A novel economy analysis for advancing CO2 capture efficiency of post combustion using sequential quadratic programming (SQP) optimization methodology

This study presents a novel comprehensive cost analysis model aimed at evaluating the economic viability of post-combustion carbon capture (PCC) processes through the application of sequential quadratic programming (SQP) optimization methodology. A CO2 capture calculation program was developed and coupled with Aspen RateSep by Fortran function for optimization. The SQP approach was employed to systematically explore and optimize various process configurations, ensuring both process and operational constraints were met. By iteratively minimizing the objective function, the optimization led to significant improvements in cost efficiency. Key findings include a 30.91 % reduction in the cost of CO2 avoided (CCA), driven by a 16.03 % decrease in thermal energy demand for regeneration. These enhancements translated into notable reductions in both Capital Expenditure (CAPEX) and Fixed Operational Expenditure (FOPEX), with decreases of 29.13 % and 32.98 %, respectively. The optimization highlighted the critical role of adjusting MEA concentration and other process parameters, resulting in a substantial increase in captured CO2, thereby enhancing overall carbon capture efficiency. Among the process parameters studied, flue gas CO2 concentration exhibited the most significant impact on reducing the cost per ton of CO2 avoided, demonstrating its superior characteristics in optimizing both the efficiency and economic viability of the carbon capture process. This research demonstrates the importance of optimizing PCC processes not only for cost reduction but also for improving environmental sustainability.