for Pipeline Engineering - UKCCSRC · for Pipeline Engineering . Tuesday 11 November 2014, York . ... Process Engineering performing research into the transport methods that will

CO2 Properties and EoS

for Pipeline Engineering Tuesday 11 November 2014, York

www.ukccsrc.ac.uk

Agenda.................................................................................................................................................................................................... 2Delegate List.................................................................................................................................................................................................... 3Speaker biographies.................................................................................................................................................................................................... 4Roland Span - Thermodynamic Property Models for Transport and Storage of CO2.................................................................................................................................................................................................... 8Javier Rodriguez - gSAFT: advanced physical properties for CCS system modelling.................................................................................................................................................................................................... 44Martin Trussler - Phase Behabiour and EoS Modelling of the CO2-H2 System.................................................................................................................................................................................................... 85Richard Graham - Understanding and predicting CO2 properties.................................................................................................................................................................................................... 108Solomon Brown - Impact of EoS on Simulating CO2 Pipeline Decompression.................................................................................................................................................................................................... 171Xiaobo Luo - Simulation-based Techno-economic Evalusation for Optical Design.................................................................................................................................................................................................... 188Chris Wareing - Numerical modelling of trans-triple point temperature....................................................................................................................................................................................................... 205Jie Ke - Phase equilibrium studies of impure CO2 systems....................................................................................................................................................................................................... 216

This meeting will bring together researchers and practitioners in the field of CO2 properties and Equations of

State relevant to the pipeline transportation of CO2. The aim of the meeting will be to share information

relating to both research needs in this area and the status of existing research projects, with a view to

developing a more co-ordinated research effort within the UK CCS research community.

The format of the meeting will include:

- Invited presentations from industrial and academic participants aimed at highlighting research needs

- Short (5 minute) presentations from researchers aimed at illuminating the topics and status of current

research efforts

- Round-table discussions aimed at improved co-ordination of existing research and identification of

opportunities for new research initiative and funding.

Convened by: Dr Richard Graham (University of Nottingham), Dr Julia Race (University of Strathclyde), Prof

Martin Trusler (Imperial College London)

AGENDA

10.00-10.30 Arrivals and registration

10.30-10.45 Welcome/introduction

10.45-11.30 KEYNOTE: Professor Roland Span, Ruhr-Universität Bochum, Germany “Thermodynamic

Property Models for Transport and Storage of CO2”

11:30-11:55 Russell Cooper, National Grid "The Certainties and Uncertainties of CO2 Transport and Storage"

11:55-12:20 Javier Rodriguez, Process Systems Enterprise Ltd "gSAFT: Advanced Physical Properties for

Carbon Capture and Storage System Modelling"

12:20-12:45 Martin Trusler, Imperial College London "Phase Behaviour and EoS Modelling of the Carbon

Dioxide-Hydrogen System"

12:45-13:10 Richard Graham, University of Nottingham "Understanding and Predicting CO2Properties for CCS

Transport”

13:15-14:15 Lunch

14.15-15.45 Group discussions with academic presentations

Solomon Brown, University College London “Impact of Equation of State on Simulating

CO2Pipeline Decompression”

Xiaobo Luo, University of Hull “Study of the Pipeline Network Planned in the Humber Region of

the UK”

Chris Wareing, University of Leeds “Numerical Modelling of Trans-Triple Point Temperature

Near-Field Sonic Dispersion of CO2 from High Pressure Dense Phase Pipelines”

15.45-16.00 Break

16.00-16.30 Discussion and wrap-up

Hamed Aghajani Newcastle University

Saif Al Ghafri Imperial College London

David Allen Doosan Babcock Limited

Emilie Brady UKCCSRC

Solomon Brown University College London

Russell Cooper National Grid

Andrew Cox Energy Intelligence & Marketing Research

Richard Graham University of Nottingham

Yong Hua Leeds University

Bilaal Hussain Birmingham University

Andrew Laughton DNV GL (Oil & Gas UK)

Chih-Wei Lin Heriot-Watt University

Xiaobo Luo University of Hull

Roger Macdonald Pipeline Industry Guild

Haroun Mahgerefteh University College London

Victor Emeka Onyebuchi Cranfield University

Javier Rodriguez Process Systems Enterprise Ltd

Roland Span Ruhr-Universität Bochum

Michael Thomson University of Nottingham

Martin Trusler Imperial College London

Meihong Wang University of Hull

Chris Wareing University of Leeds

Ben Wetenhall Newcastle University

DELEGATE LIST

SPEAKERS

Solomon Brown

University College London

Solomon Brown received his Ph.D. from University College London in 2011 and is currently a research associate

and teaching fellow in the same institution. His expertise belong to the field of mathematical and numerical

modelling of transient multiphase flows. Dr Brown has published on the use of CFD in various safety-related

aspects of CCS and is the recipient of the IChemE Frank Lees Medal for his collaborative work with HSE on CO2

pipelines safety. He has participated in several projects sponsored by the EC, EPSRC, UKCCSRC and industry. He

is a co-investigator on the UKCCSRC project “The Development and Demonstration of Best Practice Guidelines

for the Safe Start-up Injection of CO2 into Depleted Gas Fields” and EC FP7 project “CO2QUEST - Techno-

economic Assessment of CO2 Quality Effect on its Storage and Transport”.

Russell Cooper

National Grid

Russell has been working on CCS transport for over 6-years now. This started off as a side-line to his main job of

managing the design of the gas National Transmission system on behalf of National Grid.

Russell managed the transportation feed for the Longannet CCS proposal that was part of the DECC-1

competition. Since then he has managed the concept selection and consenting process for the proposed

Yorkshire and Humberside CCS transportation and Storage system. During this time Russell has also managed

the research programme that has been developed to address the safety and environmental questions relating to

the transport of CO2.

Richard Graham

University of Nottingham

Richard Graham is an Associate Professor in the School of Mathematical Sciences at the University of

Nottingham. His research broadly encompasses molecular modelling of flow dynamics and phase transitions. His

CCS interests centre on models for impure CO2, for pipeline transport and rupture. To his problem, he has

applied multi-scale techniques, including equations of state, non-parametric modelling and molecular

simulation. This work has been funded by EPSRC, UKCCSRC, E.ON and RWE npower. Previously, Richard worked

at the School of Physics and Astronomy, University of Leeds, and the Department of Chemical Engineering,

University of Michigan.

Xiaobo Luo

University of Hull

Xiaobo received his BEng in Chemical Engineering and Technology in 2001. Following this he was a process

engineer and project manager in National Engineering Research Centre of Distillation Technology of China

(based in Tianjin University) for 7 years. He obtained my MSc degree in Process System Engineering in Cranfield

University in 2008 and from 2009 to 2012 he worked for BP as a lead process optimisation engineer in BP Zhuhai

chemical plant. He them moved to the University of Hull where he is currently a research associate on

process\energy system engineering and CCS. His main research directions include:

(1) modelling, simulation and optimization of power plants integrated with post-combustion carbon capture

process;

(2) techno-economic evaluation of CO2 transport pipeline network;

(3) developing key technologies for energy saving and emission reduction for large scale chemical and

metallurgy processes;

Javier Rodriguez

Process Systems Enterprise Ltd

Javier Rodríguez is a Senior Consultant at Process Systems Enterprise (PSE). He is the lead developer of physical

property models in gCCS, a modelling and simulation package for CCS systems. He played a key role in the

execution of the ETI CCS System Modelling Tool-kit Project, and greatly contributed to the design, development

and testing of gCCS model libraries and their productisation. Before joining PSE, Javier held a position as

research associate at Imperial College and worked on the development of SAFT models for amine-based

solvents relevant to carbon capture. Javier has a PhD from Imperial College London, where he worked on

developing a hybrid strategy to enhance state estimation for nonlinear lumped and distributed parameter

systems. His research focused on model-based state and parameter estimation techniques under a stochastic

setting. Prior to his PhD, Javier obtained his Masters in Chemical Engineering from the University of Oviedo,

Spain, in 2004.

Prof Roland Span

Ruhr-Universität Bochum

2006 - Chair of Thermodynamics, Faculty Mechanical Engineering, Ruhr-Universität Bochum, Germany

2002 - 2006 Chair of Thermodynamics and Energy Technologies, Faculty Mechanical Engineering, University of

Paderborn, Germany

2001 - 2002 Project and group leader in gas-turbine research, ALSTOM Power Technology Ltd., Baden,

Switzerland

1993 - 2000 Group leader “multiparameter equations of state”, Thermodynamics (chair: Prof. Dr.-Ing. W.

Wagner), Faculty Mechanical Engineering, Ruhr-Universität Bochum, Germany

1988 - 1993 Doctoral researcher, Thermodynamics (chair: Prof. Dr.-Ing. W. Wagner), Faculty Mechanical

Engineering, Ruhr-Universität Bochum, Germany

Prof J. P. Martin Trusler

Imperial College London

Martin Trusler is Professor of Thermophysics in the Department of Chemical Engineering at Imperial College

London. He gained his bachelor’s and PhD degrees in Chemistry from UCL, and held Lindermann Trust and

Ramsay Memorial Fellowships before joining the academic staff at Imperial in 1988. Martin’s research interests

focus on measurement and modelling of the thermophysical properties and phase behaviour of fluids, especially

under extreme conditions of temperature and pressure, with applications in CCS and oil/gas exploration and

production. He is a former editor of the Journal of Chemical Thermodynamics and the author of over 120 papers

in peer-reviewed journals.

