Physics-based and data-driven hybrid modelling and dynamic adaptive multi-objective optimization of chemical reactors for CO2 capture via enhanced weathering

Enhanced weathering (EW) of alkaline minerals in chemical reactors with a controlled environment is recognized as a promising approach for gigaton-level carbon dioxide removal. However, reactor configuration and operating conditions must be optimized to balance the interfacial areas between gas, liquid and solid phases prior to industrial application. We developed a physics-based and data-driven hybrid modelling approach, coupled with multi-objective optimization, to study and compare three typical chemical reactors, i.e., trickle bed, packed bubbling columns, and stirred slurry reactors, and the optimal design to improve CO2 capture rate and reduce energy and water consumptions. Then an adaptive optimization is proposed to dynamically adjust the operating of the reactors in response to intermittent CO2 emission and renewable energy supply. Results indicated that forced stirring enhances CO2 capture rates by accelerating mass transport but increases energy consumption. Trickle bed reactor achieves the highest capture rate and lowest energy and water consumption. The AI-enabled, multi-objective, and adaptive optimization developed in response to intermittent CO2 and energy supplies shows considerable potential for industrial applications, advancing progress towards achieving SDG #13.