The optimization of reaction processes is crucial for the green

efficient

and sustainable development of the chemical industry. However

how to address the problems posed by multiple variables

nonlinearities

and uncertainties during optimization remains a formidable challenge. In this study

a strategy combining interpretable machine learning with metaheuristic optimization algorithms is employed to optimize the reaction process. First

experimental data from a biodiesel production process are collected to establish a database. These data are then used to construct a predictive model based on artificial neural network (ANN) models. Subsequently

interpretable machine learning techniques are applied for quantitative analysis and verification of the model. Finally

four metaheuristic optimization algorithms are coupled with the ANN model to achieve the desired optimization. The research results show that the methanol: palm fatty acid distillate (PFAD) molar ratio contributes the most to the reaction outcome

accounting for 41%. The ANN-simulated annealing (SA) hybrid method is more suitable for this optimization

and the optimal process parameters are a catalyst concentration of 3.00% (mass)

a methanol: PFAD molar ratio of 8.67

and a reaction time of 30 min. This study provides deeper insights into reaction process optimization

which will facilitate future applications in various reaction optimization processes.