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Research Article

A data-driven model for energy consumption analysis along with sustainable production: A case study in the steel industry

ORCID Icon, ORCID Icon, &
Pages 3360-3380 | Received 15 Nov 2021, Accepted 06 Apr 2022, Published online: 14 Apr 2022

References

  • Alidokht, M., S. Yazdani, E. Hadavandi, S. C. O. C. S. Chelgani . 2021. Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: A “Conscious-Lab” development. International Journal of Coal Science & Technology, 8:1436–1446.
  • Amado, S., H. Crispín, P. M. Haydee, O. Rafael, and Q. P. Malaquías (2015). Energy efficiency of an Electric Arc Furnace with SVM-RFE. 2015 International Conference on Electronics, Communications and Computers (CONIELECOMP), Cholula, Mexico, IEEE.
  • Amasyali, K., and N. M. El-Gohary. 2018. A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews 81:1192–205. doi:10.1016/j.rser.2017.04.095.
  • Ansari, N., and A. J. E. Seifi. 2012. A system dynamics analysis of energy consumption and corrective policies in Iranian iron and steel industry. Energy. 43 (1):334–43.
  • Argun, I. D., G. Kayakutlu, N. Y. Ozgozen, and T. U. Daim. 2021. Models for energy efficiency obligation systems through different perspectives. Technology in Society 64:101436. doi:10.1016/j.techsoc.2020.101436.
  • Björkman, B., and C. Samuelsson. 2014. Recycling of Steel. Handbook of Recycling, Elsevier. Worrell, E., Reuter, M. A. 65–83 9780123964595. https://doi.org/10.1016/B978-0-12-396459-5.00006-4.
  • Bonilla-Campos, I., N. Nieto, L. Del Portillo-valdes, B. Egilegor, J. Manzanedo, H. J. E. C. Gaztañaga . 2019. Energy efficiency assessment: Process modelling and waste heat recovery analysis. Energy Conversion and Management. 196:1180–92.
  • Brownlee, J. 2016. Master machine learning algorithms: Discover how they work and implement them from scratch v1.12 . E-Book: Machine Learning Mastery.
  • Bruckner, T., I. A. B, Y. Mulugetta, H. Chum, A. de la Vega Navarro, J. Edmonds, A. Faaij, B. Fungtammasan, A. Garg, E. Hertwich. 2014 . Energy Systems. Climate change 2014: mitigation of climate change. contribution of working group iii to the fifth assessment report of the intergovernmental panel on climate change. Parikh, K., Skea, J. (Cambridge, United Kingdom and New York, NY, USA.: Cambridge University Press), 88.
  • Carlsson, L. S., P. B. Samuelsson, and P. G. J. M. Jönsson. 2020a. Using statistical modeling to predict the electrical energy consumption of an electric arc furnace producing stainless steel. Metals. 10 (1):36.
  • Carlsson, L. S., P. B. Samuelsson, and P. G. J. P. Jönsson. 2020b. Modeling the effect of scrap on the electrical energy consumption of an electric arc furnace. Processes. 8 (9):1044.
  • Carlsson, L. S., P. B. Samuelsson, and P. G. J. S. R. I. Jönsson. 2020c. Interpretable machine learning—tools to interpret the predictions of a machine learning model predicting the electrical energy consumption of an electric arc furnace. Steel Research international. 91 (11):2000053.
  • Chang, G. W., C.-I. Chen, and Y.-J.-J. I. T. O. P. S. Liu. 2009. A neural-network-based method of modeling electric arc furnace load for power engineering study. IEEE Transactions on Power Systems. 25 (1):138–46.
  • Chavosh Nejad, M., S. Mansour, and A. Karamipour. 2021. An AHP-based multi-criteria model for assessment of the social sustainability of technology management process: A case study in banking industry. Technology in Society 65:101602. doi:10.1016/j.techsoc.2021.101602.
  • Chen, C., Y. Liu, M. Kumar, and J. J. P. C. Qin. 2018. Energy consumption modelling using deep learning technique—a case study of EAF. Procedia CIRP. 72:1063–68.
  • Chen, C., Y. Liu, M. Kumar, J. Qin, and Y. Ren. 2019. Energy consumption modelling using deep learning embedded semi-supervised learning. Computers & Industrial Engineering 135:757–65. doi:10.1016/j.cie.2019.06.052.
  • Chen, C., Y. Zuo, W. Ye, X. Li, Z. Deng, and S. P. J. A. E. M. Ong. 2020. A critical review of machine learning of energy materials. Advanced Energy Materials. 10 (8):1903242.
