Online KT CONSORTIUM Annual Meeting - Reneseng 2

Online KT CONSORTIUM Annual Meeting June 15-17, 2021

KT CONSORTIUM Annual Meeting 15-17 June 2021

Dear colleagues from KT Consortium member companies and from DTU,

Dear invited guests, participants, students and researchers from DTU Chemical Engineering associated with KT Consortium,

Welcome to 2021 Online Annual Meeting of KT Consortium!

As was the case in previous years, this year the Annual Meeting is in partial synergy with CERE Discussion Meeting, exclusively in an online format in these special times. We have kept many of the features of the physical meetings, but poster sessions and software workshops will again take place when we will have the possibility for physical meetings.

The material of the Annual Meeting includes abstracts from the presentations as well as other useful information including the “members website” overview, courses offered within the KT Consortium and past newsletters. I would particularly recommend you to get familiarized with the dedicated "members website" where you will find and download the latest ICAS version, publications, and can see previous seminar talks and PhD defenses. Shortly after the meeting, the slides and the presentations will be uploaded and available for replay.

I would very much appreciate your feedback and comments (both from KT Consortium member companies, external participants and DTU participants) via the questionnaire that will be shared during the meeting. This will help us to adjust and, when necessary, improve the meeting next year.

I hope you enjoy the meeting!

Yours sincerely,

Georgios M. Kontogeorgis

Professor of Applied Thermodynamics KT Consortium Leader Department of Chemical and Biochemical Engineering Technical University of Denmark

KT CONSORTIUM Annual Meeting

15‐17 June 2021

MEMBER COMPANIES

14 Companies

8 Countries

1 Consortium


PROGRAM OVERVIEW


Monday 14 June 2021 1300-1515 Plenary Session:

Moderator: Prof. Nicolas von Solms 1300-1305 Welcome and News

Prof. Nicolas von Solms, CERE Chairman 1305-1335 From fluid to solid: the fate of impurities in cold and cryogenic processes

Dr. Daniel Kunisch Eriksen, Shell 1335-1400 Elastic strain of chalk due to oil production

Prof. Ida Fabricius, CERE 1400-1425 Model predictive control for slug flow suppression and water treatment in daily operation of oil field

facilities Prof. John Bagterp Jørgensen, CERE

1425-1450 RAND-based saturation point calculation Assoc. Prof. Wei Yan, CERE

1450-1515 Continuous upscaling of transport equations Assoc. Prof. Alexander Shapiro, CERE

1515-1530 Break

1530-1730 Petroleum and Geoscience Moderator: Prof. Ida Fabricius

1530-1550 Determination of rock mechanical properties from seismic interface waves Emeritus Peter Klint Jensen, CERE

1550-1610 Towards a rock physical model for fine grained permafrost: Insights from velocity and NMR measurements Postdoc Leonardo Meireles, CERE

1610-1630 Effect of temperature on stiffness and strength properties of sandstones from the deep North Sea basin Assist. Prof. Tobias Orlander, CERE

1630-1650 Break 1650-1710 Inversion of seismic data from geothermal reservoirs

PhD Einar Storebø, CERE

1530-1730 Petroleum and Fluid Properties Moderator: Assoc. Prof. Alexander Shapiro

1530-1550 In-situ PH measurement in a semi-closed system: hope and reality Assist. Prof. Yi Yang, CERE

1550-1610 Measurement and modeling of gas diffusion coefficients in oil at high pressures PhD Yibo Yang, CERE

1610-1630 The influence of aquifer geochemistry on salt precipitation during CO2 injection PhD Fernando Medeiros, CERE

1630-1650 Break 1650-1710 Thermal segregation in petroleum reservoirs OR Modeling transport coefficients – diffusion and

thermodiffusion PhD Hadise Baghooee, CERE

1710-1730 Measurement of gas-oil relative permeabilities and critical saturations PhD Wael Al-Masri, CERE

1745-1845 CERE Member Companies Round Table Discussion Plenum Discussion following mutual introductions


Tuesday 15 June 2021 0815-1005 Plenary Session

Moderator: Assoc. Prof. Philip Fosbøl

0815-0840 Mission CCUS – a Danish roadmap Prof. Erling Stenby, CERE

0840-0905 New CERE activities on CO2 capture Assoc. Prof. Philip Fosbøl, CERE

0905-0930 Seismic insights into an unproven oil province: The case of Northeast Greenland Assoc. Prof. Thomas Guldborg Petersen, CERE

0930-0950 Experimental activities in CERE Prof. Nicolas von Solms, CERE

0950-1005 Honoring the life and work of Michael L. Michelsen Prof. Erling Stenby, CERE

1005-1030 Break

1030-1150 Thermodynamics, Water and Electrolytes - Session in honour of Michael L. Michelsen Moderator: Prof. Georgios M. Kontogeorgis

1030-1050 Modelling water as a mixture of two states PhD Evangelos Tsochantaris, CERE & KT Consortium

1050-1110 Investigation of the performance of e-CPA for a wide range of properties for aqueous NaCl solutions PhD Martin Due Olsen, CERE & KT Consortium

1110-1130 An evaluation study of the reliability and applicability of current electric conductance models in electrolyte solutions PhD Saman Naseri Boroujeni, CERE & KT Consortium

1130-1150 Modeling of water-hydrocarbon-salt phase equilibria with the SAFT-VR Mie equation of state Postdoc Nefeli Novak, CERE & KT Consortium

1030-1130 Petroleum properties – solids and solutions Moderator: Assoc. Prof. Thomas Guldborg Petersen, CERE

1030-1050 Detecting shear wave arrival in highly porous chalk PhD Ermis Proestakis, CERE

1050-1110 Basic principles of scale formation PhD Isaac Løge, CERE

1110-1130 BaSO4 solubility determinations Research Assist. Meng Shi, CERE

1150-1300 Break

1300-1440 Thermodynamics, Water and Electrolytes Moderator: Assoc. Prof. Xiaodong Liang

1300-1320 The (water + alcohol + alkali halide) mixed-solvent electrolyte systems: Data status and consistency analysis using electrolyte-NRTL model Postdoc Fufang Yang, CERE & KT Consortium

1320-1340 Ion pairs and properties of electrolyte solutions from molecular dynamics simulations Postdoc Jiahuan Tong, CERE & KT Consortium

1340-1400 Molecular simulations in exploring the structure and properties of water: Relevance and challenges PhD Aswin Vinod Muthachikavil, CERE & KT Consortium

1400-1420 Molecular simulation studies for electrolyte systems: Individual ion activity coefficients Postdoc Sina Hassanjani Saravi, Princeton (USA), CERE & KT Consortium

1420-1440 The Application of quantum chemistry to modelling associating systems Researcher John Towne, CERE (External Collaborator), KT Consortium


1300-1500 CCUS and related topics Moderator: Assoc. Prof. Philip Fosbøl

1300-1320 Solubility measurements of FeCO3 in relation to CO2 corrosion PhD Randi Neerup, CERE

1320-1340 Electroscrubbing for biogas cleaning: experiments and model construction PhD Sebastian Borgquist, CERE

1340-1400 Heat of absorption in CO2 in energy reducing solvents for biogas upgrading PhD Sai Hema Bhavya Vinjarapu, CERE

1400-1420 Thermodynamics of CO2 solvents PhD Lucas Farias Falcchi Corrêa, CERE

1420-1440 Hydrate swapping for CO2 capture and methane production: Challenges and innovations PhD Jyoti Shanker Pandey, CERE

1440-1500 CO2 liquefaction scenarios for CO2 transport in ships PhD Wentao Gong, CERE

1300-1440 New horizons Moderator: Senior Researcher Arne Døssing Andreasen

1300-1320 Critical Minerals for Green Energy Technologies: Greenland Assoc. Prof. Jonas Pedersen, CERE

1320-1340 Unexploded ordnance detection for offshore construction: A case study from Denmark PhD Mark Wigh & PhD Mick Kolster, CERE

1340-1400 Modelling kinetics of gas hydrate formation and decomposition PhD Carsten Frøstrup, CERE

1400-1420 Extending the reach of coiled tubing in horizontal wells using innovative methodologies PhD Sindhu Vudayagiri, CERE

1420-1440 Recycling plastic PET PhD Amirali Rezazadeh, CERE

1500-1530 Break

1530-1610 Plenary Session Moderator: Prof. Georgios M. Kontogeorgis The new paradigm in process simulation Dr. Nevin Gerek Ince, AVEVA (USA)

1630-1730 KT Consortium Advisory Board Meeting


Wednesday 16 June 2021 0830-1020 Plenary Session

Moderator: Prof. Nicolas von Solms0830-0840 Welcome & Introduction

Prof. Nicolas von Solms (CERE Chairman), Prof. Georgios M. Kontogeorgis (KT Consortium Leader) 0840-0900 Software overview

Assoc. Prof. Xiaodong Liang, CERE & KT Consortium 0900-0940 Opportunities of AI methods in process modelling

Dr. Susanna Kuitunen, NESTE (FI) 0940-1020 Compositional gradients: Equilibrium thermodynamics in a non-equilibrium world

Dr. Niels Lindeloff, TOTAL 1020-1045 Break

1045-1200 Plenary Session Moderator: Prof. Georgios M. Kontogeorgis

1045-1110 The ERC Project and other recent developments on thermodynamic modelling Prof. Georgios M. Kontogeorgis, CERE & KT Consortium

1110-1135 First principles prediction of liquid-liquid and solid-liquid interfacial properties: applications to flocculation, multiphase reactions, emulsion formulation and enhanced oil recovery Assoc. Prof. Martin Andersson, CHEC & KT Consortium

1135-1200 Biorefinery conversions with focus on syngas and CO2 as production platforms Assoc. Prof. Hariklia N. Gavala, CERE

1200-1300 Break

1300-1720 Process System Engineering Moderator: Prof. Gürkan Sin & Assoc. Prof. Seyed S. Mansouri

1300-1315 Movement, memory & distributed cognition PhD Deborah Carberry, PROSYS & KT Consortium

1315-1330 Graph Neural Networks: an AI approach to property prediction PhD Adem Rosenkvist Nielsen Aouichaoui, PROSYS & KT Consortium

1330-1350 Computer-aided formulation and solvent selection for organic paint and coating formulations PhD Markus Enekvist, CoaST, CERE & KT Consortium

1350-1405 Hybrid multi-scale modeling of flocculation processes using real-time measurement data PhD Nima Nazemzadeh, PROSYS & KT Consortium

1405-1425 Bringing digital twins to the lab: A practical demonstration of digitalizing a crystallization process PhD Rasmus Fjordbak Nielsen, PROSYS & KT Consortium

1425-1440 Multiscale modelling of biphasic reactive extraction PhD Abhimanyu Pudi, PROSYS & KT Consortium

1440-1500 Educational process simulators for the cloud: From what to how PhD Simoneta Cano de las Heras, PROSYS & KT Consortium

1500-1530 Break 1530-1550 Optimal liquefied natural gas (LNG) cold energy utilization in an Allam cycle power plant with carbon

capture and storage Postdoc Haoshui Yu, PROSYS & KT Consortium

1550-1605 Process simulation and evaluation for melamine tail gas cleaning with ionic liquids and water scrubbing PhD Yuanmeng Duan, PROSYS & KT Consortium

1605-1625 Machine learning-based aqueous biphasic systems design for the recovery of ionic liquids Postdoc Yuqiu Chen, PROSYS & KT Consortium

1625-1640 SUPPLYE - A supply chain optimization approach for S&OP planning in the pharmaceutical industry PhD Simon Brædder Lindahl, PROSYS & KT Consortium

1640-1720 Plenary Talk IBM quantum computing Dr. Florian Preis, IBM


Thursday 17 June 2021 0900-1030 Bio-Process Systems Engineering

Moderator: Prof. John M. Woodley 0900-0915 Physiological response of Yarrowia lipolytica under industrially relevant dissolved gas heterogeneities

PhD Abraham Antonius Johannes Kerssemakers, KT Consortium 0915-0930 Hybrid modelling of industrial scale fermentation process

PhD Atli Freyer Magnusson, PROSYS & KT Consortium 0930-0950 Process design for the biotechnological production of xylitol and value-added coproducts

Nikolaus Vollmer, PhD PROSYS & KT Consortium 0950-1005 A novel approach to bio-succinic acid manufacturing and downstream processing

PhD Alina Anamaria Malanca, PROSYS & KT Consortium 1005-1025 Alternative bioreaction concepts for synthesis and production

Prof. John M. Woodley, PROSYS & KT Consortium 1025-1100 Break

1100-1300 Plenary Session Moderator: Assoc. Prof. Jens Abildskov

1100-1120 IL Pro: A new software assisting ionic liquid applications Software Manager Guoliang Wang, CERE & KT Consortium

1120-1140 Quantum computing for product and process design Assoc. Prof. Seyed S. Mansouri, PROSYS & KT Consortium

1140-1200 Tray efficiency comparisons: Operation modes Assoc. Prof. Jens Abildskov, PROSYS & KT Consortium

1200-1220 Process Systems Engineering: Overview of current activities and new initiatives Prof. Gürkan Sin, PROSYS & KT Consortium Deputy Leader

1220-1240 KT Pilot Plant 4.0 Postdoc Mark N. Jones, Pilot Plant DTU

1240-1300 Discussion

1240-1400 Break

1400-1630 RENESENG II Moderator: Prof. Antonis Kokossis

1400-1410 Introduction Prof. Antonis Kokossis, National Technical University of Athens - NTUA (GR)

1410-1430 Ex-ante LCA and advanced data-driven modelling in selecting feedstocks and products Postdoc Paraskevi Karka, Chalmers University of Technology (SE) & NTUA (GR)

1430-1450 Gasification-FT-fermentation for the production of waxes and methane from syngas: ALMAGREEN R&D Project manager Enrique Montiel, Greene Waste to Energy (ES)

1450-1510 Process developments in scaling-up and scaling-down hydrothermal liquefaction Assoc. Prof. Ib Johannsen, Bio2Oil (DK) & Aarhus University (DK)

1510-1530 Early-stage capital cost estimation of biorefineries at low TRLs Dr. Mirela Tsagkari, NTUA (GR), Dr. Jean-Luc Dubois, ARKEMA (FR)

1530-1550 Systems integration for the holistic design of lignocellulosic biorefineries Postdoc Aikaterini Mountraki, NTUA (GR) & CIMV (FR)

1550-1610 Integration of CAPE models and data for the domain of biorefining: Inter-CAPE model ontology design Postdoc Linsey Koo, University of Surrey (UK)

1610-1630 The use of GVL for the holistic utilization of biomass PhD Andreas Pateromichelakis, École Polytechnique Fédérale de Lausanne (CH)

1630-1650 A semantic repository for classification and characterisation of organic waste Dr. Edlira Kalemi, University of Surrey (UK)


THERMODYNAMICS, WATER AND ELECTROLYTES

Session in honour of MICHAEL L. MICHELSEN

Tuesday 15 June 2021

1030-1150 Thermodynamics, Water and Electrolytes - Session in honour of Michael L. Michelsen Moderator: Prof. Georgios M. Kontogeorgis

1030-1050 Modelling water as a mixture of two states PhD Evangelos Tsochantaris, CERE & KT Consortium

1050-1110 Investigation of the performance of e-CPA for a wide range of properties for aqueous NaCl solutions PhD Martin Due Olsen, CERE & KT Consortium

1110-1130 An evaluation study of the reliability and applicability of current electric conductance models in electrolyte solutions PhD Saman Naseri Boroujeni, CERE & KT Consortium

1130-1150 Modeling of water-hydrocarbon-salt phase equilibria with the SAFT-VR Mie equation of state Postdoc Nefeli Novak, CERE & KT Consortium

1150-1300 Break 1300-1440 Thermodynamics, Water and Electrolytes

Moderator: Assoc. Prof. Xiaodong Liang 1300-1320 The (water + alcohol + alkali halide) mixed-solvent electrolyte systems: Data status and consistency

analysis using electrolyte-NRTL model Postdoc Fufang Yang, CERE & KT Consortium

1320-1340 Ion pairs and properties of electrolyte solutions from molecular dynamics simulations Postdoc Jiahuan Tong, CERE & KT Consortium

1340-1400 Molecular simulations in exploring the structure and properties of water: Relevance and challenges PhD Aswin Vinod Muthachikavil, CERE & KT Consortium

1400-1420 Molecular simulation studies for electrolyte systems: Individual ion activity coefficients Postdoc Sina Hassanjani Saravi, Princeton (USA), CERE & KT Consortium

1420-1440 The Application of quantum chemistry to modelling associating systems Researcher John Towne, CERE (External Collaborator), KT Consortium


Evangelos Tsochantaris Interests & Keywords PhD student • Thermodynamics

CERE, KT Consortium • Modelling [email protected] • Water

Supervisors Georgios M. Kontogeorgis

Xiaodong Liang

Modelling water as a mixture of two states

Water is the most anomalous liquid displaying many unusual thermodynamic properties. Our recent study1 showed that contemporary advanced thermodynamic models (PC-SAFT and CPA), that also consider hydrogen bonding, are unable to predict any of water’s anomalies. Modifications are needed to account for the origin of this behavior. However, the origin of these anomalies is yet unknown and a matter of debate within the scientific community. One extremely popular theory is that water is a mixture of two interconvertible states2. This presentation will present a short overview of this theory along with data that support it and in addition a semi-empirical two-state model that has been developed and applied mostly for supercooled liquid water3. Results of the model over a wide range of temperatures and pressures will be shown demonstrating the model´s strengths, but also its weaknesses. Finally, a few ideas will be presented on implementing the two-state theory in SAFT-type models along with challenges and a few promising results.

Figure 1. Hypothetical phase diagram of two liquid states of water. Water anomalies are more apparent at high pressures in the

anomalous region (after the axis break). Reproduced for [2] (Open Access CC BY 4.0)

Acknowledgement

The project was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 832460), ERC Advanced Grant project “New Paradigm in Electrolyte Thermodynamics.

References

[1] E. Tsochantaris, X. Liang, G. M. Kontogeorgis. J. Chem. Eng. Data, 65 (12), 2020, 5718–5734. [2] A. Nilsson, L. G. M. Pettersson. Nat. Commun., 6, 2015 [3] V. Holten, J. V. Sengers, M. A. Anisimov. J. Phys. Chem. Ref. Data, 43 (4), 2014


Martin Due Olsen Interests & Keywords PhD student • Thermodynamics

CERE, KT Consortium • Modelling [email protected] • Electrolytes

Supervisors Nicolas von Solms Georgios M. Kontogeorgis Xiaodong Liang

Investigation of the performance of e-CPA for a wide range of properties for aqueous NaCl solutions

The study of thermodynamics related to electrolyte solutions are highly relevant since electrolytes are present in a wide variety of industrial applications [1]. Development of accurate and predictive models for electrolytes is therefore of great importance and further work to understand and improve the models are of great importance.

The electrolyte cubic plus association (e-CPA) model is an equation of state (EoS) model that is investigated in this case. The model consists of a physical term based on the Soave-Redlich-Kwong EoS, an association term, a Debye-Hückel term and a Born term. This model has previously shown good results for properties like mean ionic activity coefficients, osmotic coefficient and solid-liquid equilibrium of many salts with both salt- and ion-specific parameters [2, 3].

It is investigated how well the model is able to describe a diverse set of properties for aqueous sodium chloride solutions with different parameter sets. Focussing on a single salt (NaCl) makes it easier to identify trends and find possible improvements of the model. Sodium chloride is used as a model salt because it has been thoroughly investigated experimentally and therefore has data for many properties in wide temperature- and pressure ranges.

New parameterizations are also proposed in an attempt to get more physically sound model parameters which are believed to make the model more predictive. The new parameterization are compared to the previously published parameter sets.

Acknowledgement

Acknowledgement to the European Research Council (ERC) for funding of this research under the European Union’s Horizon 2020 research and innovation program (grant agreement No 832460), ERC Advanced Grant project “New Paradigm in Electrolyte Thermodynamics”.

References

[1] G.M. Kontogeorgis, G.K. Folas, Thermodynamic Models for Industrial Applications: From Classical and Advanced Mixing Rules to Association Theories, 2010, John Wiley and Sons, West Sussex [2] A. Schlaikjer, K. Thomsen, G.M. Kontogeorgis, Ind. Eng. Chem. Res., 56, 2017, 1074-1089 [3] B. Maribo-Mogensen, K. Thomsen, G.M. Kontogeorgis, AIChE Journal, 61, 2015, 2933-2950 [4] B. Maribo-Mogensen G. M. Kontogeorgis, K. Thomsen, J. Phys. Chem. B, 117, 2013, 3389−3397.


Saman Naseri Boroujeni Interests & Keywords Ph.D. Student • Electrical Conductance CERE, KT Consortium • Debye–Hückel–Onsager [email protected] • Electrolyte Solutions • Primitive Model

Supervisors • Ion Association Georgios M. Kontogeorgis

Xiaodong Liang Bjørn Maribo-Mogensen, Hafnium Labs (DK)

An evaluation study of the reliability and applicability of current electric conductance models in electrolyte solutions

Electrical conductance is of great importance among the transport properties of electrolyte solutions. Since the Debye–Hückel–Onsager (DHO) theory on 1926 [1] for limiting law of equivalent conductivity, lots of scientists have attempted to improve the DHO model for higher concentrations[2], [3]. Despite the availability of experimental data for lots of electrolytes and besides the declaration of authors, the reliability and robustness of these methods haven’t been evaluated comprehensively. In this paper, we’re going to investigate remarkable models of equivalent conductivity of electrolyte solutions over a wide range of concentrations. To this aim, we have gathered experimental data for more than 100 electrolytes including Sulphates, Nitrates, Chlorides, Iodides, Bromides, Perchlorates, etc. The reliability of models has also been examined with seven different error analysis methods, and the sensitivity of models on important parameters such as distance of closest approach, static permittivity, and viscosity has been examined. It’s been tried to make a fair comparison on models based on the number of fitting parameters and the accuracy of their results. Finally, the origin of deviation from experimental results because of ignoring the physical essence of the problem has been discussed.

Figure 1. Comparison of molar conductivity of CdSO4 in water at 25 °C.

Acknowledgement

The project was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 832460)”

References

[1] P. C. Hemmer, H. Holden, and S. K. Ratkje, “The collected works of Lars Onsager: with commentary,” 1996. [2] D. Fraenkel, “An improved theory of the electric conductance of ionic solutions based on the concept of the ion-atmosphere’s smaller-ion shell,” Phys. Chem. Chem. Phys., vol. 20, no. 47, pp. 29896–29909, 2018. [3] J. M. G. Barthel, H. Krienke, and W. Kunz, Physical chemistry of electrolyte solutions: modern aspects, vol. 5. Springer Science & Business Media, 1998.


Nefeli E. Novak Interests & Keywords Postdoc • Electrolyte Solutions

CERE, KT Consortium • Water & Hydrocarbons [email protected] • Equations of State • Phase Equilibria

Supervisors Ioannis G. Economou, NCSR “Demokritos” (GR), TAMU at Qatar (QA)

Georgios M. Kontogeorgis Marcelo Castier, German-Paraguayan University, (PY)

Modeling of water-hydrocarbon-salt phase equilibria with the SAFT-VR Mie equation of state

The study of water-hydrocarbon-salt mixtures is very important both for scientific as well as industrial purposes. A relatively new equation of state (EoS), SAFT-VR Mie,1 has been evaluated in the prediction of phase equilibria of water with hydrocarbons, with and without the presence of salts. The non-electrolyte EoS is able to capture the qualitative behaviour of water with hydrocarbons, such as heteroazetropy and gas-gas equilibrium without the use of any binary interaction parameters. Quantitative predictions for the solubility of water in light hydrocarbons as well as the mutual solubility of hydrocarbons and water above the solubility minimum were also in good agreement with experimental data.2 An electrolyte version of this model3 is also employed for the water-hydrocarbon-salt mixtures in a semi-predictive framework. The model showcases an encouraging accuracy for phase equilibrium calculations under salinity.

Acknowledgement

The project was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 832460), ERC Advanced Grant project “New Paradigm in Electrolyte Thermodynamics”.

References

[1] S. Dufal, T. Lafitte, A. Galindo, G. Jackson, A. Haslam, AIChE J. 2015, 61 (9), 2891–2912. [2] N. Novak, G. Kontogeorgis, M. Castier, I. Economou, Ind. Eng. Chem. Res, 2021, 60 (14), 5278–5299 [3] M. Selam, I. Economou, M. Castier, Fluid Phase Equilib. 2018, 464, 47–63.

