Reaction condition optimization for non-oxidative conversion of methane using artificial intelligence

Abstract

Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C₂ compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize the reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH₄ conversion, C₂ yield, and selectivities for C₂ and coke) with an error of <5%. Trained ML models are applied to maximize the C₂ yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C₂ yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.