An optimal method for prediction and adjustment on gasholder level and self-provided power plant gas supply in steel works

Abstract

An optimal method for prediction and adjustment on byproduct gasholder level and self-provided power plant gas supply was proposed. This work raises the HP-ENN-LSSVM model based on the Hodrick-Prescott filter, Elman neural network and least squares support vector machines. Then, according to the prediction, the optimal adjustment process came up by a novel reasoning method to sustain the gasholder within safety zone and the self-provided power plant boilers in economic operation, and prevent unfavorable byproduct gas emission and equipment trip as well. The experiments using the practical production data show that the proposed method achieves high accurate predictions and the optimal byproduct gas distribution, which provides a remarkable guidance for reasonable scheduling of byproduct gas.

Keywords

HP filter / Elman neural network / least square support vector machine / gasholder level / self-provided power plant

References

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