A Reinforcement Learning Based Neural Multi-Agent-System for Control of a Combustion Process

Authors:
F. Wintrich;H. Wintrich
Affiliations:
-;-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Year:
2000

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Cited 1

Optimizing combustion efficiency of a circulating fluidized boiler: A data mining approach

International Journal of Knowledge-based and Intelligent Engineering Systems - Selected papers from the KES2004 conference

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Abstract

In this paper, we present a control scheme based on reinforcement learning for an industrial hard-coal combustion process in a power plant. To comply with the great demands on environmental protection, the plant operator is interested in a minimization of the nitrogen oxide emission, while other process parameters have to be kept within predefined limits. To cope with both the tremendous action and situation space of the power plant, we present a multiagent-reinforcement-system consisting of four agents, which are realized b y 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.