Analysis of data for the carbon dioxide capture domain

To tackle the global concern for adverse impact of greenhouse gas (GHG) emissions, the post combustion carbon dioxide (CO2) capture technology is commonly adopted for reducing industrial CO2 emissions, for example, from power generation plants. The research on post combustion CO2 capture has been ongoing in the last two decade, and its primary objective is to improve efficiency of the CO2 capture process while reducing specific operating problems such as solvent degradation and corrosion. This objective requires a good understanding of the intricate relationships among parameters involved in the CO2 capture process. From a review of the relevant literature, we observed that the most significant parameters influencing the CO2 production rate include: heat duty, circulation rate of the solvent, CO2 lean loading, and solvent concentration. To study the nature of relationships among the key parameters, we conducted data modeling and analysis based on the amine-based post combustion CO2 capture process at the International Test Centre for Carbon Dioxide Capture (ITC) located in Regina, Saskatchewan of Canada. In our study, the experimental data collected from ITC from year 2003 to 2006 were analyzed using the combined approach of neural network modeling and sensitivity analysis. The neural network was trained for modeling the relationships among parameters, and the sensitivity analysis method illustrated the order of significance among the parameters. The modeling results were validated by the process experts. This paper describes the procedure of our work, and discusses the results of the analysis.