Granular Computing Concept based long-term prediction of Gas Tank Levels in Steel Industry

Abstract

The converter gas, especially Linz Donawitz converter gas (LDG), is one of the most significant secondary energy resources in a large scale steel plant. For such a thing, the accurate prediction for the gas tank levels largely contributes to the energy optimization operations. Taking the LDG system of a steel plant in China into consideration, a regression model based on the Granular Computing (GrC) is proposed in this study to provide a long-term prediction for the LDG tank levels, in which the data segments are entirely considered for the prediction horizon extension rather than the generic data point-oriented modeling. For being more practical, this study specially granulates the initial data with regard to industrial semantic meaning. And, different from ordinary time series analysis, this method considers the factor related to the gas tank levels. Bearing this in mind, the fuzzy rules by adopting a fuzzy C-means based clustering is established. To verify the effectiveness of the proposed method, a series of practical experiments by using the industrial data coming from the energy data center of this plant are conducted, and the results demonstrate the practicability of the proposed approach.