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Figure 1. Prediction of water-hexane LLE


Fufang Yang Interests & Keywords Postdoc • Data analysis

CERE, KT Consortium • Mixed-Solvent [email protected] Electrolytes • Thermodynamics

Supervisors • e-NRTL

Georgios M. Kontogeorgis Jean-Charles de Hemptinne, IFP Energies Nouvelles (FR)

The (water + alcohol + alkali halide) mixed-solvent electrolyte systems: Data status and consistency analysis using electrolyte-NRTL model

Modelling of the thermodynamic properties of mixed-solvent electrolyte systems is particularly challenging. In terms of theory, the complexity originates from the very different interactions between molecule and molecule, molecule and ion, and ion and ion, and the very different solvation extent of water and organic solvent around the ions. In terms of experiment, datasets were published in different conventions that were not always clearly stated, and are difficult to reconcile. In terms of modelling, the traditional water-based reference-state framework and concentration unit definitions are not practical, as the organic solvent is treated as a solute even for mixtures with very low water concentration (and thus very high co-solvent concentration).

The aim of this work is to analyse data of the thermodynamic properties of mixed-solvent electrolyte systems through an extensive database in terms of internal and external consistency. We will use this analysis to discuss reference state and concentration definitions that are suitable for mixed-solvent electrolyte mixtures over large concentration ranges (both salt and co-solvent), and to present a benchmark database for future equation of state modelling.

The investigated properties include vapour-liquid equilibrium (VLE), liquid-liquid equilibrium (LLE), and mean ionic activity coefficients (MIAC). Experimental datasets are summarized. Data distributions are shown. The data status is not as extensive as for binary (water + salt) mixtures, but is quite adequate for data consistency analysis among the same family of salts, at least for (water + methanol) and (water + ethanol) co-solvents. The electrolyte-NRTL model is used to convert various properties to the MIAC and water activity coefficient (WAC). In this way, the various properties are reconciled. It is shown that it is possible to use a single e-NRTL parameter to analyze the solvation behaviour of mixed solvents. Doing so, the trend of this parameter is investigated for each ternary mixture.

Acknowledgement


References

[1] S. Ahmed, N. Ferrando, J.C. de Hemptinne, J.P. Simonin, O. Bernard, O. Baudouin. Fluid Phase Equilibria, 459, 2018, 138-157. [2] C.C. Chen, Y. Song. AIChE Journal, 50(8), 2004, 1928-1941.


Jiahuan Tong Interests & Keywords Postdoc • Ion pair

CERE, KT Consortium • Electrolyte [email protected] • Molecular Simulation

Supervisors Xiaodong Liang


Ion pairs and properties of electrolyte solutions from molecular dynamics simulations

Ion pairing, the association of oppositely charged ionic species, has tremendous effect on the structure and physiochemical properties for electrolyte solutions and a wide range of chemical and biological materials. The ion-pair formation in electrolytes occurs when the coulombic forces between ions of opposite charge overcome the thermal energy.[1] The ion-pair formation is favored by the lower dielectric constant of the solvent, by the higher concentration of the ions, by the higher ion charge, and by the smaller ion radii.[2] However, experimental observations of ion pairing in the electrolytes are challenging due to difficulties in differentiating ion species. In this work, the ion-pair formation was analyzed by molecular dynamics simulations which is a method based on inter-molecular interactions to study the micro-structure, dynamics and thermodynamic properties. This work aims to understand the molecular mechanisms of ion pairing with focus on single salt in pure water, to study the behavior and structure of the ion pairs, as well as to investigate the impact of ion pairs on properties of electrolyte solutions.

Figure 1. (a) Schematic diagram of sodium chloride aqueous solution simulation; (b) Self-diffusion coefficient and (c) The radial distribution function of the center of mass of Na+, Cl+ and H2O molecular.

Acknowledgement


References

[1] J. Timko, D. Bucher, S. Kuyucak. J. Chem. Phys, 132, 2010, 114510. [2] F. Moucka, I. Nezbeda, W.R. Smith. J. Chem. Phys, 138, 2013, 154102.


Aswin Vinod Muthachikavil Interests & Keywords PhD Student • Molecular Simulations

CERE, KT Consortium • Water [email protected] • Water Models

• Hydrogen Bonds

Supervisors • Order Parameters Xiaodong Liang


Molecular simulations in exploring the structure and properties of water: Relevance and Challenges

A lot of the knowledge that we have on the structure and properties of water, particularly with regards to the two states picture of water, has come from molecular simulations [1,2]. Predictions made using molecular simulations which have also been confirmed experimentally, point to the relevance of the application of this technique in studying the properties of water. For example, the liquid-liquid phase transition (LLPT) in water between the low density liquid (LDL) and high density liquid (HDL) which was predicted by molecular simulations [3], was also observed experimentally recently [4]. Molecular simulations have also been employed extensively to characterise the two structural forms in water, especially in the deeply super-cooled regimes of water, where experimental probing is difficult or next to impossible. The study of hydrogen bonding dynamics in water is also greately aided by molecular simulations, and provides insights for describing phase behavior of water-containing systems with the advanced equations of state, which account for the association phenomena explicitly [5]. There are however, a lot of models that have been developed over the years for simulation of water, and their applications range over a wide array of physical conditions. A discussion on the application of these models to explore the properties of water in different environments is presented, with an emphasis to the relevance and challenges. A brief review of the widely used models of water and a comparison based on their capability to predict the structure and properties of water is also presented.

Acknowledgement

The project is funded by PetroChina Research Institute of Petroleum Exploration and Development, and the Department of Chemical and Biochemical Engineering, DTU.

References

[1] Poole, P H., Sciortino, F., Essmann, U. and Stanley, H.E. Nature, 360(6402), 1992, 324-328 [2] Gallo, P., Amann-Winkel, K., Angell, C.A., Anisimov, M.A., Caupin, F., Chakravarty, C., Lascaris, E., Loerting, T., Panagiotopoulos,

A.Z., Russo, J. and Sellberg, J.A. Chemical reviews, 116(13), 2016, 7463-7500. [3] Pathak, H., Späh, A., Amann-Winkel, K., Perakis, F., Kim, K.K.H. and Nilsson, A. Molecular Physics, 117(22), 2019, 3232-3240. [4] Perakis, F., Amann-Winkel, K., Lehmkühler, F., Sprung, M., Mariedahl, D., Sellberg, J.A., Pathak, H., Späh, A., Cavalca, F.,

Schlesinger, D. and Ricci, A. PNAS, 114(31), 2017, 8193-8198. [5] Liang, X., Maribo-Mogensen, B., Tsivintzelis, I. and Kontogeorgis, G.M., 2016. A comment on water’s structure using monomer

fraction data and theories. Fluid Phase Equilibria, 407, pp.2-6.


Sina Hassanjani Saravi Interests & Keywords Postdoctoral Research Associate • Aqueous Electrolytes

Princeton University • Activity Coefficients [email protected] • Individual Ions

• Molecular Simulations

Supervisor Athanassios Z. Panagiotopoulos, Princeton University (US)

Molecular simulation studies for electrolyte systems: Individual ion activity coefficients

Aqueous electrolyte solutions are ubiquitous in industrial, environmental, biological, and geological applications. Developing comprehensive thermodynamic models is a key enabling step in providing accurate predictions for various thermophysical properties of these complex fluids. Molecular simulations, along with established statistical mechanical theories, have long been used to connect the liquid structure of aqueous solutions on a molecular scale to macroscopic properties of interest that are taking part in simulation and optimization of chemical processes. Of particular interest for aqueous electrolytes are the mean ionic activity coefficients (MIAC) which quantify deviations of free energies from an ideal infinite-dilution reference state. While experiments, phenomenological models, as well as molecular simulation-based approaches are well-established in measuring, correlating, and predicting MIAC for aqueous salts, there exists a knowledge gap with respect to the activity coefficients of individual ions and their behavior in the presence of different counterions. This is in part due to the controversy around measurements of individual ion activity coefficients (IIAC) through electrochemical cells consisting of ion-selective electrodes. In the present study, we address this issue by developing a thermodynamically consistent framework using explicit-water molecular dynamics simulations. We calculate IIAC by computing free energy changes for insertion of single ions from an idea gas state into aqueous solutions at specified salt concentrations. We examine several 1-1 aqueous electrolytes including NaCl, KCl, NaF, and KF in order to provide a broader picture of the behavior of individual ions in various solutions. We show that explicit-water molecular dynamics simulations are capable of predicting IIAC qualitatively, specifically with respect to the relative positioning of activity coefficients of cations and anions in different aqueous salts. The obtained values for IIAC, however, are significantly larger than the reported experimental data1. We also calculate MIAC for all studied aqueous salts from contributions of individual ions and demonstrate that the predicted values match well with the experimental data2. Overall, this study provides a sound thermodynamic framework to describe and quantify the behavior of individual ions on a microscopic level and demonstrates their unequal contributions to solution nonideality.

Acknowledgement


References

[1] G. Wilczek-Vera, E. Rodil, and J. H. Vera, AIChE journal, 50(2), 2004, 445-462. [2] R. A. Robinson and R. H. Stokes, Electrolyte Solutions, Courier Corporation: 2002.


John Towne Interests & Keywords Production Engineer • Quantum Chemistry

Aera Energy LLC, CERE, KT

• Statistical Mechanics [email protected] • Association Energy

• SAFT

Collaborators

Georgios M. Kontogeorgis Xioadong Liang

The application of quantum chemistry to modelling associating systems

Quantum chemistry can be used to increase the reliability of associating thermodynamic models for practical applications. Similarities between the chemical and perturbation theories of association create a pathway to estimate association parameters from quantum chemistry and statistical mechanics (the QC/SM approach). Association energy (𝜀𝜀𝐴𝐴𝑖𝑖𝐵𝐵𝑗𝑗) is directly related ideal gas dimerization enthalpy (∆𝐻𝐻) for low molecular weight

self-associating compounds (as shown in Figure 1). PC-SAFT and CPA can correlate saturation properties using QC/SM estimated association energy with little loss of accuracy.

Figure 1. Relationship between QC/SM estimated and CPA/PC-SAFT regressed association energies.

Quantum chemistry can also be applied to determine whether to explicitly model noncovalent interactions using an association framework. Symmetry Adapted Perturbation Theory (SAPT) provides a decomposition of interaction energy into electrostatic, exchange, induction, and dispersion components. The SAPT electrostatic to dispersion ratio (E/D ratio) is a simple measure for classifying noncovalent interactions as dispersion dominated, mixed influence, or electrostatic dominated. Application of an association framework is justified if an interaction is classified as either mixed influence or electrostatic dominated.

Acknowledgement

G. M. Kontogeorgis wishes to thank the European Research Council (ERC) for funding this research under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 832460), ERC Advanced Grant project “New Paradigm in Electrolyte Thermodynamics”.

References

[1] J. E. Towne, X. Liang, G. M. Kontogeorgis. Ind. Eng. Chem. Res., Accepted/In press, 2021.

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PLENARY SESSIONS

Tuesday 15 June 2021

1530-1610 Plenary Session Moderator: Prof. Georgios M. Kontogeorgis The new paradigm in process simulation Dr. Nevin Gerek Ince, AVEVA (USA)

Wednesday 16 June 2021

0830-1020 Plenary Session Moderator: Prof. Nicolas von Solms

0830-0840 Welcome & Introduction Prof. Nicolas von Solms (CERE Chairman), Prof. Georgios M. Kontogeorgis (KT Consortium Leader)

0840-0900 Software overview Assoc. Prof. Xiaodong Liang, CERE & KT Consortium

0900-0940 Opportunities of AI methods in process modelling Dr. Susanna Kuitunen, NESTE (FI)

0940-1020 Compositional gradients: Equilibrium thermodynamics in a non-equilibrium world Dr. Niels Lindeloff, TOTAL

1020-1045 Break

1045-1200 Plenary Session Moderator: Prof. Georgios M. Kontogeorgis

1045-1110 The ERC Project and other recent developments on thermodynamic modelling Prof. Georgios M. Kontogeorgis, CERE & KT Consortium

1110-1135 First principles prediction of liquid-liquid and solid-liquid interfacial properties: applications to flocculation, multiphase reactions, emulsion formulation and enhanced oil recovery Assoc. Prof. Martin Andersson, CHEC & KT Consortium

1135-1200 Biorefinery conversions with focus on syngas and CO2 as production platforms Assoc. Prof. Hariklia N. Gavala, CERE


Nevin Gerek Ince Process Simulation Solver & Thermo Team Lead AVEVA (US) [email protected]

The new paradigm in process simulation

The concept of a Digital Twin has been around for several decades. We can broadly define a Digital Twin as a digital replica of a physical asset such as a heat exchanger, pump, column, or entire plant. The Digital Twin can be achieved by modelling of the process, control and monitoring the health of the equipment and process simulation plays a pivotal role for modeling processes in the chemicals, refinery, gas processing, and upstream industries.

AVEVA™ Process Simulation (previously known as SimCentral) is an innovative, integrated platform covering the entire process engineering lifecycle of design, simulation, and training to deliver the process side of the Digital Twin. With this platform, users can (i) shorten the development time of processes using an open modeling approach, (ii) collaborate with other engineering disciples beyond the process world to verify equipment, piping and control design through hydraulic analysis and dynamic simulation and (iii) further simplify modeling complexity not only in conventional chemical processes but also in many special processes.

In this talk, we will discuss upcoming challenges for process simulation and how they can be utilized with different components in a digital infrastructure for sustainable process design. Specifically, we will explain AVEVA’s strategy to handle electrolyte systems that are vital for a variety of applications in the process industries including environmental, separation and electrochemical processes. Process simulation can model electrolyte solutions when there is an accurate thermodynamic method, modelling of complete or partial dissociation of electrolytes into ions and the handling of precipitating salts. This requires both phase and chemical equilibrium are handled in the process simulator. To model phase and reaction equilibrium simultaneously, AVEVATM Process Simulation utilizes a fluid object concept that comprises the thermodynamic methods and reactions. This decoupling of thermodynamic equilibrium from reaction equilibrium in a fluid object but solving simultaneously gives flexibility in modelling and provides compatibility with the equation-oriented framework leading to performance gains. The equation-oriented solver enables quick solving of flowsheet modelling by taking advantage of parallel computing, continuous solving, and open-form modelling.


Xiaodong Liang Interests & Keywords Associate Professor • Thermodynamics

CERE, KT Consortium • CAPD [email protected] • PSE

• Software

Software Overview

As a tradition, some software tools/algorithms developed in CERE and KT Consortium will be briefly introduced. It covers the following aspects this year:

(1) Over the years, we have worked on developing robust and efficient phase equilibrium calculation algorithms for modeling and simulation of various challenging situations, including multiphase equilibrium, various flash specifications, reactive systems, and equilibrium under the influence of capillarity and adsorption. Some of these algorithms were integrated into 1D or 3D simulations, e.g., 3D non-isothermal reservoir simulation and realization of many of the PHREEQC functions with a higher reliability and efficiency--the latter will be further utilized for CO2 sequestration simulation.

(2) We have developed a tool for predicting the interfacial tension and surface enrichment for a liquid-liquid or solid-liquid interface using COSMO-RS. It is in the form of a python script that uses the functionality of COSMOtherm, for which a separate licence is required: https://github.com/LasseNikolajsen/ift_from_lle.

(3) easyGSA: efficient global sensitivity analysis using mechanistic or machine learning models. It presents highly increased computational efficiency, reliably rank important parameters, quickly identify key design parameters in a design space, and tap into the power of machine learning libraries: https://github.com/gsi-lab.

(4) MOSKopt: a simulation-based stochastic black box optimizer. Evolutionary program based on infill optimization by internal optimization of expected improvement (mcFEI), utilization of machine learning metamodels (stochastic kriging), embedded Monte Carlo simulations for uncertainty quantification, allows for multiple uncertainty hedging strategies, and object-oriented programming and user-extendable infills: https://github.com/gsi-lab.

(5) INT2EOS: MATLAB and Python (Jupyter Notebook) interfaces to the Cubic Plus Association (CPA) and Perturbed-Chain SAFT (PC-SAFT) equations of state. Within the CPA equation of state, the SRK and PR equations of state are naturally available. Both the simplified and the original PC-SAFT versions are offered, while the association term has only used the simplified radial distribution function.

https://github.com/LasseNikolajsen/ift_from_lle

https://github.com/gsi-lab




Susanna Kuitunen Senior Associate (Process Modelling) Neste Engineering Solutions Oy (FI) [email protected]

Opportunities of AI methods in process modelling

Neste is the global leader in the production of renewable diesel. To support business and technology development utilization of many renewable or waste feedstocks and new process technologies are actively investigated at Neste. To develop feasible processes, accurate models and robust simulation tools are as essential as they have been previously. Additionally, these tools are needed in the optimization and troubleshooting of existing process units.

Most of the steady state models we are using at the moment are based on physico-chemical phenomena. However, in some cases, development of such models has not been successful, but we have been forced to use correlations, whose mathematical form as well as parameters have been obtained from experimental data.

In the survey on “Industrial requirements for thermodynamic and transport properties: 2020” [1], one of the questions asked from the industrial experts was about AI (artificial intelligence). Both opportunities and concerns were raised in the replies. Similarly, at Neste we have had discussions on how and where to apply AI methods. Could these help in modelling systems, for which we are not able to apply physico-chemical models? What other opportunities do they provide? How to apply them effectively?

In the field of process control and real-time process monitoring, colleagues at NAPCON, Neste’s digitalization unit, have already been active in developing AI methods to support work of process operators. In a recent project deep learning models were developed for industrial hydrotreating reactors with the objective to predict the catalyst activity in real-time. Another example is utilization of deep learning models to continuously predict the future behaviour of certain quality variables at Neste’s refining process.

These public and internal discussions as well as the development work carried out at NAPCON ignited the curiosity of the presenter and pushed her to educate herself further as well as to evaluate the possibilities & practicalities of these methods in the field of steady state process modelling. In overall, the presentation will give an introduction to Neste, present our current modelling & simulation approach, recent developments at NAPCON and discuss general findings concerning applying AI methods in process modelling.

References

[1] GM. Kontogeorgis, R. Dohrn, IG Economu, JC de Hemptinne, A. ten Kate, S. Kuitunen, M. Mooijer, LF. Žilnik, V. Vesovic. Ind. Eng. Chem. Res. 2021, 60(13), 4987–5013


Niels Lindeloff

Total (DK)

Compositional gradients – Equilibrium thermodynamics in a non-equilibrium world

Defining the initial fluid distribution is key for good reservoir models. Poorly calibrated models and incorrect forecasts often are a result of poor definition of the initial state of the reservoir.Compositional gradients in the hydrocarbon column can be approximated either as an equilibrium (for the isothermal case) or as a steady state (for the case with a temperature gradient) but the general case is more complex and cannot readily be modelled. [1-4]

Additional factors such as complex charge history with late ingress of lighter fluids, tectonic movements causing rapid uplift or burial of the formation, in-situ alteration processes like biodegradation and water washing can all cause fluid gradients that cannot be described by the above models. Aquifers are often influenced by variations in salinity and regional hydrodynamics, which for low permeable systems may cause tilting fluid contacts. [5]

Field evidence of the initial state of the reservoir is often ambiguous. Downhole PVT sampling provides key data but can be uncertain due to adverse sampling conditions and contamination by drilling fluids. Formation pressure gradients provide valuable information about in-situ fluid density and fluid contacts. Geochemical fingerprinting methods and analysis of noble gas fingerprints in fluid samples can provide an additional, valuable source of information.

Reservoir simulation models rely on equilibrium assumptions for defining initial state and approximations are thus required to impose the observed fluid distributions. A very efficient approach is to use maps of fluid property variations and fluid contact depths. These can subsequently be discretized into equilibrium regions. It is not uncommon for models defined in this way to contain thousands of equilibration regions. Because the time for the model to re-equilibrate is generally orders of magnitude larger than the simulated production period, it is feasible to emulate the observed non-equilibrium state of the reservoir in spite of the equilibrium assumptions in the model.

References

[1] Schulte, A.M. SPE 9235, 1980. [2] Onsager, L. Physical Review vol 37 & 38, 1931. [3] Pedersen, K.S., Lindeloff, N. SPE 84364, 2004. [4] Montel, F., Galliero, G., Huoang, H. Paper presented at IMT13, 2018. [5] Hubbert MK (1953) Entrapment of petroleum under hydrodynamic conditions. AAPG Bull 37(8):1954–2026


Georgios M. Kontogeorgis Interests & Keywords Professor • Thermodynamics

CERE, KT Consortium • Association Models [email protected] • Electrolytes

• Water

The ERC Project and other recent developments on Thermodynamic Modelling

Electrolyte solutions are present almost everywhere, in numerous applications in chemical, biochemical, geochemical, petroleum engineering as well as in diverse disciplines such as geology, biology and medicine.

But there are many unanswered questions e.g. which is the best electrolyte theory, what is the role of the relative permittivity, the importance and success –limitations of the Debye-Hückel theory [1] and of the Born ion charging/ion solvation term, how can we best combine electrolyte theories with physical interactions in order to develop complete models. And how can these models be developed in an optimum way and validated against thermodynamic data and benchmarked – compared to reach other? Can molecular simulation, an analysis of the terms of the models and the activity coefficients of individual ions [2,3] contribute to answering some of these fundamental questions?

These are some of the questions that a recently granted project from the European Research Council (ERC) entitled “New Paradigm in Electrolyte Thermodynamics” [4] will attempt to answer.

Most of the current results from the ERC project will be presented in separate talks during the meeting, while this talk will highlight some of the most important results, including some not presented elsewhere.

In the final part of the talk, other recent developments with association thermodynamic models will be briefly presented.

Acknowledgement

The project was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 832460), ERC Advanced Grant project “New Paradigm in Electrolyte Thermodynamics”.

References

[1] G.M.Kontogeorgis, B. Maribo-Mogensen, K. Thomsen, 2018. The Debye-Hückel theory and its importance in modeling electrolyte solutions. Fluid Phase Equilibria, 462, 130-152. [2] Sun, L., Liang, X.D., von Solms, N., Kontogeorgis, G.M., 2020. Analysis of some electrolyte models including their ability to predict the activity coefficients of individual ions. Ind. Eng. Chem. Res., 59(25): 11790-11809. [3] Lei, Q, Peng, B.L., Sun, L., Luo, J.H., Chen, Y., Kontogeorgis, G.M., Liang, X.D., 2020. Predicting activity coefficients with the Debye-Hückel theory using concentration dependent static permittivity. AICHE JOURNAL, 66(11), Article Number: e16651 [4] https://www.cere.dtu.dk/research-and-projects/framework-research-projects/new-paradigm-in-electrolyte-thermodynamics-erc-advanced-grant-project

https://www.cere.dtu.dk/research-and-projects/framework-research-projects/new-paradigm-in-electrolyte-thermodynamics-erc-advanced-grant-project

https://www.cere.dtu.dk/research-and-projects/framework-research-projects/new-paradigm-in-electrolyte-thermodynamics-erc-advanced-grant-project


Martin Andersson Interests & Keywords Associate Professor • Quantum chemistry

CHEC, KT Consortium • COSMO-RS [email protected] • Property Prediction

• Surface Chemistry

• Interfacial Tension

First principles prediction of liquid-liquid and solid-liquid interfacial properties: applications to emulsion formulation and enhanced oil recovery

We have developed methods for predicting liquid-liquid [1] and solid-liquid [2] interfacial tension (IFT), based on density functional theory and COSMO-RS. The methods require no parameterization besides relying on COSMO-RS and they apply equally well to multi-component systems as simple binary systems. Benchmarking against IFT for binary water/organic systems [1] and contact angles on self-assembled monolayers [2] demonstrate good agreement with experiments. We will show two examples of applying the methods: predicting the temperature-composition phase diagram for an oil/water/surfactant system (Figure 1) and the concept of predicting wettability alteration capacity for additives in enhanced oil recovery.

Figure 1. Composition-temperature IFT map (colour bar, [mN/m]) for the water/decane/C4E1 system. Solid lines are the boundaries of the predicted three phase region. The dashed line is the minimum IFT for each wt% surfactant.

Acknowledgement

Parts of the project were funded by the W-EOR Project from the Maersk Oil Research and Technology Centre as well as the Nano-Sand Project in the ExploRe Program with BP Exploration Operating Company, Ltd.

References

[1] M.P. Andersson, M.V. Bennetzen, A. Klamt, S.L.S. Stipp. J. Chem. Theory Comput. 2014, 10, 8, 3401–3408 [2] M.P. Andersson, T. Hassenkam, J. Matthiesen, L.V. Nikolajsen, D.V. Okhrimenko, S. Dobberschütz, S.L.S. Stipp. Langmuir 2020, 36, 42, 12451–12459

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Hariklia N. Gavala Interests & Keywords Associate Professor • Carbon Dioxide

CERE • Syngas [email protected] • Bioconversion

• Fermentation

• Fuels & Chemicals

Biorefinery conversions with focus on syngas and CO2 as production platforms

Contemporary challenges in decreasing Green House Gas emissions and finding alternative carbon and energy sources for fueling our society brought in the forefront processes based on renewable solid, liquid and gaseous feedstock. Biological conversions hold a big potential towards this direction; however, new approaches are required in order to develop processes that can be competitive, in terms of efficiencies and economy, to the already established, fossil-based ones. Valorization of a broad range of biomasses, including poorly biodegradable materials via gasification and further biological conversion of the produced syngas as well as re-use of carbon dioxide generated from big CO2 emitting activities can play a critical role in this regard.