Christopher John Wareing

University of Leeds

Dr Chris Wareing is a post-doctoral research fellow in the Department of Applied Mathematics at the University

of Leeds. He holds a cross-departmental post where he works with colleagues in the School of Chemical and

Process Engineering performing research into the transport methods that will be used in CO2 Capture and

Storage – a short-term way to stop the release of carbon dioxide into the atmosphere from emitters such as

power stations and mitigate man-made climate change. In his other role, he also supports academic researchers

through one-to-one guidance and by developing and delivering training courses on how to efficiently use the

University’s ARC supercomputing clusters to solve massively complicated problems that would on a single

computer take years to execute, but on the supercomputer can be performed in parallel in a matter of weeks or

days.

UK Carbon Capture and Storage Research Centre (UKCCSRC)

The UKCCSRC brings together over 1000 members including over 200 of the UK’s world-class CCS academics to

provide a national focal point for CCS research and development. The Centre is a virtual network where

academics, industry, regulators and others in the sector collaborate to analyse problems devise and carry out

world-leading research and share delivery, thus maximising impact. A key priority is supporting the UK economy

by driving an integrated research programme and building research capacity that is focused on maximising the

contribution of CCS to a low-carbon energy system for the UK.

The UKCCSRC is supported by the Engineering and Physical Sciences Research Council (EPSRC) www.epsrc.ac.uk

as part of the Research Councils UK Energy Programme, with additional funding from the Department of Energy

and Climate Change (DECC) www.decc.gov.uk for the UKCCSRC PACT Facilities www.pact.ac.uk

www.ukccsrc.ac.uk

http://www.epsrc.ac.uk/

http://www.decc.gov.uk/

http://www.pact.ac.uk/

RUHR-UNIVERSITÄT BOCHUM

Thermodynamic Property Models for

Transport and Storage of CO2

Roland Span

UKCCS Research Centre Meeting, York 2014

2 Span | UKCCS Research Centre Meeting | York 2014

Options for CCS

Pre-Combustion Process

(IGCC / NGRCC)

Category

Lignite

Fuel

Hard Coal

Cryogenic

O2 Supply

OTM / ITM

Physical Absorpt.

CO2 Separation

Chemical Absorpt.

H2 Membrane

CO2 Membrane

Integrated Process

(Oxy-Fuel) Cryogenic

OTM / ITM

Condensation

Chemical Looping

Post-Combustion Process

(exhaust gas cleaning) Chemical Absorpt.

Chilled Ammonia

Solid Adsorbents

Conditioning

Natural Gas

Lignite

Hard Coal

Natural Gas

Lignite

Hard Coal

Natural Gas

Membrane Reactor

Compression, Transport and Storage of CO2


Multi-Disciplinary Property Research


(IGCC / NGRCC)

Category

Lignite

Fuel

Hard Coal

Cryogenic

O2 Supply

OTM / ITM

Physical Absorpt.

CO2 Separation

Chemical Absorpt.

H2 Membrane

CO2 Membrane

Integrated Process


OTM / ITM

Condensation

Chemical Looping



Chilled Ammonia

Solid Adsorbents

Natural Gas

Lignite

Hard Coal

Natural Gas

Lignite

Hard Coal

Natural Gas

Membrane Reactor

Conditioning Compression, Transport and Storage of CO2

Property models typical for chemical engineering



(IGCC / NGRCC)

Category

Lignite

Fuel

Hard Coal

Cryogenic

O2 Supply

OTM / ITM

Physical Absorpt.

CO2 Separation

Chemical Absorpt.

H2 Membrane

CO2 Membrane

Integrated Process


OTM / ITM

Condensation

Chemical Looping



Chilled Ammonia

Solid Adsorbents

Natural Gas

Lignite

Hard Coal

Natural Gas

Lignite

Hard Coal

Natural Gas

Membrane Reactor


Property models typical for geology / geo sciences




(IGCC / NGRCC)

Category

Lignite

Fuel

Hard Coal

Cryogenic

O2 Supply

OTM / ITM

Physical Absorpt.

CO2 Separation

Chemical Absorpt.

H2 Membrane

CO2 Membrane

Integrated Process


OTM / ITM

Condensation

Chemical Looping



Chilled Ammonia

Solid Adsorbents

Natural Gas

Lignite

Hard Coal

Natural Gas

Lignite

Hard Coal

Natural Gas

Membrane Reactor


Property models typical for energy technologies




(IGCC / NGRCC)

Category

Lignite

Fuel

Hard Coal

Cryogenic

O2 Supply

OTM / ITM

Physical Absorpt.

CO2 Separation

Chemical Absorpt.

H2 Membrane

CO2 Membrane

Integrated Process


OTM / ITM

Condensation

Chemical Looping



Chilled Ammonia

Solid Adsorbents

Natural Gas

Lignite

Hard Coal

Natural Gas

Lignite

Hard Coal

Natural Gas

Membrane Reactor



Oxyflame – DFG Collaborative Research Centre

RUB with RWTH Aachen and TU Darmstadt



(IGCC / NGRCC)

Category

Lignite

Fuel

Hard Coal

Cryogenic

O2 Supply

OTM / ITM

Physical Absorpt.

CO2 Separation

Chemical Absorpt.

H2 Membrane

CO2 Membrane

Integrated Process


OTM / ITM

Condensation

Chemical Looping



Chilled Ammonia

Solid Adsorbents

Natural Gas

Lignite

Hard Coal

Natural Gas

Lignite

Hard Coal

Natural Gas

Membrane Reactor



Develop a model / a set of models which …

• describes homogeneous states with high

(reference) accuracy

• consistently describes VLE / LLE equilibria

• consistently describes equilibria with solid phases

(ice, dry ice, hydrates)


Thermodynamic Properties of Pure CO2

Span and Wagner (2003), fundamental EOS with 12 fitted coefficients (high technical quality)

Span and Wagner (1996), fundamental EOS with 42 fitted coefficients (reference quality)


Helmholtz-model for mixtures (fundamental equation of state!)

Introduced independently by Lemmon & Tillner-Roth in mid 90’s

1

0 r r

m m m m

1 1 1 1

, , , ln , ,

N N N N

i oi i i oi i j ij ij

i i i j i

x x T x x x x F

The GERG-2008 Model by Kunz and Wagner




Pure fluid equations of state (EOS)

1

0 r r

m m m m

1 1 1 1

, , , ln , ,

N N N N


i i i j i

x x T x x x x F






Mixing rules for reduced input parameters m and m

Two options:

• Mixing rules with four adjustable, binary specific parameters

• Combination rules

1

0 r r

m m m m

1 1 1 1

, , , ln , ,

N N N N


i i i j i

x x T x x x x F


0.5

rm r , , , ,2

1 1 ,

( )with ( )

N Ni j

i j T ij T ij c i c j

i j T ij i j

x xTT x x T T

T x x

xx

3

m , , 2 1 3 1 31 1r r , , ,

1 1 1 1with

( ) ( ) 8

N Ni j

i j ij ij

i j ij i j c i c j

x xx x

x x

x x






Binary excess functions

Two options:

• Binary specific excess function – Fij = 1, parameter in ij fitted

• Generalized excess function – Fij fitted, parameter in ij generalized


1

0 r r

m m m m

1 1 1 1

, , , ln , ,

N N N N


i i i j i

x x T x x x x F


, , ,

,

2

m m m m m m m m

1 1

( , ) expPol ij Pol ij Exp ij

k k k k

Pol ij

K K K

d t d t

ij k k k k k k

k k K

n n






Four levels of accuracy, depending on available experimental data and relevance of the binary subsystem

• Only combination rules for Tr, r

• Mixing rules with four adjustable parameters for Tr, r

• Adjusted mixing rules & generalized excess function

• Adjusted mixing rules & binary specific excess function


1

0 r r

m m m m

1 1 1 1

, , , ln , ,

N N N N


i i i j i

x x T x x x x F


UNIQUE!






1

0 r r

m m m m

1 1 1 1

, , , ln , ,

N N N N


i i i j i

x x T x x x x F


Methane (CH4) n-Pentane (n-C5H12) Hydrogen (H2)

Nitrogen (N2) Isopentan (i-C5H12) Carbon monoxide (CO)

Carbon dioxide (CO2) n-Hexane (n-C6H14) Hydrogen sulphide (H2S)

Ethane (C2H6) n-Heptane (n-C7H16) Water (H2O)

Propane (C3H8) n-Octane (n-C8H18) Oxygen (O2)

n-Butane (n-C4H10) n-Nonane (n-C9H20) Argon (Ar)

Isobutane (i-C4H10) n-Decane (n-C10H22) Helium (He)


5 mixtures: new excess functions

5 mixtures: new reducing parameters

EOS-CG – Improving GERG-2008 for CO2-Rich Mixtures

O 2

N 2

CO 2

CO

Ar

Binary specific excess function

r ij

Adjusted reducing functions for r and Tr

Lorentz-Berthelot combining rules for r and Tr


Property Models for CO2-Rich Mixtures – EOS-CG

Example CO2 – Ar: Phase boundaries

Improvements compared to GERG-2008 at high pressure


Example H2O – CO2: Phase Boundaries



Example H2O – CO2: Densities



Numerically stable (phase-equilibrium) algorithms available



Much Room for Improvement: IMPACTS

Binary systems for experimental work within IMPACTS

EOS

Ch

lori

ne

Hyd

roge

n C

hlo

rid

e

Die

than

ola

min

e

Mo

no

eth

ano

lam

ine

Me

than

ol

Am

mo

nia

Sulp

hu

r Tr

ioxi

de

Sulp

hu

r D

ioxi

de

Nit

roge

n D

ioxi

de

Nit

roge

n O

xid

e

Hyd

roge

n S

ulf

ide

Me

than

e

Hyd

roge

n

Car

bo

n M

on

oxi

de

Arg

on

Oxy

gen

Nit

roge

n

Wat

er

Carbon Dioxide

Water

Nitrogen

Oxygen

Argon

Carbon Monoxide

Hydrogen

Methane

Hydrogen Sulfide

Nitrogen Oxide

Nitrogen Dioxide

Sulphur Dioxide

Sulphur Trioxide

Ammonia

Methanol

Monoethanolamine Probably covered quite well by existing Helmholtz models

Diethanolamine Helmholtz models available, but accuracy unclear

Hydrogen Chloride Current work at RUB

Chlorine

Maj

or

Co

mp

on

en

tsM

ino

r C

om

po

ne

nts

NIST & WSU


• CO2 in pipelines is liquid

• Pressure loss (leaks or flooding of

evacuated pipeline segments)

results in liquid / vapor system

• Further expansion leads to

formation of a dry ice / vapor

system at about 195 K

• For a description of the process

(enthalpy) flash calculation with

solid phase

• Similar effects for low tempera-

ture transport / capture

Consistent fundamental equation

for dry ice required!

