Improving CO2 Absorption Using Artificial Intelligence and Modern Optimization for a Sustainable Environment

Abstract

One of the essential factors in maintaining environmental sustainability is to reduce the harmful effects of carbon dioxide (CO2) emissions. This can be performed either by reducing the emissions themselves or capturing and storing the emitted CO2. This work studies the solubility of carbon dioxide in the capturing solvent, which plays a crucial role in the effectiveness and cost-efficiency of carbon capture and storage (CCS). Therefore, the study aims to enhance the solubility of CO2 by integrating artificial intelligence (AI) and modern optimization. Accordingly, this study consists of two consecutive stages. In the first stage, an adaptive neuro-fuzzy inference system (ANFIS) model as an AI tool was developed based on experimental data. The mol fraction was targeted as the model’s output in terms of three operating parameters; the concentration of tetrabutylphosphonium methanesulfonate [TBP][MeSO3], temperature, and pressure of CO2. The operating ranges are (2–20 wt%), (30–60 °C), and (2–30 bar), respectively. Based on the statistical measures of the root mean squared error (RMSE) and the predicted R2, the ANFIS model outperforms the traditional analysis of variance (ANOVA) modeling technique, where the resulting values were found to be 0.126 and 0.9758 for the entire samples, respectively. In the second stage, an improved grey wolf optimizer (IGWO) was utilized to determine the optimal operating parameters that increase the solubility of CO2. The optimal values of the three operating parameters that improve the CO2 solubility were found to be 3.0933 wt%, 40.5 °C, and 30 bar, respectively. With these optimal values, the collaboration between the ANFIS and IGWO produced an increase of 13.4% in the mol fraction compared to the experimental data and the response surface methodology. To demonstrate the efficacy of IGWO, the obtained results were compared to the results of four competitive optimization techniques. The comparison showed that the IGWO demonstrates superior performance. Overall, this study provided a cost-efficient approach based on AI and modern optimization to enhance CO2 solubility in CCS.

Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning

“…Given the current challenges in carbon capture, an efficient technological framework is not merely a benefit but a necessity. , The SMART framework emerges, encompassing Scalable Modeling, Artificial Intelligence, and Rapid Theoretical computations. Unlike past reviews that solely concentrated on either materials or computational techniques, this evaluation melds high-throughput calculations, data-centric modeling, and expertise from multiple disciplines.…”