An optimal method for prediction and adjustment on gasholder level and self-provided power plant gas supply in steel works
来源期刊：中南大学学报(英文版)2014年第7期
论文作者：李红娟 WANG Jian-jun(王建军) WANG Hua(王华) MENG Hua(孟华)
文章页码：2779 - 2792
Key words：HP filter; Elman neural network; least square support vector machine; gasholder level; self-provided power plant
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.
LI Hong-juan(李红娟), WANG Jian-jun(王建军), WANG Hua(王华), MENG Hua(孟华)
(Engineering Research Center of Metallurgical Energy Conservation & Emission Reduction of Ministry of Education
(Kunming University of Science and Technology), Kunming 650093, China)
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.
Key words:HP filter; Elman neural network; least square support vector machine; gasholder level; self-provided power plant
