抄録
Since thermal plants are multi-input multi-output systems, it is important to grasp the characteristic of the system for precise combustion control. Furthermore, in refuse incinerator plants (RIPs), the fuel property is unstable and minimization of exhaust emission is required. Thus, efficiency optimization from an overall standpoint with consideration of sensor and control technology is required in RIPs. Particularly, in fluidized bed incinerators (FBIs), the combustion cycle is short in comparison with stoker incinerators and combustion processes occur in multiple layers within the incinerator. For the above characteristics, dynamic character analysis is effective for grasping the characteristics of FBIs.
In this paper, we have focused on an operating FBI, and have realized a hybrid system consisting of fuzzy systems and neural networks which speculates the fuel feeding state based on measured values and combustion image processing, and operates with low CO/NOx concentration by means of air-fuel ratio control. Furthermore, we propose a tuning method for fuzzy systems which simplifies the evaluation of speculation results and the determination of control rules by utilization of an operation support system based on a numerical model.
