Maximization of CO2 Capture Capacity Using Recent RUNge Kutta Optimizer and Fuzzy Model
Ahmed M. Nassef, Hegazy Rezk, Ali Alahmer, Mohammad Ali Abdelkareem
Abstract
This study aims to identify the optimal operating parameters for the carbon dioxide (CO2) capture process using a combination of artificial intelligence and metaheuristics techniques. The main objective of the study is to maximize CO2 capture capacity. The proposed method integrates fuzzy modeling with the RUNge Kutta optimizer (RUN) to analyze the impact of three operational factors: carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio. These factors are considered to maximize the CO2 capture. A fuzzy model was developed based on the measured data points to simulate the CO2 capture process in terms of the stated parameters. The model was then used to identify the optimal values of carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio using RUN. The results of the proposed method are then compared with an optimized performance using the response surface methodology (RSM) and measured data to demonstrate the superiority of the proposed strategy. The results of the study showed that the suggested technique increased the CO2 capture capacity from 6.39 to 6.99 by 10.08% and 9.39%, respectively, compared to the measured and RSM methods. This implies that the proposed method is an effective approach to maximize the CO2 capture capacity. The results of this study can be used to improve the performance of the CO2 capture process in various industrial applications.