Energy consumption accounting and prediction in the medium and thick plate rolling process are crucial for controlling costs, improving production efficiency, optimizing equipment management, and enhancing the market competitiveness of enterprises. Starting from the perspective of integrating process mechanism and industrial big data, we overcame the difficulties brought by complex and highly nonlinear coupling of process variables, proposed a rolling power consumption accounting algorithm based on time slicing method, and gave a calculation method for the additional power consumption of the main motor for rough rolling and finishing rolling (auxiliary system power consumption, power loss, main motor power consumption deviation); with the help of SIMS model, forward recursion, and reverse recursion pass rolling force estimation strategies are proposed, and the rated power consumption of the main motor was predicted. Furthermore, a random forest regression model of additional power consumption based on data was established, and then a prediction algorithm for the comprehensive power consumption of billet rolling was given. Experiments showed the effectiveness of the proposed method.
Citation: Qiang Guo, Zimeng Zhou, Jie Li, Fengwei Jing. Mechanism- and data-driven algorithms of electrical energy consumption accounting and prediction for medium and heavy plate rolling[J]. Electronic Research Archive, 2025, 33(1): 381-408. doi: 10.3934/era.2025019
Energy consumption accounting and prediction in the medium and thick plate rolling process are crucial for controlling costs, improving production efficiency, optimizing equipment management, and enhancing the market competitiveness of enterprises. Starting from the perspective of integrating process mechanism and industrial big data, we overcame the difficulties brought by complex and highly nonlinear coupling of process variables, proposed a rolling power consumption accounting algorithm based on time slicing method, and gave a calculation method for the additional power consumption of the main motor for rough rolling and finishing rolling (auxiliary system power consumption, power loss, main motor power consumption deviation); with the help of SIMS model, forward recursion, and reverse recursion pass rolling force estimation strategies are proposed, and the rated power consumption of the main motor was predicted. Furthermore, a random forest regression model of additional power consumption based on data was established, and then a prediction algorithm for the comprehensive power consumption of billet rolling was given. Experiments showed the effectiveness of the proposed method.
| [1] | Z. Han, Research on Strategies to Enhance the Competitiveness of China's Steel Industry, ph.D thsis, Chinese Academy of Social Sciences, 2014. |
| [2] | C. Zhang, Research on Energy Efficiency of My Country's Steel Enterprises Based on Circular Economy, ph.D thsis, University of Science and Technology Beijing, 2018. |
| [3] |
L. Malka, F. Bidaj, A. Kuriqi, A. Jaku, R. Roçi, A. Gebremedhin, Energy system analysis with a focus on future energy demand projections: the case of Norway, Energy, 272 (2023), 127107. https://doi.org/10.1016/j.energy.2023.127107 doi: 10.1016/j.energy.2023.127107
|
| [4] |
X. Liu, S. Yuan, H. Yu, Z. Liu, How ecological policy stringency moderates the influence of industrial innovation on environmental sustainability: The role of renewable energy transition in BRICST countries, Renewable Energy, 207 (2023), 194–204. https://doi.org/10.1016/j.renene.2023.01.045 doi: 10.1016/j.renene.2023.01.045
|
| [5] |
G. P. Perunov, Y. V. Inatovich, N. A. Strashkova, The power consumption optimization for thin-sheet rolling on a reversing mill, Steel Transl., 50 (2020), 897–901. https://doi.org/10.3103/S0967091220120116 doi: 10.3103/S0967091220120116
|
| [6] |
M. Tan, H. Yang, B. Duan, Y. Su, F. He, Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time‐of‐use electricity pricing, Math. Probl. Eng., 2017 (2017), 1048081. https://doi.org/10.1155/2017/1048081 doi: 10.1155/2017/1048081
|
| [7] |
