Abstract
In chemical process control, decision-making often involves balancing multiple conflicting objectives, such as maximizing production, minimizing energy consumption, and ensuring process safety. Traditional approaches for multi-objective optimization, such as linear programming and evolutionary algorithms, have proven effective but struggle to adapt in real-time to the dynamic and nonlinear nature of chemical processes. In this paper, we propose a framework that combines Reinforcement Learning (RL) with Multi-Objective Evolutionary Algorithms (MOEAs) to address these challenges. Specifically, we utilize MOEAs, such as NSGA-II, to optimize the parameter space of policy neural networks, resulting in a Pareto front of policies. This Pareto front provides a diverse set of policies that enable operators to dynamically switch control strategies based on real-time system conditions and prioritized objectives. Our proposed methodology is applied to a Controlled Stirred Tank Reactor (CSTR) case study, focusing on balancing the objectives of minimizing set point error and operational costs. The case studies highlight the adaptability of the method in dynamic environments, including scenarios involving fouling, showcasing its resilience and flexibility in complex, nonlinear systems. This work demonstrates the potential of combining RL and MOEAs to advance decision-making in chemical process control, providing a robust and adaptable framework for navigating environments with changing objectives. The methodology can also be extended to other industrial applications with similarly challenging multi-objective requirements.