Proposal to Apply the World Models to Chemical Plant Operation

Abstract

Chemical plants require sequential decision-making in response to fluctuating parameters, but this process often depends on the know-how of operators. Since the decreasing workforce has made it difficult to pass on the know-how, several AI applications have been proposed to automate chemical plant operations. A combination of planning and reinforcement learning is regarded as a effective method for AI models of sequential decision-making. In this study, we focus on one of the model-based reinforcement learning model, the world model, and propose a concept of applying the world model to chemical plant operation support.