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Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms

Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as...

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Autores principales: Ansari, Md Obaidullah, Chattopadhyaya, Somnath, Ghose, Joyjeet, Sharma, Shubham, Kozak, Drazan, Li, Changhe, Wojciechowski, Szymon, Dwivedi, Shashi Prakash, Kilinc, Huseyin Cagan, Królczyk, Jolanta B., Walczak, Dominik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778296/
https://www.ncbi.nlm.nih.gov/pubmed/35057387
http://dx.doi.org/10.3390/ma15020670
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author Ansari, Md Obaidullah
Chattopadhyaya, Somnath
Ghose, Joyjeet
Sharma, Shubham
Kozak, Drazan
Li, Changhe
Wojciechowski, Szymon
Dwivedi, Shashi Prakash
Kilinc, Huseyin Cagan
Królczyk, Jolanta B.
Walczak, Dominik
author_facet Ansari, Md Obaidullah
Chattopadhyaya, Somnath
Ghose, Joyjeet
Sharma, Shubham
Kozak, Drazan
Li, Changhe
Wojciechowski, Szymon
Dwivedi, Shashi Prakash
Kilinc, Huseyin Cagan
Królczyk, Jolanta B.
Walczak, Dominik
author_sort Ansari, Md Obaidullah
collection PubMed
description Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.
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spelling pubmed-87782962022-01-22 Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms Ansari, Md Obaidullah Chattopadhyaya, Somnath Ghose, Joyjeet Sharma, Shubham Kozak, Drazan Li, Changhe Wojciechowski, Szymon Dwivedi, Shashi Prakash Kilinc, Huseyin Cagan Królczyk, Jolanta B. Walczak, Dominik Materials (Basel) Article Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants. MDPI 2022-01-17 /pmc/articles/PMC8778296/ /pubmed/35057387 http://dx.doi.org/10.3390/ma15020670 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
Ansari, Md Obaidullah
Chattopadhyaya, Somnath
Ghose, Joyjeet
Sharma, Shubham
Kozak, Drazan
Li, Changhe
Wojciechowski, Szymon
Dwivedi, Shashi Prakash
Kilinc, Huseyin Cagan
Królczyk, Jolanta B.
Walczak, Dominik
Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
title Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
title_full Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
title_fullStr Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
title_full_unstemmed Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
title_short Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms
title_sort productivity enhancement by prediction of liquid steel breakout during continuous casting process in manufacturing of steel slabs in steel plant using artificial neural network with backpropagation algorithms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8778296/
https://www.ncbi.nlm.nih.gov/pubmed/35057387
http://dx.doi.org/10.3390/ma15020670
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