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

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 7072

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Special Issue Editors

Prof. Dr. Sheng Du

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School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
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Prof. Dr. Li Jin

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Guest Editor

School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: power system stability analysis and control; time-delay system; robust theory and application
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Dr. Zixin Huang

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School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: underdrive system control; intelligent control
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Prof. Dr. Xiongbo Wan

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1. School of Automation, China University of Geosciences, Wuhan 430074, China
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
Interests: artificial intelligence; robust control of time-delay systems
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Special Issue Information

Dear Colleagues,

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, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Hybrid Intelligent Modeling Techniques:
Exploration of advanced machine learning algorithms for modeling energy consumption patterns.
Integration of sensor data and IoT technologies for real-time data collection and analysis.
Development of predictive models for forecasting energy demand and consumption trends.

Intelligent Optimization Strategies:
Application of optimization algorithms to enhance energy efficiency in industrial processes.
Utilization of decision support systems for intelligent and adaptive energy management.
Integration of intelligent control systems for the dynamic optimization of energy consumption.

Case Studies and Applications:
Real-world case studies demonstrating the successful implementation of data-driven intelligent models in industrial set-tings.
Application of intelligent optimization strategies in diverse industrial sectors to showcase versatility and effectiveness.
Assessment of economic, environmental, and operational benefits achieved through optimized energy consumption.

Interdisciplinary Approaches:
Cross-disciplinary studies that explore the synergy between data-driven intelligence and renewable energy sources.

Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Dr. Zixin Huang
Prof. Dr. Xiongbo Wan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data-driven modeling
industrial energy consumption processes
machine learning
optimization
hybrid intelligent

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Published Papers (9 papers)

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Research

29 pages, 5928 KiB

Open AccessArticle

Energy Management Strategy for Direct Current Microgrids with Consideration of Photovoltaic Power Tracking Optimization

by Fudong Li, Zonghao Shi, Zhihao Zhu and Yongjun Gan

Energies 2025, 18(2), 252; https://doi.org/10.3390/en18020252 - 8 Jan 2025

Viewed by 485

Abstract

In response to the uncertainty of renewable energy output and the fluctuation of load, this paper proposes a hybrid energy storage management strategy based on the State of Charge (SOC) to smooth power fluctuations and thereby improve the power quality of photovoltaic energy storage DC microgrids. Firstly, a hybrid algorithm for power tracking control is formed by incorporating the Particle Swarm Optimization (PSO) algorithm into the variable step-size Incremental Conductance (INC) method, thereby optimizing the maximum power point tracking control system of the photovoltaic system. Then, a first-order filter is employed for the initial allocation of demand power. Taking the SOC of supercapacitors and energy storage batteries as a reference, a secondary power allocation energy management strategy based on rule-based control is proposed to ensure the service life and application safety of the hybrid energy storage system. Finally, simulation experiments are conducted in MATLAB/Simulink 23.2 (R2023b). The results indicate that the proposed energy management strategy can maintain the SOC of the hybrid energy storage system at a reasonable level and effectively smooth DC bus voltage fluctuations. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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20 pages, 7510 KiB

Open AccessArticle

Well-Production Forecasting Using Machine Learning with Feature Selection and Automatic Hyperparameter Optimization

by Ruibin Zhu, Ning Li, Yongqiang Duan, Gaofeng Li, Guohua Liu, Fengjiao Qu, Changjun Long, Xin Wang, Qinzhuo Liao and Gensheng Li

Energies 2025, 18(1), 99; https://doi.org/10.3390/en18010099 - 30 Dec 2024

Viewed by 516

Abstract

Well-production forecasting plays a crucial role in oil and gas development. Traditional methods, such as numerical simulations, require substantial computational effort, while empirical models tend to exhibit poor accuracy. To address these issues, machine learning, a widely adopted artificial intelligence approach, is employed to develop production forecasting models in order to enhance the accuracy of oil and gas well-production predictions. This research focuses on the geological, engineering, and production data of 435 fracturing wells in the North China Oilfield. First, outliers were detected, and missing values were handled using the mean imputation and nearest neighbor methods. Subsequently, Pearson correlation coefficients were utilized to eliminate linearly irrelevant features and optimize the dataset. By calculating the gray correlation degrees, maximum mutual information, feature importance, and Shapley additive explanation (SHAP) values, an in-depth analysis of various dominant factors was conducted. To further assess the importance of these factors, the entropy weight method was employed. Ultimately, 19 features that were highly correlated with the target variable were successfully screened as inputs for subsequent models. Based on the AutoGluon framework, model training was conducted using 5-fold cross-validation combined with bagging and stacking techniques. The training results show that the model achieved an R² of 0.79 on the training set, indicating good fitting ability. This study offers a promising approach for the development of oil and gas production forecasting models. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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18 pages, 1742 KiB

