A hybrid intelligent framework for energy consumption classification of mechanical equipment in the steel industry based on machine learning and metaheuristic optimization
-
Daming Liu
Abstract
This study presents a robust intelligent hybrid classification model to effectively classify energy consumption levels in the steel industry, thereby improving productivity and contributing to lower energy costs. By evaluating production variables, operational variables, and historical energy use data, the model produces a deep understanding of energy consumption patterns and a framework to make better decisions in achieving energy savings. The proposed approach highlights the combination of Quadratic Discriminant Analysis (QDA) and Decision Tree Classifier (DTC) classifiers with two advanced metaheuristic algorithms, Greylag Goose Optimization (GGO), and Manta Ray Foraging Optimization (MRFO) to optimise the parameters in respective classifiers and enhance the predictive ability of the classification system. The study employed a dataset of 10,000 samples containing nine inputs and one output, across 200 iterations. Experiments demonstrated that the hybrid DTC-GGO (DTGG) outperformed all the other finalist comparison systems with an overall training accuracy of 97.7 %. The lowest training accuracy with the QDA configuration was only 85.4 %. Compared to baseline models, the hybrid models yielded improvements of up to 12.3 % in classification accuracy and reduced misclassification rates. The research demonstrates several contributions including a novel intepretive hybrid framework, combining advanced machine learning classification with bio-inspired optimisation, demonstrates superior classification and computational efficiency, assesses the possibility of the ML model model in a cloud-based environment for smart grid integration, assesses the ability to train and develop deployments models in the future, and can be scaled to a broader industrial context. This integrated strategy offers a practical, high-performance solution to real-time energy management, supporting the transition toward more intelligent and sustainable industrial operations.
Acknowledgment
I would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
-
Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
-
Informed consent: This option is not neccessary due to that the data were collected from the references.
-
Author contributions: Writing-Original draft preparation, Conceptualization, Supervision, Project administration.
-
Conflict of interest: The authors declare no competing of interests.
-
Research funding: No Funding.
-
Data availability: The authors do not have permissions to share data.
References
1. Yearbook S. S., Association W. S. Worldsteel committee on economic studies. Brussels; 2010. 2011.–120 c.Suche in Google Scholar
2. Vögele, S, Grajewski, M, Govorukha, K, Rübbelke, D. Challenges for the European steel industry: analysis, possible consequences and impacts on sustainable development. Appl Energy 2020;264:114633. https://doi.org/10.1016/j.apenergy.2020.114633.Suche in Google Scholar
3. Wang, S, Watada, J, Pedrycz, W. Granular robust mean-CVaR feedstock flow planning for waste-to-energy systems under integrated uncertainty. IEEE Trans Cybern 2014;44:1846–57. https://doi.org/10.1109/tcyb.2013.2296500.Suche in Google Scholar PubMed
4. Ahmed, M, Gamal, M, Ismail, IM, El-Din, E. An AI-based system for predicting renewable energy power output using advanced optimization algorithms. J Artif Intell Metaheuristics 2024;8:1–8.10.54216/JAIM.080101Suche in Google Scholar
5. Haider, S, Mishra, PP. Benchmarking energy use of iron and steel industry: a data envelopment analysis. Benchmark Int J 2019;26:1314–35. https://doi.org/10.1108/bij-02-2018-0027.Suche in Google Scholar
6. Eed, M, Alhussan, AA, Qenawy, A-ST, Osman, AM, Elshewey, AM, Arnous, R, et al.. Potato consumption forecasting based on a hybrid stacked deep learning model. Potato Res 2024:1–25. https://doi.org/10.1007/s11540-024-09764-7.Suche in Google Scholar
7. Sun, W, Wang, Q, Zhou, Y, Wu, J. Material and energy flows of the iron and steel industry: status quo, challenges and perspectives. Appl Energy 2020;268:114946. https://doi.org/10.1016/j.apenergy.2020.114946.Suche in Google Scholar
8. Radwan, M, Alhussan, AA, Ibrahim, A, Tawfeek, SM. Potato leaf disease classification using optimized machine learning models and feature selection techniques. Potato Res 2024:1–25. https://doi.org/10.1007/s11540-024-09763-8.Suche in Google Scholar
9. Chen, M-C, Chen, L-S, Hsu, C-C, Zeng, W-R. An information granulation based data mining approach for classifying imbalanced data. Inf Sci (N Y) 2008;178:3214–27. https://doi.org/10.1016/j.ins.2008.03.018.Suche in Google Scholar
