Abstract
This paper presents the overall system of a learning, selforganizing, and adaptive controller used to optimize the combustion process in a hard-coal fired power plant. The system itself identifies relevant channels from the available measurements, classical process data and flame image information, and selects the most suited ones to learn a control strategy based on observed data. Due to the shifting nature of the process, the ability to re-adapt the whole system automatically is essential. The operation in a real power plant demonstrates the impact of this intelligent control system with its ability to increase efficiency and to reduce emissions of greenhouse gases much better then any previous control system.
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Schaffernicht, E., Stephan, V., Debes, K., Gross, HM. (2009). Machine Learning Techniques for Selforganizing Combustion Control. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_50
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DOI: https://doi.org/10.1007/978-3-642-04617-9_50
Publisher Name: Springer, Berlin, Heidelberg
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