End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The perfor...

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Main Authors: Chuang Gao, Minggang Shen, Xiaoping Liu, Lidong Wang, Maoxiang Chu
Format: Article
Language:English
Published: Wiley 2019-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2019/7408725
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author Chuang Gao
Minggang Shen
Xiaoping Liu
Lidong Wang
Maoxiang Chu
author_facet Chuang Gao
Minggang Shen
Xiaoping Liu
Lidong Wang
Maoxiang Chu
author_sort Chuang Gao
collection DOAJ
description A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.
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id doaj-art-23efb3e5a2b64bdea42df97de6a3c76e
institution OA Journals
issn 1076-2787
1099-0526
language English
publishDate 2019-01-01
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spelling doaj-art-23efb3e5a2b64bdea42df97de6a3c76e2025-08-20T02:04:13ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/74087257408725End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector RegressionChuang Gao0Minggang Shen1Xiaoping Liu2Lidong Wang3Maoxiang Chu4School of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan, Liaoning, ChinaSchool of Materials and Metallurgy, University of Science and Technology Liaoning, Anshan, Liaoning, ChinaSchool of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, Shandong, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, ChinaA static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.http://dx.doi.org/10.1155/2019/7408725
spellingShingle Chuang Gao
Minggang Shen
Xiaoping Liu
Lidong Wang
Maoxiang Chu
End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression
Complexity
title End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression
title_full End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression
title_fullStr End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression
title_full_unstemmed End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression
title_short End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression
title_sort end point static control of basic oxygen furnace bof steelmaking based on wavelet transform weighted twin support vector regression
url http://dx.doi.org/10.1155/2019/7408725
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AT minggangshen endpointstaticcontrolofbasicoxygenfurnacebofsteelmakingbasedonwavelettransformweightedtwinsupportvectorregression
AT xiaopingliu endpointstaticcontrolofbasicoxygenfurnacebofsteelmakingbasedonwavelettransformweightedtwinsupportvectorregression
AT lidongwang endpointstaticcontrolofbasicoxygenfurnacebofsteelmakingbasedonwavelettransformweightedtwinsupportvectorregression
AT maoxiangchu endpointstaticcontrolofbasicoxygenfurnacebofsteelmakingbasedonwavelettransformweightedtwinsupportvectorregression