Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking proc...

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Autores principales:	Maoqiang Gu, Anjun Xu, Hongbing Wang, Zhitong Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	converter case-based reasoning long short-term memory real-time dynamic Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/eb668bf5f52f4339a54f75f9a961184a
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spelling	oai:doaj.org-article:eb668bf5f52f4339a54f75f9a961184a2021-11-25T18:51:16ZReal-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM10.3390/pr91119872227-9717https://doaj.org/article/eb668bf5f52f4339a54f75f9a961184a2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/1987https://doaj.org/toc/2227-9717The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.Maoqiang GuAnjun XuHongbing WangZhitong WangMDPI AGarticleconvertercase-based reasoninglong short-term memoryreal-timedynamicChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 1987, p 1987 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	converter case-based reasoning long short-term memory real-time dynamic Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	converter case-based reasoning long short-term memory real-time dynamic Chemical technology TP1-1185 Chemistry QD1-999 Maoqiang Gu Anjun Xu Hongbing Wang Zhitong Wang Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM
description	The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.
format	article
author	Maoqiang Gu Anjun Xu Hongbing Wang Zhitong Wang
author_facet	Maoqiang Gu Anjun Xu Hongbing Wang Zhitong Wang
author_sort	Maoqiang Gu
title	Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM
title_short	Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM
title_full	Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM
title_fullStr	Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM
title_full_unstemmed	Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM
title_sort	real-time dynamic carbon content prediction model for second blowing stage in bof based on cbr and lstm
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/eb668bf5f52f4339a54f75f9a961184a
work_keys_str_mv	AT maoqianggu realtimedynamiccarboncontentpredictionmodelforsecondblowingstageinbofbasedoncbrandlstm AT anjunxu realtimedynamiccarboncontentpredictionmodelforsecondblowingstageinbofbasedoncbrandlstm AT hongbingwang realtimedynamiccarboncontentpredictionmodelforsecondblowingstageinbofbasedoncbrandlstm AT zhitongwang realtimedynamiccarboncontentpredictionmodelforsecondblowingstageinbofbasedoncbrandlstm
_version_	1718410685960945664

Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

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