10. Angelov, PP, Buswell, RA. Automatic generation of fuzzy rule-based models from data by genetic algorithms. Inf Sci (N Y) 2003;150:17–31.10.1016/S0020-0255(02)00367-5Suche in Google Scholar
11. Ahn, CK. Passive learning and input-to-state stability of switched hopfield neural networks with time-delay. Inf Sci (N Y) 2010;180:4582–94. https://doi.org/10.1016/j.ins.2010.08.014.Suche in Google Scholar
12. Zhao, J, Wang, W, Liu, Y, Pedrycz, W. A two-stage online prediction method for a blast furnace gas system and its application. IEEE Trans Control Syst Technol 2010;19:507–20. https://doi.org/10.1109/tcst.2010.2051545.Suche in Google Scholar
13. Liu, Y, Liu, Q, Wang, W, Zhao, J, Leung, H. Data-driven based model for flow prediction of steam system in steel industry. Inf Sci (N Y) 2012;193:104–14. https://doi.org/10.1016/j.ins.2011.12.031.Suche in Google Scholar
14. Sheng, C, Zhao, J, Liu, Y, Wang, W. Prediction for noisy nonlinear time series by echo state network based on dual estimation. Neurocomputing 2012;82:186–95. https://doi.org/10.1016/j.neucom.2011.11.021.Suche in Google Scholar
15. El-Kenawy, E-SM, Khodadadi, N, Mirjalili, S, Abdelhamid, AA, Eid, MM, Ibrahim, A, et al.. Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl 2024;238:122147. https://doi.org/10.1016/j.eswa.2023.122147.Suche in Google Scholar
16. Khafaga, DS, Ali Alhussan, A, El-Kenawy, E-SM, Ibrahim, A, Abd Elkhalik, S. H, El-Mashad, S. Y, et al.. Improved prediction of metamaterial antenna bandwidth using adaptive optimization of LSTM. Comput Mater Continua (CMC) 2022;73:865–81. https://doi.org/10.32604/cmc.2022.028550.Suche in Google Scholar
17. Hassib, EM, El-Desouky, AI, El-Kenawy, E-SM, El-Ghamrawy, SM. An imbalanced big data mining framework for improving optimization algorithms performance. IEEE Access 2019;7:170774–95. https://doi.org/10.1109/access.2019.2955983.Suche in Google Scholar
18. Halim, AH, Ismail, I, Das, S. Performance assessment of the metaheuristic optimization algorithms: an exhaustive review. Artif Intell Rev 2021;54:2323–409. https://doi.org/10.1007/s10462-020-09906-6.Suche in Google Scholar
19. Atali, G, PehlIvan, I, Gürevin, B, Şeker, Hİ. Chaos in metaheuristic based artificial intelligence algorithms: a short review. Turk J Electr Eng Comput Sci 2021;29:1354–67. https://doi.org/10.3906/elk-2102-5.Suche in Google Scholar
20. Simeone, O. A very brief introduction to machine learning with applications to communication systems. IEEE Trans Cogn Commun Netw 2018;4:648–64. https://doi.org/10.1109/tccn.2018.2881442.Suche in Google Scholar
21. Böhringer, C, García-Muros, X, González-Eguino, M. Who bears the burden of greening electricity? Energy Econ 2022;105:105705. https://doi.org/10.1016/j.eneco.2021.105705.Suche in Google Scholar
22. Carley, S, Lawrence, S, Brown, A, Nourafshan, A, Benami, E. Energy-based economic development. Renew Sustain Energy Rev 2011;15:282–95. https://doi.org/10.1016/j.rser.2010.08.006.Suche in Google Scholar
23. Nasreen, S, Anwar, S. Causal relationship between trade openness, economic growth and energy consumption: a panel data analysis of Asian countries. Energy Policy 2014;69:82–91. https://doi.org/10.1016/j.enpol.2014.02.009.Suche in Google Scholar
24. Kraft, J, Kraft, A. On the relationship between energy and GNP. J Energy Dev 1978:401–3.Suche in Google Scholar
25. Lee, C-C. Energy consumption and GDP in developing countries: a cointegrated panel analysis. Energy Econ 2005;27:415–27. https://doi.org/10.1016/j.eneco.2005.03.003.Suche in Google Scholar
26. Tsani, SZ. Energy consumption and economic growth: a causality analysis for Greece. Energy Econ 2010;32:582–90. https://doi.org/10.1016/j.eneco.2009.09.007.Suche in Google Scholar
27. Tajuddin, NII, Abdullah, R, Jabar, MA, Jusoh, YY. Effecting factors of knowledge integration through social media in small medium enterprises environment. 3c Tecnol: glosas de innovación aplicadas a la pyme 2018;7:81–92.10.17993/3ctecno.2019.specialissue.08Suche in Google Scholar
28. Lombardi, P, Schwabe, F. Sharing economy as a new business model for energy storage systems. Appl Energy 2017;188:485–96. https://doi.org/10.1016/j.apenergy.2016.12.016.Suche in Google Scholar
29. Phan, PDD, Anh, MANNM, Nguyen, BHH. Using linear regression analysis to predict energy consumption. Research Square 2024;1–21. https://doi.org/10.21203/rs.3.rs-4590592/v1.Suche in Google Scholar
30. Kabir, S, Hossain, MS, Andersson, K. An advanced explainable belief rule-based framework to predict the energy consumption of buildings. Energies 2024;17:1797. https://doi.org/10.3390/en17081797.Suche in Google Scholar