L. Giorleo, E. Ceretti, C. Giardini, Energy consumption reduction in Ring Rolling processes: A FEM analysis, Int. J. Mech. Sci., 74 (2013), 55–64. https://doi.org/10.1016/j.ijmecsci.2013.04.008 doi: 10.1016/j.ijmecsci.2013.04.008
|
| [8] |
J. Paralikas, K. Salonitis, G. Chryssolouris, Energy efficiency of cold roll forming process, Int. J. Adv. Manuf. Technol., 66 (2013), 1271–1284. https://doi.org/10.1007/s00170-012-4405-8 doi: 10.1007/s00170-012-4405-8
|
| [9] | V. Chubenko, A. Khinotskaya, T. Yarosh, L. Saithareiev, Sustainable development of the steel plate hot rolling technology due to energy-power process parameters justification, in E3S Web of Conferences, EDP Sciences, 166 (2020), 06009. |
| [10] |
J. Qiu, J. Feng, X. Huang, Z. Huang, Optimization of reheating furnace rolling delay strategies based on a minimum energy consumption principle, J. Iron. Steel Res. Int., 2024 (2024), 1–13. https://doi.org/10.1007/s42243-024-01227-0 doi: 10.1007/s42243-024-01227-0
|
| [11] | X. Chen, Y. Yang, Data-driven multi-objective optimization of power consumption and quality for finishing rolling process, in 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS), IEEE, (2024), 1262–1266. https://doi.org/10.1109/DDCLS61622.2024.10606881 |
| [12] |
Y. Zhong, J. Wang, J. Xu, S. Wu, J. Rao, K. Dang, Prediction of energy consumption in horizontal roughing process of hot rolling strip based on TDADE algorithm, IEEE Trans. Autom. Sci. Eng., 21 (2023), 555–568. https://doi.org/10.1109/TASE.2023.3236306 doi: 10.1109/TASE.2023.3236306
|
| [13] |
Y. T. Kim, B. J. Kim, S. W. Kim, Multi-level stacked regression for predicting electricity consumption of hot rolling mill, Expert Syst. Appl., 201 (2022), 117040. https://doi.org/10.1016/j.eswa.2022.117040 doi: 10.1016/j.eswa.2022.117040
|
| [14] | X. Xiong, D. Deng, Y. Xiao, Q. Guo, Y. Zhang, Research on prediction method of finish rolling power consumption of multi-specific strip steel based on random forest optimization model, in 2020 39th Chinese Control Conference (CCC), IEEE, (2020), 5977–5984. https://doi.org/10.23919/CCC50068.2020.9188937 |
| [15] |
S. Behzadipour, A. Khajepour, J. G. Lenard, J. Biglou, A new shape change quantification method for estimation of power in shape rolling, J. Mater. Process. Technol., 148 (2004), 353–361. https://doi.org/10.1016/j.jmatprotec.2004.02.047 doi: 10.1016/j.jmatprotec.2004.02.047
|
| [16] |
G. A. Orcajo, J. Rodríguez, P. Ardura, J. M. Cano, J. G. Norniella, T. R. Llera, et al., Dynamic estimation of electrical demand in hot rolling mills, IEEE Trans. Ind. Appl., 52 (2016), 2714–2723. https://doi.org/10.1109/TIA.2016.2533483 doi: 10.1109/TIA.2016.2533483
|
| [17] | Y. Sun, Model and Control of Hot Strip Rolling, Metallurgical Industry Press, Beijing, 2007. |
| [18] |
R. B. Sims, The calculation of roll force and torque in hot rolling, Proc. Inst. Mech. Eng., 168 (1954), 191–200. https://doi.org/10.1243/PIME_PROC_1954_168_023_02 doi: 10.1243/PIME_PROC_1954_168_023_02
|
| [19] |
J. Guo, X. Wang, W. Xue, Y. Zhao, System identification with binary-valued observations under data tampering attacks, IEEE Trans. Autom. Control, 66 (2021), 3825–3832. https://doi.org/10.1109/TAC.2020.3029325 doi: 10.1109/TAC.2020.3029325
|
| [20] |
J. Guo, Q. Zhang, Y. Zhao, Identification of FIR Systems with binary-valued observations under replay attacks, Automatica, 172 (2025), 112001. https://doi.org/10.1016/j.automatica.2024.112001 doi: 10.1016/j.automatica.2024.112001
|
| [21] | H. Valecha, A. Varma, I. Khare, A. Sachdeva, M. Goyal, Prediction of consumer behaviour using random forest algorithm, in 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), IEEE, (2018), 1–6. https://doi.org/10.1109/UPCON.2018.8597070 |
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Qiang Guo, Zimeng Zhou, Jie Li, Fengwei Jing. Mechanism- and data-driven algorithms of electrical energy consumption accounting and prediction for medium and heavy plate rolling[J]. Electronic Research Archive, 2025, 33(1): 381-408. doi: 10.3934/era.2025019
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