  • (1983). Cubic clustering criterion. SAS Technical Report, A-180, SAS Institute Inc, Carry.
  • Friedenthal, S., A. Moore, and R. Steiner. 2012. Chapter 18 - integrating sysml into a systems development environment. In A Practical Guide to SysML Second Edition), S. Friedenthal, A. Moore, and R. Steiner ed., 523–56. Boston: Morgan Kaufmann.
  • Gajic, D., I. Savic-Gajic, I. Savic, O. Georgieva, and S. J. E. Di Gennaro. 2016. Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks. Energy. 108:132–39.
  • Galambošová, J., V. Rataj, R. Prokeinová, and J. J. R. I. A. E. Prešinská. 2014. Determining the management zones with hierarchic and non-hierarchic clustering methods. Open Access CAAS Agricultural Journals. 60:S44–S51.
  • Ganesh, H. S., T. F. Edgar, and M. Baldea. 2019. Modeling, optimization and control of an austenitization furnace for achieving target product toughness and minimizing energy use. Journal of Process Control 74:177–88. doi:10.1016/j.jprocont.2017.09.008.
  • Hadavandi, E., J. Shahrabi, and Y. J. S. C. Hayashi. 2016. SPMoE: A novel subspace-projected mixture of experts model for multi-target regression problems. Soft Computing. 20 (5):2047–65.
  • Hadavandi, E., J. Shahrabi, and S. Shamshirband. 2015. A novel Boosted-neural network ensemble for modeling multi-target regression problems. Engineering Applications of Artificial Intelligence 45:204–19. doi:10.1016/j.engappai.2015.06.022.
  • Han, J., J. Pei, and M. Kamber. 2011. Data mining: Concepts and techniques. USA: Elsevier.
  • Haupt, M., C. Vadenbo, C. Zeltner, and S. J. J. O. I. E. Hellweg. 2017. Influence of input‐scrap quality on the environmental impact of secondary steel production. Industrial Ecology. 21 (2):391–401.
  • Hay, T., T. Echterhof, and -V.-V. J. P. Visuri. 2019. Development of an electric arc furnace simulator based on a comprehensive dynamic process model. Processes. 7 (11):852.
  • He, K., L. J. R. Wang, and S. E. Reviews. 2017. A review of energy use and energy-efficient technologies for the iron and steel industry. Renewable and Sustainable Energy Reviews. 70:1022–39.
  • Inc, S. I. 2021-2022. JMP® 16 Documentation Library. Cary, NC, USA: SAS Institute Inc.
  • Jun, H., J. Wei, P. Wenjie, C. Haoyuan, Z. Jia, C. Chao, X. Zhenjian, D. Jian, and W. Na. A data-driven distribution system scenario generation method with probabilistic assessment of pv station generation. 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Nanjing, China, IEEE.
  • Kaut, M. J. C. M. S. 2021. Scenario generation by selection from historical data. Computational Management Science. 18:411–429.
  • Khan, P. W., Y.-C. Byun, S.-J. Lee, D.-H. Kang, J.-Y. Kang, and H.-S. Park. 2020. Machine learning-based approach to predict energy consumption of renewable and nonrenewable power sources. Energies. 13 (18):4870.
  • Kirschen, M., K. Badr, and H. J. E. Pfeifer. 2011. Influence of direct reduced iron on the energy balance of the electric arc furnace in steel industry. Energy. 36 (10):6146–55.
  • Kirschen, M., T. Hay, and T. Echterhof. 2021. Process improvements for direct reduced iron melting in the electric arc furnace with emphasis on slag operation. Processes Processes 2021, 9, 402, s Note: MDPI stays neutral with regard to jurisdictional claims in published. 9 (2):402. doi:10.3390/pr9020402.
  • Kovačič, M., K. Stopar, R. Vertnik, and B. J. E. Šarler. 2019. Comprehensive electric arc furnace electric energy consumption modeling: A pilot study. Energies. 12 (11):2142.
  • Li, B., K. Sedzro, X. Fang, B.-M. Hodge, J. J. J. O. R. Zhang, and S. Energy. 2020. A clustering-based scenario generation framework for power market simulation with wind integration Journal of Renewable and Sustainable Energy . 12 (3):036301.
  • Li, Y., X. Shi, and B. J. E. P. Su. 2017. Economic, social and environmental impacts of fuel subsidies: A revisit of Malaysia Energy Policy . 110:51–61.
