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Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN

The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of...

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Detalles Bibliográficos
Autores principales: Cui, Zeqian, Han, Yang, Lu, Chaomeng, Wu, Yafeng, Chu, Mansheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387191/
https://www.ncbi.nlm.nih.gov/pubmed/34456990
http://dx.doi.org/10.1155/2021/1767308
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author Cui, Zeqian
Han, Yang
Lu, Chaomeng
Wu, Yafeng
Chu, Mansheng
author_facet Cui, Zeqian
Han, Yang
Lu, Chaomeng
Wu, Yafeng
Chu, Mansheng
author_sort Cui, Zeqian
collection PubMed
description The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.
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spelling pubmed-83871912021-08-26 Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN Cui, Zeqian Han, Yang Lu, Chaomeng Wu, Yafeng Chu, Mansheng Comput Intell Neurosci Research Article The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production. Hindawi 2021-08-17 /pmc/articles/PMC8387191/ /pubmed/34456990 http://dx.doi.org/10.1155/2021/1767308 Text en Copyright © 2021 Zeqian Cui et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Cui, Zeqian
Han, Yang
Lu, Chaomeng
Wu, Yafeng
Chu, Mansheng
Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN
title Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN
title_full Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN
title_fullStr Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN
title_full_unstemmed Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN
title_short Prediction Model of Hot Metal Silicon Content Based on Improved GA-BPNN
title_sort prediction model of hot metal silicon content based on improved ga-bpnn
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387191/
https://www.ncbi.nlm.nih.gov/pubmed/34456990
http://dx.doi.org/10.1155/2021/1767308
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