Machine Learning-Based Predictive Modeling for Chemical Reactor Performance Optimization

Abstract

Chemical reactors are the core operational units in chemical and petrochemical industries, where performance is strongly influenced by nonlinear interactions among operating variables such as temperature, pressure, feed concentration, residence time, and catalyst loading.  Conventional kinetic modeling approaches often require detailed mechanistic knowledge and high computational effort, limiting their effectiveness for real-time prediction and optimization. This paper presents a machine learning-based predictive modeling framework for optimizing chemical reactor performance. Multiple regression models, including Artificial Neural Networks, Random Forest, Support Vector Regression, and Extreme Gradient Boosting, are developed to predict key performance indicators such as conversion, yield, and selectivity. The models are trained and validated using experimental or simulation data, and their performance is evaluated using standard statistical metrics. The best-performing model is integrated with an optimization strategy to determine optimal operating conditions under process constraints. Results indicate that the proposed approach achieves high prediction accuracy with reduced computational time. The framework demonstrates strong potential for intelligent reactor operation and advanced process optimization in modern chemical industries.