Granular-computing based hybrid collaborative fuzzy clustering for long-term prediction of multiple gas holders levels

Abstract

Linz–Donawitz converter Gas (LDG), regarded as an essential secondary energy resource, plays a significant role for the entire production process of steel industry. In a LDG system, the gas holders are crucial equipment for temporary energy storage and buffers connecting with the gas generation units and the gas users. The accurate long-term prediction for the holders levels of such a system would be very necessary for energy scheduling and its optimal decision making. Given the practical characteristics of the LDG system in a steel plant, a granular-computing (GrC)-based hybrid collaborative fuzzy clustering (HCFC) algorithm is proposed in this study for the long-term prediction of the multiple holders levels. The hybrid structure considers the features regarding to a gas holder, of which the horizontal part elaborates the mutual influences among different time spaces of a holder level, while the vertical one describes them among the influence factors (denoting the gas generation units or the users). Then, the modeling algorithm is also explicitly derived in this study. To verify the performance of the proposed approach, two groups of simulation are carried out by employing the real-world industrial data coming from this plant, in which the single-output method and the iterative computing-based one are comparatively analyzed. The results indicate that the proposed approach provides a remarkable accuracy for such an industrial application.