  • Lunetto, V., P. C. Priarone, M. Galati, and P. Minetola. 2020. On the correlation between process parameters and specific energy consumption in fused deposition modelling. Journal of Manufacturing Processes 56:1039–49. doi:10.1016/j.jmapro.2020.06.002.
  • Mawson, V. J., B. R. J. E. Hughes, and Buildings. 2020. Deep learning techniques for energy forecasting and condition monitoring in the manufacturing sector Energy and Buildings . 217:109966.
  • Morariu, C., O. Morariu, S. Răileanu, and T. Borangiu. 2020. Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Computers in Industry 120:103244.
  • Mosavi, A., M. Salimi, S. Faizollahzadeh Ardabili, T. Rabczuk, S. Shamshirband, and A. R. J. E. Varkonyi-Koczy. 2019. State of the art of machine learning models in energy systems, a systematic review Energies . 12 (7):1301.
  • Narciso, D. A., and F. J. E. R. Martins. 2020. Application of machine learning tools for energy efficiency in industry: A review Energy Reports . 6:1181–99.
  • Olson, D. L., and G. Lauhoff. 2019. Descriptive data mining. In Descriptive Data Mining Wu, Desheng Dash, 129–30. USA: Springer.
  • Olson, D. L., and D. Wu. 2017. Predictive data mining models. USA: Springer.
  • Qin, J., Y. Liu, and R. Grosvenor. 2018. Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics 38:840–50.
  • Qiu, Y., Q. Li, Y. Pan, H. Yang, and W. J. I. J. O. H. E. Chen. 2019. A scenario generation method based on the mixture vine copula and its application in the power system with wind/hydrogen production International Journal of Hydrogen Energy . 44 (11):5162–70.
  • Schulze, M., H. Nehler, M. Ottosson, and P. Thollander. 2016. Energy management in industry – A systematic review of previous findings and an integrative conceptual framework. Journal of Cleaner Production 112:3692–708.
  • Shahabi, R. S., M. H. Basiri, M. R. Kahag, S. A. J, and R. P. Zonouzi. 2014. An ANP–SWOT approach for interdependency analysis and prioritizing the Iran׳ s steel scrap industry strategies Resources policy . 42:18–26.
  • Shyamal, S., and C. L. E. Swartz. 2019. Real-time energy management for electric arc furnace operation. Journal of Process Control 74:50–62.
  • Sohaili, K. J. P. E. S. 2010. The impact of improvement in Iran iron and steel production technology on environment pollution Procedia Environmental Sciences . 2:262–69.
  • Sun, B., C. Yang, Y. Wang, W. Gui, I. Craig, and L. Olivier. 2020. A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes. Journal of Process Control 86:30–43.
  • Teng, S. Y., M. Touš, W. D. Leong, B. S. How, H. L. Lam, and V. Máša. 2021. Recent advances on industrial data-driven energy savings: Digital twins and infrastructures. Renewable and Sustainable Energy Reviews 135:110208.
  • Ter Teo, P., A. A. Seman, P. Basu, and N. M. J. P. C. Sharif. 2016. Characterization of EAF steel slag waste: The potential green resource for ceramic Tile production Procedia Chemistry . 19:842–46.
  • Thiede, S., A. Turetskyy, T. Loellhoeffel, A. Kwade, S. Kara, and C. Herrmann. 2020. Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: A case of battery production. CIRP Annals 69 (1):21–24.
  • Todshki, N. E., A. J. P. E. Ranjbaraki, and Finance. 2016. The impact of major macroeconomic variables on Iran’s steel import and export Procedia Economics and Finance . 36:390–98.
  • Veleva, V., and M. Ellenbecker. 2001. Indicators of sustainable production: Framework and methodology. Journal of Cleaner Production 9 (6):519–49.
  • Xia, X., and L. J. A. R. I. C. Zhang. 2016. Industrial energy systems in view of energy efficiency and operation control Annual Reviews in Control . 42:299–308.
  • Yang, Y., M. He, and L. Li. 2020. Power consumption estimation for mask image projection stereolithography additive manufacturing using machine learning based approach. Journal of Cleaner Production 251:119710.
  • Zhang, Y., X. Zhang, and L. Tang. 2012. Energy consumption prediction in ironmaking process using hybrid algorithm of SVM and PSO, In International symposium on neural networks Wang, J., Yen, G.G., Polycarpou, M.M. Berlin, Heidelberg: Springer 594–600 978-3-642-31361-5 .

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