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Resource and Energy Saving Neural Network-Based Control Approach for Continuous Carbon Steel Pickling Process

Original scientific paper

Journal of Sustainable Development of Energy, Water and Environment Systems
Volume 7, Issue 2, pp 275-292
DOI: http://dx.doi.org/10.13044/j.sdewes.d6.0249
Oleksandr Bezsonov1 , Oleg Ilyunin1, Botagoz Kaldybaeva2, Oleksandr Selyakov1, Oleksandr Perevertaylenko3, Alisher Khusanov4, Oleg Rudenko5, Serhiy Udovenko6, Anatolij Shamraev7, Viktor Zorenko3, 8, 8
1 Department of Electronic Computer, Kharkiv National University of Radioelectronics, Nauki Ave., 14, UA-61000, Kharkiv, Ukraine
2 M. Auezov South Kazakhstan State University, Tauke Khan St., KZ-160012, Shymkent, Republic of Kazakhstan
3 Department of Integrated Technologies & Energy Saving, National Technical University “Kharkiv Polytechnical Institute”, Kyrpychova St., 2, UA-61002, Kharkiv, Ukraine
4 M. Auezov South Kazakhstan State University, Tauke Khan St., KZ-160012, Shymkent, Republic of Kazakhstan
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 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
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 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.

Keywords: Pickling solution, Process supervisor, Radial basis function network, Supervised learning.

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