Applications of three data analysis techniques for modeling the carbon dioxide capture process

Abstract

The objective of this paper is to study the relationships among the significant parameters impacting CO 2 production. An enhanced understanding of the intricate relationships among the process parameters enables prediction and optimization, thereby improving efficiency of the CO 2 capture process. Our modeling study used the operational data collected over a 3-year period from the amine-based post combustion CO 2 capture process at the International Test Centre of CO 2 Capture (ITC) located in Regina, Saskatchewan of Canada. This paper describes the data modeling process using the approaches of: (1) statistical study, (2) artificial neural network (ANN) modeling combined with sensitivity analysis (SA), and (3) neuro-fuzzy technique. It was observed that the neuro-fuzzy modeling technique generated the most accurate predictive models and best support explication of the nature of the relationships among the key parameters in the CO 2 capture process.