Open AccessArticle

Intelligent Optimization Scheduling Strategy for Energy Consumption Reduction for Equipment in Open-Pit Mines Based on Enhanced Genetic Algorithm

by Fudong Li, Zonghao Shi, Weiqiang Ding and Yongjun Gan

Energies 2025, 18(1), 60; https://doi.org/10.3390/en18010060 - 27 Dec 2024

Viewed by 395

Abstract

To achieve a rational allocation of real-time operational equipment, such as excavators and dump trucks, in open-pit mines, and thereby enhance truck–shovel coordination, this paper addresses the challenges posed by unreasonable on-site scheduling, which includes excessive truck waiting times and prolonged excavator boom-and-dipper operations. Ultimately, the paper aims to attain optimal truck–shovel coordination efficiency. To this end, we construct a scheduling optimization model, with the production capacities of trucks and shovels serving as constraints. The objective functions of this model focus on minimizing transportation costs, reducing truck waiting times, and shortening excavator boom-and-dipper operation durations. To solve this model, we have developed an improved genetic algorithm that integrates roulette wheel selection and elite preservation strategies. The experimental results of our algorithm demonstrate that it can provide a more refined operational equipment scheduling scheme, effectively decreasing truck transportation costs and enhancing equipment utilization efficiency in open-pit mines. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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13 pages, 3023 KiB

Open AccessArticle

Model Predictive Hybrid PID Control and Energy-Saving Performance Analysis of Supercritical Unit

by Qingfeng Yang, Gang Chen, Mengmeng Guo, Tingting Chen, Lei Luo and Li Sun

Energies 2024, 17(24), 6356; https://doi.org/10.3390/en17246356 - 17 Dec 2024

Viewed by 622

Abstract

In response to the escalating challenges of rapid load fluctuations and intricate operating environments, supercritical power units demand enhanced control efficiency and adaptability. To this end, this study introduces a novel model predictive hybrid PID control strategy that integrates PID with model predictive control (MPC), leveraging the operational characteristics of multi-loop systems. The proposed strategy adeptly marries the swift response of PID controllers with the foresight and optimization capabilities of MPC. A dynamic model of a supercritical unit is constructed using the subspace identification method. The model’s high precision is confirmed by its alignment with field data. Load change simulations demonstrate that the PID–MPC hybrid controller shows faster response times and more precise tracking capabilities compared to the feedforward-PID strategy. It achieves substantial improvements in the IAE index for three loops, with increases of 29.2%, 54.1%, and 57.3% over the feedforward-PID controller. An energy-saving performance analysis indicates that the proactive control actions of both the PID–MPC and MPC strategies lead to dynamic exergy efficiency and coal consumption rates with a broader range of dynamic process changes. The disturbance scenario simulation regarding the proposed controller achieves faster settling times and minimizes control deviation compared to the traditional controller. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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17 pages, 5082 KiB

Open AccessArticle

Data-Driven-Based Full Recovery Technology and System for Transformer Insulating Oil

by Feng Chen, Li Wang, Zhiyao Zheng, Bin Pan, Yujia Hu and Kexin Zhang

Energies 2024, 17(24), 6345; https://doi.org/10.3390/en17246345 - 17 Dec 2024

Viewed by 749

Abstract

This study aims to develop an efficient recovery solution for waste transformer insulating oil, addressing the challenge of incomplete separation of residual oil in existing recovery technologies. A multi-module integrated system is constructed, comprising a waste oil extraction module, a residual oil vaporization module, an exhaust gas treatment module, and an online monitoring module. By combining steps such as oil extraction, residual oil absorption, hot air circulation heating, and negative-pressure low-frequency induction heating, the complete recovery of waste oil is achieved. The recovery process incorporates oil–gas saturation monitoring and an oil–gas precipitation assessment algorithm based on neural networks to enable intelligent control, ensuring thorough recovery of residual oil from transformers. The proposed system and methods demonstrate excellent recovery efficiency and environmental protection effects during the pre-treatment of waste transformer oil. Experiments conducted on 50 discarded transformers showed an average recovery efficiency exceeding 99%, with 49 transformers exhibiting no damage to core components after the recovery process. From a theoretical perspective, this research introduces monitoring and control methods for transformer insulating oil recovery, providing significant support for the green processing and reutilization of discarded transformer insulating oil. From an application value perspective, the recovery process helps reduce environmental pollution and facilitates the disassembly of transformers. This enables better analysis of transformer operating characteristics, thereby enhancing the reliability and safety of power systems. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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13 pages, 3389 KiB

