Artificial Intelligence in Chemical Process Optimization: Recent Advances and Future Directions

Resumen

This review focuses on the use of these data-driven approaches in optimizing complex chemical processes using artificial intelligence. Non-linear reactions, multivariable control parameters and high energy demand require advanced optimization techniques in industrial chemical systems. With the increased availability of process data coming from sensors, control systems, and monitoring platforms there is a new opportunity for enhancing operational efficiency using intelligent analysis. While conventional methods principally based on first-principles modeling and mathematical programming are indispensable, they often make simplifying assumptions that hinder performance on high-dimensional, nonlinear systems, sometimes requiring prohibitive computational resources. It covers the main paradigms of AI, such as machine learning, deep learning, reinforcement learning and evolutionary algorithms and describes their application in the context of process simulation and digital twin technologies. A comprehensive review of recent applications in reactor optimization, separation through distillation, energy management and predictive maintenance is presented. Based on these findings, it can be concluded that AI-based models have the potential to analyze big operational data and help in achieving optimal operating conditions while also enhancing process stability and reducing energy consumption. These advancements enable industrial development to realize sustainability by improving energy efficiency, resource utilization and reducing pollution from chemical manufacturing process.