A NEW CONTROL SCHEME FOR COMBUSTION PROCESSES USING REINFORCEMENT LEARNING BASED ON NEURAL NETWORKS

Abstract

We present a new control scheme for an industrial hard-coal combustion process in a power plant based on reinforcement-learning in combination with neural networks. To comply with the great requirements for environmental protection, the plant operator is interested in a minimization of the nitrogen oxides emission and a maximization of the efficiency factor, while other process parameters have to be kept within predefined limits. To cope with both the tremendous action and state space of the power plant, we present a multiagent-reinforcement-system consisting of 4 agents, which are realized by relatively simple neural function approximators. We demonstrate that our multiagent-system was able to significantly reduce the overall air consumption of the real combustion process of the power plant.