Advancing Process Control in Fluidized Bed Biomass Gasification Using Model-Based Deep Reinforcement Learning

Abstract

This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a pilot-scale FBG plant to approximate FBG process dynamics. The reinforcement learning-based optimizer employs a specially designed reward function, determining optimal control policies for FBG. Moreover, the controller includes an online learning component, ensuring periodic updates to the DNN model training. The performance of the controller is evaluated by testing its control accuracy for regulating synthetic gas composition, flow rate, and CO concentration in the FBG. The evaluation also includes a comparison with a model predictive controller. The results demonstrate the superior control performance of MB-DRL, surpassing MPC by over 15% in regulating synthetic gas composition and flow rate, with similar effectiveness observed in synthetic gas temperature control. Additionally, this study also includes systematic investigations into factors like DNN layer count and learning update intervals to provide insights for the practical implementation of the controller. The results, presenting a 50% reduction in control error with the addition of a single layer to the DNN model, highlight the significance of optimizing MB-DRL for effective implementation.