The presentation will focus on summarizing the status of the technology and highlighting recent advances within syngas and carbon dioxide biological transformations for addressing the main challenges, i.e. the low solubility of the gaseous substances and the relatively low growth rates of the microbes. Future directions complying with the UN Sustainability goals and perspectives for further development of the bio-syngas and CO2 production platform will also be discussed, i.e. production of biological Synthetic Natural Gas - bio-SNG – as well as production of advanced fuels and higher-value products.


PROCESS SYSTEM ENGINEERING

Wednesday 16 June 2021

1300-1720 Process System Engineering Moderator: Prof. Gürkan Sin & Assoc. Prof. Seyed S. Mansouri

1300-1315 Movement, memory & distributed cognition PhD Deborah Carberry, PROSYS & KT Consortium

1315-1330 Graph Neural Networks: an AI approach to property prediction PhD Adem Rosenkvist Nielsen Aouichaoui, PROSYS & KT Consortium

1330-1350 Computer-aided formulation and solvent selection for organic paint and coating formulations PhD Markus Enekvist, CoaST, CERE & KT Consortium

1350-1405 Hybrid multi-scale modeling of flocculation processes using real-time measurement data PhD Nima Nazemzadeh, PROSYS & KT Consortium

1405-1425 Bringing digital twins to the lab: A practical demonstration of digitalizing a crystallization process PhD Rasmus Fjordbak Nielsen, PROSYS & KT Consortium

1425-1440 Multiscale modelling of biphasic reactive extraction PhD Abhimanyu Pudi, PROSYS & KT Consortium

1440-1500 Educational process simulators for the cloud: From what to how PhD Simoneta Cano de las Heras, PROSYS & KT Consortium

1500-1530 Break 1530-1550 Optimal liquefied natural gas (LNG) cold energy utilization in an Allam cycle power plant with carbon

capture and storage Postdoc Haoshui Yu, PROSYS & KT Consortium






Deborah Carberry Interests & Keywords PhD Student • Distributed Cognition

PROSYS, KT Consortium • Education [email protected] • Neuroscience

• Evolution

Supervisors • Extended Reality Seyed Soheil Mansouri

Martin Andersson Christian Beenfeldt, Knowledge Hub Zealand (DK)

Movement, memory & distributed cognition

Student populations are growing, domain specific knowledge is deepening, and, complex technologies are emerging. As a result, there is an increasing need to teach more students more skills in less time. Moreover, innovation across industries and technologies is accelerating at such a rate that we can no longer rely on master-novice systems of education. In response, developing scalable learning tools based on theories of distributed cognition could help Students (and Teachers) to keep pace with Industry.

In Figure 1, the SULEX model points to how the brain can be conceptualised within an evolutionary context to distinguish between mentalism (of mind) and embeddedness (of place) cognitive systems. The corresponding presentation will use the SULEX model to illustrate how languaging and imagination can be leveraged to reduce cognitive load using techniques that exploit extended cognition and cognitive enhancement.

These learning techniques are particularly suitable for practice-based or situated learning, e.g. for operator training, and can be applied in Augmented Reality (AR) and other Extended Reality (XR) platforms.

Figure 1. The SULEX Model


Adem R.N. Aouichaoui Interests & Keywords PhD student • Thermodynamics PROSYS, KT Consortium • CAPE [email protected] • Property Prediction

• AI Supervisors • Software Development

Gürkan Sin Jens Abildskov Seyed Soheil Mansouri

Graph neural networks (GNN): An AI approach to property prediction

Many chemical engineering applications are heavily dependent on the availability of various thermophysical properties. They are used for assessing the safety of processing chemicals, performing phase-equilibria calculations as well as identifying potential chemical substitutes for a given application. QSPR models offer fast and inexpensive for predicting these properties from structural information of the molecule. Classical hand-crafted molecular descriptors such as the Group-Contribution groups suffer many drawbacks such as the absence of interaction information between neighbor groups (proximity effects) and often result in a sparse representation of the chemical design space making them unable to describe many compounds. Graph Neural Networks are a special type of neural network that can operate on irregular graph representations such as molecular graphs where the nodes are embedded with information concerning the atoms and the edges with bond information [1]. GNN provides an end-to-end learning approach where feature information is extracted from the raw molecular information and mapped to the target property through error backpropagation. This results in a model capable of characterizing a larger chemical space and better at generalizing to previously unseen compounds [2]. The presentation will aim to demystify the concept of Graph Neural Networks by explaining its core concept, demonstrating the workflow for developing such models, and presenting the latest results and applications of such models.

Figure 1. Main steps in developing GNN based property prediction models

References

[1] K. Yang, K. Swanson, W. Jin, C. Coley, P. Eiden, H. Gao, A. Guzman-Perez, T. Hopper, B. Kelley, M. Mathea, A. Palmer, V. Settels, T. Jaakkola, K. Jensen, R. Barzilay, J. Chem. Inf. Model. 59 (2019) 3370–3388.

[2] C.W. Coley, R. Barzilay, W.H. Green, T.S. Jaakkola, K.F. Jensen, J. Chem. Inf. Model. 57 (2017) 1757–1772.


Markus Enekvist Interests & Keywords PhD Student • Product Design CERE, CoaST, KT Consortium • Coatings Science

[email protected] • Property Modelling • Sustainability Supervisors

Georgios M. Kontogeorgis Xiaodong Liang Kim Dam-Johansen Xiangping Zhang, Chinese Academy of Science (CN)

Computer-aided design and solvent selection for organic paint and coating formulations

The current method of developing new paints is highly practical in nature, built on the knowledge of experienced formulators and a general understanding of interactions within the paint system. Instead of testing and revising samples until the target needs are met, computational tools and methods can be used to minimize the time and resources necessary. Using estimation methods, property models, and product interactions to screen ingredient candidates in order to find a more accurate starting point for experimental testing is known as an integrated experiment-modelling approach. This approach maintains reliability due to experimental verification, while also providing improvements to innovation and development time due to the model-based structure.

A methodology for selection of coating ingredients and solvents was designed and tested for three case studies. The case studies show that the extended computer-aided framework can solve both general product design problems, and find alternative formulations for existing products to avoid ingredients, or improve safety. Two distinct solvent selection methods are applied, shown in Figure 1. A mixture design algorithm selects and optimizes binary solvent mixtures from a database of practical coating solvents, while a molecular design approach uses group contribution methods to design structures which fulfils the formulation needs.

Figure 1. Property constraints set for solvent selection, and number of designed molecular structures or solvent mixtures fulfilling set

constraints.

Acknowledgement

The project was funded by the Sino-Danish Center for Education and Research (SDC) and the Hempel Foundation Coatings Science and Technology Center (CoaST).


Nima Nazemzadeh Interests & Keywords PhD Student • Flocculation Process

PROSYS, KT Consortium • Computational [email protected] Chemistry

• Theoretical Chemistry Supervisors • Hybrid Modelling

Martin Andersson • Process Monitoring Seyed Soheil Mansouri

Krist V. Gernaey

Hybrid multi-scale modelling of flocculation processes using real-time measurement data

Considerable efforts have been dedicated in the past decade to monitoring chemical and biochemical processes to enhance process operation in different ways. Various monitoring techniques have been proposed to be used in industry. Especially real-time measurement techniques have received a lot of attention, as they can facilitate the use of process real-time optimization for further improvements in product quality, production cost, safety and environmental requirements against key process variables [1]. The application of such techniques is increasingly relevant in bio-manufacturing as the processes involved are inherently complex. Additionally, the knowledge about these processes is limited and lacks in many cases predictive models to fully describe the processes. Flocculation is one of the major down-stream bio-manufacturing processes with a wide range of application in many industries such as wastewater treatment, food industry, brewing, and etc. The existing knowledge of the process is limited, and industry relies mostly on heuristic control to achieve desired product quality. The heuristic control approach has led to extensive product losses due to sub-optimal process operation. This work tries to tackle these issues using a hybrid modelling approach that uses data-driven modelling component together with existing first-principle models to provide a potential to develop a predictive model of the process and most likely optimize the process against its process variables. In this work, the application of such hybrid model is demonstrated through a laboratory case study of flocculation process.

Acknowledgement

The project received financial support from the Greater Copenhagen Food Innovation project (CPH-Food), the Technical University of Denmark and Novozymes A/S. Laura Wind Sillesen is acknowledged for carrying out a great portion of experiments and Alina Anamaria Malanca is acknowledged for helping in developing the population balance model.

References

[1] B. Chachuat, B. Srinivasan, D. Bonvin, Computers and Chemical Engineering, 33(10), 2009, 1557-67. [2] C.R. Pearson, M. Heng, M. Gebert, C.E. Glatz, Biotechnology and Bioengineering, 87(1), 2004, 61-8


Rasmus Fjordbak Nielsen Interests & Keywords PhD student • PSE

PROSYS, KT Consortium • CAPE [email protected] • Hybrid Modelling

• Digital Twinning

Supervisors • Machine Learning Krist V. Gernaey

Seyed Soheil Mansouri

Bringing digital twins to the lab: A practical demonstration of digitalizing a crystallization process

It is expected that the 2020s will be the decade in which digital twins will make their big inroad into many industries, with market adoptions projected going from 5% in 2021 to as high as 30-50% by 2030 (reported for life science industries [1]). Some of the major driving factors for this rapid adoption are the expected improved process operations, resulting in better product qualities and/or lower material consumptions. Many of these improvements are expected to be obtained using model-based design strategies in the development of new processes/products and with more advanced control strategies such as model-predictive control. Even so, the price of developing and implementing a digital twin of a process can easily become an implementation barrier. Especially the developing of a sufficient process model can be both time-consuming and costly to a degree where it starts to dilute the potential benefits. Pragmatic modelling strategies, such as hybrid process models [2], where machine learning and first principles modelling are combined, are therefore expected to play a key role in a successful adaption of digital twins in the years to come.

In this presentation, we will present our take on a full-fledged digital twin. The presentation will be based a practical case study of creating a digital twin of a lab-scale crystallization process, where the digital twinning process will be demonstrated using an in-house developed digital twinning software, DelphiTwin. Furthermore, we will illustrate how hybrid semi-parametric models can be integrated seamlessly into this digital twin, allowing for rapid prototyping of process models and the possibility of an on-line parameter-estimation, using a hybrid modelling framework suggested by Nielsen et al. [3]. Lastly, we will illustrate experimentally how digital twins can pave the road for easier implementations of advanced control strategies.

Acknowledgement

The project was funded by the Greater Copenhagen Food Innovation project (CPH-Food), Novozymes, from EU’s regional fund (BIOPRO-SMV project) and from Innovation Fund Denmark through the BIOPRO3 strategic research centre (Grant number 4105-00020B).

References

[1] Accenture, Dassault Systèmes. The critical role of virtual twins in accelerating sustainability, 2021 [2] R.F. Nielsen, N. Nazemzadeh, M.P. Andersson, K.V. Gernaey, S.S. Mansouri. Dansk Kemi, 101(8), 2020, 18-20. [3] R.F. Nielsen, N. Nazemzadeh, L.W. Sillesen, M.P. Andersson, K.V. Gernaey, S.S. Mansouri. Computers & Chemical Engineering, 140, 2020, 106916.


Abhimanyu Pudi Interests & Keywords PhD Student • Process Intensification

PROSYS, KT Consortium • Multiscale Modelling [email protected] • CAPE

• PSE

Supervisors • Quantum Chemistry Martin Andersson

Seyed Soheil Mansouri

Multiscale modelling of biphasic reactive extraction

Over the past couple of decades, process intensification has emerged as a sustainable toolbox toward more capital- and energy-efficient processes via the synergistic combination of process blocks (such as unit operations, tasks, and phenomena) or the application of innovative techniques that enhance mass and heat transfer rates. Since the advent of process intensification, the combination of reaction and separation into a single process unit has found widespread interest and use in both academia and industry. Biphasic reactive extraction (BRE) or extractive reaction is one example of such an intensified configuration where reaction and liquid-liquid extraction are carried out simultaneously. The reaction step is intensified via a shift in equilibrium from the continuous removal of one or more of the products, while the extraction step is intensified due to lower by-product formation, easier recycling of catalyst, and/or easier product purification.1 BRE involves simultaneous reaction and liquid–liquid phase separation in two immiscible phases. The immiscibility may naturally occur within the reactive system or may be deliberately introduced by the addition of a solvent.

Mathematical modelling can be valuable for efficiently analysing and designing these complex systems. For example, solution and reaction properties of the many chemicals involved (reactants, solvents, products, coproducts, and catalysts) need to be described; the extent of miscibility (totally, partially, or effectively immiscible) must be established; the phases where reactions occur need to be identified; and the reaction and mass transfer mechanisms must be established. Also, the effects of chemically inert species on partitioning and of mixture composition on reaction rates must be characterized. However, the commonly used thermodynamic models lack the necessary thermodynamic parameters for every case and are inherently limited to the portion of the chemical design space for which every binary interaction parameter is available.

In this work, an integrated and multiscale modelling architecture is presented to design and simulate BRE systems based on COSMO-based thermodynamic models which do not require any binary interaction parameters. The framework is showcased via an application case study.

Acknowledgement

The project is funded by the Danish Hydrocarbon Research & Technology Centre and the Sino-Danish Center for Education and Research.

References

[1] A. Stankiewicz. Chem. Eng. Process, 42 (3), 2003, 137–144.


Simoneta Caño de las Heras Interests & Keywords PhD student • CAPE

PROSYS Center, KT Consortium • PSE [email protected] • Biotechnology

• Education

Supervisors • Gamification Ulrich Krühne Carina L. Gargalo Seyed Soheil Mansouri

Educational process simulators for the cloud: from what to how

The internet era has evolved into a great revolution pushing forward, among others, the digitalization of processes. Meanwhile, to fully exploit the digitalization paradigm, process simulators will need to be connected to the cloud. Even though it can be a complex problem with multiple steps, this work aims to show how to develop an online simulator in under 20 minutes. We will present the different steps that should be taken to succeed in this task. Furthermore, possible challenges that developing such an online tool might face will be elaborated. To present this stepwise approach, we will demonstrate the educational software we developed, named BioVL (www.biovl.com). BioVL has been fully developed through open-source platforms at the Department of Chemical and Biochemical Engineering, DTU. It targets the training of undergraduates for digitalized bio-manufactories, focusing on explanatory modeling and a “hands-on” approach for teaching model implementations.

Figure 1. Graphical abstract

http://www.biovl.com/


Haoshui Yu Interests & Keywords Postdoc • LNG Cold Energy

PROSYS • ORC [email protected] • Process Synthesis

• Carbon Capture

Supervisors • Decarbonization Gürkan Sin

Optimal liquefied natural gas (LNG) cold energy utilization in an Allam cycle power plant with carbon capture and storage

Oxy-combustion power cycles are an alternative technology for electricity generation to facilitate carbon capture and storage (CCS). Among oxy-combustion power cycles, the Allam cycle is one of the most promising technologies for power generation in terms of both efficiency and economics. Besides, the Allam cycle can also achieve a near-zero emission target at a much lower cost compared to conventional fossil fuel power plants. On the other hand, the flue gas carbon capture process and the recycled flue gas compression process in the Allam cycle consume considerable work. If the compression work can be decreased, the energy efficiency of the system can be further improved, which can enhance the competitiveness over other power generation technologies. When the fuel of the power plant is Liquified Natural Gas (LNG) instead of conventional natural gas, the LNG cold energy can be utilized to reduce the compression work of the carbon capture process and recycled flue gas compression work in the Allam cycle. In this study, we investigated different ways to utilize the LNG cold energy for both a stand-alone power plant and a combined power plant and LNG regasification cogeneration system. A simulation-based optimization framework is adopted to optimize the different systems. Based on the optimization results, the optimal way to utilize LNG cold energy is determined. The results indicate that direct integration of LNG regasification and flue gas liquefaction performs well for the stand-alone power plant, while the organic Rankine cycle integration scheme is the best choice for the cogeneration system.

Fig. 1 Flowsheet of Allam cycle

Acknowledgement

The project was funded by MIT Energy Initiative CCUS Low Carbon Energy Center and the H2020 Marie Skłodowska-Curie Actions-Individual Fellowships (891561).


Yuanmeng Duan Interests & Keywords PhD • Ionic Liquid

PROSYS, KT Consortium • Melamine Tail Gas [email protected] • Process Simulation • Process Evaluation

Supervisors Jakob Kjøbsted Huusom

Jens Abildskov Xiangping Zhang, Chinese Academy of Science (CN)

Process simulation and evaluation for melamine tail gas cleaning with ionic liquids and water scrubbing

With the development of industry, the emission of tail gas such as CO2, NH3, SO2, etc. from industrial tail gas is becoming more and more serious, which is harmful to human body and environment. Melamine production is known to produce tail gas will a significant amount of both NH3 and CO2. At present, the main treatment methods of melamine tail gas are water scrubbing and urea co-production technology. As a new type of solvent, the ionic liquid is composed of positive and negative ions and is a liquid organic salt at room temperature. Because of its designable structure, extremely low vapor pressure, and high gas solubility, it has received wide attention in the field of gas separation. Therefore, a new enhanced process technology based on ionic liquids treatment of melamine tail gas is employed and evaluated for energy and cost efficiency. In this work, a protic ionic liquid named [Bim][NTf2][1] is selected for the evaluation on the melamine tail gas cleaning process. Based on experimental data, a thermodynamic model suitable for process simulations are regressed with the NRTL equation [2]. The steady-state model of the process is built and investigated through sensitivity analysis in Aspen Plus, energy/economic evaluation is carried out by making a comparison with the more traditional water scrubbing technology. The enhanced ionic liquid-based process purification cost is reduced as 50 % of that of water scrubbing process. Moreover, the flowsheet based on the ionic liquid is simpler than water scrubbing method and avoid the wastewater discharge. In summary, the enhanced ionic liquid process could realize energy saving and environment friendly NH3-containing gas purification, which provides a perspective purification technology for the future.

Acknowledgement

The project was funded by Sino-Danish Center for Education and Research (SDC), National Natural Science Foundation of China (21890764, U1610222, 21776277, 21978306), Beijing Municipal Natural Science Foundation (2182071), Major Scientific and Technological Innovation Project in Shandong Province (2019JZZY010518), and Hebei Natural Science Foundation (B2019103011)

References

[1] Shang, D., Zhang, X., …, Zhang, S. Green Chemistry, 19(4), 2017, 937-945. [2] Huang, Y., C.D. Dong, H., …, Zhang, S. AIChE Journal, 59(4), 2013, 1348-1359.


Yuqiu Chen Interests & Keywords Postdoc • Ionic Liquids

KT Consortium • Property Modeling [email protected] • Computer-Aided Design

• Process Optimization

Supervisors • Machine Learning Georgios M. Kontogeorgis

Xiaodong Liang

Machine learning-based aqueous biphasic systems design for the recovery of ionic liquids

Ionic liquids (ILs) have attracted much attention in both academics and industries as promising solvents for a diverse range of applications. Great efforts have been made to facilitate their applications in catalytic processes, extraction, desulfurization, gas separation, hydrogenation, electronic manufacturing, etc. However, there were little industrial processes employing ILs as current time due to the low economic efficiency and environmental concerns of using ILs. To reduce the cost and environmental effects, different technologies have been proposed to recover the ILs from aqueous solutions after their application [1,2]. Among them, aqueous two-phase extraction (ATPE) that based on the formation of the aqueous two-phase systems (ABS) is being widely studied due to it allows the ILs to be efficiently concentrated or recovered in the IL-rich phase [3].

For most IL involved aqueous solutions, the ABS could be formed through adding salting-out agents or changing temperature. Since ABS with diffent ILs and salting-out agents at different temperature generally present different phase behaviors, it is challenging to find optimal ABS for the recovery of ILs. Due to the number of the potential IL-based ABS is so huge, it would be time consuming and expensive by using the trial-and-error approach to search optimal ABS. On the other hand, the optimal design of compounds/systems through manipulating properties at the molecular level is often the key to considerable scientific advances and improved process systems performance. To do this, it is important to have a good thermodynamic model to describe and predict liquid-liquid equilibrium conditions. However, currently such a thermodynamic model is not available for IL-based ABS due to the high complexity of these systems. For this reason, a machine learning-based model, i.e., artificial neural network (ANN)- group contribution (GC) model, is developed for a such purpose. Based on this ANN-GC model, the optimal design of IL-based ABS can be identified by solving a formulated mixed-integer non-linear programming (MINLP) problem. As a proof of the concept, results of the recovery of a hydrophilic IL from aqueous solution are presented.

Acknowledgement

The project was funded by KT Consortium.

References

[1] N.L. Mai, K. Ahn, Y.-M. Koo. Process Biochem. 2014, 49, 872-881. [2] J. Zhou, H. Sui, Z. Jia, Z. Yang, L. He, X. Li. Rsc Advances 2018, 8, 32832-32864. [3] M.G. Freire. Springer, 2016. Germany.


Simon Brædder Lindahl Interests & Keywords Industrial PhD Student • Optimization

PROSYS, KT Consortium • Production Planning [email protected] • Supply Chain Analysis

• Capacity Planning

Supervisors • Pharmaceuticals Gürkan Sin • Industrial Application

Krist V. Gernaey • Knowledge Sharing

Deenesh K. Babi, Novo Nordisk (DK) Marianne Langfrits, Novo Nordisk (DK)

SUPPLYE (Sustainable Upstream Production Planning for Lot-sizing and Yield Evaluation) A Supply Chain Optimization approach for S&OP Planning in the Pharmaceutical Industry

In the pharmaceutical industry, supply of life changing medicine to patients is critical. The topology of the pharmaceutical supply chain up until the active pharmaceutical ingredient (API) manufacturing can be generically categorized. First, is (early) Research where, given a medical conditional, one or a combination of molecules are generated to effectively treat that condition. Second, is Development, typically referred to as CMC (chemistry manufacturing & control) where, given the generated molecule, a feasible prototype of the upstream process (USP) and downstream process (DSP) inclusive of pilot scale is generated. Third, is Technology Transfer where, given the protype process, a near optimal, full manufacturing process to satisfy current and projected market demand is generated. Finally, this is followed by Full scale manufacturing where, given the near optimal USP and DSP, the final integrated design inclusive of USP, DSP plus auxiliary processes, for example the utility process, solvent mixing process etc is generated. Whether new and or existing molecules, the quantity of API produced (kg) plus the timing (t) to do so is critical [1]. Therefore, to analyse the interfaces and interdependences within the supply chain in order to ensure that kg and t are satisfied, a multi-period optimization problem needs to be formulated in terms of (1) Planning, (2) Sales and Operations Planning (S&OP), (3) Scheduling, (4) Sales and Operations Execution (S&OE) [2]. Notwithstanding, the complexity of data, data alignment, data transfer, data curation and data usefulness. The objective of this work is to develop a systematic, SUPPLYE optimization framework that consists of method, algorithms and tools for analysing relevant scenarios for decision making under uncertainty for S&OP and S&OE in API manufacturing.

Acknowledgement

The project is funded by Diabetes API, Novo Nordisk A/S

References

[1] D. F. Pereira, J. F. Oliveira, and M. A. Carravilla, Int. J. Prod. Econ., vol. 228, pp. 1-28, 2020. [2] R. C. Leachman, L. Johnston, S. Li, and Z.-J. Shen, Eur. J. Oper. Res., vol. 238, no. 1, pp. 327-338, 2014.

Figure 1 SUPPLYE framework


Florian Preis

Data Scientist & IBM Quantum Ambassador IBM Client Innovation Center (AT) [email protected]

IBM quantum computing

We discuss what quantum computing is in a nutshell and take a detailed look at the roadmap for scaling quantum computing and how IBM is rethinking its open quantum software ecosystem. In view of this outlook we will see how quantum computing might be leveraged for computational chemistry on error corrected quantum computers as well as on near term systems. We will then have a look at IBM's publicly available development environment for quantum and conclude with an overview of the IBM Quantum Network - a partner ecosystem for advancing quantum computing.