Low-Temperature Phase Equilibria for CO2


Phase Equilibria with Solid Phases – Dry Ice

Fundamental equations for solid CO2 (dry ice) developed

Jäger and Span (2010, 2012): Gibbs enthalpy as a function of pressure and temperature

Approach by Tillner-Roth (1998) adapted

Fitted only to data for solid CO2

Trusler (2011): Helmholtz energy as a function of molare volume and temperature

Fitted also phase equilibrium with adjacent fluid-phase

0 0 0

0

0 0 0

( , )( , ) ( , )d d ( , )d

pT Tp

p

T T p

c T pg p T h Ts c T p T T T v p T p

T


Phase Equilibria with Solid Phases – Dry Ice

Intersection with Gibbs enthalpies from EOS for fluid states yields consistent SVE / SLE data (sublimation / melting pressure)

Allows for flash calculations into the melting / sublimation region

The property model used for the fluid phases has a significant impact!

Intersection with

Span and Wagner (1996)

Intersection with

Ely (1987)


Phase Equilibria in the System CO2 / H2O

Solid H2O: Feistel and Wagner (2006)

Solid CO2: Trusler (2011) / Jäger and Span (2010, 2012)

Hydrates: Jäger, Vinš, Hrubý, and Span, R. (2013)

Fluid region:

H2O: Wagner and Pruss (2002)

CO2: Span and Wagner (1996)

Mixing rules: Gernert and Span (2013)


• The model of Ballard und Sloan (2002) was chosen and

slightly modified:

,, , , ln 1 ,H

w J w i i J J

i J

T p f g T p RT v C T p f

Phase Equilibria with Solid Phases – Hydrates


• The model of Ballard und Sloan (2002) was chosen and

slightly modified:

• Adjustable parameters of the model:

,, , , ln 1 ,H

w J w i i J J

i J

T p f g T p RT v C T p f

1 2, ,0 ,0,w wg h ,

Reference state Pressure dependence

of the molar volume

Potential-

parameters


fitted but almost

unchanged fitted to (T,p)-data generated from

experimental data & fluid model

consistency to reference

state of fluid model



Accurate description of CO2 / H2O hydrate formation

Consistent to accurate VLE / LLE / homogeneous phase model

Intersection with EOS-CG yields

equilibrium temperatures with

deviations < 1 K to exp. data

LcH

VH

VIw

HIc

VLc

VLw


Allowable Water Content in CO2


Allowable Water Content in CO2


TREND – Software Made Available

• TREND 1.1 was made available early in 2014

• Launch of TREND 2.0 is expected by the end of 2014

Preview

TREND 2.0


Other Hydrates

• Hydrate models (consistent to accurate multiparam. mixture

models for fluid phase) will soon be published for water with

- Nitrogen

- Oxygen

- Argon

- Carbon monoxide

- Methane

- Ethane

- Propane

• A corresponding model for mixed hydrates is still pending


Avoiding Inconsistencies – Injection of CO2

• Engineers working on pipelining of CO2 use GERG-2008 / EOS-CG

• Reservoir engineers use cubic EOS (+ hydrate / electrolyte models)

• Severe inconsistencies, e.g., for injection of CO2 (-rich mixtures)

Pipeline engineer delivers

500 to/h 577 m3/h at 19.6 MPa

Reservoir engineer receives

577 m3/h at 18.1 MPa 440 to/h


Avoiding Inconsistencies

• CO2 in geological storage

Mixtures with brines instead of water

• CO2 capture from natural gas / two phase gas pipelines

Mixed hydrates

Mixtures with hydrate inhibitors

• CO2 scrubbing from flue gas / natural gas

Systems containing scrubbing agents

• CO2 ….

In the long run the main challenge will

be to ensure that property models used

in adjacent process steps are consistent

to accurate models applied for CO2 transport


Thank you for your attention!

The author is grateful to all organizations that

contributed funding to the presented work, namely to

- E.ON for awarding an E.ON Research Award

- E.ON Ruhrgas for the contract "Calculation of Complex

Phase Equilibria"

- the federal government of Nordrhein Westfalen in

conjunction with EFRE for funding under contract

315-43-02/2-005-WFBO-011Z

- the European Commission for the contract

"Seventh Framework Program, Nr. 308809, IMPACTS“

- the DFG for the framework of the collaborative research

centre ”Oxyflame“


The „Killer Application“

• If a CO2 pipeline leaks, the

mantle cools down drastically, the

material becomes brittle

• The crack propagates in both

directions

• The pressure loss propagates

with about speed of sound

• If the crack propagates faster

than speed of sound, small

cracks result in a disaster

The issue is a safety issue,

accurate properties required for

homogeneous, VLE, VLSE states

• Accurate properties are certainly

more important for other

applications, but who cares …

liquid CO2 in the pipeline

cold CO2 escapes

Pipeline wall

SINTEF


International Cooperation

Focus on CO2-rich mixtures

• Measurement of thermodynamic properties (pvT, w)

• Measurement of phase equilibria (VLE, LLE, VLSE)

• Measurement of transport properties (viscosity)

• Accurate property models for CO2-rich mixtures

• Description of phase equilibria including solid phases

• Improvement of phase equilibrium algorithms

• Test of new property models for various applications

© 2014 Process Systems Enterprise Limited

CO2 Properties and EoS for pipeline engineering York 11 November 2014

J. Rodriguez, M. Calado, E. Dias, A. Lawal, N. Samsatli, A. Ramos, T. Lafitte, J. Fuentes, C. Pantelides

gSAFT: advanced physical properties for carbon capture and storage system modelling

THE ADVANCED PROCESS MODELING COMPANY


Overview

gCCS whole-chain system modelling environment

ETI’s CCS system modelling tool-kit project

Challenges in providing physical properties for the systems downstream of the capture plant

gSAFT technology

Based on a predictive molecular equation of state

gSAFT for the compression, transmission and injection subsystems within gCCS

Application to typical CCS flowsheets

Using gCCS libraries

Conclusions


gCCS whole-chain system modelling environment

ETI’s CCS system modelling tool-kit project


The CCS System Modelling Tool-kit Project 2011-2014

Energy Technologies Institute (ETI)

gPROMS modelling platform & expertise

Project Management

~$5m project commissioned & co-funded by the ETI

Objective: “end-to-end” CCS modelling tool

http://www.eti.co.uk/


gCCS initial scope (2014/Q2)

Process models

Power generation

Conventional: pulverised-coal, CCGT

Non-conventional: oxy-fuelled, IGCC

Solvent-based CO2 capture

CO2 compression & liquefaction

CO2 transportation

CO2 injection in sub-sea storage

Materials models

cubic EoS (PR 78)

flue gas in power plant

Corresponding States Model

water/steam streams

SAFT-VR SW/ SAFT- Mie

amine-containing streams in CO2 capture

SAFT- Mie

near-pure post-capture CO2 streams

Open architecture allows incorporation of 3rd party models 5


Physical properties for subsystems downstream of the capture plant

Challenges


Physical properties for downstream of the capture plant

CO2 phase diagram

Physical properties for pure CO2 predicted very accurately by Span & Wagner EoS Span, Wagner. "A new equation of state for carbon dioxide covering the fluid region from the triple‐point

temperature to 1100 K at pressures up to 800 MPa." Journal of physical and chemical reference data 25 (1996): 1509.

Best choices for CO2 transmission liquid-like density gas-like viscosity



Challenges I: Impurities

From post-combustion (dry basis):

CO2 (>99%), N2 (<0.17%), O2 (<0.01%), SOx (10 ppmv), traces of Ar

From pre-combustion (dry basis):

CO2 (>95.6%), H2S (<3.4%), H2 (<3%), N2 (<0.6%), CO (<0.4%), Ar (<0.05%), CH4 (350 ppmv)

From oxyfuel (dry basis):

CO2 (>74.66%), N2 (<15%), Ar (<2.5%), O2 (<6.15%), SOx (<2.5%), traces of CO

…plus H2O

The presence of impurities significantly affects physical properties (densities, phase envelope, critical temperature and pressure,…)

impact on compressor/pump power, pipeline capacity, potential for hydrate formation & two phase flow, distance between booster stations…



Challenges II : Wide range of conditions

Compression subsystem

Pressures Inlet 0.5 to 5 bara

Outlet 10 to 200 bara

Temperatures Inlet 20-41 °C

Outlet 40-130 °C

Transmission subsystem

Pressures 50-200 bara

Temperatures -5-40 °C

Compression Transmission



Challenges III: Limited experimental data

Recent literature review of experimental data Li, Hailong, et al.

"PVTxy properties of CO2 mixtures relevant for CO2 capture, transport and storage: Review of available experimental data and theoretical models." Applied Energy 88.11 (2011): 3567-3579.

