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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
ARTICLE IN PRESS (volume, issue and page numbers will be assigned later)
DOI: http://dx.doi.org/10.13044/j.sdewes.d6.0249 (registered soon)
Oleksandr Bezsonov1 , Oleg Ilyunin2, Botagoz Kaldybaeva3, Oleksandr Selyakov2, Oleksandr Perevertaylenko4, Alisher Khusanov3, Oleg Rudenko5, Serhiy Udovenko6, Anatolij Shamraev7, Viktor Zorenko4, 8, 8
1 Department of Electronic Computer, Kharkiv National University of Radioelectronics, Nauki Ave., 14, UA-61000, Kharkiv,Ukraine
2 Department of Electronic Computer, Kharkiv National University of Radioelectronics, Nauki Ave., 14, UA-61000, Kharkiv, Ukraine
3 M.Auezov South Kazakhstan State University, Tauke Khan St., KZ-160012, Shymkent, Republic of Kazakhstan
4 Department of Integrated Technologies & Energy Saving, National Technical University “Kharkiv Polytechnical Institute”, Kyrpychova St.,2, UA-61002, Kharkiv, Ukraine
5 Department of Information Systems ,S.Kuznets Kharkiv National University of Economics, Nauki Ave., 9A, UA-61001, Kharkiv, Ukraine
6 Department of Informatics and Computer Technology, S.Kuznets Kharkiv National University of Economics, Nauki Ave., 9A, UA-61001, Kharkiv, Ukraine
7 Department of information control systems, Belgorod State National Research University, Pobedy St., 85, RU-308015, Belgorod, Russian Federation
8

Abstract

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 sulphuric acid pickling process together with scale removal. the partial dissolving of steel surface takes place because of sulphuric acid attack takes place. Continuous sulphuric acid carbon steel pickling in existing plants is very energy and water consumptive. An innovative approach is proposed for modernization of continuous sulphuric acid pickling process performance. The proposed neural network model may be used to optimize consumption of sulphuric 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 sulphuric acid and concentration of ferrous sulphate 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.

Keywords: Pickling solution, process supervisor, Radial Basis Function Network, supervised learning

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