BIO-PROCESS SYSTEM ENGINEERING

Thursday 17 June 2021

0900-1030 Bio-Process Systems Engineering Moderator: Prof. John M. Woodley

0900-0915 Physiological response of Yarrowia lipolytica under industrially relevant dissolved gas heterogeneities PhD Abraham Antonius Johannes Kerssemakers, KT Consortium

0915-0930 Hybrid modelling of industrial scale fermentation process PhD Atli Freyer Magnusson, PROSYS & KT Consortium

0930-0950 Process design for the biotechnological production of xylitol and value-added coproducts Nikolaus Vollmer, PhD PROSYS & KT Consortium

0950-1005 A novel approach to bio-succinic acid manufacturing and downstream processing PhD Alina Anamaria Malanca, PROSYS & KT Consortium

1005-1025 Alternative bioreaction concepts for synthesis and production Prof. John M. Woodley, PROSYS & KT Consortium






Abraham A. J. Kerssemakers Interests & Keywords PhD student • Fermentation

DTU Biosustain, KT Consortium • Yarrowia Lipolytica [email protected] • Transcriptomics

• Dissolved Gasses

Supervisors • Physiology Gürkan Sin

Suresh Sudarsan

Physiological response of Yarrowia lipolytica under industrially relevant dissolved gas heterogeneities

The non-conventional yeast Yarrowia lipolytica is gaining attention because of its versatility in substrate usage, product spectrum, general robustness and increased metabolic understanding [1]. To facilitate the transition to an industrial workhorse, it is crucial to make it into a scale-insensitive strain. Mid- and large scale reactors can pose many potential stressors that are absent in the laboratory, thereby complicating upscaling efforts. For this project specifically, the focus lies on dissolved gasses (O2 and CO2) and their effect on the yeasts physiology. A better understanding of these interactions would ultimately facilitate a more efficient process development.

The potential stresses induced by these gasses are inherently connected to both cellular metabolism and reactor engineering. To better understand the cellular regulatory mechanisms, a transcriptomic study under a wide variety of conditions is deployed. The large dataset is, together with publicly available data, used in an independent component analysis (ICA) to identify, extract and map the co-regulated and independently-modulated gene sets (iModulon) to the transcriptional regulatory network of Y. lipolytica [2]. Together with a better basic understanding of kinetics and growth parameters through chemostat experiments, this sets the baseline for metabolic understanding of Y. lipolytica. Subsequent oscillation experiments with varying O2 and CO2 concentrations and analysis with a variety of omics and growth kinetic data should assist in identifying the metabolic and transcriptional dynamics of this industrial production host when exposed to large scale challenges. As a case study, a previously modified succinic acid producing strain, originating from wild-type Y. lipolytica W-29 will be used for this research [3].

Acknowledgement

The project was funded by the Novo Nordisk Foundation in the framework of the Fermentation Based Biomanufacturing initiative (NNF17SA0031362)

References

[1] J.M. Nicaud. "Yarrowia lipolytica." Yeast 29.10 (2012): 409-418 [2] K. Rychel, K. Decker, A.V. Sastry, P.V. Phaneuf, S. Poudel, B.O. Palsson. (2021). iModulonDB: a knowledgebase of microbial

transcriptional regulation derived from machine learning. Nucleic Acids Research, 49(D1), D112-D120. [3] M. Babaei, K. Rueksomtawin Kildegaard, A. Niaei, M. Hosseini, S. Ebrahimi, S. Sudarsan, I. Borodina. (2019). Engineering

oleaginous yeast as the host for fermentative succinic acid production from glucose. Frontiers in bioengineering and biotechnology, 7, 361.


Atli Freyr Magnússon Interests & Keywords Industrial PhD • Hybrid-Modelling

LEO Pharma A/S, KT Consortium • Pharmaceuticals [email protected] • Bioprocess

Supervisors Gürkan Sin

Stuart M. Stocks, LEO Pharma A/S (DK) Jari Pajander, LEO Pharma A/S (DK)

Hybrid modelling of industrial scale fermentation process

The production of pharmaceuticals has always had a strong focus on quality. A common method for producing Active Pharmaceutical Ingredients (API) is via biological processes or fermentation of high producing bacteria or fungal strains. These processes have been subject to mechanistic modeling for use in batch optimization, monitoring and control[1][2][3]. However, the metabolic pathways responsible for the main product can lead to accumulation of related substances in the batch that hamper the final product quality and may be impossible to remove in the downstream process. Mechanistic models rarely take batch quality into account and most model development is focused on main product only. Additionally, biological systems are notoriously complex so sufficient knowledge to build a biologically structured mechanistic model is rare and extremely difficult to obtain.

The biotechnological industry increasingly applies mechanistic models because it has realized their significance [4]. Due to the complexity of the biological systems a hybrid model approach is used as an alternative. The concept of hybrid modelling in this context is the combination of a first principles mechanistic model and machine learning models into single model. Machine learning and Artificial Intelligence algorithms such as Artificial Neural Networks (ANN) have seen an increase in popularity in various research fields and the use of Hybrid modelling has seen success in chemical engineering such as in particle processes [5]

Figure 1. Hybrid model structure for a fermentation batch


This study focuses on the application of a hybrid modelling framework on the fermentation of a filamentous fungi that produces Fusidic Acid. A first principles biochemical model is built influenced by existing literature based on penicillin fermentation models[6]. Sophisticated ANN models are subsequently trained to predict the kinetic expressions for the evolution of by-products. The fully integrated hybrid model is subsequentially used for predicting productivity and quality of industrial scale batches currently in production and further process optimization to maximize the yield of a single batch without compromising the quality of the final product.

Acknowledgement

The project is an Industrial PhD program with collaboration between DTU and LEO Pharma A/S with additional funding from Innovationsfonden.

References

[1] K.V. Gernaey, A.E. Lantz, P. Tufvesson, J.M. Woodley, G. Sin. Trends in Biotechnology, 28(7), 2010, 346-354. [2] L. Mears, S.M. Stocks, M.O. Albaek, G. Sin, K.V. Gernaey. Trends in Biotechnology, 35(10), 2017, 914-924. [3] R.L. Fernandes, V.K. Bodla, M. Carlquist, A.L. Heins, A.E. Lantz, G. Sin, K.V. Gernaey. Advances in Biochemical Engineering/Biotechnology, 132(Dec 2012), 2013, 137-166. [4] R. Spann, C. Roca, D. Kold, A.E. Lantz, K.V. Gernaey, G. Sin. Biochemical Engineering Journal, 135, 2018, 49-60. [5] R.F. Nielsen, N. Nazemzadeh, L.W. Sillesen, M.P. Andersson, K.V. Gernaey, S.S. Mansouri. Computers and Chemical Engineering, 140, 2020, 106916 [6] G. Birol, C. Ündey, A. Çinar. Computers & Chemical Engineering, 26(11), 2002, 1553-1565


Nikolaus I. Vollmer Interests & Keywords PhD Student • CAPE

PROSYS, KT Consortium • PSE [email protected] • Process Design

• Bioprocesses

Supervisors • Optimization

Gürkan Sin Solange I. Mussatto Krist V. Gernaey

Process design for the biotechnological production of xylitol and value-added coproducts

Xylitol is a sugar substitute with manifold beneficial health properties that gained significant attraction in the last decade [1]. There exists a great potential to be produced biotechnologically with engineered cell factories, as opposed to the current chemical production process, which imposes high requirements regarding, e.g., purity and consequently leads to high product prices [2]. Moreover, lignocellulosic biomass as feedstock for the biotechnological process adds further benefits in terms of sustainability. However, the use of lignocellulosic biomass as feedstock introduces several challenges for the conceptual design of the biotechnological processes, as considerations about potential value-added co-products, pretreatment technologies, and possibilities for process integration amongst others, as shown in Figure 1.

In the presented study for a base-case process design, a synergistic optimization-based process design framework (S3O) is used to overcome the named hurdles and to conceptually design this process [3]. In this base case, succinic acid and sustainable aromatic kerosene are chosen as value-added co-products and wheat straw as feedstock. Also, the generation of heat as a product for possibly integrating it with the other products' downstream processes is considered. As the objective function in the framework, key performance indicators (e.g., net present value) are selected. The resulting process itself is evaluated against both criteria of being economically viable compared to the chemical process and being sustainable.

Figure 1. Set of primary and value-added co-products for the biotechnological production of xylitol

Acknowledgments

The project is part of the Fermentation-Based Biomanufacturing Initiative at DTU and is funded by the Novo Nordisk Foundation under the grant NNF17SA0031362.

References

[1] A.F. Hernández-Pérez et al. Critical Reviews in Biotechnology, Vol. 39(7), 2019, pp. 924-943. [2] Y.D. Arcaño et al. Catalysis Today, Vol. 344, 2020, pp. 2-14. [3] N.I. Vollmer et al. Frontiers in Chemical Science and Engineering, under review.


Alina Anamaria Malanca Interests & Keywords PhD Student • Biorefinery

PROSYS, KT Consortium • Membrane Technology [email protected] • Downstream Separation

Supervisors • Fermentation

Manuel Pinelo • Process design Seyed S. Mansouri

Hariklia N. Gavala Ioannis V. Skiadas Jianquan Luo, Chinese Accademy of Science (CN)

A novel approach to bio-succinic acid manufacturing and downstream processing

Succinic acid is one of the most import chemical building-blocks. Its fermentative production from renewable carbon source is a promising alternative to the petrochemical synthesis, due to a more sustainable and green process. Production of succinic acid from biomass have indeed been successfully achieved and implemented at full scale. However, it is not yet economically competitive with its equivalent oil-based version.

Recently, an intensive research has been done in terms of process synthesis and techno-economic analysis in order to find the best-processing roots for the production of bio-succinic acid. One of the most important results of these studies was to identify glycerol as the best feedstock and the electrolytic cell as a novel unit operation which could potentially substitute many other unit operations.

This work is focused mainly on the integration of fermentation and separation process in an electrolytic device. Membrane electrolysis is a novel electrochemical extraction technique in which electrodes are present in the fermentation broth to drive (bio)electrochemical reactions while utilizing electro-motive forces to transport charged acid anions from a cathode chamber across an anion exchange membrane into an anode site. The advantages of this set up are several, both economic and environmental: (i) continuous extraction of products, avoiding product inhibition of microorganism; (ii) reducing the base addition to the fermentation, due to the production of OH- in the cathode as a consequence of water electrolysis; (iii) acidification of the succinate to succinic acid by the H+ ions produced in the anode; (iv) H2 produced in the cathode can regenerate NADH in many microorganisms, leading to improved conversion yields.

The above-mentioned hypothesis has been implemented with a first simple setup made by electrochemical cell, anionic exchange membrane and broth produced by A. succinogenes, obtaining a very promising proof of concept. The work will proceed by integrating the electrolytic cell with a continuous fermentation of A. succinogenes in which the fermentation broth will recirculate between the fermenter and the cathode of the electrochemical cell. Eventually, a new membrane reactor will be designed with anionic exchange membrane submerged directly into the fermenter.

A further attractive option would be the integration of the bioethanol-production process with the bio-succinic acid production process. Indeed, the glycerol and the CO2 are by-products of bioethanol production, but they can represent a secure long-time supply of feedstock to the bio-succinic acid production.


Acknowledgement

The project was funded by the Sino-Danish Center.

References

[1] E. Mancini, S.S. Mansouri, K.V. Gernaey, J. Luo, M. Pinello. Critical Reviews in Environmental Science and Technology, 50:18, 2019, 1829-1873. [2] R. Dickson, E. Mancini, N. Garg, J. M. Woodley, K. Gernaey, M. Pinelo, J. Liu, S. S. Mansouri. Energy & Environmental Science, 2021. [3] C. Pateraki, S. J. Andersen, D. Ladakis, A.Koutinas, K. Rabaey. Green Chemistry, 21, 2019, 2401-2411.


John M Woodley Interests & Keywords Professor • Bioprocesses

PROSYS, KT Consortium • Bioreaction [email protected] • Fermentation

• Biocatalysis

Alternative bioreaction concepts for synthesis and production

It is widely believed that synthesis and production of fuels, chemicals and proteins via fermentation will deliver sustainable manufacturing in the future. Such bio-manufacturing may indeed prove very attractive in some cases where sustainable (renewable, cheap and available) feed-stocks can be used, combined with efficient bioconversion technology. However, not all cases will prove sustainable. Indeed one challenge today is that fermentation (growth-associated product formation) has several limitations when applied at larger scales. In the last decade, several alternative bioreaction concepts have been developed and in the last few years driven by increasing emphasis on sustainability, they have gained considerable interest as an alternative. These concepts include two-stage fermentation (decoupled growth and conversion) [1], cell-free bioprocessing [2] and biocatalysis [3]. Combinations of these technologies are also possible. In this brief presentation, recent developments in each of these areas will be presented. There is still research to be done to make these concepts widely applicable, but already it is clear that new opportunities will be forthcoming.

References

[1] J.M. Burg, C.B. Cooper, Z. Ye, B.R. Reed, E.A. Moreb, M.D. Lynch. Curr. Opin. Chem. Eng., 14, 2016, 121-136. [2] N.J. Claassens, S. Burgener, B. Vögeli, T.J. Erb, A. Bar-Even. Curr Opin. Biotechnol., 60, 2019, 221-229. [3] J.M. Woodley. Appl. Microb. Biotechnol., 103, 2019, 4733-4739.


PLENARY SESSION


1100-1240 Plenary Session Moderator: Assoc. Prof. Jens Abildskov

1100-1120 IL Pro: A new software assisting ionic liquid applications Software Manager Guoliang Wang, CERE & KT Consortium

1120-1140 Quantum computing for product and process design Assoc. Prof. Seyed S. Mansouri, PROSYS & KT Consortium

1140-1200 Tray efficiency comparisons: Operation modes Assoc. Prof. Jens Abildskov, PROSYS & KT Consortium

1200-1220 Process Systems Engineering: Overview of current activities and new initiatives Prof. Gürkan Sin, PROSYS & KT Consortium Deputy Leader

1220-1240 KT Pilot Plant 4.0 Postdoc Mark N. Jones Sin, Pilot Plant DTU

1240-1300 Discussion


Guoliang Wang Interests & Keywords Software Manager • IL Pro

AT-CERE, KT Consortium • Ionic Liquid [email protected] • Property Database

• Property Prediction

IL Pro: A new software assisting ionic liquid application

Ionic liquids (ILs) have many potential applications in various industries and they represent a rapid growing market. IL Pro, a new comprehensive software assisting ionic liquids application, is under development at KT Consortium. The aim of our software is to evaluate and identify (new) IL at a fast pace by using reliable methods.

The software is developed mainly based on the results of the Ph.D. project by Yuqiu Chen (currently Postdoc) supervised by Prof. John M. Woodley and Prof. Georgios M. Kontogeorgis. A large number of experimental data, as well as validated models covering 22 commonly used physical and thermodynamic properties have been integrated into the software. These properties include density, heat capacity, viscosity, surface tension, melting point, electric conductivity, gas solubility (13 gases) and so on. IL Pro is designed to have various toolboxes such as IL DB Manager, IL ProPred, IL Molecular Design, IL Pro Tutorial and more. The IL DB Manager enables users to search different property data. In case experimental data does not exist, or if ILs have not even been synthesized, IL ProPred can be used to calculate the missing data using integrated models. This is essential to find novel high-performance ILs for different industry applications. The IL Molecular Design can be used to identify IL compounds that match specific requirements set by the user. The first version of IL Pro will have IL DB Manager and IL ProPred toolboxes and it will be released in June 2021. The IL Molecular Design and other toolboxes will be integrated into IL Pro in future versions.

Figure 1. IL ProPred toolbox screenshot.


Seyed Soheil Mansouri Interests & Keywords Associate Professor • Quantum computing

PROSYS, KT Consortium • Process design [email protected] • Product design

• PSE

Quantum computing for product and process design

While quantum computing has been in development for quite some time, the development of the technology to the point of making commercial use of such resources is quite recent, and still quite limited in scope. However, projections point to a very rapid development of quantum computing resources becoming available to academia and industry, which opens up new potential application

areas. With the advent of hybrid algorithms, able to take advantage of both classical computing and quantum computing resources dynamically as quantum computing grows, more and more problems relevant for chemical product design become solvable. In this paper, we have given our views for some of these upcoming applications, such as quantum chemistry-based property prediction, protein folding, complex multi-step chemical reactions, multivariate process monitoring and molecular reaction dynamics.


Jens Abildskov Interests & Keywords Associate Professor • Separation Models

PROSYS, KT Consortium • Thermodynamics [email protected] • Energy Systems

• Process Design

Tray efficiency comparisons: Operation modes

In the area of periodic separations, there has been traditionally 2 types of operating modes of the draining period.

One way is to drain the reboiler first as bottoms product. Then, the bottom tray is drained into the now empty reboiler, the next tray is drained into the bottom tray, and so forth. This is the sequential1-2 liquid movement operating procedure.

An alternate operating procedure that has been demonstrated on commercial scale, transfers all the liquid simultaneously with no liquid mixing between trays. This is the simultaneous3 liquid movement operating procedure.

In either case, draining takes some fraction of time out of the full cyclic period.

In this work, exact expressions of efficiencies of either operational form are derived from models of simple separations4.

The results are compared for various types of counter-flow separations (stripping, absorption), in order to arrive at a more complete understanding of the pros and cons of either operational form.

References

[1] Toftegård B., et al., Ind. Eng. Chem. Res., 55 (6), 2016, 1720. [2] Fazlollahi F., Wankat P., Ind. Eng. Chem. Res., 59, 2020, 21914−21929. [3] Maleta V.N., Kiss A. A., Taran V.M., Maleta B.V., Chemical Engineering and Processing: Process Intensification, 50(7), 2011, 655-664. [4] Toftegård B., Bay Jørgensen S., Ind. Eng. Chem. Res., 27(3), 1988, 481-485.


Gürkan Sin Interests & Keywords Professor • PSE

PROSYS, KT Consortium • Monte Carlo Simulation [email protected] • ML/AI • Process modeling

• Process design • Process synthesis

Process Systems Engineering – overview of current activities and new initiatives

In today’s chemical industries commercial software tools employing state-of-the-art models and advanced optimization and control algorithms are used at different stages of the project life cycle, from early stages performing front-end engineering design to retrofitting and optimization studies at the plant commissioning/operation stage. The impact of advanced process modeling and simulation, optimization and control is profound and has become mainstream in the chemical industries due to the significant economic benefits achieved. These are amongst crown achievements of the process systems engineering community in Chemical Engineering discipline through research in mathematical programming, modeling, process synthesis and design and process control that has been performed in the past decades. Today there are new driving forces affecting the bottom line of chemical industries but also universities alike namely digitalization, machine learning/Artificial Intelligence (AI), climate change, decarbonization, sector coupling through renewable energy, etc. These technologies open up new horizons for industry to become more efficient, to decrease CO2 footprint and to develop innovative products and services. In this talk, I will present an overview of current research activities and new initiatives to study and exploit these new paradigms and integrate them with process systems engineering in the process industries. A number of examples from ongoing research projects will be used to highlight this new vision for a model-based engineering research: (1) application of big data analytics and deep learning to plant data for process analysis, understanding and modeling – an industrial example for N2O (greenhouse gas) emission control on WWTP; (ii) identify and take advantage of new methods and tools within machine learning and simulation based optimization strategies to directly use first principles/mechanistic models in process synthesis; (iii) integrate knowledge-based (mechanistic) and data-driven (machine learning) approaches to develop hybrid predictive models with better accuracy for advancing process modeling –two examples from biopharmaceuticals (iv) Application of deep learning approaches and techniques in the field of pure component property predictions. I will finally comment with new initiatives to drive forward the PSE agenda.


Mark Nicholas Jones Interests & Keywords

Postdoc • Industry 4.0 PILOT PLANT, DTU • Digital Twins

[email protected] • OPC • Automation

• Education, VR/AR Khosrow Bagherpour

Chief Consultant PILOT PLANT, DTU [email protected]

KT Pilot Plant 4.0

The pilot plant of the Chemical and Biochemical Engineering Department at DTU (DTU Kemiteknik) serves as a research & education facility with process equipment applied within the chemical and biochemical engineering domain. PILOT PLANT accommodates many units of operation, which are the building blocks for highly relevant industrial processes in up- and down-stream processing. PILOT PLANT accommodates units such as reactors, fermenters, membranes, distillation columns, extractors, crystallizers and chromatography columns. These units are perfectly suited in combination with KT’s laboratory facilities to perform scale up studies with the capabilities of a modern digital infrastructure. Over the years, PILOT PLANT has been extended with new units to various setups with different degrees of automation. Some units are only manually operable while other can be operated through human machine interfaces (HMI).

In line with DTU’s strategic objectives, DTU Kemiteknik focuses on the development and application of an Industry 4.0 framework for its research and educational activities. Therefore, the department’s pilot plant and laboratory facilities are going through a digital transformation by creating a suitable infrastructure that provides remote accessibility to all research and operation data. Full connectivity will be provided between distributed pilot plants and equipment. Furthermore, the developed infrastructure includes virtual and augmented reality applications while digital twins of the physical units of operation will allow applying machine learning (ML) for novel control methods and new sensor technologies. We believe this will enable our researchers to make even more rapid advancements within product design, process control and production across chemistry, biotechnology, food, pharma and energy.

This presentation aims to give a current overview of the progress we are making and to get some feedback from all members of KT-Consortium to accommodate to their needs and research interest for future collaboration projects. The PILOT PLANT 4.0 team aims to provide a basis for excellent and future cutting-edge research for everybody at our great department and KT-Consortium.


RENESENG II


1400-1410 Introduction Prof. Antonis Kokossis, National Technical University of Athens - NTUA (GR)

1410-1430 Ex-ante LCA and advanced data-driven modelling in selecting feedstocks and products Postdoc Paraskevi Karka, Chalmers University of Technology (SE) & NTUA (GR)

1430-1450 Gasification-FT-fermentation for the production of waxes and methane from syngas: ALMAGREEN R&D Project manager Enrique Montiel, Greene Waste to Energy (ES)

1450-1510 Process developments in scaling-up and scaling-down hydrothermal liquefaction Assoc. Prof. Ib Johannsen, Bio2Oil (DK) & Aarhus University (DK)

1510-1530 Early-stage capital cost estimation of biorefineries at low TRLs Dr. Mirela Tsagkari, NTUA (GR), Dr. Jean-Luc Dubois, ARKEMA (FR)

1530-1550 Systems integration for the holistic design of lignocellulosic biorefineries Postdoc Aikaterini Mountraki, NTUA (GR) & CIMV (FR)

1550-1610 Integration of CAPE models and data for the domain of biorefining: Inter-CAPE model ontology design Postdoc Linsey Koo, University of Surrey (UK)

1610-1630 The use of GVL for the holistic utilization of biomass PhD Andreas Pateromichelakis, École Polytechnique Fédérale de Lausanne (CH)

1630-1650 A semantic repository for classification and characterisation of organic waste Dr. Edlira Kalemi, University of Surrey (UK)


Paraskevi Karka Interests & Keywords Postdoc • Early Stage Assessment

Chalmers University of Technology • Classification Trees [email protected] • Data Mining

Co-authors Stavros Papadokonstantakis, Chalmers University of Technology (SE) Antonis Kokossis, National Technical University of Athens (GR)

Ex-ante LCA and advanced data-driven modelling in selecting feedstocks and products

The study aims at the development of a data science-based framework for the assessment of environmental sustainability of bio-based processes in early design phases. These processes in their greatest majority exist in pilot and lab scale, where process related information for life cycle inventories (LCI), which are required for the LCA methodology, are sparse. The proposed framework advocates the development of ex-ante LCA as an alternative approach to evaluate sustainability. The framework is data driven and exploits knowledge gathered from simulations and pilot scale data providing estimations of LCA “ahead of detailed design”.

The proposed computational framework employs machine learning techniques, i.e. neural networks in combination with classification trees for mapping relations among input and output data. Predictor variables refer to the molecular structure of the bio-chemical or bio-fuel product of interest and production process related information that may be known in early design phases.

The database of bio-processes that was used to derive the data science models is based on previous work of Karka et al. (2017, 2019). It consists of 138 data sets of biomass process chains and their respective LCA metrics according to the ReCiPe and the CED impact assessment methods, and various allocation approaches (mass, economic and substitution). The black-box neural network models of this work complement the classification trees by providing point estimations of the LCA metrics. This model complementarity of the proposed framework increases the robustness of the prospective LCA estimations for bio-based processes. About half of the neural network models have coefficient of determination values (Q2) higher than 0.6. Their performance is mainly affected by the allocation scenario and the number of predictors. ANN models for selected LCA metrics of good performance are identified (e.g., CC, CED, for mass allocation and the reduced dataset) are further applied in out of sample case studies in parallel with classification trees. The parallel use of both techniques demonstrates how they can complete each other to streamline LCA estimations ahead of detailed design stages.

Acknowledgement

This work has received funding from European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie RISE action, grant agreement No 778332, project RENESENG II.

References

[1] Karka P, Papadokonstantakis S, Kokossis A. Int J Life Cycle Assess 2017;22(9):1418–1440 [2] Karka P, Papadokonstantakis S, Kokossis A. Int J Life Cycle Assess 2019;24(9):1675e700


Enrique Montiel R&D Project managemer Greene Waste to Energy (ES) [email protected]

Gasification-FT-fermentation for the production of waxes and methane from syngas: ALMAGREEN

The ALMAGREEN project aims to establish the technological bases of a flexible and reconfigurable system for the production and storage of renewable energy, depending on the demand and availability of input material. We use waste of different nature and origin (as feedstock we will use from biomass to CDR / plastics) conditioned (composition, humidity and size) and we apply thermochemical and biological technologies for its transformation. As process technologies, we combine pyrolysis, gasification and fermentation with SOEC electrolysis as an adjuvant for the supply of hydrogen and oxygen. The final products will be renewable methane obtained by fermentation of syngas, hydrogen and renewable liquid fuels obtained by hydrotreating / hydrocracking of pyrolytic oils.