Limited range of conditions

Gaps for several binary mixtures

some mixtures (e.g. CO2-SO2) are very corrosive experimentation problematic

Very scarce data for ternaries and beyond

Working on solving this • Release of experimental data from several projects • Experimental plan at University of Nottingham for VLE measurements of near-

pure CO2 mixtures


applied to mixtures of CO2, CO, H2O, Ar…..

small molecules single group each


Challenges

Impurities

Wide range of conditions

Limited experimental data

A predictive equation of state

is required


gSAFT

A commercial implementation of the SAFT-γ Mie equation of state


gSAFT

The Statistical Association Fluid Theory I

Molecular-based EOS are a very appealing alternative to more classical approaches, such as cubic EOS

The Statistical Association Fluid Theory (SAFT) is especially relevant for its ability to deal with complex fluids

SAFT-based EOS are rooted on statistical mechanics, so

they involve a limited number of parameters with a clear physical meaning

can be fitted to a limited amount of experimental data can predict phase behaviour and physical properties for a wide range of

conditions, including those far from the ones employed for parameter estimation


gSAFT

The Statistical Association Fluid Theory II

PSE’s gSAFT is a commercial implementation of one of the most advanced SAFT-based EOS

SAFT- Mie, developed by Imperial College London

SAFT: Chapman, Gubbins, Jackson, Radosz, Ind. Eng. Chem. Res., 29, 1709 (1990)

SAFT-VR: Gil-Villegas, Galindo, Whitehead, Mills, Jackson, Burgess, J. Chem. Phys., 106, 4168 (1997)

SAFT-γ: Lymperiadis, Adjiman, Jackson, Galindo, Fluid Phase Equilib., 274, 85 (2008)

SAFT-γ Mie: Papaioannou, Lafitte, Avendaño, Adjiman, Jackson, Muller, Galindo, in preparation (2014)


gSAFT

SAFT-γ Mie molecular model

Molecules are modelled as chains of spheres

Interactions

dispersion/repulsion (van der Waals) forces

hydrogen bonding via off-centre electron donor/acceptor (“association”) sites

ionic (coulombic) forces

Mie potential

( )R A

U r Cr r

Incr

eas

ing

stre

ngt

h


gSAFT

Transferability of parameter values

The values of the interaction parameters

are assumed to be constant across

different molecules and mixtures

in different phases

under different temperatures, pressures and compositions

An approximation based on SAFT- Mie’s fundamental molecular basis supported by practical evidence


gSAFT for near-pure CO2 streams in gCCS


gSAFT for compression/transmission in CCS

The gSAFT Databank

H2O

H2S

CO2

CH3OH

CH4

Ar H2

SO2

O2

N2 CO



Comparisons: Pure CO2



Comparisons: Binary mixture H2O + CO2

Isotherms:

T=323.2 K (red)

T=333.2 K (yellow)

T=353.1 K (green)

CPA: Cubic+Association EoS

CO2 rich phase

Bamberger et al., 2000



Comparisons: Binary mixture H2O + CO2

CO2 rich phase – low temperatures

King et al., 1992



Comparisons: Binary CO2 + impurities

CO2+CH4

CO2+H2S

CO2+O2



Predictions: Bubble point of CO2+H2

Chapoy et al., 2011



Predictions: CO2+N2 densities

Brugge et al., 1997



Predictions: CO2+N2+Ar densities

University of Nottingham

COZOC project, University of Nottingham

T=303.15 T=313.15

T=313.15


University of Nottingham measurements

Experimental plan

Mixture Name Component 1 Component 2 Component 3 x1 x2 x3

E1 CO2 N2 Ar 0.90 0.05 0.05

E2 CO2 N2 Ar 0.98 0.01 0.01

E3 CO2 Ar H2 0.95 0.02 0.03

gSAFT predictive accuracy being tested Specifically two-body interaction assumption

gSAFT model parameters will be readjusted if necessary

Dew-point and bubble-point lines for the following mixtures



VLE predictions

Pure CO2

CO2 + N2 (x=0.05 )+ Ar (x=0.05)

Dew-point line



VLE predictions

Pure CO2

Dew-point line

CO2 + N2 (x=0.01 )+ Ar (x=0.01)

Pure CO2



VLE predictions

Dew-point line

Pure CO2

CO2 + H2 (x=0.03)+ Ar (x=0.02)


Application to typical CCS compression, transmission and injection flowsheets

Using gCCS libraries


Compression


gCCS model libraries

Compression

SourceCO2

ElectricDrive

CompressorSection

CoolerKODrum Dehydrator

SinkCO2


Transmission & injection


gCCS model libraries

Transmission and Injection

Well

PipeSegment

Emergency shutdown valve (ESD)

Gate Valve

Vertical Riser

CO2 Flowmeter

Distribution header

Choke Valve

Reservoir

Wellhead connection


Case Study

Line-packing operation

• Assumed constant inlet flowrate at CO2Source (275tonnes CO2 per day)

• Gas phase injection with discharge pressure in CO2 sink ~ 21bara

• Total pipeline length – 132.2km • Pipeline is located offshore (in water)

System dynamics

Simulating line-packing operation: Sudden valve closure


System dynamics

Simulating line-packing operation: Sudden valve closure

Case Study

Line-packing operation

Warning: Phase change identified!


Conclusions


Conclusions

Providing physical properties for a modelling tool for the systems downstream of the capture plant is challenging

Experimental data are limited

gSAFT is an implementation of a SAFT equation of state, perfectly suited to address these challenges

a parameter databank for the relevant components has been developed

excellent correlations and predictions have been demonstrated

gSAFT physical properties are already available within gCCS, an “end-to-end” modelling tool for CCS

for the simulation of compression/transmission/injection flowsheets


Acknowledgements

ETI Tool-kit development consortium

Energy Technologies Institute

E.On

EdF

Rolls-Royce

CO2DeepStore

E4Tech

http://www.eti.co.uk/


PSE’s CCS Technology Team

Gerardo Sanchis Power plant

Mário Calado Compression Systems

Capture processes

Dr Adekola Lawal Capture processes

Transmission & injection

Dr Javier Rodríguez Capture processes

Physical properties (gSAFT)

Dr Tom Laffite

Physical properties (gSAFT)

Dr Nouri Samsatli Power plant

Product development

Dr Javier Fuentes Software development

Alfredo Ramos Technology Manager

Mark Matzopoulos Marketing & Business

Development

Prof Costas Pantelides Chief Technologist


Advanced Process Modelling

World leaders in …

Software & services

Phase Behaviour and EoS Modelling

of the Carbon Dioxide-Hydrogen

System

J P Martin Trusler

Department of Chemical Engineering

Imperial College London, UK

11 November 2014

1

Acknowledgements

Researchers

David Vega Maza (now at University of Aberdeen, Scotland)

Olivia Fernandez Torres (now at University of Gelph, Canada)

Sponsors

Costain Energy & Process

Energy Technologies Institute

2

CO2 + H2 CO2 + N2

Background to Project

Original motivation for study: generation of VLE data to support pre-

combustion decarbonisation of fuel gases.

Examples include:

• processing of high-CO2 natural gases

• hydrogen production from synthesis gas

Technologies include:

• traditional solvent processes (e.g. MEA process)

• membrane separations

• cryogenic flash or distillation processes

• hybrids of the above

3

Costain’s Next Generation Capture Technology (NGCT)

• Process for electricity production from coal with 95% carbon capture

• Based on synthesis gas production and CO2 separation to yield H2

• Combustion/electricity production in a combined a cycle process

• NGCT achieves primary CO2 removal by low-temperature flash processes

Sour Water

Gas Shift Gasifier

Air Separation

Unit

CO2

Comp.

Combined

Cycle

O2

Coal

Claus Plant Sulphur CO2

12 MPa

Power

Syn

Gas

Syn

Gas

H2

Steam

CO2

H2S

Low Temp.

CO2 Removal

Syn

Gas H2S Removal

4

Phase Behaviour Project Plan

• Development of new VLE apparatus for studying CO2-rich mixtures at

low temperatures and high pressures

• Measurements of VLE (and also SVLE) for the CO2 + H2 binary system

• Pressures up to 16 MPa

• Temperatures approx. triple-point to critical point of CO2

• Fully analytical approach

• Modelling of VLE data in a form suitable for process design

• Measurement range covers two areas of interest:

• T < 270 K (cryogenic separations)

• T > 270 K (pipeline engineering)

5

Apparatus for VLE and SVLE Measurements

6

Emphasis on:

• uncertainty

• reliability

• automation

• safety

Working ranges:

• pressure to 20 MPa

• temperature 193 to 474 K

H2

N2

Gas 4

Gas 5

CO2

V-4

E-2

P-3RD-2

V-3

V-5

E-1

V-8 V-9

P-2 P-1

V-1

I-1

E-3V-7

Burner

V-2

RD-1

CG

T-1

E-4

Phase-Equilibrium Cell

• pmax = 200 bar

• V = 0.15 L

• Stainless steel

• Gold-plated, N2-filled

stainless-steel o-ring

• Magnetic stirrer

• Rolsi electro-

magnetic sampling

valves

7

Phase-Equilibrium Cell

8

GC Calibration for H2 and CO2

2,1 Component / 2 iAbAaρVn iiii

0.00

0.02

0.04

0.06

0.08

0.10

0 50000 100000 150000 200000

ρ/(

mol·dm

-3)

A / (25 μVs)

H2

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 5000 10000 15000 20000

ρ/(

mol·dm

-3)

A / (25 μVs)

CO2

-0.002

-0.001

0.000

0.001

0.002

0 50000 100000 150000 200000

Δρ/(

mol·dm

-3)

A / (25 μVs)

H2

-0.002

-0.001

0.000

0.001

0.002

0 5000 10000 15000 20000

Δρ/(

mol·dm

-3)

A / (25 μVs)

CO2

Parameters: ai and bi for each

gas

9

Verification: Vapour Pressure of Pure CO2

Vapour pressure Deviations from Span-Wagner EoS

10

0

2

4

6

8

220 240 260 280 300

p/M

Pa

T/K

Experiment

Span-Wagner EoS

0.000

0.002

0.004

0.006

0.008

220 240 260 280 300

Δp/M

Pa

T/K

Experiment

Uncertainty

Overall standard uncertainties: u(T) = 0.01 K; u(p) = 0.003 MPa; u(x) = 0.011x(1 - x); u(y) = 0.011y(1 - y)

