Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process
Steel pickling processes are very important for steelmaking production quality. Pickling process is based on chemical reaction of acidic pickling solution with scale impurities on steel strip surface. In sulfuric acid pickling process together with scale removal. The partial dissolving of...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SDEWES Centre
2019-06-01
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| Series: | Journal of Sustainable Development of Energy, Water and Environment Systems |
| Subjects: | |
| Online Access: |
http://www.sdewes.org/jsdewes/pid6.0249
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| _version_ | 1818518034846842880 |
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| author | Oleksandr Bezsonov Oleg Ilyunin Botagoz Kaldybaeva Oleksandr Selyakov Oleksandr Perevertaylenko Alisher Khusanov Oleg Rudenko Serhiy Udovenko Anatolij Shamraev Viktor Zorenko |
| author_facet | Oleksandr Bezsonov Oleg Ilyunin Botagoz Kaldybaeva Oleksandr Selyakov Oleksandr Perevertaylenko Alisher Khusanov Oleg Rudenko Serhiy Udovenko Anatolij Shamraev Viktor Zorenko |
| author_sort | Oleksandr Bezsonov |
| collection | DOAJ |
| description | Steel pickling processes are very important for steelmaking production quality. Pickling process is based on chemical reaction of acidic pickling solution with scale impurities on steel strip surface. In sulfuric acid pickling process together with scale removal. The partial dissolving of steel surface takes place because of sulfuric acid attack takes place. Continuous sulfuric acid carbon steel pickling in existing plants is very energy and water consumptive. An innovative approach is proposed for modernization of continuous sulfuric acid pickling process performance. The proposed neural network model may be used to optimize consumption of sulfuric acid, decrease energy consumption, reduce steel losses and, respectively, reduce harmful wastes and emissions from continuous steel pickling lines. This is possible because of quick adaptation of neural network model to changing environment through fast training algorithms. The developed model identifies the temperature necessary to provide the set process rate at the current variable values of the parameters: concentration of sulfuric acid and concentration of ferrous sulfate multi-hydrates in solution and transmits the temperature value as a current task to regulator in each discrete moment of the process. The results of application of the developed neural network, included as a part of the presented process supervisor, prove its efficiency in use for pickling process operational control: steam consumption for pickling process was decreased by 8%, acid consumption for pickling process was decreased by 26%, while the process efficiency and quality remain unaffected. |
| first_indexed | 2024-12-11T01:04:32Z |
| format | Article |
| id | doaj.art-387883557f9049df8737783dce6156a8 |
| institution | Directory Open Access Journal |
| issn | 1848-9257 |
| language | English |
| last_indexed | 2024-12-11T01:04:32Z |
| publishDate | 2019-06-01 |
| publisher | SDEWES Centre |
| record_format | Article |
| series | Journal of Sustainable Development of Energy, Water and Environment Systems |
| spelling | doaj.art-387883557f9049df8737783dce6156a82022-12-22T01:26:12ZengSDEWES CentreJournal of Sustainable Development of Energy, Water and Environment Systems1848-92572019-06-017227529210.13044/j.sdewes.d6.024900249Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling ProcessOleksandr Bezsonov0Oleg Ilyunin1Botagoz Kaldybaeva2Oleksandr Selyakov3Oleksandr Perevertaylenko4Alisher Khusanov5Oleg Rudenko6Serhiy Udovenko7Anatolij Shamraev8Viktor Zorenko9 Department of Electronic Computer, Kharkiv National University of Radioelectronics, Nauki Ave., 14, UA-61000, Kharkiv, Ukraine Department of Electronic Computer, Kharkiv National University of Radioelectronics, Nauki Ave., 14, UA-61000, Kharkiv, Ukraine M. Auezov South Kazakhstan State University, Tauke Khan St., KZ-160012, Shymkent, Republic of Kazakhstan Department of Electronic Computer, Kharkiv National University of Radioelectronics, Nauki Ave., 14, UA-61000, Kharkiv, Ukraine Department of Integrated Technologies & Energy Saving, National Technical University “Kharkiv Polytechnical Institute”, Kyrpychova St., 2, UA-61002, Kharkiv, Ukraine M. Auezov South Kazakhstan State University, Tauke Khan St., KZ-160012, Shymkent, Republic of Kazakhstan Department of Information Systems, S. Kuznets Kharkiv National University of Economics, Nauki Ave., 9A, UA-61001, Kharkiv, Ukraine Department of Informatics and Computer Technology, S. Kuznets Kharkiv National University of Economics, Nauki Ave., 9A, UA-61001, Kharkiv, Ukraine Department of Software Engineering for Computers and Computer-based Systems, Belgorod State Technological University named after V.G. Shukhov, Kostyukova St., 46, RU-308012, Belgorod, Russian Federation Department of Integrated Technologies & Energy Saving, National Technical University “Kharkiv Polytechnical Institute”, Kyrpychova St., 2, UA-61002, Kharkiv, Ukraine Steel pickling processes are very important for steelmaking production quality. Pickling process is based on chemical reaction of acidic pickling solution with scale impurities on steel strip surface. In sulfuric acid pickling process together with scale removal. The partial dissolving of steel surface takes place because of sulfuric acid attack takes place. Continuous sulfuric acid carbon steel pickling in existing plants is very energy and water consumptive. An innovative approach is proposed for modernization of continuous sulfuric acid pickling process performance. The proposed neural network model may be used to optimize consumption of sulfuric acid, decrease energy consumption, reduce steel losses and, respectively, reduce harmful wastes and emissions from continuous steel pickling lines. This is possible because of quick adaptation of neural network model to changing environment through fast training algorithms. The developed model identifies the temperature necessary to provide the set process rate at the current variable values of the parameters: concentration of sulfuric acid and concentration of ferrous sulfate multi-hydrates in solution and transmits the temperature value as a current task to regulator in each discrete moment of the process. The results of application of the developed neural network, included as a part of the presented process supervisor, prove its efficiency in use for pickling process operational control: steam consumption for pickling process was decreased by 8%, acid consumption for pickling process was decreased by 26%, while the process efficiency and quality remain unaffected. http://www.sdewes.org/jsdewes/pid6.0249 Pickling solutionProcess supervisorRadial basis function networkSupervised learning. |
| spellingShingle | Oleksandr Bezsonov Oleg Ilyunin Botagoz Kaldybaeva Oleksandr Selyakov Oleksandr Perevertaylenko Alisher Khusanov Oleg Rudenko Serhiy Udovenko Anatolij Shamraev Viktor Zorenko Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process Journal of Sustainable Development of Energy, Water and Environment Systems Pickling solution Process supervisor Radial basis function network Supervised learning. |
| title | Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process |
| title_full | Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process |
| title_fullStr | Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process |
| title_full_unstemmed | Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process |
| title_short | Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process |
| title_sort | resource and energy saving neural network based control approach for continuous carbon steel pickling process |
| topic | Pickling solution Process supervisor Radial basis function network Supervised learning. |
| url |
http://www.sdewes.org/jsdewes/pid6.0249
|
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