Soft Actor-Critic Reinforcement Learning Improves Distillation Column Internals Design Optimization

Amid the advancements in computer-based chemical process modeling and simulation packages used in commercial applications aimed at accelerating chemical process design and analysis, there are still certain tasks in design optimization such as distillation column internals design that become bottlenecks due to inherent limitations in such software packages. This work demonstrated the use of soft actor-critic (SAC) reinforcement learning (RL) in automating the task of determining the optimal design of trayed multi-stage distillation column. The design environment was created using the AspenPlus® software with its RadFrac module for the required rigorous modeling of column internals. The RL computational work was achieved by developing a Python package that allows interfacing with AspenPlus®, and by implementing in OpenAI’s Gymnasium module the learning space for the state and action variables. The results evidently show that: (1) SAC RL works as automation approach for the design of distillation column internals, (2) the reward scheme in the SAC model significantly affects SAC performance, (3) column diameter is a significant constraint in achieving column internals design specification in flooding, and (4) SAC hyperparameters have varying effect on SAC performance. SAC RL can be implemented as a one-shot learning model that can significantly improve the design of multistage distillation column internals by automating the optimization process.