VLE Results for (CO2 + H2): Low Temperatures

11

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8 1.0

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8 1.0

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8 1.0

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8

p/M

Pa

x2, y2

T = 218.15 K T = 233.15 K

T = 243.15 K T = 258.15 K

VLE Results for (CO2 + H2): Pipeline Regime

12

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.1 0.2 0.3 0.4 0.5

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.1 0.2 0.3 0.4

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.00 0.05 0.10 0.15 0.20 0.25

p/M

Pa

x2, y2

(a)

T = 273.15 K T = 280.65 K

T = 288.15 K

T = 295.65 K

VLE Results for (CO2 + H2): ... & close to CO2 critical point

13

7.0

7.2

7.4

7.6

7.8

8.0

0.00 0.01 0.02

p/M

Pa

x2, y2

Modelling

• Standard Peng-Robinson Equation of State

• Quadratic mixing rules for a and b parameters:

• lij = constant

• kij = kij,1 + kij,2/T

• Up to three parameters to fit nine isotherms

• ai, bi from critical (or effective critical) constants and acentric factor

14

jiiji jj i aakxxa )1(

2/))(1( jiiji jj i bblxxb

Quantum Effects for H2

• Typical equations of state (including cubic EoS and SAFT models)

regress pure-component parameters against pure-component data:

• e.g. critical constants, vapour pressure, saturated liquid density

• Hydrogen is a quantum gas with large quantum-mechanical effects

below its critical temperature (Tc = 33 K)

• Quantum effects diminish at higher temperatures, so that H2 behaves

essentially classically at the present experimental temperatures

• Errors arise when EoS parameters are fitted to pure-H2 VLE data

because of the quantum effects that prevail under those conditions

• Thus for cubic EoS models (which base parameters on Tc, pc and ω)

effective critical constants fitted to virial coefficients are used

15

Fit for Virial Coefficients of H2

16

0

200

400

600

800

1000

-20

-10

0

10

20

50 150 250 350 450

C/(

cm

6 m

ol-2

)

B/(

cm

3 m

ol-1

)

T/K

Parameters Tc/K pc/MPa ω

True 33.15 1.296 -0.219

Effective 31.76 1.276 -0.063

Critical Constants of Normal Hydrogen

Modelling (CO2 + H2) with Peng-Robinson Equation

• Objective function based on composition deviations at given T and p:

• Up to three parameters: kij,1, kij,2 and lij

• Common approach is to set lij = 0

• ... but this leads to a relatively poor fit for CO2 + H2

• Fits with lij ≠ 0 lower objective function by a factor of 4

• Global fit to all isotherms (excluding a few near-critical states):

S = 0.004

17

N

i

i,ii,i yyxxN

S1

2calc,,22

2calc,,22

2 )()(1

Experiment vs Model for (CO2 + H2) at Low Temperatures

18

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8 1.0

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8 1.0

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8 1.0

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6 0.8

p/M

Pa

x2, y2

T = 218.15 K T = 233.15 K

T = 243.15 K T = 258.15 K

Experiment vs Model for (CO2 + H2) at PipelineTemperatures

19

0

2

4

6

8

10

12

14

16

0.0 0.2 0.4 0.6

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.1 0.2 0.3 0.4 0.5

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.0 0.1 0.2 0.3 0.4

p/M

Pa

x2, y2

0

2

4

6

8

10

12

14

16

0.00 0.05 0.10 0.15 0.20 0.25

p/M

Pa

x2, y2

(a)

T = 273.15 K T = 280.65 K

T = 288.15 K

T = 295.65 K

Experiment vs Model for (CO2 + H2)

20

7.0

7.2

7.4

7.6

7.8

8.0

0.00 0.01 0.02

p/M

Pa

x2, y2

General conclusions:

• Model with 3-parameters provides a reasonable global fit

• Fails near to the critical locus (as expected)

• Not within experimental uncertainty – especially bubble curves at higher temperatures

• Improved fits for restricted regions can be obtained

Limited Model for Pipeline Applications

• Same Peng-Robinson model but parameters fitted:

• T ≥ 273 K

• xH2 ≤ 0.06 with coexisting values of yH2

• Much improved representation with S = 0.002

21

Phase Envelopes

(0.95 CO2 + 0.05 H2): ,

(0.98 CO2 + 0.02 H2): ,

PR model:

Critical locus:

CO2 VP curve:

Summary

Experiment:

• New equipment constructed and validated

• High-quality calibrations and low overall uncertainties

• (CO2 + H2) measured at temperatures between the triple point and

critical points of CO2

Modelling:

• ‘Standard’ Peng-Robinson equation used

• ‘Effective’ critical constants for H2

• Quadratic mixing rules for both a and b parameters

• Provides a fair global fit

• Local fit for pipeline regime gives a good representation of the data

22

PE EoS

23

)()(

)(

mmmm bVbbVV

Ta

bV

RTp

c2

c /)()(457235.0 pTαRTa

2c2 /126992.054226.137464.01)( TTωωTα

cc /077796.0 pRTb

jiiji jj i aakxxa )1( 2/))(1( jiiji jj i bblxxb

Single substance: Mixture:

Understanding and predicting CO2 properties

Richard Graham Tom Demetriades, Alex Cresswell, Martin Nelson,

Richard Wilkinson and Simon Preston School of Mathematical Sciences, University of Nottingham.

Overview!

•Parametric equations of state (pressure explicit)

•Non-parametric EoS (pressure explicit or free energy formulation).

•Molecular simulations

Overview!

•Parametric equations of state (pressure explicit)

•Non-parametric EoS (pressure explicit or free energy formulation).

•Molecular simulations!

•Uncertainty quantification

Potential applications: Avoiding pipeline issues

Two-phase flow

Vapour-solid mix

JetSolid entrainment

Warm air entrainment Solar heating

Solid sublimation

Snow/ dry ice Ground heat flux

Pipe rupture

Coexistence - Impurities


Liquid

Gas

xG: Gas composition vG: Gas volume

xL: Liquid composition vL: Liquid volume


CO2+N2 dataLi

quid

Gas

Molar Volume (litres/mol)

Pres

sure

(M

Pa)

Liqu

id

Gas

Pres

sure

(M

Pa)

Mole fraction of impurity

Liquid

Gas



Uncertainty quantification



Economic recovery!


Huge uncertainty!


Must account for uncertainty due to: •Incomplete data •Measurements errors •Model imperfection

Huge uncertainty!

R

A generalised equation of state

Peng-Robinson

This work

R


Peng-Robinson

This work

Higher order terms enable a longer plateau and improved critical volume

R


Peng-Robinson

This work

Higher order singularity provides a sharper ‘liquid’ region

Higher order terms enable a longer plateau and improved critical volume

10-4 10-3

Molar volume [m^3/mol]

2

4

6

8

10

12

Pres

sure

[MPa

]

294K

Fitting methodFitting criterion

10-4 10-3


2

4

6

8

10

12

Pres

sure

[MPa

]

294K


10-4 10-3


2

4

6

8

10

12

Pres

sure

[MPa

]

294K


10-4 10-3


2

4

6

8

10

12

Pres

sure

[MPa

]

294K


10-4 10-3


2

4

6

8

10

12

Pres

sure

[MPa

]

294K

Fitting method

Numerically minimise the sum of these 4 quantities over the parameters a...g

Fitting criterion

MCMC: an example

θ1

θ2

2 4 6 8 10 12 14

4

6

8

10

12

14

16

18

The search algorithm explores the fitting criterion, spending more time in regions of good fit.

Markov-Chain Monte-Carlo: an example

MCMC: an example

θ1

θ2

2 4 6 8 10 12 14

4

6

8

10

12

14

16

18



MCMC: an example

θ1

θ2

2 4 6 8 10 12 14

4

6

8

10

12

14

16

18



MCMC: an example

θ1

θ2

2 4 6 8 10 12 14

4

6

8

10

12

14

16

18



MCMC: an example

θ1

θ2

2 4 6 8 10 12 14

4

6

8

10

12

14

16

18



MCMC: an example

2 4 6 8 10 12 14

4

6

8

10

12

14

16

18

θ1

θ2

The result is samples of the probability distribution of the parameters


Predictions (pure CO2)R

10-4 10-3


2

4

6

8

10

12

Pre

ssur

e [M

Pa]

304.3K (Tc)294K285K

Predictions (pure CO2)R

10-4 10-3


2

4

6

8

10

12

Pre

ssur

e [M

Pa]

304.3K (Tc)294K285K

Predictions (pure CO2)

10-4


4

6

8

Pre

ssur

e [M

Pa]

Coexisting liquidCoexisting vapour

R

Mixture modelling CO2+N2

Introduction to non-parametric methods

[6]


•Model for pressure against volume, as with an equation of state. •However, no need to specify terms or parameters •Model ‘learns’ the P(v) functional form from the measurements [6]

0 0.2 0.4 0.6 0.8 1x0

0.2

0.4

0.6

0.8

1

f(x)


•Model for pressure against volume, as with an equation of state. •However, no need to specify terms or parameters •Model ‘learns’ the P(v) functional form from the measurements

•Basic examples include splines and other interpolation techniques•Modern implementations are significantly more sophisticated

[6]

0 0.2 0.4 0.6 0.8 1x0

0.2

0.4

0.6

0.8

1

f(x)

Gaussian processes

a) Generate random functions from a distribution that favours smooth functions

0 0.2 0.4 0.6 0.8 1x0

0.2

0.4

0.6

0.8

1

f(x)

Gaussian processes

a) Generate random functions from a distribution that favours smooth functions

b) Keep only the functions that pass through the data points

Mean of accepted functions = Model Variance of accepted functions = Uncertainty quantification

0 0.2 0.4 0.6 0.8 1x0

0.2

0.4

0.6

0.8

1

f(x)

Data Mean

Variance

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re

A Gaussian process for pure CO2P

ress

ure/

(Crit

ical

Pre

ssur

e)

Molar volume/(Ideal gas volume)

Temperature=290K

CO2 data Gaussian Process mean. 95% confidence interval Individual Gaussian Processes

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re


ress

ure/

(Crit

ical

Pre

ssur

e)


Temperature=290K


Gaussian Process accurately captures the data

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re


ress

ure/

(Crit

ical

Pre

ssur

e)


Temperature=290K



Uncertainty is only significant in the coexistence region

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re

0.2 0.4 0.6 0.8 1.0

−2

−1

01

2

volume

pre

ssu

re


ress

ure/

(Crit

ical

Pre

ssur

e)


Temperature=290K



Uncertainty is only significant in the coexistence region

Generalisation to mixtures is ongoing

Molecular simulationComputer model of individual molecules within a small box of fluid.

