A Proposal of Identification Method for Prediction Model of Oxygen Component Using ID3 and Fuzzy Partition Based on Mutual Entropy

Abstract

Residue Fluid Catalytic Cracking Unit (RFCCU) is the heart of the modern petroleum refinery. There has been a strong demand for decreasing the electric power and manpower in operating RFCCU. A combined control system has been constructed for satisfying this demand by controlling the reactor/regenerator differential pressure of RFCCU. The problem of this control system is that the analyzer of oxygen component, which is one of the important input variables of this control system, lacks reliability. A modeling method using neural networks and a linear regressive model has been developed. But the accuracy of this predictor became unsatisfactory in the case of drastic changes of operating conditions, such as feed oil change. This paper presents a fuzzy partition method based on mutual entropy for identification of prediction model of oxygen component. This method uses ID3 algorithm to select predictor variables. The input space is fuzzily divided based on the mutual entropy. A linear regressive equation is identified in each sub-space, and a fuzzy model is constructed. The obtained predictor shows a satisfactory performance even in the case where the operating conditions are changed drastically. Continuous operation of the combined controller is made possible with this predictor. The differential pressure is stabilized, and a considerable amount of electric power is reduced. The implementation of this control system can also reduce the manpower needed for the operation of RFCCU.