The proposed process consists of the recovery of waste through its transformation to renewable methane, fuels and hydrogen. For this, it starts from a waste stream to which, firstly, its moisture content will be reduced to 5-10%. Once conditioned, the waste will be treated in a pyrolyser, from which a solid fraction (2-5%) and a gaseous fraction (95-98%) will be obtained and / or in a gasifier. The solid fraction, depending on its ash content, will be considered for a gasification treatment with O2 with which a stream of H2, CO and CH4 would be obtained mainly to be used in the process (electrolysis). The gaseous fraction, or pyrogas, consists of a mixture of H2 and different hydrocarbons, many of them condensable. Thus, a liquid fraction (pyrolytic oils) and a gaseous one (H2 and C1-C4) can be obtained from said pyrogas stream by condensation.


Ib Johannsen Honorary Associate Professor Bio2Oil & Aarhus University (DK) [email protected]

Process developments in scaling-up and scaling-down hydrothermal liquefaction

Creating a future void of fossil fuels requires rethinking common practices even in chemical engineering. One of the promising technologies is the hydrothermal conversion of (waste) biomass using hydrothermal liquefaction (HTL) to produce biocrude, a high energy density feedstock for production of liquid fuels and materials. This process involves handling aqueous biomass slurries at high temperature and pressures (>600K, >20MPa) and a classical chemical engineering approach would be to go for economy of scale with central utilities. However, since biomass sources are scattered as opposed to fossil sources, and transporting the voluminous or water filled feed streams are detrimental for economy and environment, such large central facilities often turn out to be non-competitive. Moreover, one of the main CAPEX cost drivers is the heat exchange, a unit operation that barely show improved economy of scale.

In this talk, a decentral approach to HTL using innovative new technologies to scale a 25000 ton feed/yr plant fit into just two shipping containers for decentral deployment in a cost and energy efficient manner, which will allow scaling the process simply by the number of units.

Acknowledgement



Mirela TSAGKARI Interests & Keywords Cost Estimation Manager • CAPEX

ERAS Engineering (FR) • Investment [email protected] • Biorefineries

• PSE

Co-authors • Estimation Antonis Kokossis, NTUA (GR)

Jean-Luc Dubois, Arkema (FR)

Early-stage capital cost estimation of biorefineries at low TRLs

This work outlines a framework for rapid capital and operating cost estimation of evolving biorefineries. The cost models are addressed to the biorefinery community and aim to assist chemists in selecting economically viable biorefinery routes and engineers in avoiding laborious flowsheeting with uncertain economic analyses at process conception. The models assist in early-stage decision making for budget allocation in a portfolio of different projects and estimation of budget overrun risks. The framework sets the basis for the systematic cost modelling and estimating of biorefineries and is reproducible and extendable to other process groups.

Figure 1. Risk S- curve for a biodiesel plant

Acknowledgement

This work has received funding from European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie ITN Network, grant agreement No FP7-607415, project RENESENG and Marie Skłodowska-Curie RISE action, grant agreement No 778332, project RENESENG II.

References

[1] M. Tsagkari, Methodology of rapid evaluation of capital and operating cost of biorefineries with applications in multi-scale problems, NTUA, School of Chemical Engineering, 2017 [2] M. Tsagkari, AC. Kokossis, JL. Dubois, Biofuels, Bioproducts and Biorefining 14 (5), 1061-1088 [3] M. Tsagkari, JL. Couturier, AC. Kokossis, JL. Dubois, ChemSusChem 9 (17), 2515-2515


Aikaterini Mountraki Interests & Keywords Postdoc • Biorefinery

AU (DK), NTUA (GR), CIMV (FR) • CAPE [email protected] • Optimisation

• PSE

Co-authors • Energy Integration Bouchra Benjelloun-Mlayah, • Process Development CIMV (FR) • Green Deal

Antonis Kokossis, NTUA (GR)

Systems integration for the holistic design of lignocellulosic biorefineries

Biorefineries can be the cornerstone for the shift to a sustainable economy since they assist in the decarbonization of multiple sectors (e.g., energy, food, chemical industry). In order to achieve carbon neutrality by 2050, more technologies need to reach the industrial scale. However, the development of integrated biorefineries is a multidimensional problem. The traditional process design problem is combined with the complex problem of synthesizing product portfolios and multiple processing paths at various technological readiness levels. The complexity and the size of the combined problem do not allow the development of a single optimization model.

This work introduces procedures to tackle systematically the design decision problems that come up during the industrial development of second-generation biorefineries. The framework uses models of different detail levels to explore the trade-offs between operating and capital cost throughout the value chain. Options for various feedstocks, pre-treatment technologies, product portfolios, production paths, and waste treatment technologies are evaluated. Implications of utility consumption are also addressed. Industrial synergies, retrofit, and grassroots design are considered for steady-state operation. The procedures combine tools for process flowsheeting, surrogate-based optimization, pinch analysis, graphs, superstructures, process synthesis, and mathematical optimization. The aim is not to propose a single optimization method for finding the optimum process design. The intention is to develop and combine tools of different design scales that can help engineers and investors set targets and screen design options during the different phases of industrial process development.

Challenges include (i) the management of the data acquired by various sources (literature or experimental) and at various accuracy levels due to different technology readiness levels (pilot, laboratory, or theoretical stage), (ii) the systematic screening of processing paths while considering the technological attributes, (iii) the impact of alternative designs on the utility consumption (mass and energy), (iv) the endogenous symbiotic options when revamping existing installations, and (v) the integrated waste management.

Acknowledgement



Linsey Koo Interests & Keywords Postdoc • Biorefining

University of Surrey (UK) • CAPE Models [email protected] • Ontology Design

• Model Integration

Co-authors Edlira Vakaj, University of Surrey, UK Nilay Shah, Imperial College London (UK) Franjo Cecelja, University of Surrey (UK)

Integration of CAPE models and data for the domain of biorefining: Inter-CAPE model ontology design

With increasing number of specialised CAPE (computer aided process engineering) tools and methods, the search ability of models and data developed by heterogeneous tools in biorefining and concomitant integration of these models and data remains a challenge. The best way to retain these valuable models and data that often resides only within individuals, is to store in such a way to make their future retrieval and reuse as easy as possible. In order to tackle this challenge, this paper introduces a semantic based decision support framework that enables interoperability between process models and data. Following on already established CAPE-OPEN framework for model integration, new techniques benefiting from semantic technology have emerged as a solution to improve knowledge capture, knowledge reuse and knowledge sharing. Semantic modeling is used to depict all relevant concepts in an ontology by capturing the associations, ensuring the understanding of the exchanged knowledge during models and data interoperation. In order to establish the automated integration of heterogeneous models and data in the domain of biorefining, a well-defined domain ontology, OntoCAPEmodel ontology, has been presented.

The domain ontology, InterCAPEmodel Ontology, is designed to provide a robust and shared understanding of the domain by providing common vocabulary and consequently improved visibility of such models and data to be discovered and shared. It is based on the textual specification of rules and interfaces defined in CAPE-OPEN standard (Belaud and Pons, 2002). The conceptual design of semantic architecture of models and data are formulated by a Semantic Web Services (SWS) using Web Ontology Language semantic mark-up for service (OWL-S) framework. Establishing interoperability is supported by each stages of semantic web services lifecycle with semantics expressed in OWL-S. The domain ontology, InterCAPEmodel ontology, allows richer semantic specification, which supports registration stage. The ontology allowed users to navigate through the taxonomy and assist in registering a model or data (i.e. process, supply chain models or data) as an instance in a semantic repository where can be searched, retrieved and reused by the community. The matching process was employed as a method to enable more flexible automation of models and data discovery, using partial matching techniques for inputs and outputs. Users are then given choices of ranked discovered models and data based on the requirements to assist them to make an inform decision. Once the relevant models and data sets are chosen, the demonstration of establishing interoperability was performed by composition and execution process. In this paper, the ontological approach is demonstrated the full utilisation of modelling


knowledge that can be coupled with the potential of individual expertise to identify the best suited model for the integration of CAPE models and data in the domain of biorefining.

Acknowledgement


References

[1] Belaud, J. P. and Pons, M. (2002) ‘Open software architecture for process simulation: The current status of CAPE-OPEN standard’, Computer Aided Chemical Engineering, 10(C), pp. 847–852. doi: 10.1016/S1570-7946(02)80169-9.


Andreas Pateromichelakis PhD EPFL (CH) Co-authors Francois Marechal, EPFL (CH) Antonis Kokossis, NTUA (GR)

The use of GVL for the holistic utilization of biomass

The work presents an integrated approach for the holistic utilization of lignocellulosic biomass based on the “lignin-first” concept. Biomass is fractionated by means of γ-Valerolactone (GVL) and Formaldehyde solvents to effectively extract and protect lignin and xylose ingredients. The process flowsheet and simulation of a large-scale biomass fractionation technology – recently validated in laboratory scale by Shuai et al. [1] – are developed to test and build performance in use of energy, water, and materials. The biorefinery value chain additionally integrates chemistries, for the production of platform chemicals and biofuels (furfural, levulinic acid and lignin-aromatics), while the C6 sugars fraction is partially converted into GVL to offset solvent losses. Finally, energy integration and techno-economic analysis resulted in up to 6% steam savings, 1.8 MW power cogeneration and 37 Μ€/yr net revenues for the overall biorefinery.

Acknowledgement


References

[1] L. Shuai, M.T. Amiri, Y.M. Questell-Santiago, F. Héroguel, Y. Li, H. Kim, R. Meilan, C. Chapple, J. Ralph, J.S. Luterbacher, 2016. Formaldehyde stabilization facilitates lignin monomer production during biomass depolymerization. Science, 354, 6310, 329-333.


Edlira Vakaj (Kalemi) Interests & Keywords Lecturer of Computer Science • Organic Waste Birmingham City University (UK) • Semantic Technologies [email protected] • Ontologies • Semantic Matching

Co-authors Linsey Koo, University of Surrey (UK)

Franjo Cecelja, University of Surrey (UK)

A semantic repository for classification and characterisation of organic waste

Waste management and recycling for social and environmental impact play a crucial role in circular economy [1]. Despite that, there is great unrealised potential in the high volumes of organic waste. There is a vast number of models and data sources from various research or industrial communities that can be reused. However, this comes from various research, industrial, and governmental entities and in different formats i.e. data sets from experiments, research studies, libraries, etc. The heterogeneous nature of this information is a major source for inefficient assessments.

To address the mentioned challenges, this paper proposes a semantic platform for classifying organic waste and explicitly defining its characteristics that links cross-domain information, infers implicit knowledge, and empowers researchers and decision-makers with insightful assessments and reuse.Semantic web technologies are well recognized for digesting heterogeneous data formats and for overcoming interoperability issues in cross-domain problems. To give semantic context to this linked dataspace, previous research has defined some ontologies (e.g. OntoCAPEmodel, BiOnto) for specific domains of biorefining as well as ontologies for establishing interoperability and integration of data and processes (e.g. OWL-S, InterCAPEmodel Ontology) [2]. The semantic repository semantically describes and classifies organic waste, its characteristics, related processes, and technologies. The knowledge that resided in the repository can be discovered and flexibly reused for discovering unlocked knowledge or simulating different pathways of data and model integration.

Furthermore, the repository is being tested for processing text resources such as research papers to continuously enrich the knowledge graph of organic waste with recent research developments. The technique used converts plain text to ontology using Natural Language Processing (NLP) that extract structured data (entities and relationships) from unstructured data (i.e. text).

Acknowledgement


References

[1] E Kalemi, L Koo, N Trokanas, F Cecelja, CAPE: A Circular Economy Perspective, - 2019 American Institute of Chemical Engineers (AICHE) Annual Meeting, Orlando, 2019. [2] L. Koo, N. Trokanas, and F. Cecelja, “A semantic framework for enabling model integration for biorefining,” Computers & Chemical Engineering, vol. 100, pp. 219-231, 2017.


KT CONSORTIUM MATERIALS

Software updates IL Pro – a Comprehensive Software Assisting Ionic Liquid Application

KT Consortium Publication Record 2019-2021

KT Consortium Member Website presentation

Course Offer Advanced On-line Course on Thermodynamic Models: Fundamentals & Computational Aspects

Summer School on Uncertainty and Sensitivity Analysis of Numerical Models in Chemical and Biochemical and Environmental Engineering

Course on Advanced Process Optimization: Deterministic & Stochastic Optimization

KT Consortium Newsletters December 2020

April 2021


IL Pro Features • IL Database Manager

• IL ProPred • Computer Aided IL

Design Development • IL Process Design

Guoliang Wang, • IL Pro Tutorial Software manager

Yuqiu Chen, Postdoc

IL Pro – a Comprehensive Software Assisting Ionic Liquid Application

IL Pro is a brand new software developed at KT-Consortium, aiming at assisting application of ionic liquid in various industries. The software is based on the results of the Ph.D. project by Yuqiu Chen (currently Postdoc) supervised by Prof. John M. Woodley and Prof. Georgios M. Kontogeorgis, and developed by our Software Manager Guoliang Wang. Three main toolboxes have been included into IL Pro: IL DB Manager, IL ProPred and IL Pro Tutorial.

IL DB Manager enable users to search among the large number of experimental data covering 22 commonly used physical and thermodynamic properties, such as density, viscosity, melting point, heat capacity, and gas solubility (13 gases). In case experimental data does not exist, or if IL compounds have not even been synthesized, IL ProPred can be used to calculate the missing property data using integrated models. This is essential to find new high-performance ILs for different industry applications. All these models integrated into IL Pro were developed at KT-Consortium. These models would be further developed for tools including “Computer Aided IL Design” and “IL-related process Design”. With these two tool boxes, users could design task-oriented IL compound for specific applications. Furthermore, IL-related industry process could be designed or optimized to obtain the best performance economically and environment-friendly. These tool boxes would be released in our future versions.

Figure 1. IL Pro overview

References

[1] Y. Chen, G. M. Kontogeorgis, J. M. Woodley. Industrial & Engineering Chemistry Research, 58(10), 2019, 4277-4292. [2] Y. Chen, X. Liu, J. M. Woodley, G. M. Kontogeorgis. Industrial & Engineering Chemistry Research, 59, 2020, 16805-16821.

Develop property models based on database

DBdata of 19 properties can be searched from database

IL Property prediction

Computer Aided IL Design

IL Process DesignFuture versions

publications


KT CONSORTIUM PUBLICATION RECORD 2019-2021

PhD Theses (2019-present)

Book Chapters (2019 – present)

Peer-Reviewed Conference Proceedings

CERE-KT Consortium (2019-present)

PROSYS – KT Consortium (2019-present)

Peer-reviewed publications in International Journals

KT Consortium (2019 – present)

CERE-KT Consortium (2019 – present)

PROSYS-KT Consortium (2019 – present)


PhD Theses (2019-present) PhD-63 Spardha Virendra Jhamb, 2019, “Computer-Aided Design of Sustainable Product

Formulations”, PhD-Thesis

PhD-64 Xinyan Liu, 2019, “Energy Efficient Hybrid Gas Separation Process with Ionic Liquids”, PhD-Thesis

PhD-65 Li Sun, 2019, “Analysis and Applications of the Electrolyte Cubic Plus Association Equation of State”, PhD-Thesis

PhD-66 Nipun Garg, 2019, “Phenomena-based Process Synthesis-Intensification”, PhD-Thesis

PhD-67 Asimakopoulos, K., 2019, “Fermentation of Synthesis Gas and Design of Bioreactors”, PhD-Thesis.

PhD-68 Edgar L. Camacho Vergara, 2020, “Phase Behavior of Inhomogeneous Fluids: A Classical Density Functional Theory Approach”, PhD-Thesis

PhD-69 Öner Merve, 2020, “Advanced Modeling, Simulation and Optimization for In Silico Process Design”, PhD-Thesis.

PhD-70 Al Resul, 2020, “Simulation-based framework for design and optimization of wastewater treatment plants”, PhD-Thesis.

PhD-71 Yuqiu Chen, 2020, “Computer-aided design methodology for separation processes with ionic liquids”, PhD-Thesis.

PhD-72 Rowan Malan Lindeque, 2020, “Continuous Oxidative Biocatalysis”, PhD-Thesis

PhD-73 Jiahuan Tong, 2021, “Theory, simulation and models for electrolyte systems with focus on ionic liquids”, PhD-Thesis

Book Chapters (2019 – present) Chapter-38 Nazemzadeh, N., A. Udugama, I., Fjordbak Nielsen , R., Meyer, K., Perez-Cisneros, E. S.,

Sales-Cruz, M., Huusom, J. K., Abildskov, J., Mansouri, S. S., 2019, “Graphical tools for designing intensified distillation processes: Methods and applications”, in “Process Intensification: Design Methodologies”, F. I. Gómez-Castro, J. G. Segovia-Hernández (Eds.), 145–179, De Gruyter (https://doi.org/10.1515/9783110596120-006)

Chapter-39 Mansouri, S. S., Gargalo, C. L., Udugama, I. A., Ramin, P., Sales-Cruz, M., Sin, G., Gernaey, K. V. (2019). Economic Risk Analysis and Critical Comparison of Biodiesel Production Systems. In M. Tabatabaei, & M. Aghbashlo (Eds.), Biodiesel. From Production to Combustion (pp. 127-148). Springer. Biofuel and Biorefinery Technologies, Vol. 8. Doi: https://doi.org/10.1007/978-3-030-00985-4_6

https://doi.org/10.1515/9783110596120-006

https://doi.org/10.1007/978-3-030-00985-4_6


Peer-Reviewed Conference Proceedings

CERE-KT Consortium (2019-present) CERE 2007

Olivia A. Perederic, Franjo Cecelja, John M. Woodley, Georgios M. Kontogeorgis, Antonis Kokossis, 2019, “Waste Streams Property Characterisation in Biorefinery Systems Engineering Using an Ontology Approach”, In 3rd International Conference on Functional Materials and Chemical Engineering, 1(1).

CERE 2036

Olivia A. Perederic, Aikaterini Mountraki, Electra Papadopoulou, John M. Woodley, Georgios M. Kontogeorgis, 2020, “Life Cycle Analysis of Phenol – Formaldehyde Resins Substituted with Lignin”, In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 607-612.

PROSYS – KT Consortium (2019-present) PROSYS C-12

J. Frutiger, M, N. Jones, N. G. Ince, G. Sin, 2019, “From property uncertainties to process simulation uncertainties – Monte Carlo methods in SimSci PRO/II process simulator”, Computer aided chemical Engineering, 44, 1489-1494.

PROSYS C-13

M. Öner, S. M. Stocks, J. Abildskov, G. Sin, 2019, “Scale-up Modeling of a Pharmaceutical Antisolvent Crystallization via a Hybrid Method of Computational Fluid Dynamics and Compartmental Modeling” Accepted in 29th European Symposium on Computer-Aided Process Engineering, June 16th to 19th, 2019, Eindhoven, The Netherlands.

PROSYS C-14

R. F. Nielsen, N. A. Kermani, L. la Cour Freiesleben, K. V. Gernaey, S. S. Mansouri, 2019, “Novel strategies for predictive particle monitoring and control using advanced image analysis”, Accepted at 29th European Symposium on Computer Aided Process Engineering, June 16th to 19th, 2019, Eindhoven, The Netherlands.

PROSYS C-15

R. Al, C. R. Behera, K. V. Gernaey, G. Sin, 2019, “Towards development of a decision support tool for conceptual design of wastewater treatment plants using stochastic simulation optimization”, Accepted in 29th European Symposium on Computer-Aided Process Engineering, June 16th to 19th, 2019, Eindhoven, The Netherlands.

PROSYS C-16

Jones, M. N., Jones, S. A., Sin, G. (2019). A Modular Modelling Environment for Computer-Aided Process Design. In S. G. M., C. D. L., & M. J. R. (Eds.), Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design (Vol. 47, pp. 23-28). Elsevier. Computer Aided Chemical Engineering. Doi: https://doi.org/10.1016/B978-0-12-818597-1.50004-7

PROSYS C-17

Hwangbo, S., Öner, M., Sin, G. (2019). Design of smart liquid-liquid extraction columns for downstream separations of biopharmaceuticals using deep Q-learning algorithm. In K. Anton, E. Zondervan, R. Lakerveld, & L. Özkan (Eds.), Proceedings of the 29th European Symposium on Computer Aided Process Engineering (pp. 271-276). Elsevier. Computer Aided Chemical Engineering, Vol. 46. Doi: https://doi.org/10.1016/B978-0-12-818634-3.50046-1

PROSYS C-18

Caño de Las Heras, S., Gutschmann, B., Gernaey, K. V., Krühne, U., & Mansouri, S. S. (2019). Facilitating learning by failure through a pedagogical model-based tool for bioprocesses. In K. Anton, E. Zondervan, R. Lakerveld, & L. Özkan (Eds.), Proceedings of the 29th European Symposium on Computer Aided Process Engineering (pp. 1825-1830). Elsevier. Computer Aided Chemical Engineering, Vol. 46. Doi: https://doi.org/10.1016/B978-0-12-818634-3.50305-2

https://doi.org/10.1016/B978-0-12-818597-1.50004-7

https://doi.org/10.1016/B978-0-12-818634-3.50046-1

https://doi.org/10.1016/B978-0-12-818634-3.50046-1

https://doi.org/10.1016/B978-0-12-818634-3.50305-2


PROSYS C-19

Nazemzadeh, N., Udugama, I. A., Taube, M. A., Abildskov, J., Mansouri, S. S. (2019). Molecular tracking: A novel approach for multicomponent distillation column design. In A. A. K., E. Z., R. L., & L. Ö. (Eds.), Computer Aided Chemical Engineering (pp. 313-318). Elsevier. Computer Aided Chemical Engineering, Vol. 46. Doi: https://doi.org/10.1016/B978-0-12-818634-3.50053-9

PROSYS C-20

Pudi, A., Karcz, A.P., Shadravan, V., Andersson, M.P., Mansouri, S.S. (2020). Modeling of Liquid-Liquid Phase Transfer Catalysis: Process Intensification via Integration of Process Systems Engineering and Computational Chemistry. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 43-48. (Accepted)

PROSYS C-21

Nazemzadeh, N., Sillesen, L.W., Nielsen, R.F., Jones, M.N., Gernaey, K.V., Andersson, M.P., Mansouri, S.S. (2020). Integration of Computational Chemistry and Artificial Intelligence for Multi-scale Modeling of Bioprocesses. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 295-300. (Accepted)

PROSYS C-22

Dickson, R., Mancini, E., Garg, N., Liu., J., Pinelo, M., Mansouri, S.S. (2020). Sustainable Process Synthesis, Design and Innovation of Bio-succinic Acid Production. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 787-792. (Accepted)

PROSYS C-23

Nielsen., R.F., Gernaey, K.V., Mansouri, S.S. (2020). A Hybrid Model Predictive Control Strategy using Neural Network Based Soft Sensors for Particle Processes. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 1177-1182. (Accepted)

PROSYS C-24

Pahija, E., Spann, R., Sin, G. (2020). Robust Monitoring of Lactic Acid Bacteria with Sequential Monte Carlo. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 1615-1620. (Accepted)

PROSYS C-25

Vollmer, N.I., Gernaey, K.V., Mussatto, S.I., Sin, G. (2020). Surrogate Modelling Based Uncertainty and Sensitivity Analysis for the Downstream Process Design of a Xylitol Biorefinery. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 1663-1668. (Accepted)

PROSYS C-26

Cano, S., Jones, M.N., Gernaey, K.V., Krune, U., Mansouri, S.S. (2020). An E-learning Bot for Bioprocess Systems Engineering. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 2023-2028

PROSYS C-27

Pahija, E., Hui, C.W., Woodley, J.M., Sin, G. (2020). Effect of Selective Size Extraction of Microalgae from a Photobioreactor. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 331-336. (Accepted)

PROSYS C-28

Magnusson, A.F., Al, R., Sin, G. (2020). Development and Application of Simulation-based Methods for Engineering Optimization Under Uncertainty. In Proceedings of the 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48, 451-456. (Accepted)

PROSYS C-29

Wahlgreen, M. R., Schroll-Fleischer, E., Boiroux, D., Ritschel, T., Wu, H., Huusom, J. K., Jørgensen, J. B. (2020). Nonlinear Model Predictive Control for an Exothermic Reaction in an Adiabatic CSTR. In Proceedings of 6th International Conference on Advances in Control and Optimization of Dynamical Systems (pp. 500-505). Elsevier.