Can predict: •Pressure-‐volume •Coexistence •Effect of impurity •Most other quanBBes of interest

[7]



Can be used where experiments are unavailable?

[7]



Can be used where experiments are unavailable?

Can be used to derive an EquaBon of State?

[7]

Gibbs ensemble simulaBonsGas

Liquid

Gibbs ensemble simulaBonsTwo simulaBon boxes, represenBng coexisBng phases

Gas

Liquid

Gibbs ensemble simulaBonsTwo simulaBon boxes, represenBng coexisBng phases

The system approaches equilibrium by making a series of moves, consistent with staBsBcal mechanics

Once in equilibrium, the system predicts the coexistence properBes !

Gas

Liquid

ParBcle displacement Volume change

ParBcle transfer

�23

Molecular force-fields

�23

Molecular force-fields•All physical proper-es are ulBmately determined by interac-ons between molecules•Force-‐fields that describe these interacBons are a key input to simula-ons

�23

Molecular force-fields•All physical proper-es are ulBmately determined by interac-ons between molecules•Force-‐fields that describe these interacBons are a key input to simula-ons•InteracBons of CO2 with itself and with impuri-es must be specified

!

Semi-empirical forcefields CO2+N2


Simulations using literature force fields


Simulations after optimising the force field

Simulations using literature force fields

Bubble point comparison CO2 + 5%H2

Phase boundary measurements by Jie Ke, Mike

George et al

Two phase re

gion

Simulation aids EoS development

Liqu

id

Gas


Pres

sure

(M

Pa)

Liqu

id

Gas

Pres

sure

(M

Pa)


Liquid

Gas



Two phase re

gion


Liqu

id

Gas


Pres

sure

(M

Pa)

Liqu

id

Gas

Pres

sure

(M

Pa)


Liquid

Gas



Two phase re

gion


Liqu

id

Gas


Pres

sure

(M

Pa)

Liqu

id

Gas

Pres

sure

(M

Pa)


Liquid

Gas



Ab initio force fields CO2+H2

Quantum Chemistry calculations of CO2-H2 interaction

Gaussian Process fit for use in simulations

+

Ab initio force fields CO2+H2

Quantum Chemistry calculations of CO2-H2 interaction

Force field computed from first principles

Potential for accurate predictions without data fitting⇒

Gaussian Process fit for use in simulations

+

Making it all work together!

•Parametric equations of state

•Non-parametric EoS

•Semi-empirical molecular simulation

•Ab-initio molecular simulation











• Fast, flexible models for computational studies • Fit to experiments, simulation data more advanced

EoS







EoS

• Rigorous uncertainty quantification - optimise choice of experiments

• (Somewhat) expensive but very accurate EoS







EoS



• Accurate treatment of temperature variation • Completes coexistence measurements to help EoS fitting







EoS



• Accurate treatment of temperature variation • Completes coexistence measurements to help EoS fitting

• Most physically realistic but also most expensive. • Can augment or replace experiments

1

Impact of Equation of State on

Simulating CO2 Pipeline Decompression

Dr Solomon Brown

UCL

CO2 Properties and EoS for Pipeline Engineering

11 August 2013, Athens, Greece

http://www.ucl.ac.uk/

Structure

2

1. Background

2. Equations of State

3. Impact on simulation of pipeline decompression

4. Conclusions


Structure

3

1. Background


4

A ductile fracture will come

to rest when the fluid

pressure at the crack tip,

Pt falls below the Crack

Arrest Pressure, Pa.

Running fractures

represent a threat to CO2

pipelines

Pipeline ductile fracture

5

Pressurised CO2

Rupture

plane: 1 atm

• At the rupture plane the fluid is exposed to ambient air

• Following the rupture, the rarefaction wave starts propagating along the

pipe at the speed of sound

• The vapour phase emerges in the expansion wave reducing the mixture

speed of sound

• Due to rapid cooling of the fluid in the decompression wave, the solid

phase may also be released from the pipe

Pipeline decompression


Structure

6

1. Background



7

Generalized cubic EoS

3 * * 2 * *2 * *2 * * *2 *3

* *

2

(1 ) ( ) 0

,

z B uB z A B uB uB z A B B B

aP bPA B

RT RT

EoS

RK

u=1, w=0

SRK

u=1, w=0

PR

u=2, w=-1

PR/G

u=2, w=-1

Cubic Equations of State

8

( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , ) ( , )res ideal ref disp hs chain assoc dispa T a T a T a T a T a T a T a T a T

RT RT RT RT RT RT RT RT RT

The SAFT (Statistical Associating Fluid Theory) equation of state is written as a summation of residual Helmholtz free energy terms that occur due to different types of molecular interactions in the system under study.

Hard sphere term: Chain term: Association term: Dispersion term:

22

1

34

n

nn

RT

Ahs

4

1

9

1i j

ji

ij

disp n

kT

uD

RT

A

31

5.01ln1

n

nm

RT

Achain

M

A

AA

assoc

MX

XRT

A

1 2

1

2ln

Carnahan-Starling EoS for hard spheres based on Wertheim’s TPT1 based on TPT1

Alder equation from molecular dynamics

SAFT and PC-SAFT Equations of State

Structure

9

1. Background




10

Saturated density predictions

Saturated liquid and vapour density predictions

using the various Equations of State


11

SAFT PC-SAFT RK PR SRK Yokozeki

Density 1.81 1.14 6.25 4.89 5.97 3.35

Cv 6.37 3.77 9.96 3.94 8.80 58.75

Cp 11.09 3.53 10.35 4.54 3.35 28.76

Sound Speed 6.73 3.26 16.65 13.53 11.86 15.39

Joule-Thomson 113.11 44.20 75.70 102.08 66.65 85.62

Accuracy of derivative properties

Comparison of the predictive accuracy of EoS (AAD%) for

important derivative properties.


Saturated vapour phase speed of sound

12

Saturated liquid phase speed of sound

Speed of Sound predictions


13

Pressure history during decompression

13.5 m from open end

Pressure history during decompression

18 m from open end

Vapour phase decompression


14

0

2

4

6

8

10

12

14

16

0 0.5 1 1.5 2 2.5 3

Clo

sed E

nd P

ressure

(M

Pa)

Time (s)

SRK

SAFT

PC-SAFT

Experimental

Liquid

Pressure history during decompression 143 m

from open end

Liquid phase decompression


Structure

1

5

1. Background



4. Conclusions


16

• Accuracy of speed of sound in liquid predicted by even complex EoS remarkably low

• This greatly affects the modelling of the decompression wave, given that its front moves at the speed of sound

• Very little experimental data for dense phase speed of sound available for development of better EoS

• Results presented relate to pure CO2 only, the little impure CO2 data we have indicates that the same observations are true

17

Acknowledgements and Disclaimer

The research leading to the results described in this

presentation has received funding from the European

Union 7th Framework Programme FP7-ENERGY-2012-1-

2STAGE under grant agreement number 309102.

The presentation reflects only the authors’ views and the

European Union is not liable for any use that may be

made of the information contained therein.


Study of the CO2 Pipeline Network Planned in the Humber Region of the UK:

Simulation-based Techno-economic Evaluation for

Optimal Design

School of Engineering

University of Hull

11th NOV 2014

Xiaobo Luo, Meihong Wang

in collaboration with Ketan Mistry, Russell Cooper

OUTLINE

Pipeline Scheme in Humber Region

Work Package Overview

Techno-economic evaluation

Methodology

Evaluation of compression

Evaluation of trunk pipeline

Whole pipeline system

Findings

Work Package Overview

CO2 transportation pipeline network

Optimal design and operation by using process engineering system techniques

Aspen HYSYS gPROMS APEA

(Aspen Process Economic Analyser)

Dynamic simulation

Steady state simulation

Economic evaluation

Simulation-based techno-economic evaluation for Optimal Design

Methodology-model development

EOS selection in the literature

Span and Wanger ( for pure CO2)

GERG (for CO2 and impurities)

Peng-Robinson (for CO2 and impurities)

SAFT (for CO2 and impurities)

Equation of state (EOS) selection

Table 1. EOS used in published studies

Papers/studies EOS used

Hein et al. 1985 Soave-Redlich-Kwong (SRK) equation

Hein et al. 1986 Peng-Robinson (PR) equation for CO2 mixture

Zhang et al. 2006 Peng-Robinson (PR) equation with Boston -Mathias modification for CO2 mixture

Seevam et al. 2008 Peng-Robinson (PR) equation

Mahgerefteh et al. 2008 Peng-Robinson (PR) equation

E.ON's report , 2010 Span and Wagner EOS for pure CO2

Nimtz et al. 2010 Span and Wagner EOS for pure CO2

Munkejord et al. 2010 Soave–Redlich–Kwong EOS

Liljemark et al.2011 Span and Wagner EOS for pure CO2 and GERG-2004 for the CO2 mixtures

Klinkby et al. 2011 Span and Wagner EOS for pure CO2

Chaczykowski et al. 2012 GERG-2004 for CO2 mixture

An entry specification was agreed to be 96 mole% CO2 and a mixture of nitrogen, oxygen, hydrogen, argon and methane with hydrogen limited to 2.0 mole% and oxygen limited to 10 ppmv.


