Workshop: Machine Learning for Engineering Modeling, Simulation and Design

An Industrial Application of Deep Reinforcement Learning for Chemical Production Scheduling

Christian Hubbs · Adam Kelloway · John Wassick · Nikolaos Sahinidis · Ignacio Grossmann


We discuss the implementation of a deep reinforcement learning based agent to automatically make scheduling decisions for a continuous chemical reactor currently in operation. This model is tasked with scheduling the reactor on a daily basis in the face of uncertain demand and production interruptions. The reinforcement learning model has been trained on a simulator of the scheduling process that was built with historical demand and production data. The model has been successfully implemented to develop schedules on-line for an industrial reactor and has exhibited improvements over human made schedules. We discuss the process of training, implementation, and development of this system and the application of reinforcement learning for complex, stochastic decision making in the chemical industry.

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