Dr. Dobb's Journal
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Improving the efficiency of the carbon dioxide (CO"2) capture process requires a good understanding of the intricate relationships among parameters involved in the process. The objective of this research is to study the nature of relationships among the key parameters using the approaches of artificial neural network and statistical analysis. Our modeling study used the three-year operational data 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. The goal of CO"2 capture is to capture and remove CO"2 from industrial gas streams before they are released into the atmosphere. The amine solution is used at ITC for absorbing CO"2 from the industrial flue gas, and then the CO"2 is separated from the amine solution. The amine solution recycles for further CO"2 capture and the CO"2 stream can be stored or used for other industrial purposes. This paper describes the data modeling process using the approaches of: (1) statistical analysis and (2) neural network modeling combined with sensitivity analysis. The results from the two modeling process were compared from the perspectives of predictive accuracy, inclusion of parameters, support for exploration and explication of problem space, modeling uncertainty, and involvement of experts. It was observed that the approach of neural network modeling combined with sensitivity analysis achieved much higher accuracy on predicting CO"2 production rate than the statistical study.