AI-Driven Advancements in Carbon Capture and Mineralization: Optimizing Efficiency and Scalability for a Sustainable Future

Abstract This study investigates the integration of advanced technologies in Carbon Capture and Mineralization (CCM) to enhance efficiency and scalability in carbon management systems. Against the backdrop of escalating climate change concerns, we examine recent advances in material science, process optimization, and artificial intelligence-driven monitoring systems. Our research demonstrates significant improvements in capture efficiency (exceeding 90%), a substantial reduction in mineralization energy consumption (by 20%), and the successful application of AI/ML techniques to predict and optimize operational outcomes. Through a combined experimental and computational approach, we developed advanced sorbents and catalysts, optimized reaction kinetics, and implemented machine learning models for real-time process control. Results indicate a 92% CO2 capture efficiency for metal-organic framework (MOF)-based sorbents, 25% reduction in mineralization reaction times, and 22% decrease in energy consumption. AI/ML integration enabled process efficiency predictions with an R2 value of 0.96 and reduced system downtime by 18% through early fault detection. This work establishes a new paradigm in CCM by integrating advanced materials with AI-driven process optimization, creating a path toward economically viable and industrially scalable carbon management solutions.