Reinforcement Learning-Based Fed-Batch Optimization with Reaction Surrogate Model

In this paper, we implement a framework which combines Reinforcement Learning (RL) based reaction optimization with first principle model and plant historical data of the reaction system. Here we employ a Long-Short-Term-Memory (LSTM) network for reaction surrogate modeling, and Proximal Policy Optimization (PPO) algorithm for the fed-batch optimization. The proposed reaction surrogate model combines simulation data with real plant data for an...