PROSYS C-30

Jørgensen, J. B., Ritschel, T. K. S., Boiroux, D., Schroll-Fleischer, E., Wahlgreen, M. R., Nielsen, M. K., Wu, H., Huusom, J. K. (2020). Simulation of NMPC for a Laboratory

https://doi.org/10.1016/B978-0-12-818634-3.50053-9


Adiabatic CSTR with an Exothermic Reaction. In Proceedings of 2020 European Control Conference (pp. 202-207). International Federation of Automatic Control.

PROSYS C-31

Udugama, I. A., Mansouri, S. S., Gernaey, K. V., Bayer, C., Young, B. R. (2019). Achieving value from process intensification through better process control. In Proceedings of the 2019 Moratuwa Engineering Research Conference (MERCon) (pp. 376-381). IEEE. Doi: https://doi.org/10.1109/MERCon.2019.8818773

PROSYS C-32

Bähner, F. D., Santacoloma, P. A., Huusom, J. K. (2019). Assessment of the Plantwide Control Structure in a Pectin Production Plant. IFAC-PapersOnLine, 52(1), 251-256. Doi: https://doi.org/10.1016/j.ifacol.2019.06.070

PROSYS C-33

Horsholt, A., Christiansen, L. H., Meyer, K., Huusom, J. K., Jorgensen, J. B. (2019). A Discontinuous-Galerkin Finite-Element Method for Simulation of Packed Bed Chromatographic Processes. IFAC-PapersOnLine, 52(1), 346-351. Doi: https://doi.org/10.1016/j.ifacol.2019.06.086

Peer-reviewed publications in International Journals

KT Consortium (2019 – present) 245 Georgios M. Kontogeorgis, Michele Mattei, Ka M. Ng, Rafiqul Gani, 2019, “An Integrated

Approach for the Design of Emulsified Products”, AIChE Journal 65(1), 75-86.

246 Hongliang Qian, Wei Chen, Weiwei Zhu, Chang Liu, Xiaohua Lu, Xiaojing Guo, Dechun Huang, Xiaodong Liang, Georgios M. Kontogeorgis, 2019, “Simulation and evaluation of utilization pathways of biomasses based on thermodynamic data prediction”, Energy 173, 610-625.

247 Xinyan Liu, Teng Zhou, Xiangping Zhang, Suojiang Zhang, Xiaodong Liang, Rafiqul Gani, Georgios M. Kontogeorgis, 2018, “Application of the COSMO-RS and UNIFAC for Ionic Liquids based Gas Separation”, Chemical Engineering Science 192, 816-828.

248 Saeed Eini, Spardha Jhamb, Mahdi Sharifzadeh, Davood Rashtchian, Georgios M. Kontogeorgis, 2018,” Developing group contribution models for the estimation of Atmospheric Lifetime and Minimum Ignition Energy”, (Submitted to Chemical Engineering Research and Design)

249 Yuqiu Chen, Georgios Kontogeorgis, John Woodley, 2019,” Group Contribution-based estimation method for properties of ionic liquids”, Industrial and Engineering Chemistry Research 58, 4277-4292.

250 Yuqiu Chen, Georgios Kontogeorgis, John Woodley, 2019,” Integrated ionic liquids and process design involving azeotropic separation processes”, Chemical Engineering Sciences 203, 402-414.

251 Spardha Jhamb, Xiaodong Liang, Rafiqul Gani, Georgios M. Kontogeorgis, 2019, “Systematic Model-Based Methodology for Substitution of Hazardous Chemicals”, ACS Sustainable Chemistry & Engineering 7, 7652-7666.

252 Nipun Garg, John M. Woodley, Rafiqul Gani, Georgios M. Kontogeorgis, 2019, “Sustainable solutions by integrating process synthesis-intensification”, Computers & Chemical Engineering 126, 499-519.

https://doi.org/10.1109/MERCon.2019.8818773

https://doi.org/10.1016/j.ifacol.2019.06.070

https://doi.org/10.1016/j.ifacol.2019.06.086


253 Bingye Dai, Weiwei Zhu, Xiaojing Guo, Hongliang Qian, Xiaodong Liang, Georgios M. Kontogeorgis, 2019, “Effect of the composition of biomass on the quality of syngas produced from thermochemical conversion based on thermochemical data prediction”, Energy and Fuels, doi: https://doi.org/10.1021/acs.energyfuels.9b00106

254 Chen, Y., Koumaditi, E., Gani, R., Kontogeorgis, G. M., & Woodley, J. M., 2019, “Computer-aided design of ionic liquids for hybrid process schemes”. Computers & Chemical Engineering, 130, doi: https://doi.org/10.1016/j.compchemeng.2019.106556

255 Saeed Eini, Georgios M. Kontogeorgis, , Davood Rashtchian, Mahdi Sharifzadeh, 2019,” Multi-criteria optimization of process and refrigerant mixture for a small-scale natural gas liquefaction: enhancing energy efficiency, environmental benignness, and safety”, (Submitted to Applied Energy)

256 Liu, X., Chen, Y., Zeng, S., Zhang, X., Zhang, S., Liang, X., Gani, R., Kontogeorgis, G. M. (2020). Structure Optimization of Tailored Ionic Liquids and Process Simulation for Shale Gas Separation. AIChE Journal, 66(2), doi: https://doi.org/10.1002/aic.16794

257 Jhamb, S. V., Liang, X., Dam-Johansen, K., Kontogeorgis, G. M. (2020). A model-based solvent selection and design framework for organic coating formulations. Progress in Organic Coatings, 140, doi: https://doi.org/10.1016/j.porgcoat.2019.105471

258 Chen, Y., Cai, Y., Thomsen, K., Kontogeorgis, G. M., Woodley, J. M. (2020). A group contribution-based prediction method for the electrical conductivity of ionic liquids. Fluid Phase Equilibria, 509, doi: https://doi.org/10.1016/j.fluid.2020.112462

259 Chen, Y., Liu, X., Kontogeorgis, G. M., Woodley, J. M. (2020). Ionic-Liquid-Based Bioisoprene Recovery Process Design. Industrial and Engineering Chemistry Research, 59(16), 7355–7366, doi: https://doi.org/10.1021/acs.iecr.0c00146

260 Spardha Jhamba, Irène Hospitala, Xiaodong Liang, Francis Pilloud, Patrick M. Piccione，Georgios M. Kontogeorgis, 2019, “A Group Contribution Method to Estimate the Biodegradability of Organic Compounds”, (Submitted to ACS Industrial and Engineering Chemistry.)

261 Markus Enekvist, Xiaodong Liang, Xiangping Zhang, Kim Dam-Johansen, Georgios M. Kontogeorgis, 2020, “New group contribution method for estimating the Hansen Solubility Parameters and application to organic pigments”, (Submitted to Chinese Journal of Chemical Engineering)

262 Yuqiu Chen, Xinyan Liu, John Woodley, Georgios Kontogeorgis, 2020, “Gas solubility in ionic liquids (ILs): UNIFAC-IL model extension”, (Submitted to Industrial & Engineering Chemistry Research)

263 Edgar L. Camacho Vergara, Georgios M. Kontogeorgis, Xiaodong Liang, 2020, “On the study of the vapor-liquid interface of associating fluids with classical density functional theory”,( Submitted to Fluid Phase Equilibria)

264 Ioannis Tsivintzelis, Eirini Karakatsani, Georgios M. Kontogeorgis, 2020, “Costa Tsonopoulos – his legacy and some personal reflections on cubic equations of state and beyond”, (Submitted to Fluid Phase Equilibria)

265 Li Sun, Xiaodong Liang, Nicolas von Solms, Georgios M. Kontogeorgis, 2020, “Solubility Modeling of Air in Aqueous Electrolyte Solutions with the e-CPA Equation of State”, (Submitted to Industrial & Engineering Chemistry Research)

266 Chenyang Zhu, Xiangyang Liu, Maogang He, Georgios M. Kontogeorgis, Xiaodong Liang, 2020, “Heat Capacities of Fluids: The Performance of Various Equations of State”, (Submitted to Journal of Chemical & Engineering Data)

https://doi.org/10.1021/acs.energyfuels.9b00106

https://doi.org/10.1016/j.compchemeng.2019.106556

https://doi.org/10.1002/aic.16794

https://doi.org/10.1016/j.porgcoat.2019.105471

https://doi.org/10.1016/j.fluid.2020.112462

https://doi.org/10.1021/acs.iecr.0c00146


267 Evangelos Tsochantaris, Xiaodong Liang, Georgios Kontogeorgis, 2020, “Evaluating the performance of the PC-SAFT and CPA equations of state on water’s anomalous properties”, (Submitted to Journal of Chemical & Engineering Data - Special SAFT issue)

268 Antoon J.B. ten Kate, Jan Gerretzen, Henk-Jan van Manen, Georgios M. Kontogeorgis, Gerrald Bargeman, 2020, “A methodology to predict thermodynamic data from spectroscopic analysis” (Submitted to Industrial & Engineering Chemistry Research)

269 Nipun Garg, Georgios M. Kontogeorgis, Rafiqul Gani, John M. Woodley, 2020, “A Process synthesis-intensification method for generation of novel and intensified solutions” (Submitted for publication)

CERE-KT Consortium (2019 – present) CERE 1945

Jamali, A., Vinhal, A. P. C. M., Behnejad, H., Yan, W., Kontogeorgis, G. M. (2019). Comparison of two crossover procedures for describing thermodynamic behavior of normal alkanes from singular critical to regular classical regions. Fluid Phase Equilibria, 495, 33-46. Doi: https://doi.org/10.1016/j.fluid.2019.04.030

CERE 1810

Wang, T., Guittard, P., Coquelet, C., El Ahmar, E., Baudouin, O., & Kontogeorgis, G. M. (2019). Corrigendum to “Improvement of the PR-CPA equation of state for modelling of acid gases solubilities in aqueous alkanolamine solutions” [Fluid Phase Equilibria, vol. 471, 2018, 74–87]. Fluid Phase Equilibria, 485, 126-127. Doi: https://doi.org/10.1016/j.fluid.2018.12.010

CERE 1904

Sun, L., Liang, X., Solms, N. V., Kontogeorgis, G. M. (2019). Modeling Tetra-n-butyl ammonium halides aqueous solutions with the electrolyte cubic plus association equation of state. Fluid Phase Equilibria, 486, 37 - 47. Doi: https://doi.org/10.1016/j.fluid.2018.12.033

CERE 1906

Camacho Vergara, E. L., Kontogeorgis, G. M., Liang, X. (2019). Gas Adsorption and Interfacial Tension with Classical Density Functional Theory. Industrial & Engineering Chemistry Research, 58(14), 5650-5664. Doi: https://doi.org/10.1021/acs.iecr.9b00137

CERE 1911

Meng, X., Ju, Z., Zhang, S., Liang, X., von Solms, N., Zhang, X., Zhang, X. (2019). Efficient transformation of CO2 to cyclic carbonates using bifunctional protic ionic liquids under mild conditions. Green Chemistry, 21(12), 3456-3463. Doi: https://doi.org/10.1039/c9gc01165j

CERE 1912

Mondejar, M. E., Regidor, M., Kontogeorgis, G., Haglind, F. (2019). Challenges in the development of a database of thermophysical properties of nanofluids. Paper presented at The 1st International Conference on Nanofluids (ICNf) and the 2nd European Symposium on Nanofluids (ESNf), Castellón, Spain.

CERE 1913

Kontogeorgis, G. M., Liang, X., Arya, A., Tsivintzelis, I. (2020). Equations of state in three centuries. Are we closer to arriving to a single model for all applications? Chemical Engineering Science: X, 7. Doi: https://doi.org/10.1016/j.cesx.2020.100060

CERE 1915

Abunahman, S. S., dos Santos, L. C., Tavares, F. W., Kontogeorgis, G. M. (2020). A computational tool for parameter estimation in EoS: New methodologies and natural gas phase equilibria calculations. Chemical Engineering Science, 215. Doi: https://doi.org/10.1016/j.ces.2019.115437

CERE 1916

Tsivintzelis, I., Ali, S., Kontogeorgis, G. M. (2020). Modeling systems relevant to the biodiesel production using the CPA equation of state. Part 2. Systems with supercritical CO2. Fluid Phase Equilibria, 504. Doi: https://doi.org/10.1016/j.fluid.2019.112337

https://doi.org/10.1016/j.fluid.2019.04.030



https://doi.org/10.1021/acs.iecr.9b00137

https://doi.org/10.1039/c9gc01165j

https://doi.org/10.1016/j.cesx.2020.100060

https://doi.org/10.1016/j.ces.2019.115437



CERE 1917

Saeed Eini, Georgios M. Kontogeorgis, Davood Rashtchian, and Mahdi Sharifzadeh, 2019, “Multi-criteria optimization of process and refrigerant mixture for a small- scale natural gas liquefaction: enhancing energy efficiency, environmental benignness, and safety” (Submitted for publication)

CERE 1919

Kontogeorgis, G. M., Privat, R., Jaubert, J-N. (2019). Taking Another Look at the van der Waals Equation of State – Almost 150 Years Later. Journal of Chemical and Engineering Data, 64(11), 4619-4637. Doi: https://doi.org/10.1021/acs.jced.9b00264

CERE 1922

Tong, J., Xiao, X., Liang, X., von Solms, N., Huo, F., He, H., Zhang, S. (2019). Insights into the solvation and dynamic behaviors of a lithium salt in organic- and ionic liquid-based electrolytes. Physical Chemistry Chemical Physics, 21, 19216-19225. Doi: https://doi.org/10.1039/c9cp01848d

CERE 1923

Stefano Lillia, Davide Bonalumi, Philip L. Fosbøl, Kaj Thomsen, Indira Jayaweera, and Gianluca Valenti, 2019, “Thermodynamic and kinetic properties of NH3-K2CO3-CO2-H2O system for carbon capture applications”, International Journal of Greenhouse Gas Control, 85, 121-131.

CERE 1925

Vinhal, A. P. C. M., Yan, W., Kontogeorgis, G. M. (2020). Modeling the Critical and Phase Equilibrium Properties of Pure Fluids and Mixtures with the Crossover Cubic-Plus-Association Equation of State. Journal of Chemical and Engineering Data, 65(3), 1095-1107. Doi: https://doi.org/10.1021/acs.jced.9b00492

CERE 1926

Kruger, F., Varsos, A. A., Kontogeorgis, G. M., von Solms, N. (2019). High-pressure experimental vapour-liquid-liquid equilibrium measurements and modelling for natural gas processing: Equipment validation, and the system CH4+nC6H14+H2O. Fluid Phase Equilibria, 501. Doi: https://doi.org/10.1016/j.fluid.2019.112276

CERE 1931

Jhamb, S. V., Liang, X., Dam-Johansen, K., & Kontogeorgis, G. M. (2020). A model-based solvent selection and design framework for organic coating formulations. Progress in Organic Coatings, 140, doi: https://doi.org/10.1016/j.porgcoat.2019.105471

CERE 1941

Jhamb, S., Enekvist, M., Liang, X., Zhang, X., Dam-Johansen, K., Kontogeorgis, G. M. (2020). A review of computer-aided design of paints and coatings. Current Opinion in Chemical Engineering, 27, 107-120. Doi: https://doi.org/10.1016/j.coche.2019.12.005

CERE 2003

Perederic, O. A., Mansouri, S. S., Appel, S., Sarup, B., Gani, R., Woodley, J. M., Kontogeorgis, G. (2020). Process analysis of shea butter solvent fractionation using a generic systematic approach. Industrial & Engineering Chemistry Research, 59(19), 9152-9164. Doi: https://doi.org/10.1021/acs.iecr.9b06719

CERE 2008

Xiaodong Liang, Baoliang Peng, Yuan Chen, Jianhui Luo, Michael L. Michelsen, Georgios M. Kontogeorgis, 2020, “Matching the critical point of associating fluids with the Cubic Plus Association equation of state” (submitted for publication)

CERE 2015

Sun, L., Liang, X., von Solms, N., & Kontogeorgis, G. M. (2020). Analysis of Some Electrolyte Models Including Their Ability to Predict the Activity Coefficients of Individual Ions. Industrial and Engineering Chemistry Research, 59(25), 11790-11809, doi: https://doi.org/10.1021/acs.iecr.0c00980

CERE 2016

Ala Bazyleva, Jens Abildskov, Andrzej Anderko, Olivier Baudouin, Yury Chernyak, Jean-Charles de Hemptinne, Vladimir Diky, Ralf Dohrn, J. Richard Elliott, Johan Jacquemin, Jean-Noel Jaubert, Kevin G. Joback, Ursula R. Kattner, Georgios Kontogeorgis, Herbert Loria, Paul M. Mathias, John P. OĆonnell, Wolfram Schröer, G. Jeffrey Smith, Ana Soto, Shu Wang, and Ronald D. Weir.

https://doi.org/10.1021/acs.jced.9b00264

https://doi.org/10.1039/c9cp01848d



https://doi.org/10.1016/j.porgcoat.2019.105471

https://doi.org/10.1016/j.coche.2019.12.005




(2020) “Good reporting practice for thermo-physical and thermochemical property measurements” (IUPAC Technical Raport) (Submitted for publication)

CERE 2017

Xiaodong Liang, Baoling Peng, Yuan Chen, Jianhui Luo, Michael Locht Michelsen, Georgios Kontogeorgis. (2020) “Matching the critical point of associating fluids with the Cubic Plus Association equation of state” (Submitted for publication)

CERE 2019

Markus Enekvist, Xiaodong Liang, Xiangping Zhang, Kim Dam-Johansen, and Georgios M. Kontogeorgis. (2020) “New group contribution method for estimating the Hansen solubility parameters and application to organic pigments”.

CERE 2021

Spardha Jhamb, Irène Hospital, Xiaodong Liang, Francis Pilloud, Patrick M. Piccione, and Georgios M. Kontogeorgis. (2020) “A group contribution method to estimate the biodegradability of organic compounds” (Submitted for publication)

CERE 2024

Yuqiu Chen, Xinyan Liu, John M. Woodley, Georgios M. Kontogeorgis. (2020) Gas solubility in ionic liquids (ILs): UNIFAC-IL model extension. Ind. Eng. Chem. Res. 2020, 59, 16805−16821. Doi: https://dx.doi.org/10.1021/acs.iecr.0c02769

CERE 2026

Camacho Vergara, E. L., Kontogeorgis, G. M., & Liang, X. (2020). On the study of the vapor-liquid interface of associating fluids with classical density functional theory. Fluid Phase Equilibria, 522. Doi: https://doi.org/10.1016/j.fluid.2020.112744

CERE 2031

Li Sun, Xiaodong Liang, Nicolas von Solms, and Georgios M. Kontogeorgis. (2020) Solubility modeling of air in aqueous electrolyte solutions with the e-CPA equation of state”, Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX, doi: https://dx.doi.org/10.1021/acs.iecr.0c03164

CERE 2033

Evangelos Tsochantaris, Xiaodong Liang, and Georgios Kontogeorgis. (2020) “Evaluating the performance of the PC-SAFT and CPA equations of state on water´s anomalous properties” (Submitted for publication)

CEER 2034

Maria E. Mondejar, Maria Regidor, Joerg Krafczyk, Christian Ihmels, Bastian Schmid, Georgios M. Kontogeorgis, Fredrik Haglind. (2020) “An open-access database of the thermophysical properties of nano fluids” (submitted to Journal of Molecular Liquids)

CERE 2035

Georgios M. Kontogeorgis, Ralf Dohrn, Ioannis G. Economou, Jean-Charles de Hemptinne, Antoon ten Kate, Susanna Kuitunen, Miranda Mooijer, Ljudmila Fele Žilnik, Velisa Vesovic.(2020) “Industrial Requirements for Thermodynamic and Transport Properties - 2020” (Submitted to Industrial & Engineering Chemistry Research)

CERE 2037

Pedro Velho, Xiaodong Liang, Eugénia A. Macedo, Elena Gómez, Georgios M. Kontogeorgis. (2020) “Towards a predictive Cubic Plus Association equation of state” (Submitted to Fluid Phase Equilibria)

CERE 2101

John Towne, Xiaodong Liang, Georgios M. Kontogeorgis. (2021) “Application of Quantum Chemistry Insights to the Prediction of Phase Equilibria in Associating Systems” (Submitted to Industrial & Engineering Chemistry Research)

CERE 2102

Yuqiu Chen, Nipun Garg, Hao Luo, Georgios M. Kontogeorgis, John M. Woodley. (2021) “Ionic liquid-based in-situ product removal (ISPR) design for small molecule fermentation” (Submitted to Journal of Cleaner Production)

CERE

2103

Nefeli Novak, Georgios M. Kontogeorgis, Marcelo Castier,Ioannis G. Economou. (2021) “Water - hydrocarbon phase equilibria with SAFT-VR Mie equation of state” (Submitted to The Journal of Physical Chemistry)

CERE

2104

Chenyang Zhu, Maogang He, Xiangyang Liu, Georgios M. Kontogeorgis, Xiaodong Liang. (2021) “Quantification of Dipolar Contribution and Modeling of Green Polar Fluids with the Polar Cubic-Plus-Association Equation of State” (Submitted Green Energy & Environment)

https://dx.doi.org/10.1021/acs.iecr.0c02769


https://dx.doi.org/10.1021/acs.iecr.0c03164


CERE 2105

Yuqiu Chen, Xiaodong Liang, John M.Woodley, Georgios M. Kontogeorgis. (2021) “Modelling study on phase equilibria behavior of ionic liquid-based aqueous biphasic systems” (Submitted to Chemical Engineering Science)

CERE 2106

Xinyan Liu, Yuqiu Chen, Shaojuan Zeng, Xiangping Zhang, Xiaodong Liang, Rafiqul Gani, Georgios M. Kontogeorgis. (2021) “Thermodynamic properties prediction and process evaluation of ionic liquid-based NH3/CO2 separation from melamine tail gas” (Submitted for publication)

CERE 2107

Martin Due Olsen, Georgios M. Kontogeorgis, Xiaodong Liang, Nicolas von Solms. (2021) “Investigation of the performance of e-CPA for a wide range of properties for aqueous NaCl solutions” (Submitted to Fluid Phase Equilibria)

CERE 2108

Georgios M. Kontogeorgis*, Spardha Jhamb, Xiaodong Liang, Kim Dam-Johansen. (2021) “Computer-Aided Design of Formulated Products” (Submitted to Current Opinion in Colloid & Interface Science)

CERE 2109

Markus Enekvist, Xiaodong Liang, Xiangping Zhang, Kim Dam-Johansen, Georgios M. Kontogeorgis. (2021) “Computer-Aided Design and Solvent Selection for Organic Paint and Coating Formulations” (Submitted to Progress in Organic Coatings)

PROSYS-KT Consortium (2019 – present) PROSYS J-04

M.N. Jones, J. Frutiger, N. G. Ince, G. Sin, 2019, “The Monte Carlo driven and Machine Learning enhanced Process Simulator” Computers & Chemical Engineering, 125, 324-338.

PROSYS J-05

R. M. Lindeque and J. M. Woodley, 2019, “Reactor Selection for Effective Continuous Biocatalytic Production of Pharmaceuticals”, Catalysts, 9(3), p.262.

PROSYS J-06

R. Al, C. R. Behera, A. Zubov, K. V. Gernaey, G. Sin, 2019, “Meta-modeling based efficient global sensitivity analysis for wastewater treatment plants–An application to the BSM2 model”, Computers & Chemical Engineering, 127, 233-246.

