Machine Learning‐Driven Multi‐Objective Optimization of Microchannel Reactors for CO₂ Conversion

Abstract Recently, the power‐to‐gas (PtG) concept, specifically thermocatalytic CO₂ conversion via the Sabatier process, emerges as a promising route for mitigating greenhouse gas emissions. The process transforms CO₂ and H₂ into methane and water under low‐temperature methanation conditions. This study suggests a new way to improve the performance of a microchannel reactor by combining computational fluid dynamics (CFD), response surface methodology (RSM), machine learning (ML), and multi‐objective optimization. Key design variables include inlet velocity, temperature, and channel length ratios. The RSM approach is for generating datasets for simulation; while, data augmentation assists ML model training. Six ML models—linear, ensemble, tree, Gaussian, support vector machine (SVM), and neural networks are evaluated for regression accuracy against RSM‐based correlation. The Gaussian process model is found superior and integrated with a multi‐objective optimization algorithm. A decision‐making score (DMS) levels and normalizes performance indicators. It finds the best reactor designs with CO₂ conversion rates of ≈78.6% and CH₄ selectivity close to 99.9%. These results demonstrate an advanced approach for significantly reducing computational demand (24 h to 1.471 ms) against CFD simulations; while, maintaining accuracy, thereby enabling cost‐effective, efficient solutions for reactor design optimization across various engineering applications in real‐world PtG applications.