Optimization of CO2 capture plants with surrogate model uncertainties

• Published in: Computers & chemical engineering 2024-07, Vol.186, p.108709, Article 108709
• Main Authors: Pedrozo, A., Valderrama-Ríos, C.M., Zamarripa, M.A., Morgan, J., Osorio-Suárez, J.P., Uribe-Rodríguez, A., Diaz, M.S., Biegler, L.T.
• Format: Article
• Language: English
• Subjects: Equation-oriented model; MEA-based absorption; Model uncertainty; Optimal design; Surrogate models
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2024.108709

•Equation-oriented optimization using rigorous models in Aspen Plus.•Efficient approach to generate data from large-scale optimization problems.•Optimal design of amine-based absorption process under different flue gas conditions and CO2 recoveries.•Surrogate models for economic indicators considering parameter uncertainty. CO2 capture plants can help reduce the cost of deploying capture systems across the globe. However, the CO2 variability and model uncertainty represent operational challenges to capture CO2 from different sources. This work proposes a framework for analyzing the optimal plant design considering different flue gas sources. We show a methodology to generate large data sets from optimization runs using rigorous models in Aspen Plus®. The efficiency of the approach allows its application to large-scale optimization problems, with an average CPU time per run of 176 s. We additionally build surrogate models (SMs) for the capital and operating costs of the capture plants, employing an iterative procedure to generate SMs using ALAMO. We systematically reject SMs with high uncertainty in the estimated parameters. This approach results in SMs with favorable bias-variance tradeoffs, enabling their effective application to optimization problems under uncertainty, as demonstrated with a pooling problem of CO2 streams.