PROSYS J-07

A. Udugama, I., Alvarez Camps, M., Taube, M. A., Thawita, C., Anantpinijwatna, A., Mansouri, S. S., Young, B. R., Yu, W. (2019). A novel soft sensor for measuring and controlling recovery in a high-purity, multi-component, side-draw distillation column. Industrial & Engineering Chemistry Research, 58(43), 20026-20035. Doi: https://doi.org/10.1021/acs.iecr.9b04594

PROSYS J-08

Caroço, R. F., Kim, B., Santacoloma, P. A., Abildskov, J., Lee, J. H., Huusom, J. K. (2019). Analysis and model-based optimization of a pectin extraction process. Journal of Food Engineering, 244, 159-169. Doi: https://doi.org/10.1016/j.jfoodeng.2018.09.0

PROSYS J-09

Mussati, S. F., Mansouri, S. S., Gernaey, K. V., Morosuk, T., Mussati, M. C. (2019). Model-Based Cost Optimization of Double-Effect Water-Lithium Bromide Absorption Refrigeration Systems. Processes, 7(1). Doi: https://doi.org/10.3390/pr7010050

PROSYS J-10

Al, R., Behera, C. R., Zubov, A., Gernaey, K. V., Sin, G. (2019). Meta-modeling based efficient global sensitivity analysis for wastewater treatment plants – An application to the BSM2 model. Computers & Chemical Engineering, 127, 233-246. Doi: https://doi.org/10.1016/j.compchemeng.2019.05.015

PROSYS J-11

Ramirez-Castelan, C. E., Hidalgo-Vivas, A., Brix, J., Jensen, A. D., Huusom, J. K. (2019). Mathematical Modelling and Simulation of a Trickle-Bed Reactor for Hydrotreating of Petroleum Feedstock. International Journal of Chemical Reactor Engineering, 17(7). Doi: https://doi.org/10.1515/ijcre-2018-0176

PROSYS J-12

Woodley, J. M. (2019). New Frontiers in Biocatalysis for Sustainable Synthesis. Current Opinion in Green and Sustainable Chemistry, 21, 22-26. Doi: https://doi.org/10.1016/j.cogsc.2019.08.006


https://doi.org/10.1016/j.jfoodeng.2018.09.0

https://doi.org/10.3390/pr7010050

https://doi.org/10.1016/j.compchemeng.2019.05.015

https://doi.org/10.1515/ijcre-2018-0176

https://doi.org/10.1016/j.cogsc.2019.08.006


PROSYS J-13

Papadakis, E., Huusom, J. K., Abildskov, J. (2019). Economic analysis of a horizontal diabatic separation system. Chemical Engineering Research and Design, 147, 709-720. Doi: https://doi.org/10.1016/j.cherd.2019.05.023

PROSYS J-14

Frutiger, J., Cignitti, S., Abildskov, J., Woodley, J. M., Sin, G. (2019). Computer-aided molecular product-process design under property uncertainties – A Monte Carlo based optimization strategy. Computers and Chemical Engineering, 122, 247-257. Doi: https://doi.org/10.1016/j.compchemeng.2018.08.021

PROSYS J-15

Cignitti, S., Rodriguez-Donis, I., Abildskov, J., You, X., Shcherbakova, N., Gerbaud, V. (2019). CAMD for entrainer screening of extractive distillation process based on new thermodynamic criteria. Chemical Engineering Research and Design, 147, 721-733. Doi: https://doi.org/10.1016/j.cherd.2019.04.038

PROSYS J-16

Meyer, K., Huusom, J. K., Abildskov, J. (2019). A stabilized nodal spectral solver for liquid chromatography models. Computers and Chemical Engineering, 124, 172-183. Doi: https://doi.org/10.1016/j.compchemeng.2019.02.017

PROSYS J-17

Lopez-Arenas, T., Mansouri, S. S., Sales-Cruz, M., Gani, R., Pérez-Cisneros, E. S. (2019). A Gibbs energy-driving force method for the optimal design of non-reactive and reactive distillation columns. Computers & Chemical Engineering, 128, 53-68. Doi: https://doi.org/10.1016/j.compchemeng.2019.05.024

PROSYS J-18

Baehner, F. D., Huusom, J. K. (2019). A Debottlenecking Study of an Industrial Pharmaceutical Batch Plant. Industrial & Engineering Chemistry Research, 58(43), 20003-20013. Doi: https://doi.org/10.1021/acs.iecr.9b03134

PROSYS J-19

Woodley, J. M. (2019). Accelerating the implementation of biocatalysis in industry. Applied Microbiology and Biotechnology, 103(12), 4733–4739. Doi: https://doi.org/10.1007/s00253-019-09796-x

PROSYS J-20

Arias, A., Behera, C. R., Feijoo, G., Sin, G., Moreira, M. T. (2020). Unravelling the environmental and economic impacts of innovative technologies for the enhancement of biogas production and sludge management in wastewater systems. Journal of Environmental Management, 270. Doi: https://doi.org/10.1016/j.jenvman.2020.110965

PROSYS J-21

Woodley, J. M. (2020). Towards the sustainable production of bulk-chemicals using biotechnology. New Biotechnology, 59, 59-64. Doi: https://doi.org/10.1016/j.nbt.2020.07.002

PROSYS J-22

Lindeque, R. M., Woodley, J. M. (Accepted/In press). The effect of dissolved oxygen on kinetics during continuous biocatalytic oxidations. Organic Process Research And Development. Doi: https://doi.org/10.1021/acs.oprd.0c00140

PROSYS J-23

A. Udugama, I., Petersen, L. A. H., Falco, F. C., Junicke, H., Mitic, A., Flores Alsina, X., Mansouri, S. S., Gernaey, K. V. (2020). Resource recovery from waste streams in a Water- Energy-Food nexus perspective: toward more sustainable food processing. Food and Bioproducts Processing, 119, 133-147. Doi: https://doi.org/10.1016/j.fbp.2019.10.014

PROSYS J-24

Tula, A. K., Wang, J., Chen, X., Mansouri, S. S., Gani, R. (2020). ProCACD: A computer-aided versatile tool for process control. Computers and Chemical Engineering, 136. Doi: https://doi.org/10.1016/j.compchemeng.2020.106771

PROSYS J-25

O’Connell, J. P., Woodley, J. M., Abildskov, J. (2020). On the thermodynamics of biocatalytic reactions with application of group-contribution correlation and prediction. Fluid Phase Equilibria, 518. Doi: https://doi.org/10.1016/j.fluid.2020.112623

PROSYS J-26

Chen, X., Rodríguez, Y., López, J. C., Muñoz, R., Ni, B-J., Sin, G. (2020). Modelling of Polyhydroxyalkanoates Synthesis from Biogas by Methylocystis hirsuta. ACS Sustainable Chemistry and Engineering. Doi: https://doi.org/10.1021/acssuschemeng.9b07414

https://doi.org/10.1016/j.cherd.2019.05.023






https://doi.org/10.1007/s00253-019-09796-x

https://doi.org/10.1007/s00253-019-09796-x

https://doi.org/10.1016/j.jenvman.2020.110965

https://doi.org/10.1016/j.nbt.2020.07.002

https://doi.org/10.1021/acs.oprd.0c00140

https://doi.org/10.1016/j.fbp.2019.10.014



https://doi.org/10.1021/acssuschemeng.9b07414


PROSYS J-27

Schenkendorf, R., Gerogiorgis, D. I., Mansouri, S. S., Gernaey, K. V. (2020). Model-Based Tools for Pharmaceutical Manufacturing Processes. Processes, 8(1). Doi: https://doi.org/10.3390/pr8010049

PROSYS J-28

Lindblom, E., Jeppsson, U., Sin, G. (Accepted/In press). Identification of behavioural model input data sets for WWTP uncertainty analysis. Water Science and Technology. Doi: https://doi.org/10.2166/wst.2019.427

PROSYS J-29

Nielsen, R. F., Nazemzadeh, N., Sillesen, L. W., Andersson, M. P., Gernaey, K. V., Mansouri, S. S. (2020). Hybrid machine learning assisted modelling framework for particle processes. Computers & Chemical Engineering, 140. Doi: https://doi.org/10.1016/j.compchemeng.2020.106916

PROSYS J-30

Mancini, E., Mansouri, S. S., Gernaey, K. V., Luo, J., Pinelo, M. (2020). From second generation feed-stocks to innovative fermentation and downstream techniques for succinic acid production. Critical Reviews in Environmental Science and Technology, 50(18), 1829-1873. Doi: https://doi.org/10.1080/10643389.2019.1670530

PROSYS J-31

Hansen, R. B., Agerbaek, M. A., Nielsen, P. M., Rancke-Madsen, A., Woodley, J. M. (2020). Esterification using a liquid lipase to remove residual free fatty acids in biodiesel. Process Biochemistry, 97, 213-221. Doi: https://doi.org/10.1016/j.procbio.2020.06.005

PROSYS J-32

Sin, G., Espuña, A. (2020). Editorial: Applications of Monte Carlo Method in Chemical, Biochemical and Environmental Engineering. Frontiers in Energy Research, 8, 1-2. Doi: https://doi.org/10.3389/fenrg.2020.00068

PROSYS J-33

Bähner, F. D., Prado-Rubio, O. A., Huusom, J. K. (2020). Discrete-Continuous Dynamic Simulation of Plantwide Batch Process Systems in MATLAB. Chemical Engineering Research and Design, 159, 66-77. Doi: https://doi.org/10.1016/j.cherd.2020.03.030

PROSYS J-34

Hwangbo, S., Sin, G., Rhee, G., Yoo, C. K. (2020). Development of an integrated network for waste-to-energy and central utility systems considering air pollutant emissions pinch analysis. Journal of cleaner production, 252. Doi: https://doi.org/10.1016/j.jclepro.2019.119746

PROSYS J-35

Cheung, H., Frutiger, J., Bell, I. H., Abildskov, J., Sin, G., Wang, S. (2020). Covariance-Based Uncertainty Analysis of Reference Equations of State. Journal of Chemical and Engineering Data, 65(2), 503-522. Doi: https://doi.org/10.1021/acs.jced.9b00689

PROSYS J-36

Öner, M., Stocks, S. M., Sin, G. (2020). Comprehensive sensitivity analysis and process risk assessment of large scale pharmaceutical crystallization processes. Computers and Chemical Engineering, 135. Doi: https://doi.org/10.1016/j.compchemeng.2020.106746

PROSYS J-37

Forero-Hernandez, H., Jones, M. N., Sarup, B., Jensen, A. D., Abildskov, J., Sin, G. (2020). Comprehensive development, uncertainty and sensitivity analysis of a model for the hydrolysis of rapeseed oil. Computers and Chemical Engineering, 133. Doi: https://doi.org/10.1016/j.compchemeng.2019.106631

PROSYS J-38

Meyer, K., Leweke, S., von Lieres, E., Huusom, J. K., & Abildskov, J. (2020). ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatography. Computers and Chemical Engineering, 141. Doi: https://doi.org/10.1016/j.compchemeng.2020.107012

PROSYS J-39

Taboada-Santos, A., Behera, C. R., Sin, G., Gernaey, K. V., Mauricio, M., Carballa, M., Lema, J. M. (2020). Assessment of the fate of organic micropollutants in novel wastewater treatment plant configurations through an empirical mechanistic model. Science of the Total Environment, 716. Doi: https://doi.org/10.1016/j.scitotenv.2020.137079

PROSYS J-40

Behera, C. R., Al, R., Gernaey, K. V., Sin, G. (2020). A process synthesis tool for WWTP – An application to design sustainable energy recovery facilities. Chemical Engineering Research and Design, 156, 353-370. Doi: https://doi.org/10.1016/j.cherd.2020.02.014

https://doi.org/10.3390/pr8010049

https://doi.org/10.2166/wst.2019.427


https://doi.org/10.1080/10643389.2019.1670530

https://doi.org/10.1016/j.procbio.2020.06.005

https://doi.org/10.3389/fenrg.2020.00068


https://doi.org/10.1016/j.jclepro.2019.119746





https://doi.org/10.1016/j.scitotenv.2020.137079



PROSYS J-41

Hwangbo, S., Al, R., Sin, G. (2020). An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations. Computers and Chemical Engineering, 143. Doi: https://doi.org/10.1016/j.compchemeng.2020.107071

PROSYS J-42

Shi, M., Woodley, J. M., von Solms, N. (2020). An Experimental Study on Improved Production Performanceby Depressurization Combined with CO2-Enriched AirInjection. Energy and Fuels, 34(6), 7329–7339. Doi: https://doi.org/10.1021/acs.energyfuels.0c00779

PROSYS J-43

Segovia-Hernández, J. G., Sánchez-Ramírez, E., Alcocer-García, H., Quíroz-Ramírez, J. J., Udugama, I. A., Mansouri, S. S. (2020). Analysis of Intensified Sustainable Schemes for Biobutanol Purification. Chemical Engineering and Processing, 147. Doi: https://doi.org/10.1016/j.cep.2019.107737

PROSYS J-44

Gani, R., Bałdyga, J., Biscans, B., Brunazzi, E., Charpentier, J-C., Drioli, E., Feise, H., Furlong, A., Geem, K. M. V., Hemptinne, J-C. D., Kate, A. J. B. T., Kontogeorgis, G. M., Manenti, F., Marin, G. B., Mansouri, S. S., Piccione, P. M., Povoa, A., Rodrigo, M. A., Sarup, B., Woodley, J. M. (2020). A multi-layered view of chemical and biochemical engineering. Chemical Engineering Research and Design, 155, 133-145. Doi: https://doi.org/10.1016/j.cherd.2020.01.008

PROSYS J-45

Silk, D., Mazzali, B., Gargalo, C. L., Pinelo, M., Udugama, I. A., Mansouri, S. S. (2020). A decision-support framework for techno-economic-sustainability assessment of resource recovery alternatives. Journal of cleaner production, 266. Doi: https://doi.org/10.1016/j.jclepro.2020.121854

PROSYS J-46

Rasmussen, J. B., Mansouri, S. S., Zhang, X., Abildskov, J., Huusom, J. K. (2020). A mass and energy balance stage model for cyclic distillation. AIChE Journal, 66(8). Doi: https://doi.org/10.1002/aic.16259

PROSYS J-47

Öner, M., Montes, F. C. C., Ståhlberg, T., Stocks, S. M., Bajtner, J. E., Sin, G., (2020). Comprehensive evaluation of a data driven control strategy: Experimental application to a pharmaceutical crystallization process, Chemical Engineering Research and Design, 163, 248-261. Doi: https://doi.org/10.1016/j.cherd.2020.08.032

PROSYS J-48

Forero-Hernandez, H., Jones, M. N., Sarup, B., Jensen, A. D., Abildskov, J., & Sin, G. (2020). Comprehensive development, uncertainty and sensitivity analysis of a model for the hydrolysis of rapeseed oil. Computers & Chemical Engineering, 133, [106631]. https://doi.org/10.1016/j.compchemeng.2019.106631

PROSYS J-49

Meyer, K., Leweke, S., von Lieres, E., Huusom, J. K., & Abildskov, J. (2020). ChromaTech: A discontinuous Galerkin spectral element simulator for preparative liquid chromatography. Computers & Chemical Engineering, 141, [107012]. https://doi.org/10.1016/j.compchemeng.2020.107012

PROSYS J-50

Ji, M., Li, X., Omidvarkordshouli, M., Sigurdardóttir, S. B., Woodley, J. M., Daugaard, A. E., Luo, J., & Pinelo, M. (2020). Charge exclusion as a strategy to control retention of small proteins in polyelectrolyte-modified ultrafiltration membranes. Separation and Purification Technology, 247, [116936]. https://doi.org/10.1016/j.seppur.2020.116936

PROSYS J-51

Behera, C. R., Al, R., Gernaey, K. V., & Sin, G. (2020). A process synthesis tool for WWTP – An application to design sustainable energy recovery facilities. Chemical Engineering Research and Design, 156, 353-370. https://doi.org/10.1016/j.cherd.2020.02.014

PROSYS J-52

Hwangbo, S., Al, R., & Sin, G. (2020). An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations. Computers & Chemical Engineering, 143, [107071]. https://doi.org/10.1016/j.compchemeng.2020.107071


https://doi.org/10.1021/acs.energyfuels.0c00779

https://doi.org/10.1016/j.cep.2019.107737







https://doi.org/10.1016/j.seppur.2020.116936




PROSYS J-53

Shi, M., Woodley, J. M., & von Solms, N. (2020). An Experimental Study on Improved Production Performanceby Depressurization Combined with CO2-Enriched AirInjection. Energy and Fuels, 34(6), 7329–7339. https://doi.org/10.1021/acs.energyfuels.0c00779

PROSYS J-54

Segovia-Hernández, J. G., Sánchez-Ramírez, E., Alcocer-García, H., Quíroz-Ramírez, J. J., Udugama, I. A., & Mansouri, S. S. (2020). Analysis of Intensified Sustainable Schemes for Biobutanol Purification. Chemical Engineering and Processing, 147, [107737]. https://doi.org/10.1016/j.cep.2019.107737

PROSYS J-55

Rasmussen, J. B., Mansouri, S. S., Zhang, X., Abildskov, J., & Huusom, J. K. (2020). A mass and energy balance stage model for cyclic distillation. AIChE Journal, 66(8), [e16259]. https://doi.org/10.1002/aic.16259

PROSYS J-56

Woodley, J. M. (2020). Advances in biological conversion technologies: New opportunities for reaction engineering. Reaction Chemistry and Engineering, 5(4), 632-640. https://doi.org/10.1039/c9re00422j

PROSYS J-57

Silk, D., Mazzali, B., Gargalo, C. L., Pinelo, M., Udugama, I. A., & Mansouri, S. S. (2020). A decision-support framework for techno-economic-sustainability assessment of resource recovery alternatives. Journal of cleaner production, 266, [121854]. https://doi.org/10.1016/j.jclepro.2020.121854

PROSYS J-58

Kumar, V. V., Carberry, D., Beenfeldt, C., Andersson, M. P., Mansouri, S. S., & Gallucci, F. (2021). Virtual Reality in Chemical and Biochemical Engineering Education and Training. Education for Chemical Engineers, 36, 143-153. https://doi.org/10.1016/j.ece.2021.05.002

PROSYS J-59

L. Gargalo, C., Caño de Las Heras, S., Jones, M. N., Udugama, I., Mansouri, S. S., Krühne, U., & Gernaey, K. V. (2021). Towards the Development of Digital Twins for the Bio-manufacturing Industry. In Advances in Biochemical Engineering/Biotechnology (pp. 1-35). Springer. Advances in Biochemical Engineering/Biotechnology https://doi.org/10.1007/10_2020_142

PROSYS J-60

Wang, J., Ren, Y., Zhang, H., Luo, J., Woodley, J. M., & Wan, Y. (2021). Targeted modification of polyamide nanofiltration membrane for efficient separation of monosaccharides and monovalent salt. Journal of Membrane Science, 628, [119250]. https://doi.org/10.1016/j.memsci.2021.119250

PROSYS J-61

Sánchez-Ramírez, E., Segovia-Hernandez, J. G., Lund, N. L., Pinto, T., Udugama, I. A., Junicke, H., & Mansouri, S. S. (2021). Sustainable Purification of Butanol from a Class of a Mixture Produced by Reduction of Volatile Fatty Acids. Industrial & Engineering Chemistry Research, 60(13), 4975–4986. https://doi.org/10.1021/acs.iecr.0c06164

PROSYS J-62

Dickson, R., Mancini, E., Garg, N., Woodley, J. M., Gernaey, K., Pinelo, M., Liu, J., & Mansouri, S. S. (Accepted/In press). Sustainable bio-succinic acid production: Superstructure optimization, techno-economic, and lifecycle assessment. Energy & Environmental Science. https://doi.org/10.1039/D0EE03545A

PROSYS J-63

Mansouri, S. S., Briesen, H., Gernaey, K. V., & Nopens, I. (2021). Special Issue on “Recent Advances in Population Balance Modeling”. Processes, 9(1), [122]. https://doi.org/10.3390/pr9010122

PROSYS J-64

Anderson, S. R., Bommarius, B. R., Woodley, J. M., & Bommarius, A. S. (2021). Sparged but not stirred: Rapid, ADH-NADH oxidase catalyzed deracemization of alcohols in a bubble column. Chemical Engineering Journal, 417, [127909]. https://doi.org/10.1016/j.cej.2020.127909

PROSYS J-65

Nachtergaele, P., Sin, G., De Meester, S., Ruysbergh, E., Lauwaert, J., Dewulf, J., & Thybaut, J. W. (2021). Simulation of an Industrial-Scale Reactive Liquid–Liquid Extraction Tower Using Polar PC-

https://doi.org/10.1021/acs.energyfuels.0c00779

https://doi.org/10.1016/j.cep.2019.107737


https://doi.org/10.1039/c9re00422j


https://doi.org/10.1016/j.ece.2021.05.002

https://doi.org/10.1007/10_2020_142

https://doi.org/10.1016/j.memsci.2021.119250


https://doi.org/10.1039/D0EE03545A

https://doi.org/10.3390/pr9010122

https://doi.org/10.1016/j.cej.2020.127909


SAFT Toward Understanding and Improving the Hydrolysis of Triglycerides. ACS Sustainable Chemistry and Engineering, 9(13), 4735-4743. https://doi.org/10.1021/acssuschemeng.0c08757

PROSYS J-66

Zverina, L., Pinelo, M., Woodley, J. M., & Daugaard, A. E. (Accepted/In press). Monolithic flow reactor for enzymatic oxidations. Journal of Chemical Technology and Biotechnology. https://doi.org/10.1002/jctb.6771

PROSYS J-67

Nazemzadeh, N., Udugama, I. A., Karcz, A. P., Andersson, M. P., Abildskov, J., & Mansouri, S. S. (2021). Molecular tracking: A concept for side-draw distillation column design. AIChE Journal, e17070. https://doi.org/10.1002/aic.17070

PROSYS J-68

Montes, F. C. C., Öner, M., Gernaey, K. V., & Sin, G. (2021). Model-Based Evaluation of a Data-Driven Control Strategy: Application to Ibuprofen Crystallization. Processes, 9(4), [653]. https://doi.org/10.3390/pr9040653

PROSYS J-69

Hwangbo, S., Al, R., Chen, X., & Sin, G. (2021). Integrated Model for Understanding N2O Emissions from Wastewater Treatment Plants: A Deep Learning Approach. Environmental Science and Technology, 55(3), 2143–2151. https://doi.org/10.1021/acs.est.0c05231

PROSYS J-70

Iftakher, A., Mansouri, S. S., Nahid, A., Tula, A. K., Choudhury, M. A. A. S., Lee, J. H., & Gani, R. (2021). Integrated Design and Control of Reactive Distillation Processes Using the Driving Force Approach. AIChE Journal, 67(6), [e17227]. https://doi.org/10.1002/aic.17227

PROSYS J-71

Öner, M., Tran, F., Jensen, G. H., Ståhlberg, T., Bisgaard-Frantzen, K., Stocks, S. M., Abildskov, J., & Sin, G. (2021). Independent Validation of an In Silico Tool for a Pilot-Scale Pharmaceutical Crystallization Process Development. Processes, 9(4), [640]. https://doi.org/10.3390/pr9040640

PROSYS J-72

Duman-Özdamar, Z. E., Ünlü, A., Ünal, H., Woodley, J. M., & Bınay, B. (2021). High-yield production of active recombinant S. simulans lysostaphin expressed in E. coli in a laboratory bioreactor. Protein Expression and Purification, 177, [105753]. https://doi.org/10.1016/j.pep.2020.105753

PROSYS J-73

Obermeier, A., Vollmer, N., Windmeier, C., Esche, E., & Repke, J-U. (2021). Generation of linear-based surrogate models from non-linear functional relationships for use in scheduling formulation. Computers and Chemical Engineering, 146, [107203]. https://doi.org/10.1016/j.compchemeng.2020.107203

PROSYS J-74

Ziaei-Halimejani, H., Zarghami, R., Mansouri, S. S., & Mostoufi, N. (2021). Data-Driven Fault Diagnosis of Chemical Processes Based on Recurrence Plots. Industrial & Engineering Chemistry Research, 60(7), 3038–3055. https://doi.org/10.1021/acs.iecr.0c06307

PROSYS J-75

Caño de las Heras, S., Kensington-Miller, B., Young, B., Gonzalez, V., Krühne, U., Mansouri, S. S., & Baroutian, S. (2021). Benefits and Challenges of a Virtual Laboratory in Chemical and Biochemical Engineering: Students’ Experiences in Fermentation. Journal of Chemical Education, 98(3), 866–875. https://doi.org/10.1021/acs.jchemed.0c01227

PROSYS J-76

Bhatt, C., Nielsen, P. M., Rancke-Madsen, A., & Woodley, J. M. (Accepted/In press). Combining technology with liquid-formulated lipases for In-Spec biodiesel production. Biotechnology and Applied Biochemistry. https://doi.org/10.1002/bab.2074

PROSYS J-77

Zhang, H., Luo, J., Woodley, J. M., & Wan, Y. (Accepted/In press). Confining the motion of enzymes in nanofiltration membrane for efficient and stable removal of micropollutants. Chemical Engineering Journal, [127870]. https://doi.org/10.1016/j.cej.2020.127870

https://doi.org/10.1021/acssuschemeng.0c08757

https://doi.org/10.1002/jctb.6771


https://doi.org/10.3390/pr9040653

https://doi.org/10.1021/acs.est.0c05231


https://doi.org/10.3390/pr9040640

https://doi.org/10.1016/j.pep.2020.105753


https://doi.org/10.1021/acs.jchemed.0c01227

https://doi.org/10.1002/bab.2074

https://doi.org/10.1016/j.cej.2020.127870


PROSYS J-78

Caño de Las Heras, S., L. Gargalo, C., Weitze, C. L., Mansouri, S. S., Gernaey, K. V., & Krühne, U. (2021). A framework for the development of Pedagogical Process Simulators (P2Si) using explanatory models and gamification. Computers and Chemical Engineering, 151, [107350]. https://doi.org/10.1016/j.compchemeng.2021.107350

PROSYS J-79

Sin, G., & Al, R. (2021). Activated sludge models at the crossroad of artificial intelligence—A perspective on advancing process modeling. npj Clean Water, 4(1), [16]. https://doi.org/10.1038/s41545-021-00106-5


https://doi.org/10.1038/s41545-021-00106-5


K

KT Consortium at KT-DTU is an industry academia collaboration where:

• Faculty, students and researchers work on variety of academic and industrial topics and disseminate their work in form of journals articles, presentations & software tools, etc.

