Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes
Abstract
:1. Introduction
2. Review of Published Articles
3. Conclusions
Funding
Conflicts of Interest
References
- Akbulut, O.; Cavus, M.; Cengiz, M.; Allahham, A.; Giaouris, D.; Forshaw, M. Hybrid intelligent control system for adaptive microgrid optimization: Integration of rule-based control and deep learning techniques. Energies 2024, 17, 2260. [Google Scholar] [CrossRef]
- Du, J.; Cao, H.; Li, Y.; Yang, Z.; Eslamimanesh, A.; Fakhroleslam, M.; Mansouri, S.S.; Shen, W. Development of hybrid surrogate model structures for design and optimization of CO2 capture processes: Part I. Vacuum pressure swing adsorption in a confined space. Chem. Eng. Sci. 2024, 283, 119379. [Google Scholar] [CrossRef]
- Li, X.; Wang, Z.; Yang, C.; Bozkurt, A. An advanced framework for net electricity consumption prediction: Incorporating novel machine learning models and optimization algorithms. Energy 2024, 296, 131259. [Google Scholar] [CrossRef]
- Bakare, M.S.; Abdulkarim, A.; Shuaibu, A.N.; Muhamad, M.M. A hybrid long-term industrial electrical load forecasting model using optimized ANFIS with gene expression programming. Energy Rep. 2024, 11, 5831–5844. [Google Scholar] [CrossRef]
- Zhuang, X.; Wang, W.; Su, Y.; Yan, B.; Li, Y.; Li, L.; Hao, Y. Multi-objective optimization of reservoir development strategy with hybrid artificial intelligence method. Expert Syst. Appl. 2024, 241, 122707. [Google Scholar] [CrossRef]
- Jiabao, W.; Jianliang, Z.; Yaozu, W.; Zhengjian, L.; Qingke, S.; Xiaoran, S.; Zhen, L. Cost and energy synergy optimization model for ironmaking processes: Hybrid knowledge and data driven. J. Clean. Prod. 2025, 486, 144420. [Google Scholar] [CrossRef]
- Lou, S.; Yang, C.; Zhang, X.; Zhang, H.; Wu, P. Data/mechanism hybrid-driven modeling of blast furnace smelting system and global sequential optimization. J. Process Control 2024, 139, 103235. [Google Scholar] [CrossRef]
- Zheng, R.; Bao, Y.; Zhao, L.; Xing, L.-D. Enhanced steelmaking cost optimization and real-time alloying element yield prediction: A ferroalloy model based on machine learning and linear programming. J. Iron Steel Res. Int. 2024, 1–16. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, X.; Liu, R.; Li, H.; Duan, Y.; Li, X.; Sun, Y.; Li, L. A new method for prediction and decision-making of silicon content in hot metal: A hybrid approach based on machine learning and process optimization. Preprints 2024, 1–16. [Google Scholar] [CrossRef]
- Du, S.; Huang, C.; Ma, X.; Fan, H. A review of data-driven intelligent monitoring for geological drilling processes. Processes 2024, 12, 2478. [Google Scholar] [CrossRef]
- Du, S.; Ma, X.; Wu, M.; Cao, W.; Pedrycz, W. Time series anomaly detection via rectangular information granulation for sintering process. IEEE Trans. Fuzzy Syst. 2024, 32, 4799–4804. [Google Scholar] [CrossRef]
- Du, S.; Ma, X.; Fan, H.; Hu, J.; Cao, W.; Wu, M.; Pedrycz, W. Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review. Comput. Ind. 2025, 165, 104215. [Google Scholar] [CrossRef]
- Özdemir, M.E.; Beşkardeş, A.; Hameş, Y. Intelligent sinter machine speed control system using optimized fuzzy logic controller: An experimental study in iron and steel plant. Arab. J. Sci. Eng. 2024, 49, 16391–16406. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhang, P.; Du, S.; Dong, H. Economic optimal scheduling of integrated energy system considering wind–solar uncertainty and power to gas and carbon capture and storage. Energies 2024, 17, 2770. [Google Scholar] [CrossRef]
- Zhang, Y.; Zhou, Y.; Wang, B.; Song, J. MMD-TSC: An adaptive multi-objective traffic signal control for energy saving with traffic efficiency. Energies 2024, 17, 5015. [Google Scholar] [CrossRef]
- Meng, L.; Zhong, J.; Luo, S.; Zhu, X.; Wang, Y.; Zhang, S. Multimodal operation data mining for grid operation violation risk prediction. Energies 2024, 17, 5424. [Google Scholar] [CrossRef]
- Wang, X.; Huang, Z.; Zhang, D.; Yuan, H.; Cai, B.; Liu, H.; Wang, C.; Cao, Y.; Zhou, X.; Dong, Y. Dynamic prediction of proton-exchange membrane fuel cell degradation based on gated recurrent unit and grey wolf optimization. Energies 2024, 17, 5855. [Google Scholar] [CrossRef]
