  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.