• Members are provided with networking opportunities along with state-of-the-art methods and tools for chemical and biochemical engineering.

The purpose of KT Consortium member website is to provide a platform to access research activities and deliverables from KT Consortium and can be accessed here: http://www.kt.dtu.dk/english/research/kt-consortium using a unique id and password. For queries please contact Guoliang Wang ([email protected])

ICAS, ASSOCIATED TOOLS & DOCUMENTS

Software and Tools: This section of the website contains a full list of tools description, softwares, their manuals, tutorials etc. that can be downloaded directly.

Annual Meetings: This section contains all the documents including presentations from previous and current meetings (2017 onwards).

KT Consortium Member Website

http://www.kt.dtu.dk/english/research/kt-consortium

mailto:[email protected]


KT Consortium Department of Chemical and Biochemical Engineering, DTU, Denmark

Please stay tuned! In the following weeks, we plan a major upgrade in the structure of the website for better organization and ease of access

www.cere.dtu.dk, Department of Chemical and Biochemical Engineering - Søltofts Plads - Building 229 - DK2800 Kgs. Lyngby

August 9 - 20, 2021

CEREDept. of Chemical & Biochemical EngineeringDept. of ChemistryTechnical University of Denmark

Advanced On-line Courseon

Thermodynamic Models:Fundamentals & Computational Aspects

CERECenter for Energy Resources Engineering

The course will provide the participants with a knowledge of the fundamentals of thermodynamics, an overview of the most important thermodynamic models currently used in industrial practice, including how efficient computer codes for such models are written and checked for errors.

In addition the course will emphasize the development of efficient procedures for calculation of phase equilibria under a variety of conditions.

The practical part of the course, where the participants have to develop their own codes, emphasizes in particular this aspect.

The course is of relevance for researchers engaged in the development and implemen-tation of thermodynamic models for process simulation or for those who just want to learn how to develop and write an efficient and consistent computer code. Fundamentals:

The state functions, conditions of equilibrium and stability, properties of mixtures, calculation of the derivatives of the thermodynamic functions, checking model expressions and model consistency. Models:

Equations of state and activity coefficient models, EoS/GE mixing rules, association models (the CPA and SAFT equations of state), mixtures with electrolytes and polymers, applications including carbon capture and storage as well as flow assurance and trends in thermodynamic models

Computational methods:

General equilibrium relations and material balances. The PT-flash: Successive substitution, the Rachford-Rice equation, acceleration, higher order methods and stability analysis.

The multiphase flash. General state function based specifications.Dew- and bubble points. Critical point calculation. Chemical equilibrium calculation. Gravitational segregation. Miscible displacement.

Course description

Further information:A maximum of 30 participants can be accepted. The course credits are 7.5 ECTS points.

Teachers:Wei Yan - [email protected] Kontogeorgis - [email protected] Alexander Shapiro [email protected] L. Fosbøl - [email protected] Textbook:M.L. Michelsen and J.M. Mollerup, Thermodynamic Models: Fundamentals & Compu-tational Aspects. 2nd Edition December 2007. Tie-Line Publications. Price € 110 (incl VAT) at www.tie-tech.com/shop - Students will receive a discount of 50%.

PrerequisitesBasic chemical engineering thermodynamics, including knowledge about Equations of State, the concept of fugacity, and basic chemical equilibrium. For the practical part, a working knowledge of Fortran or Matlab is needed.

Curriculum and exam:Two weeks of lectures, classroom problems and computer exercises (in teams of two). A third week, which does not require presence at DTU, for completing the exercises. A report is due at latest August 30, 2021.

Course history & style

The course is strongly inspired by the book written by Michelsen & Mollerup and the lectures on Computational aspects by Prof. Michelsen. These lectures will be available on the course website during the course and for a short period after that. This year, due to COVID-19 situation, the course will be given on-line. The course will consist of recorded lectures, on-line sessions for questions and dis-cussions, on-line sessions for exercises and potentially some additional sessions depending on the interests and questions of the participants.

Center for Energy Resources EngineeringDepartment of Chemical and Biochemical Engineering & Dept. of Chemistry

Technical University of DenmarkSøltoft PladsBuilding 229

Registration: www.cere.dtu.dk/education/phd-courses

Further information: Anne Louise Biede ([email protected] )

General course information

Venue:

On-line. Information about course site and links will be provided in advance to the course participants.

Registration deadline: June 15 2021 Payment deadline July 1, 2021

Prices: Industrial participant (up to 3 registrations) € 2,500*CERE / KT Consortium members (up to 3 registrations) € 1,400* PhD student € 300Academic € 1,400* The price for each company is the same for 1-3 registrations.

Since there is a maximum of about 30 participants, an early registration is recommended.

Photo: Christian Ove Carlsson

Summer School on Uncertainty and

Sensitivity Analysis of Numerical Models in Chemical, Biochemical and Environmental Engineering

August 9 – 13, 2021

Gürkan Sin & Resul Al

PROSYS Research Centre, Department of Chemical and Biochemical

Engineering, Technical University of Denmark, Lyngby, DK

Keywords:

Probability, Inference, Uncertainty, Sensitivity, Bayesian inference, Frequentist’s approach, Bootstrap, Monte Carlo, Metropolis-Hasting, Morris screening, Variance decomposition, Sobol’s sampling, Modeling, Simulation.

General course objectives:

Modeling is actively used in various scientific and engineering disciplines for a variety of

ends: from the development of process understanding to design, control , and operation of engineering and natural systems.

Most numerical models simulating such systems tend to be complex with many parameters, state variables, and non-linear relations resulting in many degrees of freedom. Using a fine -tuning method (manually or statistically), these models can be made to produce virtually any desired behavior to fit the observations on the system in question. What is challenging, however, is to ascertain a degree of reliability and credibility of the models before one applies them in reality.

The objective of this course is to introduce modern techniques of uncertain ty and sensitivity analysis of data and models. We aim to provide participants with a sound understanding of theory and hands-on practice (in Matlab®) on applying a range of methods from local to global techniques for model-based engineering applications.

Course content:

Fundamental as well as contemporary methods in advanced uncertainty and sensitivity analysis

will be covered in the course as presented below:

For parameter estimation uncertainties, two fundamental theories—MLE and Bayesian inference—in addition to the bootstrap method are covered. For sensitivity analysis, local as well as global contemporary techniques will be covered. For global analysis, methods developed for both independent inputs and correlated inputs will also be covered.

As t he course aims at giving hands -on experience with the topics studied, therefore, the lectures are structured as follows: first the theory will be introduced in class, which is followed by practical exercises and examples. Examples are taken from the textbook, the literature, and also from ongoing research work at PROSYS Research Centre covering models from simple linear to complex

nonlinear types as typically encountered in engineering studies.

Course literature:

Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. Global Sensitivity Analysis: The primer. West Sussex, England, John Wiley & Sons, 2008.

Gelman, Carlin, Stern and Rubin, Bayesian data analysis, 2nd ed, Chapman & Hall, CRC, 2004. Plus, several more relevant articles. Remarks:

Knowledge of Matlab at the start of the course is required. If requested, an introduction material to Matlab with exercises can be sent to participants in advance of the course. Participants are expected to have their own laptops to carry out the computer exercises.

Course type and credits:

The summer school is registered as a Ph.D. level course equivalent to 7.5 ECTS.

Scope and form:

About one week of teaching and computer exercises, followed by two or more weeks

where the methods are applied to the student’s own model or system. Lectures will typically take 1 to 1.5 hours followed by practical sessions to work with the methods introduced. Later on, students are to apply the methods to a model/system agreed between students and teachers (as a case study format). The evaluation is based on the submission of a final report about the case study including the codes.

Registration: There is a registration fee charged to follow this summer school. The registration fee grants you access to the uncertainty and sensitivity analysis toolbox as well as include social activities and networking events (lunches and summer school dinner). Normal Ph.D. student: 2500 DKK Postdocs/Industrial Ph.D.: 5000 DKK Industrial participants: Regular fee: 12,500 DKK KTC members: 7,500 DKK Online participation (limited): 7500 DKK The participants are expected to cover their own expenses related to traveling and accommodation. Registration takes place via

the summer school website (contact Anja Ninett Jensen) and will be confirmed once the payment is received. There is room for 25 persons in the course and the deadline to register is July 30th, 2021. Contact information: Course responsible: Prof. Gürkan Sin ([email protected]). Course secretariat: Anja Ninett Jensen ([email protected]) Course website & registration: https:/ / www.conferencemanager.dk/ 2021summerschool Department of Chemical and Biochemical Engineering, DTU. Søltofts Plads, Building 229, DK-2800 Kgs. Lyngby Direct/ Mobile +45 4525 2802 http:/ / www.kt.dtu.dk/ english/ Research/ PROSYS EAN-nr. 5798000430280 Sted-nr. 2800



https://www.conferencemanager.dk/2021summerschool

https://www.conferencemanager.dk/2021summerschool

http://www.kt.dtu.dk/english/Research/PROSYS%20EAN-nr.%205798000430280%20Sted-nr.%202800

http://www.kt.dtu.dk/english/Research/PROSYS%20EAN-nr.%205798000430280%20Sted-nr.%202800

Advanced Process Optimization

Deterministic & Stochastic Optimization

August 2021

A course by Assoc. Prof. Seyed S. Mansouri

Assoc. Prof. Jens Abildskov

For more information please write to [email protected] or [email protected]



KT CONSORTIUM NEWS DECEMBER 2020

KT Consortium Annual Meeting: 15-17 June 2021 We would like to thank you for your participation at the first Online KT Consortium Annual Meeting. Despite the challenges of a difficult year, we are glad we have managed to meet in a virtual space to present our latest research results.

We would like to invite you to reserve 15-17 June 2021 dates for the next Annual Meeting. More details will come in the following months.

The most popular videos from each session were T07, T08, P02, P08, W01 and W02. These videos together with all the material from the Annual Meeting 2020 is available on the members website under the conference banner button. Feel free to contact our affiliated researchers if you have further questions or inquires related to the presented topics.

Software Plans for 2021 The work of PhD student Yuqiu Chen on ionic liquids will be implemented in our software as a new tool which will be part of ICAS or as a stand-alone program. The tool will include visualization and search of ionic liquid experimental property (density, heat capacity, viscosity, melting point, gas solubility and more) database. Group-Contribution and UNIFAC models will be integrated for predicting property data not available in the database. The expected release date of the tool is June 2021.

Gürkan Sin Appointed Professor at DTU Chemical Engineering In October, our Deputy Leader Associate Professor Gürkan Sin was appointed Professor at the Department of Chemical and Biochemical Engineering at DTU. Prof. Sin research aims to advance systematic computer-aided methods for the design, control and optimization of processes and systems for life sciences, biopharma, biotech and water industries.

Prof. Sin pioneered innovative use of Monte Carlo methods within process innovation, scale-up, control and optimization in different process industries. His research plans for the following years is “to expand process systems engineering discipline with emerging artificial intelligence (AI) and digitalization paradigm. New AI techniques are expanding our modelling capabilities in many ways that were not possible before. This new era is promising deeper understanding and better models of process industries”.

Changes in CERE Chairmanship After more than 6 years as CERE chairman, Prof. Kontogeorgis steps down from the CERE chairman position, leaving it to Prof. Nicolas von Solms. Prof. Kontogeorgis continues being the leader of AT-CERE (CERE group at DTU Chemical Engineering Department) and KT Consortium.

Happy Holidays!

https://share.dtu.dk/sites/KT-Consortium_223000/SitePages/Home.aspx

KT CONSORTIUM NEWS DECEMBER 2020

KT Consortium Annual Meeting Survey Results Thank you for taking the time to complete the survey sent at the end of the Online Annual Meeting. This represents a good tool for us in planning future work within the consortia. We are glad you have enjoyed the first online edition. As suggested in the survey and the advisory board meeting, and hopefully with a better pandemics’ situation, we aim for a hybrid version for the Annual Meeting 2021, where the focus will be on having more presentations from our PhDs, Postdocs and invited guests, having more discussion time and enhancing the networking. The survey results point out that Process Synthesis and Development together with Properties and Thermodynamics remain the main areas of interest for the member companies, with an emerging interest within the Circular Economy, Artificial Intelligence and Machine Learning. We will try to address the main concerns and recommendations received, such as integration of methods and tools (with respect to data science), online tailor-made ICAS presentations and workshops, constant updates to member website and publication list.

PhD Defenses Two PhD defenses from KT Consortium associated students will take place at the end of this year. The defenses will be online via Zoom. The links will be shared a few days before the defense, while the video links will be available on the members website afterwards.

PhD student Yuqiu Chen will defend his PhD thesis: “Computer-aided design methodology for separation processes with ionic liquids” supervised by Prof. John M. Woodley and Prof. Georgios M. Kontogeorgis on 16 December 2020. The examiners are Prof. Christos Maravelias (Princeton University, USA), Assoc. Prof. Yansong Zhao (Western Norway University of Applied Sciences, Norway), and Prof. Gürkan Sin (DTU).

PhD student Rowan Malan Lindeque will defend his PhD thesis: “Continuous Oxidative Biocatalysis”, supervised by Prof. John M. Woodley, Prof. Kim Dam-Johansen and Assoc. Prof. Ulrich Krüne on 17 December 2020. The examiners are Principal Scientist Shane T. Grosser (Merck and Co., USA) Professor Roland Wohlgemuth (Lodz University of Technology, Poland), Prof. Krist V. Gernaey (DTU).

Welcome to New KT Consortium Co-workers PhD Student Aswin Vinod Muthachikavil (01.05.2020) will work on the project “Exploring Structure and Properties of Water Through Molecular Simulations” under the supervision of Assoc. Prof. Xiaodong Liang and Prof. Georgios M. Kontogeorgis.

Postdoc Fufang Yang (31.08.2020) will work on electrolyte thermodynamics within ERC Grant under the supervision of Prof. Georgios M. Kontogeorgis and Prof. Jean-Charles de Hemptinne (IFP Energies Nouvelles, FR).

PhD Student Deborah Carberry (01.09.2020) will work on the project “Education Design for Emerging Technologies in Systems Engineering Education” under the supervision of Assist. Prof. Seyed S. Mansouri and Assoc. Prof. Martin Andersson.

PhD Student Saman Naseri Boroujeni will work on the “Multi-phase Modelling of Electrolyte Systems” project part of thr ERC Grant under the supervision of Prof. Georgios M. Kontogeorgis, Assoc. Prof. Xiaodong Liang and Dr. Bjørn Maribo-Mogensen

(Hafnium Labs, DK).

KT Consortium Management Prof. Georgios M. Kontogeorgis, [email protected] Prof. Kim Dam-Johansen, [email protected] Prof. Gürkan Sin, [email protected] General contact: Mrs. Eva Mikkelsen, [email protected]

KT Consortium Website & Members’ Website: www.kt.dtu.dk/english/research/kt-consortium

The official Newsletter of the KT Consortium

Department of Chemical and Biochemical Engineering Technical University of Denmark (DTU) Kgs. Lyngby, Denmark

Issue 07, December 2020





KT CONSORTIUM NEWS APRIL 2021

Online KT CONSORTIUM Annual Meeting June 15-17, 2021

Online Annual Meeting 2021 15-17 June It is with a heavy heart we announce that this year’s Annual Meeting will be held only as an online event. We came to this decision due to uncertainties regarding Denmark’s opening conditions. We did all in our power to have a hybrid meeting, where we could meet both physically and virtually, but the risks and uncertainties were too high. As in the previous years, the meeting is joined with the CERE Discussion Meeting. Both meetings are free to attend and we encourage you to share the news with your colleagues within the company. We will have several plenary talks from invited industrial presenters (Dr. Nevin Gerek Ince, AVEVA; Dr. Susanna Kuitunen, Neste Engineering Solutions) and KT Consortium Associated faculties, along with numerous PhD presentations. The program includes 3 sessions: Thermodynamics, Water and Electrolytes, Process System Engineering, and Bio-Process System Engineering. This year we have one extra session with invited industrial and academia speakers from the EU project RENESENG II, where DTU was part of. More information about the meeting including updated program, and registration will come soon. We are looking to virtually meet you in June!

Good Reporting Practice and Industrial Requirements for Property Data – Out Now KT Consortium associated faculty, Assoc. Prof. Jens Abildskov and Prof. Georgios M. Kontogeorgis, participated in a recently completed IUPAC project, under NIST leadership, which has published useful guidelines for good experimental practice for thermodynamic measurements. The paper is Golden Open Access (OA) and can be accessed here. Industrial Requirements for Thermodynamic and Transport Properties – 2020 by Prof. Georgios M. Kontogeorgis and several academic and industrial colleagues has been recently published OA in Ind. Eng. Chem. Res. The paper has as co-authors Dr. Susanna Kuitunen (Neste Engineering Solutions), and Dr. Antoon ten Kate (Nouryon). The paper presents the “opinion of the industry” and identifies the most relevant aspects of thermodynamics for industry, and the evolution in the past 10 years.

The paper has been selected as ACS Editor’s Choice which offers read access to new research of high importance to the global scientific community selected by a specialized panel. The paper can be accessed here. Both papers are available on the KT Consortium Members Website as well.

https://www.degruyter.com/document/doi/10.1515/pac-2020-0403/html

https://pubs.acs.org/doi/10.1021/acs.iecr.0c05356




IL Pro – New KT Consortium Software for Ionic Liquids As previously announced in our Newsletter, a new comprehensive software for ionic liquids, IL Pro, is under development at KT Consortium. Ionic liquids (ILs) have many potential applications in various industries and they represent a rapid growing market. The aim of our software is to evaluate and identify (new) IL at a fast pace by using reliable methods. The software is based on the results of the PhD project by Yuqiu Chen (currently Postdoc) supervised by Prof. John M. Woodley and Prof. Georgios M. Kontogeorgis, and developed by our Software Manager Guoliang Wang. A large number of experimental data and models covering 22 commonly used physical and thermodynamic properties, such as density, viscosity, melting point, heat capacity, and gas solubility (13 gases) have been already integrated into the software. IL Pro is designed to have various toolboxes such as IL DB Manager, IL ProPred, IL Molecular Design, and IL Pro Tutorial. The IL DB Manager enables users to search different property data. In case experimental data does not exist, or if ILs have not yet been synthesized, IL ProPred can be used to calculate the missing data using integrated models. This is essential to find new high-performance ILs for different industry applications. The IL Molecular Design can be used to identify IL compounds that match specific requirements set by the user. The first version of IL Pro will have IL DB Manager and IL ProPred toolboxes and it will be released in June 2021. The IL Molecular Design and other toolboxes will be integrated into IL Pro in future versions.

ERC Advanced Grant awarded to Prof. John M. Woodley We congratulate Professor John M. Woodley for the recently awarded ERC Advanced Grant for the project FLUIZYME - Understanding the Effect of Non-natural Fluid Environments on Enzyme Stability. The project aims to improve our understanding of enzyme stability and broaden the field of potential applications for biocatalysts. More information about the project can be found here.

Welcome to new KT Consortium co-workers Assoc. Prof. Martin Andersson joined officially the KT Consortium very recently. He is affiliated with the research center of Combustion and Harmful Emission Control (CHEC) at DTU. His research is focused on molecular simulations for thermodynamic and kinetic properties applied to catalysis, property prediction, materials science, PSE etc. He is the supervisor of several KT Consortium associated PhD students and postdocs.

Postdoc Mattia Turchi (01.10.2019-30.09.2021) is working together with Assoc. Prof. Martin Andersson on a project regarding first-principles prediction for critical micellar concentrations of surfactants.

Postdoc Haoshui Yu (01.12.2020-30.11.2022) is working on the project Integrated Organic Rankine Cycle (ORC) system for simultaneous utilization of solar energy and LNG cold energy together with Prof. Gürkan Sin. Postdoc Sina Hassanjani Saravi (01.03.2021) affiliated at Princeton University (USA) will collaborate together with Prof. Georgios M. Kontogeorgis and Prof. Athanassios Z. Panagiotopoulos (Princeton University) in the project Activity Coefficients and Solubilities of Aqueous Electrolytes from Molecular Dynamics Simulations part of the ERC grant. PhD Saeed Miri Ramsheh (01.01.2020-31.12.2023) is working on Molecular modelling of stone wool dissolution under the supervision of Assoc. Prof. Martin Andersson, Assoc. Prof. Seyed S. Mansouri, and Prof. Susan Stipp.

IL ProPred toolbox screenshot

https://www.dtu.dk/english/news/Nyhed?id=%7b6DD2579B-836E-4A60-BE4A-46DCA7F5029D%7d


PhD Simon Brædder Lindahl (01.01.2021-31.12.2023) is working on SUPPLY - sustainable Upstream Production Planning for Lot-sizing and Yield Evaluation under the supervision of Prof. Gürkan Sin, Dr. Deenesh K. Babi (Novo Nordisk) and Marianne Langfrits (Novo Nordisk)

Abraham A. J. Kerssemakers (01.10.2019-30.09.2022) is working under the supervision of Prof. Gürkan Sin and Senior Bioprocess Engineer Suresh Sudarsan on the project Understanding and overcoming physiological stress responses in Yarrowia lipolytica to dissolved O2 and CO2 in fermentation processes at the Novo Nordisk Foundation Center for Biosustainability, DTU.

PhD Alina Anamaria Malanca (01.01.2021-31.12.2022) will work on the project High performance fermentation by integration with membrane technologies under the supervision of Prof. Manuel Pinelo, Assoc. Prof. Seyed S. Mansouri, Assoc. Prof. Hariklia Gavala, Assoc. Prof. Ioannis V. Skiadas, and Jianquan Luo (Chinese Academy of Science).

Courses provided by KT Consortium Faculties Several courses and workshops are provided by the KT Consortium affiliated faculty members. They can be accessed at a special price by the KT Consortium member companies. Summer School on Uncertainty and Sensitivity Analysis of Numerical Models in Chemical, Biochemical and Environmental Engineering (09-13 August 2021) by Prof. Gürkan Sin and Dr. Resul Al (Novo Nordisk, DK). The course program and registration page can be accessed here. Advanced Process Optimization (August 2021) by Assoc. Prof. Seyed Soheil Mansouri and Assoc. Prof. Jens Abildskov – for more information please write to [email protected] or [email protected]. Advanced Course on Thermodynamic Models: Fundamentals & Computational Aspects (09-20 August 2021) by Prof. Georgios M. Kontogeorgis, Assoc. prof. Wei Yan and other faculty. More details and registration page can be found here.

Conferences & Workshops We attend the following conferences: ESCAPE31 is organized during 06-09 June 2021 in Istanbul, Turkey, but the whole event will take place online. KT Consortium will have 11 contributions as virtual posters and presentations. ESAT 2021 will take place during 05-09 July 2021 in Paris, France as a virtual event. A total of 4 contributions will be presented by KT Consortium co-workers.

Several of the KT Consortium co-workers together with other faculty from Department of Chemical and Biochemical engineering will be involved in organizing following workshops: ProBioRefine 2021 Workshop (06-07 May 2021) is a collaboration between DTU and KAIST for boosting international collaboration on developing methods and tools to analyze and design biorefinery networks based on chemical and biological processes, to convert biomass feedstocks into valuable chemicals and biofuels. The event is held online via Zoom platform and it is free for participation. Details about registration, and program can be found here. Quantum Computing Applications in Chemical and Biochemical Engineering Workshop (21-23 October 2021) will showcase advances of emerging and rapidly growing field of Quantum Computing. The workshop is organized together with AIChE. More details about registration and program can be found here. EFCE Early Career Engineers' Forum (22 April 2021) will feature networking opportunities as well as best practice exchange on activities targeting engineers at the start of their careers. The workshop is organized together with EFCE, will take place on zoom and it is free to attend. More details can be found here.

KT Consortium Management Prof. Georgios M. Kontogeorgis, [email protected] Prof. Kim Dam-Johansen, [email protected] Prof. Gürkan Sin, [email protected] General contact: Dr. Olivia Ana Perederic, [email protected] Mrs. Eva Mikkelsen, [email protected]

KT Consortium Website & Members’ Website: www.kt.dtu.dk/english/research/kt-consortium

The official Newsletter of the KT Consortium

Department of Chemical and Biochemical Engineering Technical University of Denmark (DTU) Kgs. Lyngby, Denmark

Issue 08, April 2021

https://www.conferencemanager.dk/2021summerschool/conference



https://www.conferencemanager.dk/actm2021

https://www.escape31.org/

http://www.esat2020.com/

https://conferencemanager.events/probiorefine2021/program

https://www.aiche.org/sbe/conferences/quantum-computing-applications-chemical-and-biochemical-engineering-workshop/2021






Online KT CONSORTIUM Annual Meeting - Reneseng 2

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