Abstract
Abstract Since thermal plants are multi-input, multi-output systems, it is important to capture the characteristics of the system for precise combustion control. Furthermore, in refuse incinerator plants (RIPs), the properties of the fuel are unstable, and minimization of the exhaust emissions is required. Thus, optimization of efficiency from an overall standpoint, including consideration of sensor and control technology, is required in RIPs. In particular, in comparison with stoker incinerators, the combustion cycle in fluidized bed incinerators (FBIs) is short, and combustion processes occur in multiple layers within the incinerator. As a result, analysis of the dynamic characteristics is considered most the effective way of grasping the characteristics of FBIs. This paper has focused on an operating FBI, where a hybrid system consisting of fuzzy systems and neural networks has been realized, which assesses the fuel-feeding state on the basis of measured values and combustion image processing, and operates with low CO/NO x concentrations by means of air–fuel ratio control. Furthermore, a proposed tuning method for the fuzzy systems simplifies the evaluation of speculative results, and the determination of control rules, by the utilization of an operation support system based on a numerical model.
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