- Chen, F.; Wang, L.; Zheng, Z.; Pan, B.; Hu, Y.; Zhang, K. Data-driven-based full recovery technology and system for transformer insulating oil. Energies 2024, 17, 6345. [Google Scholar] [CrossRef]
- Yang, Q.; Chen, G.; Guo, M.; Chen, T.; Luo, L.; Sun, L. Model predictive hybrid PID control and energy-saving performance analysis of supercritical unit. Energies 2024, 17, 6356. [Google Scholar] [CrossRef]
- Li, F.; Shi, Z.; Ding, W.; Gan, Y. Intelligent optimization scheduling strategy for energy consumption reduction for equipment in open-pit mines based on enhanced genetic algorithm. Energies 2025, 18, 60. [Google Scholar] [CrossRef]
- Zhu, R.; Li, N.; Duan, Y.; Li, G.; Liu, G.; Qu, F.; Long, C.; Wang, X.; Liao, Q.; Li, G. Well-production forecasting using machine learning with feature selection and automatic hyperparameter optimization. Energies 2025, 18, 99. [Google Scholar] [CrossRef]
- Li, F.; Shi, Z.; Zhu, Z.; Gan, Y. Energy management strategy for direct current microgrids with consideration of photovoltaic power tracking optimization. Energies 2025, 18, 252. [Google Scholar] [CrossRef]
- Kang, K.; Shi, N.; Cai, S.; Zhang, L.; Shao, X.; Cao, H.; Fei, M.; Zhou, S.; Wan, X. Distributed model predictive load frequency control for virtual power plants with novel event-based low-delay technique under cloud-edge-terminal framework. Energies 2025, 18, 1380. [Google Scholar] [CrossRef]
No. | DOI | Research Area | Focus | Type of Research | Industry | Country |
---|---|---|---|---|---|---|
[14] | 10.3390/en17112770 | Optimization algorithms | Integrated energy systems; economically optimized dispatch; landscape uncertainty; carbon trading; P2G-CCS | Mathematical modeling | Energy systems | China |
[15] | 10.3390/en17195015. | Control strategies | Sustainable transition; energy saving; reinforcement learning; meta-learning | Mathematical modeling | Transportation | China |
[16] | 10.3390/en17215424 | Decision support strategies | Violation dictionary construction; operation risk prediction; random forests; independent component analysis; mutual information | Mathematical modeling | Power systems | China |
[17] | 10.3390/en17235855 | Prediction algorithms | Proton-exchange membrane fuel cells; degradation prediction; durability test; gated recurrent unit; gray wolf optimizer; accuracy; complexity | Mathematical modeling | Energy technology | China |
[18] | 10.3390/en17246345 | Process optimization transformer | Transformer insulating oil recovery; data-driven monitoring; thermal cycle of insulating oil; recovery rate | Mathematical modeling | Power systems | China |
[19] | 10.3390/en17246356 | Control strategies | Supercritical unit; coordination control; model predictive control; energy-saving analysis; rapid load change | Mathematical modeling | Energy systems | China |
[20] | 10.3390/en18010060 | Optimization algorithms | Open-pit mine; transportation costs; truck waiting times; excavator boom-and-dipper operation durations; improved genetic algorithm | Mathematical modeling | Mining | China |
[21] | 10.3390/en18010099 | Prediction algorithms | Machine learning; production forecast; data preprocessing; principal component analysis; AutoGluon | Mathematical modeling | Energy extraction | China |
[22] | 10.3390/en18020252 | Energy management strategies | Hybrid energy storage; energy management; state of charge; DC microgrid | Mathematical modeling | Power systems | China |
[23] | 10.3390/en18061380 | Control strategies | Load frequency control; dynamic event-triggered mechanism; distributed model predictive control; cloud-edge-terminal; virtual power plant | Mathematical modeling | Power systems | China |
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Du, S.; Jin, L.; Huang, Z.; Wan, X. Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes. Energies 2025, 18, 1939. https://doi.org/10.3390/en18081939
Du S, Jin L, Huang Z, Wan X. Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes. Energies. 2025; 18(8):1939. https://doi.org/10.3390/en18081939
Chicago/Turabian StyleDu, Sheng, Li Jin, Zixin Huang, and Xiongbo Wan. 2025. "Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes" Energies 18, no. 8: 1939. https://doi.org/10.3390/en18081939
APA StyleDu, S., Jin, L., Huang, Z., & Wan, X. (2025). Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes. Energies, 18(8), 1939. https://doi.org/10.3390/en18081939