A comparison of two data analysis techniques and their applications for modeling the carbon dioxide capture process

作者：Wu Yuxiang; Zhou Qing; Chan Christine W^*

来源：Engineering Applications of Artificial Intelligence, 2010, 23(8): 1265-1276.

DOI：10.1016/j.engappai.2010.06.012

• CO2 capture process
• Statistical analysis
• Neural network
• Sensitivity analysis
• Data modeling

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 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 CO2 capture process at the International Test Centre of CO2 Capture (ITC) located in Regina Saskatchewan of Canada The goal of CO2 capture is to capture and remove CO2 from industrial gas streams before they are released into the atmosphere The amine solution is used at ITC for absorbing CO2 from the industrial flue gas and then the CO2 is separated from the amine solution The amine solution recycles for further CO2 capture and the CO2 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 app

• 出版日期2010-12