Transfer learning for process design with reinforcement learning

TL;DR

This paper introduces a transfer learning approach integrated with reinforcement learning to improve process design automation, reducing simulation time and increasing economic feasibility of process flowsheets.

Contribution

It presents a novel combination of transfer learning with RL for process design, enabling faster learning and more accurate, economically viable flowsheets using rigorous simulations.

Findings

Achieved 8% higher revenue in flowsheet design.

Reduced learning time by a factor of 2.

Enabled stable interaction with detailed process simulator DWSIM.

Abstract

Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, reinforcement learning (RL) has shown some success in automating process design by integrating data-driven models that learn to build process flowsheets with process simulation in an iterative design process. However, one major challenge in the learning process is that the RL agent demands numerous process simulations in rigorous process simulators, thereby requiring long simulation times and expensive computational power. Therefore, typically short-cut simulation methods are employed to accelerate the learning process. Short-cut methods can, however, lead to inaccurate results. We thus propose to utilize transfer learning for process design with RL in combination with rigorous simulation methods. Transfer learning is an established approach from machine learning that stores knowledge gained while solving one problem and reuses this information on a different target domain. We integrate transfer learning in our RL framework for process design and apply it to an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles, our method can design economically feasible flowsheets with stable interaction with DWSIM. Our results show that transfer learning enables RL to economically design feasible flowsheets with DWSIM, resulting in a flowsheet with an 8% higher revenue. And the learning time can be reduced by a factor of 2.