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Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling
The blast furnace ironmaking process is the core of steel manufacturing, and the optimization of this process can bring enormous economic and environmental benefits. However, previous data-driven optimization methods neglect the uncontrollability of part of the variables in the predictive modeling p...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227773/ https://www.ncbi.nlm.nih.gov/pubmed/35746308 http://dx.doi.org/10.3390/s22124526 |
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author | Li, Junfang Yang, Chunjie Yang, Chong |
author_facet | Li, Junfang Yang, Chunjie Yang, Chong |
author_sort | Li, Junfang |
collection | PubMed |
description | The blast furnace ironmaking process is the core of steel manufacturing, and the optimization of this process can bring enormous economic and environmental benefits. However, previous data-driven optimization methods neglect the uncontrollability of part of the variables in the predictive modeling process, which brings great uncertainty to the optimization results and adversely affects the optimization effect. To address this problem, a consistency optimization framework based on controllability assurance soft sensor modeling is proposed. The method achieves the information extraction of uncontrollable variables in a process-supervised way, and improves the posterior distribution prediction accuracy. The method also proposes an integrated self-encoder regression module, which uses the regression to guide the encoding, realize the construction of latent features, and further improve the prediction accuracy of the model. Integrating the prediction module and the multi-objective gray wolf optimizer, the proposed model achieves the optimization of the blast furnace ironmaking process with only controllable variables as prediction model inputs while being capable of giving uncertainty estimates of the solutions. Empirical data validated the optimization model and demonstrated the effectiveness of the proposed algorithm. |
format | Online Article Text |
id | pubmed-9227773 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92277732022-06-25 Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling Li, Junfang Yang, Chunjie Yang, Chong Sensors (Basel) Article The blast furnace ironmaking process is the core of steel manufacturing, and the optimization of this process can bring enormous economic and environmental benefits. However, previous data-driven optimization methods neglect the uncontrollability of part of the variables in the predictive modeling process, which brings great uncertainty to the optimization results and adversely affects the optimization effect. To address this problem, a consistency optimization framework based on controllability assurance soft sensor modeling is proposed. The method achieves the information extraction of uncontrollable variables in a process-supervised way, and improves the posterior distribution prediction accuracy. The method also proposes an integrated self-encoder regression module, which uses the regression to guide the encoding, realize the construction of latent features, and further improve the prediction accuracy of the model. Integrating the prediction module and the multi-objective gray wolf optimizer, the proposed model achieves the optimization of the blast furnace ironmaking process with only controllable variables as prediction model inputs while being capable of giving uncertainty estimates of the solutions. Empirical data validated the optimization model and demonstrated the effectiveness of the proposed algorithm. MDPI 2022-06-15 /pmc/articles/PMC9227773/ /pubmed/35746308 http://dx.doi.org/10.3390/s22124526 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Junfang Yang, Chunjie Yang, Chong Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling |
title | Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling |
title_full | Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling |
title_fullStr | Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling |
title_full_unstemmed | Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling |
title_short | Consistent Optimization of Blast Furnace Ironmaking Process Based on Controllability Assurance Soft Sensor Modeling |
title_sort | consistent optimization of blast furnace ironmaking process based on controllability assurance soft sensor modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9227773/ https://www.ncbi.nlm.nih.gov/pubmed/35746308 http://dx.doi.org/10.3390/s22124526 |
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