31. Tian, S, Ren, W, Deng, Q, Zou, S, Li, Y. A predictive energy consumption scheduling algorithm for multiprocessor heterogeneous system. IEEE Trans Green Commun Netw 2021;6:979–91. https://doi.org/10.1109/tgcn.2021.3131323.Suche in Google Scholar
32. Fernandes, RDA, Seppe, FR, Júnior, CTC, Torné, IG, Ds Filho, C, Rego, SBT, et al.. A bayesian optimization approach of ensemble and decision tree learning applied to industrial energy consumption prediction. In: São Bernardo do Campo, Brazil: 2023 15th IEEE International Conference on Industry Applications (INDUSCON). New York City: IEEE; 2023:842–9 pp.10.1109/INDUSCON58041.2023.10374661Suche in Google Scholar
33. Etem, T. The bagged tree ensemble model for predicting steel industry energy consumption profile. In: Riyadh, Saudi Arabia: 2025 8th International Conference on Data Science and Machine Learning Applications (CDMA). New York City: IEEE; 2025:43–7 pp.10.1109/CDMA61895.2025.00013Suche in Google Scholar
34. Hussain, I, Ching, KB, Uttraphan, C, Tay, KG, Memon, I, Memon, SA, et al.. Predicting monthly wind speeds using XGBoost: a case study for renewable energy optimization. Processes 2025;13:1763. https://doi.org/10.3390/pr13061763.Suche in Google Scholar
35. Zulfiqar, M, Gamage, KAA, Kamran, M, Rasheed, MB. Hyperparameter optimization of bayesian neural network using bayesian optimization and intelligent feature engineering for load forecasting. Sensors 2022;22:4446. https://doi.org/10.3390/s22124446.Suche in Google Scholar PubMed PubMed Central
36. Lan, Y-B, Chien, C-F. Knowledge-based genetic algorithm for energy-efficient scheduling for steel making and an empirical study. Available at SSRN 4234207. 1–23. https://dx.doi.org/10.2139/ssrn.4234207.10.2139/ssrn.4234207Suche in Google Scholar
37. Lan, Y-B, Chien, C-F. Ensemble machine learning and knowledge-based genetic algorithm for energy-efficient scheduling and an empirical study. Available at SSRN 4352008, 2023:1–34. https://dx.doi.org/10.2139/ssrn.4352008.10.2139/ssrn.4352008Suche in Google Scholar
38. Chahbi, I, Ben Rabah, N, Ben Tekaya, I. Towards an efficient and interpretable machine learning approach for energy prediction in industrial buildings: a case study in the steel industry. In: Abu Dhabi, United Arab Emirates: 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA). New York City: IEEE; 2022:1–8 pp.10.1109/AICCSA56895.2022.10017816Suche in Google Scholar
39. Wu, W, Mallet, Y, Walczak, B, Penninckx, W, Massart, D, Heuerding, S, et al.. Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data. Anal Chim Acta 1996;329:257–65.10.1016/0003-2670(96)00142-0Suche in Google Scholar
40. Pranckevičius, T, Marcinkevičius, V. Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Baltic Journal of Modern Computing 2017;5:221. https://doi.org/10.22364/bjmc.2017.5.2.05.Suche in Google Scholar
41. Smyth, P, Gray, A, Fayyad, UM. Retrofitting decision tree classifiers using kernel density estimation. In: Machine Learning Proceedings 1995. Tahoe City, California: Elsevier; 1995:506–14 pp.10.1016/B978-1-55860-377-6.50069-4Suche in Google Scholar
42. Swain, PH, Hauska, H. The decision tree classifier: design and potential. IEEE Trans Geosci Electron 1977;15:142–7. https://doi.org/10.1109/tge.1977.6498972.Suche in Google Scholar
43. Quinlan, JR. Learning decision tree classifiers. ACM Comput Surv 1996;28:71–2. https://doi.org/10.1145/234313.234346.Suche in Google Scholar
44. Safavian, SR, Landgrebe, D. A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 1991;21:660–74. https://doi.org/10.1109/21.97458.Suche in Google Scholar
45. Savitha, R, Ambikapathi, A, Rajaraman, K. Online RBM: growing restricted boltzmann machine on the fly for unsupervised representation. Appl Soft Comput 2020;92:106278. https://doi.org/10.1016/j.asoc.2020.106278.Suche in Google Scholar
46. El-Kenawy, E-SM, Khodadadi, N, Mirjalili, S, Abdelhamid, AA, Eid, MM, Ibrahim, A, et al.. Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl 2024;238:122147. https://doi.org/10.1016/j.eswa.2023.122147.Suche in Google Scholar
47. El-Kenawy, E-SM, Khodadadi, N, Mirjalili, S, Abdelhamid, AA, Eid, MM, Ibrahim, A, et al.. Greylag goose optimization: nature-inspired optimization algorithm. Expert Syst Appl 2024;238:122147. https://doi.org/10.1016/j.eswa.2023.122147.Suche in Google Scholar
48. Kleindorfer, S, Heger, B, Tohl, D, Frigerio, D, Hemetsberger, J, Fusani, L, et al.. Cues to individuality in greylag goose faces: algorithmic discrimination and behavioral field tests. J Ornithol 2024;165:27–37. https://doi.org/10.1007/s10336-023-02113-4.Suche in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
