M. H. Wathsala N. Jinadasa
Faculty of Technology, Natural Sciences and Maritime Sciences – University College of Southeast Norway, Kongsberg, Norway
Klaus J-Jens
Faculty of Technology, Natural Sciences and Maritime Sciences – University College of Southeast Norway, Kongsberg, Norway
Carlos F. Pfeiffer
Faculty of Technology, Natural Sciences and Maritime Sciences – University College of Southeast Norway, Kongsberg, Norway
Sara Ronasi
Norner Research, Asdalstrand 291, N-3962, Stathelle, Norway
Carlos Soler
Norner Research, Asdalstrand 291, N-3962, Stathelle, Norway
Maths Halstensen
Faculty of Technology, Natural Sciences and Maritime Sciences – University College of Southeast Norway, Kongsberg, Norway
Ladda ner artikel
http://dx.doi.org/10.3384/ecp17142207Ingår i: Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Linköping Electronic Conference Proceedings 142:30, s. 207-213
Publicerad: 2018-12-19
ISBN: 978-91-7685-399-3
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
Carbon dioxide absorption by mixtures of propylene oxide / polypropylene carbonate at 600C was monitored by Raman spectroscopy at 20, 40 and 60 bar in a 2 L autoclave reactor. Multivariate preprocessing techniques were used to process raw Raman spectra and Principal Component Analysis was performed. Simulation data from the Peng- Robinson equation of state were used to model the absorbed CO2 amount and spectroscopic signals. Results showed that Principal Component Analysis can be used to explore the dynamics of the system at different pressure levels and to track the CO2 absorption. A similar analysis was carried out to monitor CO2 absorption by four different amines at room temperature and pressure in a batch reactors. The CO2 content was determined from titration and was used to model the spectroscopic data. Principal Component Analysis proved to be able to identify CO2 absorption capacity in the amines. This feasibility study confirms that Raman spectroscopy together with multivariate analysis can effectively report chemical information and dynamics in these CO2 absorption systems and hence can be used for developing regression models for online monitoring and control.
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