DURATION: 2017-2019 PROJECT LEADER: Konstantinos Kyprianidis FUNDED BY: KKS. PARTNERS: Bestwood, Mälarenergi, Eskilstuna Energi och Miljö. KEY INVESTIGATORS Jan Skvaril (MDH), Konstantinos Kyprianidis (MDH), Erik Dahlquist (MDH), Anders Avelin (MDH), Monica Odlare (MDH). SUMMARY Near Infrared Reflectance Spectroscopy (NIRS) offers rapid on-line analysis of biomass feedstocks that can be utilized for process control of biomass-based CHP plants. We carry out a full-scale on- site implementation of NIRS for online powerplant control at the facilities of Mälarenergi (ME) and Eskilstuna Sträng- näs Energi och Miljö (ESEM). We focus on developing robust NIRS soft-sensors for fuel properties and composition and combining them with dynamic models for on-line feed-forward process moni- toring and control. Expected benefits include reduced risk of agglomeration and pollutant emissions formation as well as improved production control and identification of fossil content in munici- pal waste fuel. OBJECTIVES • Transfer existing laboratory NIR calibrations for biomass and waste material properties to a combined heat and power plant online bio- mass handling system. • Use the soft sensor signals as input to powerplant control and evaluate the possibility for process control. • Implement the system on-line along with other available sensors and dynamic models at ME and ESEM powerplants. • Use the integrated system for determining key parameters such as fuel feed volume and providing useful input for control to the line operators. • Evaluate the benefits from the on- line implementations at the ME and ESEM powerplants. • Evaluate the potential for high technology readiness level perma- nent implementation of the on-line measurements at ME and ESEM power plants. TASKS WP1: Data collection, qualitative analysis and NIR measurements of wood chips and other biomass products • Including spectroscopy on-line cha- racterization of incoming feedstock, such as wood chips, recycled wood etc. in a full-scale installation. WP2: Multivariate modeling, monitoring and evaluation • Statistical and machine learning pre-processing, analysis and eva- luation of the results from the NIR measurements. WP3: Dynamic model development • Development of new physical dyna- mic models as well as modification of existing models already develo- ped within the research group. • Feedback from regular measure- ments performed at the power plants to be added to further train the models into self-learning. WP4: Operational settings and model imple- mentation • Implementation, testing and evalua- tion of the NIR equipment and dyna- mic models in full-scale and on-site experiments at the industrial sites of the partner companies. SPECTRA SPECTRAL MEASUREMENTS FOR PREDICTION OF BIOMASS PROPERTIES 2.0 RECENT PUBLICATIONS Skvaril, Jan, Konstantinos G. Kypriani- dis, and Erik Dahlquist. ”Applications of near-infrared spectroscopy (NIRS) in biomass energy conver- sion processes: A review.” Applied Spectroscopy Reviews 52.8 (2017): 675-728. Skvaril, Jan. ”Near-Infrared Spectroscopy and Extractive Probe Sampling for Biomass and Combustion Characterization.” Mälardalen University Doctoral Dissertation (2017). Kyprianidis, Konstantinos, and Jan Skvaril. ”Developments in Near- Infrared Spectroscopy.” InTech (2017). Updated November 2017