An optimal method for prediction and adjustment on gasholder level and self-provided power plant gas supply in steel works
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 pre...
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| Published in | Journal of Central South University Vol. 21; no. 7; pp. 2779 - 2792 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Central South University
01.07.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2095-2899 2227-5223 |
| DOI | 10.1007/s11771-014-2241-8 |
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| 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. |
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| AbstractList | 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. |
| Author | Li, Hong-juan Wang, Jian-jun Wang, Hua Meng, Hua |
| Author_xml | – sequence: 1 givenname: Hong-juan surname: Li fullname: Li, Hong-juan organization: Engineering Research Center of Metallurgical Energy Conservation & Emission Reduction of Ministry of Education (Kunming University of Science and Technology) – sequence: 2 givenname: Jian-jun surname: Wang fullname: Wang, Jian-jun email: 7336250@qq.com organization: Engineering Research Center of Metallurgical Energy Conservation & Emission Reduction of Ministry of Education (Kunming University of Science and Technology) – sequence: 3 givenname: Hua surname: Wang fullname: Wang, Hua organization: Engineering Research Center of Metallurgical Energy Conservation & Emission Reduction of Ministry of Education (Kunming University of Science and Technology) – sequence: 4 givenname: Hua surname: Meng fullname: Meng, Hua organization: Engineering Research Center of Metallurgical Energy Conservation & Emission Reduction of Ministry of Education (Kunming University of Science and Technology) |
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| CitedBy_id | crossref_primary_10_1016_j_compchemeng_2022_107692 crossref_primary_10_1016_S1006_706X_15_30057_1 crossref_primary_10_1016_j_compchemeng_2024_108719 crossref_primary_10_3390_en11102727 crossref_primary_10_1109_ACCESS_2019_2904299 |
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| Keywords | least square support vector machine gasholder level HP filter self-provided power plant Elman neural network |
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