Modeling of the carbon dioxide capture process system using machine intelligence approaches

Improving the efficiency of the carbon dioxide (CO2) capture process requires a good understanding of the intricate relationships among parameters involved in the process. The objective of this paper is to study the relationships among the significant parameters impacting CO2 production. An enhanced understanding of the intricate relationships among the process parameters supports prediction and optimization, thereby improving efficiency of the CO2 capture process. Our modeling study used the 3-year operational data collected from the amine-based post combustion CO2 capture process system at the International Test Centre (ITC) of CO2 Capture located in Regina, Saskatchewan of Canada. This paper describes the data modeling process using the approaches of (1) neural network modeling combined with sensitivity analysis and (2) neuro-fuzzy modeling technique. The results from the two modeling processes were compared from the perspectives of predictive accuracy, inclusion of parameters, and support for explication of problem space. We conclude from the study that the neuro-fuzzy modeling technique was able to achieve higher accuracy in predicting the CO2 production rate than the combined approach of neural network modeling and sensitivity analysis.