E.ON’s report (2010) show PR EOS is not very accurate in the near-critical region. In this T/P range(4oC - 20oC/ 101 bar -150 bar) in this study, the deviation of pure CO2 density is from -4.8% to 0.1% in E.ON’s report.

Peng-Robinson with calibrated binary interaction parameters

AAD% between experimental data and PR EOS for corresponding kij values

kij Bubble pressure Liquid volume

Reference Temperature (K)

Pressure (Mpa)

AAD% Temperature (K)

Pressure (Mpa)

AAD%

CO2 - N2 -0.007 220-301 1.4-16.7 3.73 209-320 1.4-16.7 1.54 Diamantonis et al. (2013)

Li & Yan (2009)

CO2 - Ar 0.141 288 7.5-9.8 2.32 288 2.4-14.5 1.83 Diamantonis et al. (2013)

CO2 – H2 0.1470 290.2 5.0-20.0 5.6% - - - Foster et al.


Model flow sheet in Aspen HYSYS

CO2 from Drax

CO2 from Don Valley

Common onshore pipeline Common offshore pipeline

Pump station

Mid-compressor train for Don Valley

Compressor train

Model validation by comparing the results of PIPEFLO®

no available operating and experimental data

PIPEFLO® is used for the concept design of the project

GERG-2008 EOSwas used in PIPEFLO® for the project

Entry pressure at

White Rose

Entry pressure at

Don Valley

DP of mid-booster for

Don Valley

DP of pump

station

Arrival

pressure

barg barg bar bar barg

Aspen HYSYS® 119.50 34.0 86.92 43.0 125.0

PIPEFLO® 119.20 34.0 86.70 42.4 125.0

Relative difference 0.25% - 0.25% 1.40% -

Methodology-economic evaluation

Economic evaluation using APEA

Aspen Process Economic Analyzer (APEA)

CAPEX • total capital investment cost (capital return factor is 0.15 for annualized capital cost)

Equipments purchase Engineering Construction others during project implement

OPEX •Fixed OPEX

O&M cost (per year) •Available OPEX

Energy and utilities cost (per year)

Diameter calculation in different

correlation methods

Input information

Simulations of trunk pipelines

Economic evaluations of trunk pipelines

Comparison with the literature and analysis

Simulation of whole pipeline network

Compression technology analysis

Simulation of compression train

Economics evaluation of compression train

Comparison with the literature and analysis

Comparison of compression options

to select optimal option

Comparison of pipeline options to

select optimal option

Economics evaluation of whole pipeline

network

Evaluation of CO2 compression

Table 5. Compression technology options and their process definition

Option Unit Base Case C1 C2 C3 C4

Description

Centrifugal

5 stage with

4

intercoolers

Centrifugal 16 stage 4

intercoolers

8 stage

centrifugal

geared with 7

intercoolers

6 stage integrally

geared with 5

intercoolers to 20

oC +pumping

6 stage integrally

geared with 5

intercoolers to 38

oC +pumping

Capacity t/h 245 245 245 245 245

Suction pressure MPa 0.101325 0.101325 0.101325 0.101325 0.101325 Suction temp. oC 20 20 20 20 20

Pumping suck

pressure MPa - - - 8.0 8.0 Pumping suck temp. oC - - - 20 20

Exit pressure MPa 13.5 13.5 13.5 13.5 13.5

Stage - 5 16 8 6 6

Isentropic efficiency % 75 75 75 75 75

Interstage cooler exit

temperature oC 20 38 38 20 38 Last stage exit temp. oC 20 20 20 20 20

Base case Case 1

Case 2 Case 4 Case 3

Compression technology options and their process definition

Evaluation of CO2 compression

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

Base case C1 C2 C3 C4

Brea

k do

wn

annu

al c

osts

(M€2

012/

a)

Options

Annual Energy and utilities cost

Annual O&M cost

Annualized Capital cost

0

2

4

6

8

10

12

Basecase

C1 C2 C3 C4

Ann

ual c

ost (

M€/

a)

McCollum and Ogden (2006)

0

2

4

6

8

10

12

Basecase

C1 C2 C3 C4

Ann

ual c

ost (

M€/

a)

IEA GHG (2002)

0

2

4

6

8

10

12

Basecase

C1 C2 C3 C4

Ann

ual c

ost (

M€/

a)

This study

Annual O&M cost

Annual capital cost

The comparison of levelized cost of different cost model in the literature

Comparison of annual costs of different compression options

Evaluation of trunk pipelines

Hydraulic performance and energy requirement of trunk pipelines in different

diameters

The calculation results of different diameter calculation methods in literature

Common onshore pipeline Common offshore pipeline

Pump station

Pipeline

diameter

Actual initial

velocity

Pressure drop of

onshore pipeline

Pressure drop of

offshore pipeline

Boosting pressure

of pump station

Energy required of

pump station (inch) (m/s) (bar) (bar) (bar) (kWh)

28 1.08 5.9 10.0 5.9 301.5

24 1.49 13.5 20.6 24.1 1243

22 1.81 22.1 32.2 44.3 2305

Diameter calculation method

Calculated

diameter Velocity

Selected diameter

in APEA

Unit (m) (m/s) DN (inch)

Velocity based equation

0.699 1.0 28

0.5713 1.5 24

0.4948 2 20

Hydraulic equation 0.5262 1.77 22

Extensive hydraulic equation 0.6173 1.29 24

McCoy and Rubin model 0.5672 1.52 22

Evaluation of trunk pipelines

0.00

20.00

40.00

60.00

80.00

100.00

120.00

28 in. 24 in. 22 in.

Bre

ak d

ow

n a

nn

ual

co

sts

(M€

20

12

/a)

Pipeline diamter

Annual energy and utilities cost

Annual O&M cost

Annual capital cost

Comparison of capital cost of different cost models in the literature

Annual cost comparison for different diameters of the pipelines

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

22 in. 24 in. 28 in.

Ca

pit

al

cost

(M€

/k

m)

Pipeline diamter

This study

IEA GHG, 2002

MaCollum and Ogden, 2006

McCoy and Rubin, 2008

Piessense et al., 2008

Van Den Broek et al., 2010

Overall cost of whole pipeline network

Comparison of levelized cost of the optimal case and COCATE project (Roussanaly et al., 2013)

Optimal case

COCATE project

Levelized energy and utilities cost €/t-CO2 7.6 8

Levelized CAPEX of the trunk pipeline €/t-CO2 6.0 5.5

Levelized CAPEX of collecting system €/t-CO2 4.4 0.2

Levelized O&M cost €/t-CO2 1.0 2

Levelized total cost €/t-CO2 19.1 15.7

Base case Optimal case

Annual energy and utilities cost M€/a 68.7 68.0

Annual CAPEX of the trunk pipeline M€/a 69.4 53.6

Annual CAPEX of collecting system M€/a 45.2 39.5

Annual O&M cost M€/a 9.2 9.2

Annual total cost M€/a 192.5 170.3

Comparison of annual costs of base case and optimal case

0.00

50.00

100.00

150.00

200.00

250.00

Base case Optimal case

An

nu

al

cost

(M€

/a

)

Annual capital cost of trunk pipelines

Annual capital cost of collecting system

Annual energy and utilities cost

Annual O&M cost

0.00

5.00

10.00

15.00

20.00

25.00

Optimal case COCATE project

Le

ve

lize

d c

ost

(€

/t-

CO

2)

Levelized capital cost of trunk pipelines

Levelized capital cost of collecting system

Levelized energy and utilities cost

Levelized O&M cost

key findings

For CO2 compression, lower intercooler exit temperature (20 oC vs 38oC in this study) contributes lower both energy cost and capital cost.

The O&M cost of CO2 compression is found to be low in other published models.

The pipeline diameter models in literature are generally reliable. With optimal diameter of pipelines, the initial velocity of CO2 mixture in dense phase is about 1.7m/s in this study.

The cost range of the pipelines are large for different models. The weight based model (Piessense et al. 2008) has the prediction close to this study.

Simulation-based techno-economics evaluation method offers a powerful tool for optimal designs for the projects, especially for the decision making support about the detailed technical options selection.

Reference Lists

Li H, Yan J. Evaluating cubic equations of state for calculation of vapor–liquid equilibrium of CO2 and CO2-mixtures for CO2 capture and storage processes. Applied Energy 2009;86(6):826-836.

Diamantonis NI, Boulougouris GC, Mansoor E, Tsangaris DM, Economou IG. Evaluation of Cubic, SAFT, and PC-SAFT Equations of State for the Vapor–Liquid Equilibrium Modeling of CO2 Mixtures with Other Gases. Industrial & Engineering Chemistry Research 2013;10.1021/ie303248q:130227083947003.

Foster NR, Bezanehtak K, Dehghani F. Modeling of Phase Equilibria for Binary and Ternary Mixture of Carbon Dioxide, Hydrogen and Methanol. [cited 2014 02/06]. Available from: http://www.isasf.net/fileadmin/files/Docs/Versailles/Papers/PTs15.pdf

IEA GHG, 2002. Pipeline transmission of CO2 and energy. Transmission study report. PH4/6, 1–140.

McCollum, D.L., Ogden, J.M., 2006. Techno-economic models for carbon dioxide compression, transport, and storage & Correlations for estimating carbon dioxide density and viscosity. UCD-ITS-RR-06-14, 1–87.

