Gaussian process surrogate modelling and multi-objective optimization for energy-efficient CO2 capture using functionalized oil palm ash

Agricultural waste-derived adsorbents offer a sustainable approach for carbon capture while supporting biomass valorization. However, identifying operating conditions that simultaneously maximize CO 2 adsorption and minimize energy consumption remains challenging because adsorption performance depends on multiple interacting variables. This study presents a machine learning-assisted optimization framework for CO 2 capture using KOH-functionalized oil palm ash (OPA). A dataset comprising 346 fixed-bed adsorption experiments was used to develop Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Tree models, employing the OPA-to-KOH ratio, gas flow rate, adsorption temperature, inlet CO 2 concentration, and contact time as input variables. Among the models evaluated, GPR achieved the highest predictive performance, with an R 2 of 0.9984, a root mean square error of 0.0262 mmol g −1 , and a mean absolute error of 0.0119 mmol g −1 . The validated GPR model was integrated with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to maximize adsorption capacity, and with Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize both adsorption capacity and electrical energy consumption. Both GA and PSO predicted a maximum adsorption capacity of 2.954 mmol g −1 , while the multi-objective algorithms identified Pareto-optimal operating conditions that balanced adsorption performance with energy demand. The proposed framework demonstrates the potential of integrating machine learning with multi-objective optimization to support the design of energy-efficient and sustainable CO 2 adsorption processes.