Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes
This is to explore the multifaceted aspects of 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. It...
में बचाया:
| स्वरूप: | Online |
|---|---|
| भाषा: | अंग्रेज़ी |
| प्रकाशित: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| विषय: | |
| ऑनलाइन पहुंच: | ONIX_20250812T110751_9783725838837_228 |
| टैग: |
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
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| _version_ | 1869519652547198976 |
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| collection | Directory of Open Access Books |
| description | This is to explore the multifaceted aspects of 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. It explores 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. Research areas include hybrid intelligent modeling techniques, intelligent optimization strategies, case studies and applications, and interdisciplinary approaches. 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. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes, providing readers with valuable ideological inspiration in the field. |
| format | Online |
| id | doab-20.500.12854ir-165473 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1654732025-08-12T09:34:36Z Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes Du, Sheng Wan, Xiongbo Jin, Li Huang, Zixin integrated energy systems economically optimized dispatch landscape uncertainty carbon trading P2G-CCS sustainable transition energy saving reinforcement learning meta-learning violation dictionary construction operation risk prediction random forests independent component analysis mutual information proton-exchange membrane fuel cells degradation prediction durability test gated recurrent unit grey wolf optimizer accuracy complexity transformer insulating oil recovery data-driven monitoring thermal cycle of insulating oil recovery rate supercritical unit coordination control model predictive control energy-saving analysis rapid load change open-pit mine transportation costs truck waiting times excavator boom-and-dipper operation durations improved genetic algorithm machine learning production forecast data preprocessing principal component analysis AutoGluon hybrid energy storage energy management state of charge DC microgrid load frequency control dynamic event-triggered mechanism distributed model predictive control cloud-edge-terminal virtual power plant hybrid intelligent modeling industrial processes optimization strategy artificial intelligence energy conservation and consumption reduction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology This is to explore the multifaceted aspects of 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. It explores 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. Research areas include hybrid intelligent modeling techniques, intelligent optimization strategies, case studies and applications, and interdisciplinary approaches. 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. By sharing their practice and insights in the development and application of these new technologies, the authors of the articles in this reprint have demonstrated the value of hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes, providing readers with valuable ideological inspiration in the field. 2025-08-12T09:34:34Z 2025-08-12T09:34:34Z 2025 book ONIX_20250812T110751_9783725838837_228 9783725838837 9783725838844 https://directory.doabooks.org/handle/20.500.12854/165473 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/10896 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-3884-4 10.3390/books978-3-7258-3884-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725838837 9783725838844 204 open access |
| spellingShingle | integrated energy systems economically optimized dispatch landscape uncertainty carbon trading P2G-CCS sustainable transition energy saving reinforcement learning meta-learning violation dictionary construction operation risk prediction random forests independent component analysis mutual information proton-exchange membrane fuel cells degradation prediction durability test gated recurrent unit grey wolf optimizer accuracy complexity transformer insulating oil recovery data-driven monitoring thermal cycle of insulating oil recovery rate supercritical unit coordination control model predictive control energy-saving analysis rapid load change open-pit mine transportation costs truck waiting times excavator boom-and-dipper operation durations improved genetic algorithm machine learning production forecast data preprocessing principal component analysis AutoGluon hybrid energy storage energy management state of charge DC microgrid load frequency control dynamic event-triggered mechanism distributed model predictive control cloud-edge-terminal virtual power plant hybrid intelligent modeling industrial processes optimization strategy artificial intelligence energy conservation and consumption reduction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes |
| title | Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes |
| title_full | Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes |
| title_fullStr | Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes |
| title_full_unstemmed | Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes |
| title_short | Hybrid Intelligent Modeling Technology and Optimization Strategy for Industrial Energy Consumption Processes |
| title_sort | hybrid intelligent modeling technology and optimization strategy for industrial energy consumption processes |
| topic | integrated energy systems economically optimized dispatch landscape uncertainty carbon trading P2G-CCS sustainable transition energy saving reinforcement learning meta-learning violation dictionary construction operation risk prediction random forests independent component analysis mutual information proton-exchange membrane fuel cells degradation prediction durability test gated recurrent unit grey wolf optimizer accuracy complexity transformer insulating oil recovery data-driven monitoring thermal cycle of insulating oil recovery rate supercritical unit coordination control model predictive control energy-saving analysis rapid load change open-pit mine transportation costs truck waiting times excavator boom-and-dipper operation durations improved genetic algorithm machine learning production forecast data preprocessing principal component analysis AutoGluon hybrid energy storage energy management state of charge DC microgrid load frequency control dynamic event-triggered mechanism distributed model predictive control cloud-edge-terminal virtual power plant hybrid intelligent modeling industrial processes optimization strategy artificial intelligence energy conservation and consumption reduction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | integrated energy systems economically optimized dispatch landscape uncertainty carbon trading P2G-CCS sustainable transition energy saving reinforcement learning meta-learning violation dictionary construction operation risk prediction random forests independent component analysis mutual information proton-exchange membrane fuel cells degradation prediction durability test gated recurrent unit grey wolf optimizer accuracy complexity transformer insulating oil recovery data-driven monitoring thermal cycle of insulating oil recovery rate supercritical unit coordination control model predictive control energy-saving analysis rapid load change open-pit mine transportation costs truck waiting times excavator boom-and-dipper operation durations improved genetic algorithm machine learning production forecast data preprocessing principal component analysis AutoGluon hybrid energy storage energy management state of charge DC microgrid load frequency control dynamic event-triggered mechanism distributed model predictive control cloud-edge-terminal virtual power plant hybrid intelligent modeling industrial processes optimization strategy artificial intelligence energy conservation and consumption reduction thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20250812T110751_9783725838837_228 |