Modeling and optimization of CO2 capture into mixed MEA-PZ amine solutions using machine learning based on ANN and RSM models

Abstract

Carbon dioxide (CO2) sequestration by chemical absorption is widely regarded as the most effective method for its reduction in natural gas streams or flue gases from fossil fuel power plants. In this article, modeling and optimization of CO2 mass transfer flux (NCO₂) are investigated. A combination of Piperazine (PZ) and Monoethanolamine (MEA) amines has been used for CO2 absorption. Artificial neural networks (ANN) and Response Surface Methodology (RSM) were used to achieve goals. The dimensionless numbers for the input of ANNs and RSM was obtained using Buckingham's Pi theory. The resulting models can provide acceptable results in an effect of independent variables and the interaction between them by the impact on the objective function, to optimize the process of CO2 capture. In the RSM approach, the quadratic model is used. Optimized ANNs and a structure with the least error and the most matching with the experimental data were obtained. Both ANNs and RSM models showed acceptable prediction of experimental data with maximum R2 value of 0.9974 and 0.9723 respectively. Due to the mean squared error of5.2 × 10−4, the ANN is recommended for the development of absorption simulation models.