Open AccessArticle

Dynamic Prediction of Proton-Exchange Membrane Fuel Cell Degradation Based on Gated Recurrent Unit and Grey Wolf Optimization

by Xiangdong Wang, Zerong Huang, Daxing Zhang, Haoyu Yuan, Bingzi Cai, Hanlin Liu, Chunsheng Wang, Yuan Cao, Xinyao Zhou and Yaolin Dong

Energies 2024, 17(23), 5855; https://doi.org/10.3390/en17235855 - 22 Nov 2024

Viewed by 593

Abstract

This paper addresses the challenge of degradation prediction in proton-exchange membrane fuel cells (PEMFCs). Traditional methods often struggle to balance accuracy and complexity, particularly under dynamic operational conditions. To overcome these limitations, this study proposes a data-driven approach based on the gated recurrent unit (GRU) neural network, optimized by the grey wolf optimizer (GWO). The integration of the GWO automates the hyperparameter tuning process, enhancing the predictive performance of the GRU network. The proposed GWO-GRU method was validated utilizing actual PEMFC data under dynamic load conditions. The results demonstrate that the GWO-GRU method achieves superior accuracy compared to other standard methods. The method offers a practical solution for online PEMFC degradation prediction, providing stable and accurate forecasting for PEMFC systems in dynamic environments. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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16 pages, 3331 KiB

Open AccessArticle

Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction

by Lingwen Meng, Jingliang Zhong, Shasha Luo, Xinshan Zhu, Yulin Wang and Shumei Zhang

Energies 2024, 17(21), 5424; https://doi.org/10.3390/en17215424 - 30 Oct 2024

Viewed by 578

Abstract

With the continuous expansion of the power grid, the issue of operational safety has attracted increasing attention. In power grid operation control, unauthorized operations are one of the primary causes of personal accidents. Therefore, preventing and monitoring unauthorized actions by power grid operators is of critical importance. First, multimodal violation data are integrated through information systems, such as the power grid management platform, to construct a historical case database. Next, word vectors for three types of operation-related factors are generated using natural language processing techniques, and key vectors are selected based on generalized correlation coefficients using mutual information, enabling effective dimensionality reduction. Independent component analysis is then employed for feature extraction and further dimensionality reduction, allowing for the effective characterization of operational scenarios. For each historical case, a risk score is derived from a violation risk prediction model constructed using the Random Forests (RF) algorithm. When a high-risk score is identified, the K-Nearest Neighbor (KNN) algorithm is applied to locate similar scenarios in the historical case database where violations may have occurred. Real-time violation risk assessment is performed for each operation, providing early warnings to operators, thereby reducing the likelihood of violations, and enhancing the safety of power grid operations. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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18 pages, 3643 KiB

Open AccessArticle

MMD-TSC: An Adaptive Multi-Objective Traffic Signal Control for Energy Saving with Traffic Efficiency

by Yuqi Zhang, Yingying Zhou, Beilei Wang and Jie Song

Energies 2024, 17(19), 5015; https://doi.org/10.3390/en17195015 - 9 Oct 2024

Viewed by 925

Abstract

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people’s mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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26 pages, 9291 KiB

Open AccessArticle

Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage

by Yunlong Zhang, Panhong Zhang, Sheng Du and Hanlin Dong

Energies 2024, 17(11), 2770; https://doi.org/10.3390/en17112770 - 5 Jun 2024

Cited by 2 | Viewed by 1195

Abstract

With the shortage of fossil energy and the increasingly serious environmental problems, renewable energy based on wind and solar power generation has been gradually developed. For the problem of wind power uncertainty and the low-carbon economic optimization problem of an integrated energy system with power to gas (P2G) and carbon capture and storage (CCS), this paper proposes an economic optimization scheduling strategy of an integrated energy system considering wind power uncertainty and P2G-CCS technology. Firstly, the mathematical model of the park integrated energy system with P2G-CCS technology is established. Secondly, to address the wind power uncertainty problem, Latin hypercube sampling (LHS) is used to generate a large number of wind power scenarios, and the fast antecedent elimination technique is used to reduce the scenarios. Then, to establish a mixed integer linear programming model, the branch and bound algorithm is employed to develop an economic optimal scheduling model with the lowest operating cost of the system as the optimization objective, taking into account the ladder-type carbon trading mechanism, and the sensitivity of the scale parameters of P2G-CCS construction is analyzed. Finally, the scheduling scheme is introduced into a typical industrial park model for simulation. The simulation result shows that the consideration of the wind uncertainty problem can further reduce the system’s operating cost, and the introduction of P2G-CCS can effectively help the park’s integrated energy system to reduce carbon emissions and solve the problem of wind and solar power consumption. Moreover, it can more effectively reduce the system’s operating costs and improve the economic benefits of the park. Full article

(This article belongs to the Special Issue Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes)

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