Artificial Intelligence in Chemical Process Optimization: Techniques, Applications, Challenges, and Future Directions

Chemical processes are often complex nonlinear reactions, with large numbers of operating variables, and high energy consumption; therefore, optimization is not an easy task. With the industrial data from sensors and monitoring systems becoming more widely available, artificial intelligence (AI) is being deployed to enable process efficiency as well as better decision making. Further, unlike the more traditional optimization methods used, especially for complex and high dimensional systems as in molecular design, AI techniques can do so much more efficiently than exploring the whole computational landscape thanks to their powerful predictive capabilities. This review provides an overview of recent developments in machine learning, deep learning (DL), reinforcement learning, and evolutionary algorithms for chemical process optimization across four key industrial applications: reactor design; distillation; energy management; and predictive maintenance. Results suggest that AI-driven solutions enhance process performance, energy savings and promote sustainable industrialization in the context of SDGs 7, 9, 12 and 13.