Machine Learning and Reinforcement Learning-Driven Optimization of Carbon Capture and Storage Processes and Their Environmental Impact Assessment

Abstract The increasing global carbon footprint necessitates advanced solutions for mitigating greenhouse gas emissions, with Carbon Capture and Storage (CCS) emerging as a critical strategy. However, optimizing CCS processes for efficiency, cost-effectiveness, and environmental sustainability remains a significant challenge. This study proposes an artificial intelligence (AI)-driven framework for optimizing CCS operations, integrating machine learning models, deep reinforcement learning, and process simulation techniques to enhance capture efficiency, reduce energy consumption, and improve storage security. The proposed AI models leverage historical and real-time data to predict CO_2 capture rates, optimize absorption and adsorption parameters, and dynamically control injection strategies in geological storage sites. Furthermore, an environmental impact assessment framework is incorporated to evaluate the sustainability and long-term effects of CCS applications. Comparative analyses with conventional CCS optimization methods demonstrate the superior performance of AI-driven approaches in reducing operational costs and enhancing system stability. The results highlight AI’s transformative role in advancing CCS technologies, supporting global decarbonization efforts, and fostering sustainable energy transitions.