Prediction for Abnormal Combustion State of Energy-from-Waste Plants with Multiple Fuzzy Relational Maps of Sensors — Prediction of the Time to Transfer to Abnormal State

Abstract

In Energy-from-Waste plants, combustion is dependent on many complex factors and a sign of abnormal states appears in other sensors. We have proposed fuzzy relational maps of many sensors (FuRMS) for expressing relationships between many sensors as relations between fuzzy sets of sensor values. We constructed several fuzzy relational maps for combustion states (a stable state and three abnormal states) using the data set to be their states after a certain time. With evaluating unknown data for the maps, we predict the combustion state after a certain time, but cannot do it very well when combustion state transfers to an abnormal state, i.e., a certain sensor value exceeds a threshold one. In this paper, we separate the data set into two data sets, to transfer to the other state and to remain the same states after a certain time. We construct several fuzzy relational maps using the data set to transfer to the other state, to predict the combustion state after a certain time with the maps. The result shows that the maps using the separated data can predict with higher accuracy than the unseparated data.