Piessens, K., Laenen, B., Nijs, W., Mathieu, P., Baele, J.M., et al., 2008. Policy Support System for Carbon Capture and Storage. SD/CP/04A, 1–269.

Roussanaly S, Bureau-Cauchois G, Husebye J. 2013. Costs benchmark of CO2 transport technologies for a group of various size industries. International Journal of Greenhouse Gas Control. 12(0):341-350.

S.T. McCoy, E.S. Rubin, 2008. An engineering-economic model for pipeline transport of CO2 with application to carbon capture and storage. International Journal of Greenhouse Gas Control, 2 , pp. 219–229

M. Van den Broek, A. Ramírez, H. Groenenberg, F. Neele, P. Viebahn, W. Turkenburg, A. Faaij, 2010. Feasibility of storing CO2 in the Utsira formation as part of a long term Dutch CCS strategy: An evaluation based on a GIS/MARKAL toolbox. International Journal of Greenhouse Gas Control, 4 , pp. 351–366

Acknowledgement

Financial support of UK NERC Energy Programme (NE/H013865/2)

Information and discussion from Dr. Russell Cooper Mr. Ketan Mistry

Mr. Julian Field

from National Grid

Related publications: Luo, X., Mistry, K., Okezue, C., Wang, M., Cooper, R., Oko, E., Field, J. (2014), Process simulation and analysis for CO2 transport pipeline design and operation – case study for the Humber Region in the UK, European symposium on computer-aided process engineering (ESCAPE24), Budapest, Hungary. Published in Computer Aided Chemical Engineering, Vol. 33, p1633-1639 Luo X, Wang M, Oko E, Okezue C. Simulation-based techno-economic evaluation for optimal design of CO2 transport pipeline network. Applied Energy 2014;132(0):610-620

Thanks

Thank you for you attention!

Questions are welcome.

Contact us if you are interested in our works.

Meihong Wang

[email protected]

UK office: 01482 466688

Xiaobo Luo

[email protected]

mailto:[email protected]

mailto:[email protected]

www.co2quest.eu

School of something FACULTY OF OTHER

School of Chemical and Process Engineering FACULTY OF ENGINEERING

Numerical modelling of trans-triple point

temperature near-field sonic dispersion

of CO2 from high pressure

dense phase pipelines

Dr Chris Wareing

UKCCSRC Equation of State Workshop,

11th November 2014, York

C J Wareing, R M Woolley, M Fairweather, S Falle

University of Leeds, Leeds, LS2 9JT, United Kingdom COOLTRANS: The Don Valley CCS Project is co-financed by the European Union’s European Energy Programme for Recovery

COOLTRANS / CO2PIPEHAZ / CO2QUEST: The sole responsibility of this content lies with the author.

The European Union is not responsible for any use that may be made of the information contained herein

Overview

Carbon capture and storage, the short term option for reducing

CO2 emissions, is likely to proceed with transportation from

source to storage along high-pressure dense phase pipelines

• Pipelines fail. Complex CFD simulations can validate pragmatic

approaches used for quantified risk assessment (QRA).

• Leeds: near-field sonic dispersion of carbon dioxide (CO2) from

high pressure pipelines

• Examples

• Thermodynamic model

• Recent developments

• Requirements for impurities

Venting: COOLTRANS dense phase Temperature

Dense phase release from a 150bar reservoir

through a 25mm ventpipe Measurements at:

• 4m (165D)

• 7m (288D)

Near-field shock containing region:

20D x 20D (0.5m x 0.5m)

COOLTRANS punctures and ruptures

Puncture:

Rupture:

Near-field dispersion model

(COOLTRANS & CO2PIPEHAZ)

• Thermodynamic model: (Wareing et al. 2013, AIChE Journal 59 3928-3942)

• Near-field dispersion of pure CO2 in the gas, liquid and solid phases into

dry air.

• Novel composite equation of state for pure CO2 employing:-

• the Peng-Robinson equation of state in the gas phase;

• tabulated data derived from the Span & Wagner equation of state for

the liquid phase and vapour pressure;

• and NIST/DIPPR data for the solid phase and latent heat of fusion.

• Calculations were undertaken using the Helmholtz free energy in terms

of temperature and molar volume, as all other thermodynamic properties

can be readily obtained from it.

• Novel combination of the simple Peng-Robinson equation and tabulated

data on the saturation line allows for crucially a fast implementation

numerically, when updating tens of millions of grid cells per second.

• Internal energy on the saturation line. • Tcrit marks the critical

temperature.

• The triple point can be

identified by the steep

connection between the

liquid and solid phases –

the latent heat of fusion.

• Numerical method, with

unstructured AMR

Near-field dispersion model

(COOLTRANS & CO2PIPEHAZ)

Comparative performance

• Comparison of ideal, Peng-Robinson and composite EoSs

Temperature:- CO2 fraction:-

(a) Ideal

(b) P-R

(c) Composite

Comparative performance

• Comparison of Peng-Robinson and composite EoSs

Condensed

phase fraction:-

(a) P-R

(b) Composite

Recent developments

• Different equations of state

• Solid phase. Now able to run with tables generated from Jager and

Span – Cp and internal energy are very different. Also considered the

Trusler solid phase EoS.

• Gas and liquid phases. Several equations of state under consideration

and comparison:-

• Physical Properties Library (SAFT/pcSAFT) from the National

Research Centre for Physical Sciences, Greece.

• Tables based on Span and Wagner

• Tables based on Richard Graham’s work.

• New considerations of EOS-CG.

• Have previously considered PRSV, PRSV2, Yokozeki, PROPATH &

others and discounted them for our use.

Requirements for impurities

(CO2QUEST)

• EITHER a simple fast equation of state that can be directly embedded

(advantages of speed and solutions over a wide range).

• OR a complex equation that can generate tables (disadvantage – solvers

routinely require solutions outside the tabular ranges).

• Mixing rules with known interaction parameters for e.g. N2, O2, Ar, H2S.

• Pipeline spec.: 96% CO2, 4% impurities (<2% N2, <2% O2 + trace).

Temperature range: 50K – 400K. Pressure range: 0.01 to ~20 MPa.

• Options:-

• A simple option: e.g. Peng-Robinson. But, there are known issues

(incorrect densities, speed of sound, etc. noted again at GHGT12)

• Trust a library function e.g. PPL. Current issues under investigation.

• More complex option: EOS-CG in tabular form. Possible issues with

H2O (presented at GHGT12)?

Thank you for listening. Discussion?

Phase equilibrium studies of impure CO2 systems to underpin

developments of CCS technologies

Jie Ke, Martyn Poliakoff and Michael W. George

School of Chemistry

The University of Nottingham

11 November, 2014, CO2 Properties and EOS for Pipeline Engineering, York

Acknowledgements

National Grid

E.ON

Rolls-Royce

PSE

Our collaborators from the COZOC, MATTRAN and COOLTRANS projects

Dr. Stéphanie Foltran

Dr. Yolanda Sanchez-Vicente

Dr. Andrew J. Parrot

Dr. James Calladine

Dr. Maria-José Tenorio

Dr. Alisdair Wriglesworth

Matthew E. Vosper

Norhidayah Suleiman

Prof. Trevor Drage

UKCCSRC

ETI

EPSRC

TSB

High-pressure facilities in Nottingham

Sensors

t=0 t=0

Sound Wave

t

IR

Holey fibre + GC

ATR Shear-mode quartz Optical fibre

Density meter

J. Phys. Chem. 1996, 100, 9522. J. Phys. Chem. 1997, 101, 5853. Fluid Phase Equilib. 1998, 150, 493. J. Supercrit. Fluids 2004, 30, 259. Phys . Chem. Chem. Phys. 2004, 6, 1258. J. Chem. Eng. Data 2009, 54, 1580.

Vapour-liquid-equilibrium and other thermodynamic properties of CO2 mixtures

Density

CO2 + N2, CO2 + H2, CO2 + N2 + H2 and CO2 + N2 + Ar

VLE Without water With water

o CO2 + N2 (4 mixtures)

o CO2 + H2 (3 mixtures)

o CO2 + N2 + H2 (2 mixtures)

o CO2 + N2 + Ar (in progress, funded by ETI and PSE)

o CO2 + H2 + Ar (in progress, funded by ETI and PSE)

o CO2 + H2O o CO2 + N2 + H2O o CO2 + H2 + H2O

260 270 280 290 300

2

4

6

8

10

12

14

p / M

Pa

T / K

p – T phase boundary of CO2 + N2 and CO2 + H2

260 270 280 290 300

2

4

6

8

10

p / M

Pa

T / K

CO2 + N2 CO2 + H2

Pure CO2

14%

2% 5%

Pure CO2

9.1%

3% 4%

260 270 280 290 300

4

8

12

0.95 CO2 + 0.05H

2

0.95 CO2 + 0.05N

2

p / M

Pa

T / K

0.95 CO2 + 0.02 N

2 + 0.03 H

2

p – T phase boundary of the ternary system of CO2 + N2 + H2

0.93 CO2 + 0.04 N2 + 0.03 H2

Pure CO2

Solubility of H2O in CO2 + N2 (40 oC)

yH2O - mole fraction of H2O

Pure CO2 95% CO2 + 5% N2

90% CO2 + 10% N2

What is next?

• The VLE data of multicomponent mixtures (more than 5

components) of CO2, N2 and O2, H2, Ar, etc.

• The solubility of water in the mixture of CO2 + N2 + H2.

• CO2 and N2 solubilities in sea water.

• Karl-Fischer titration

• Spectroscopic methods

for Pipeline Engineering - UKCCSRC · for Pipeline Engineering . Tuesday 11 November 2014, York . ... Process Engineering performing research into the transport methods that will

Documents