From neural network to neuro-fuzzy modeling: Applications to the carbon dioxide capture process

Abstract

Abstract Research on improving efficiency of the amine-based post combustion carbon dioxide (CO 2 ) capture process has been ongoing during the past decade. A good understanding of the intricate relationships among parameters involved in the CO 2 capture process is important for process optimization. The objective of this study is to uncover relationships among the significant parameters impacting CO 2 production by modeling the historical real-time process data. The data were collected 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. Relevant literature review and opinions from the experienced engineers of the ITC CO 2 capture plant suggested that the four parameters of reboiler heat duty, lean loading, CO 2 absorption efficiency and CO 2 production rate are the key parameters for assessing efficiency of the process. The eight process parameters that influence these four consequent or output parameters were identified as the conditional or input parameters. In this study, two artificial intelligence techniques were applied for modeling the relationships among the conditional and consequent parameters: (1) artificial neural network combined with sensitivity analysis and (2) neuro-fuzzy modeling. The results from the two modeling processes were compared, and it was observed that the neuro-fuzzy modeling technique was able to achieve on average higher accuracies than the combined approach of neural network modeling and sensitivity analysis.