Recent Progress in Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes

Abstract

This editorial discusses recent progress in hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes. With the increasing emphasis on sustainable practices, efficient management of industrial energy consumption has become a critical concern. This editorial aims to explore innovative approaches that use artificial intelligence to model and optimize energy use in industrial processes. The integration of advanced technologies such as machine learning, artificial intelligence, and data analytics play a pivotal role in achieving energy efficiency, reducing environmental impacts and ensuring the sustainability of industrial operations. These studies collectively contribute to the body of knowledge on hybrid intelligent modeling technology and optimization strategy, offering practical solutions and theoretical frameworks to address energy conservation and consumption reduction.

1. Introduction

Efficient management of industrial energy consumption has become increasingly vital with the global shift towards sustainable practices. Recent advancements in hybrid intelligent modeling technologies and optimization strategies offer promising avenues for addressing energy conservation and reducing industrial energy consumption. Hybrid intelligent modeling, which combines machine learning algorithms and data-driven techniques, has emerged as an innovative solution to address the complexities associated with variable renewable energy sources and dynamic industrial demands [1,2,3,4].

Recent developments in artificial intelligence and machine learning, such as CatBoost, XGBoost [3], adaptive neuro-fuzzy inference systems, and genetic programming techniques [4], have demonstrated significant potential in accurately modeling and predicting energy consumption patterns. By integrating these intelligent models with evolutionary optimization algorithms like Non-dominated Sorting Genetic Algorithms, researchers have successfully tackled multi-objective optimization challenges across various energy-intensive industries, including ironmaking, steel production, and vacuum-pressure swing-adsorption processes [5,6,7].

Further advancements have been made through integrating data-driven analytics and knowledge-based predictions, complemented by real-time optimization frameworks, to achieve considerable improvements in energy efficiency and operational cost-effectiveness [8,9]. Additionally, intelligent monitoring and anomaly detection techniques, particularly those employing information granules and comprehensive soft-sensing methods, have significantly enhanced operational reliability and the accuracy of predictive maintenance in industrial systems [10,11,12]. The adoption of optimized fuzzy logic controllers has also provided robust adaptive control solutions, significantly enhancing the performance of complex energy-intensive processes [13].

This editorial discusses recent progress in hybrid intelligent modeling technologies and optimization strategies for industrial energy consumption processes. It examines the innovative integration of machine learning, artificial intelligence, and data analytics, highlighting their crucial roles in enhancing energy efficiency, minimizing environmental impacts, and fostering sustainable industrial operations. Through a critical analysis of contemporary advancements and identification of existing challenges, this study provides valuable insights and theoretical frameworks that contribute practically and theoretically to the ongoing discourse on energy conservation and consumption reduction.

2. Review of Published Articles

In the era of Industry 4.0 and the ever-growing emphasis on sustainable practices, the efficient management of industrial energy consumption has become a critical concern. This Special Issue aims to explore innovative approaches that leverage data-driven intelligence to model and optimize energy use in industrial processes. The integration of advanced technologies such as machine learning, artificial intelligence, and data analytics will play a pivotal role in achieving energy efficiency, reducing environmental impacts and ensuring the sustainability of industrial operations.

The main objective of this Special Issue is to promote research and innovation in the field of hybrid intelligent modeling and optimization for industrial energy consumption processes, especially in the fields of steel metallurgy, chemical engineering, geological drilling, marine exploration, textile, pharmaceutical, and other large-scale industries. In this Special Issue, the research areas include hybrid intelligent modeling techniques, intelligent optimization strategies, case studies and applications, and interdisciplinary approaches. The analysis of the published contributions in the Special Issue is shown in

Table 1. Analysis of the published contributions in the Special Issue.

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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The articles published in this Special Issue comprehensively explore cutting-edge hybrid intelligent modeling technologies and optimization strategies tailored specifically for complex industrial processes. These studies collectively address critical challenges in areas such as energy management, intelligent scheduling, predictive analytics, advanced control strategies, and operational efficiency. Detailed analyses of these articles are summarized below.

