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A SOM-BASED TOOL FOR PROCESS STATE MONITORING AND OPTIMIZATION

Self-organizing maps (SOM) have been successfully applied in many fields of research. In this paper, we demonstrate the use of a neural-network-based tool for a data analysis in fluidized bed energy plants. The software is based on selforganizing maps. Reference vectors of SOMs can be classified by K-means algorithm into clusters, which represented different states of processes. The differences between reference vectors could further be used for determination of reasons of non-optimal process states. The results show that this method can also be successfully applied to process state monitoring and optimisation in fluidized bed energy plants.