Energy management and optimal scheduling are prominently discussed in the literature [14,20,22]. Study [14] presents a sophisticated economic optimal scheduling method for integrated energy systems, explicitly considering uncertainties in wind and solar power generation. By integrating innovative approaches such as power-to-gas and carbon capture and storage technologies, the proposed strategy effectively enhances economic performance while simultaneously achieving significant carbon emission reductions, aligning with sustainable energy objectives. Study [20] introduces an intelligent optimization scheduling approach, applying an enhanced genetic algorithm to reduce energy consumption significantly in open-pit mining equipment operations. This study not only improves energy efficiency but also highlights the adaptability of genetic algorithms in complex industrial environments. Study [22] offers a comprehensive energy management strategy specifically designed for direct current microgrids, emphasizing the optimization of photovoltaic power-tracking. This research addresses crucial issues of renewable energy integration, improving energy utilization efficiency and contributing to the stability of renewable-powered microgrids.

Advanced control methodologies and decision-support strategies form the central focus of the literature [15,19,23]. Study [15] proposes an adaptive multi-objective traffic-signal control model, carefully designed to balance energy savings with enhanced traffic efficiency. The model effectively adapts to dynamic traffic conditions, demonstrating its practical value in urban transportation networks by simultaneously reducing energy consumption and traffic congestion. Study [19] introduces a hybrid predictive-control model combined with proportional-integral-derivative methods tailored specifically for supercritical power units. This novel hybrid control approach effectively maintains operational stability while maximizing energy savings, demonstrating substantial improvements over conventional control strategies. Study [10] explores a distributed model predictive load frequency control approach for virtual power plants, employing an innovative event-based low-delay communication technique under a cloud-edge-terminal integrated framework. The proposed strategy greatly reduces control delays, enhances system reliability, and is particularly valuable for future grid applications involving extensive distributed energy resources.

Predictive modeling, data-driven fault detection, and process optimization techniques are thoroughly investigated in the literature [16,17,18,21]. Study [16] develops multimodal operation data-mining methods, significantly enhancing the prediction of operational violation risks within power grids. The study effectively leverages complex data sources, thus improving the safety and reliability of grid operations by enabling early identification and prevention of potential disruptions. Study [17] proposes an advanced dynamic degradation prediction model for proton-exchange membrane fuel cells, employing gated recurrent units optimized by gray wolf optimization. This approach substantially enhances predictive accuracy, effectively prolonging the fuel cell lifespan by enabling timely preventive maintenance. Study [18] introduces a comprehensive data-driven system for the full recovery of transformer-insulating oil, leveraging advanced analytics to ensure the reliability and longevity of transformer operations. The innovative integration of data-driven methods significantly contributes to equipment maintenance and operational efficiency. Study [21] implements machine learning techniques, combined effectively with feature selection methods and automatic hyperparameter optimization, to accurately forecast oil well production performance. The research demonstrates the strong capability of intelligent analytics in achieving robust and reliable prediction outcomes, providing valuable decision-support tools for petroleum exploration and production management.

In summary, these articles collectively highlight the profound impact of advanced data-driven intelligent modeling and optimization techniques on enhancing the efficiency, reliability, sustainability, and adaptability of industrial systems. They provide valuable insights into both theoretical innovations and practical implementations, laying a robust foundation for future advancements in intelligent industrial systems under Industry 4.0. Finally, the topics covered in these studies include energy management and optimal scheduling, advanced control strategies, and predictive modeling for complex industrial processes across power systems, transportation, and oil and gas. The authors who have contributed to our Special Issue are all from China.

3. Conclusions

This editorial presents the recent progress in hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes. These intelligent algorithms leverage data for control, decision-making, and parameter optimization to significantly lower energy usage in industrial systems. In addition, these algorithms not only drive unprecedented levels of efficiency and reliability but also play a critical role in quality improvement. In the era of Industry 4.0, such hybrid intelligent modeling technologies and optimization strategies are proving essential for increasing production efficiency, promoting sustainability, and ensuring high product quality by directly targeting energy reduction.