Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes
Energy efficiency in industrial systems remains a critical challenge, with traditional data-driven models often limited by model accuracy and data availability. Incorporation of physical laws governing energy systems can improve performance and physical consistency, but the model often struggles wit...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546825000072 |
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| author | Santi Bardeeniz Chanin Panjapornpon Moonyong Lee |
| author_facet | Santi Bardeeniz Chanin Panjapornpon Moonyong Lee |
| author_sort | Santi Bardeeniz |
| collection | DOAJ |
| description | Energy efficiency in industrial systems remains a critical challenge, with traditional data-driven models often limited by model accuracy and data availability. Incorporation of physical laws governing energy systems can improve performance and physical consistency, but the model often struggles with the calculation of loss and ignores dynamic interplays between sub-systems, which can result in oversimplification and a lack of practical applicability. Therefore, this study investigated a theoretical framework for developing a law of conservation-guided neural network aimed at enhancing energy efficiency prediction in industrial systems. The framework integrates physical principles directly into floating nodes constructed using a long short-term memory architecture to help the model formulate the relationship between process variables, while gradient aggregation increases liquidity and interpretability. Through evaluation of two large-scale case studies—vinyl chloride monomer and detergent powder production—the proposed model produced substantial improvements in prediction accuracy and model reliability, with a test prediction improvement of 12.2 % and 5.87 % over published methods. Compared to network architecture modification approaches, the proposed model provided higher reliability and reproducibility in energy efficiency predictions. Moreover, the model successfully identified energy inefficiencies, resulting in a 4.21 % reduction in energy consumption and a corresponding 377.35 tons of carbon emissions reduction. |
| format | Article |
| id | doaj-art-f72b4d60f5da4b9db82cfd6d7e56f910 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-f72b4d60f5da4b9db82cfd6d7e56f9102025-01-22T05:44:09ZengElsevierEnergy and AI2666-54682025-05-0120100475Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processesSanti Bardeeniz0Chanin Panjapornpon1Moonyong Lee2Department of Chemical Engineering, Center of Excellence on Petrochemicals and Materials Technology, Faculty of Engineering, Kasetsart University, Bangkok 10900, ThailandDepartment of Chemical Engineering, Center of Excellence on Petrochemicals and Materials Technology, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand; Corresponding author.School of Chemical Engineering, Yeungnam University, Gyeongsan-si 38541, South KoreaEnergy efficiency in industrial systems remains a critical challenge, with traditional data-driven models often limited by model accuracy and data availability. Incorporation of physical laws governing energy systems can improve performance and physical consistency, but the model often struggles with the calculation of loss and ignores dynamic interplays between sub-systems, which can result in oversimplification and a lack of practical applicability. Therefore, this study investigated a theoretical framework for developing a law of conservation-guided neural network aimed at enhancing energy efficiency prediction in industrial systems. The framework integrates physical principles directly into floating nodes constructed using a long short-term memory architecture to help the model formulate the relationship between process variables, while gradient aggregation increases liquidity and interpretability. Through evaluation of two large-scale case studies—vinyl chloride monomer and detergent powder production—the proposed model produced substantial improvements in prediction accuracy and model reliability, with a test prediction improvement of 12.2 % and 5.87 % over published methods. Compared to network architecture modification approaches, the proposed model provided higher reliability and reproducibility in energy efficiency predictions. Moreover, the model successfully identified energy inefficiencies, resulting in a 4.21 % reduction in energy consumption and a corresponding 377.35 tons of carbon emissions reduction.http://www.sciencedirect.com/science/article/pii/S2666546825000072Energy efficiencyLaw of conservationPhysical guided neural networkLong short-term memoryGradient aggregation |
| spellingShingle | Santi Bardeeniz Chanin Panjapornpon Moonyong Lee Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes Energy and AI Energy efficiency Law of conservation Physical guided neural network Long short-term memory Gradient aggregation |
| title | Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes |
| title_full | Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes |
| title_fullStr | Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes |
| title_full_unstemmed | Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes |
| title_short | Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes |
| title_sort | law of conservation guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes |
| topic | Energy efficiency Law of conservation Physical guided neural network Long short-term memory Gradient aggregation |
| url | http://www.sciencedirect.com/science/article/pii/S2666546825000072 |
| work_keys_str_mv | AT santibardeeniz lawofconservationguidedneuralnetworkwithgradientaggregationforimprovedenergyefficiencyoptimizationinindustrialprocesses AT chaninpanjapornpon lawofconservationguidedneuralnetworkwithgradientaggregationforimprovedenergyefficiencyoptimizationinindustrialprocesses AT moonyonglee lawofconservationguidedneuralnetworkwithgradientaggregationforimprovedenergyefficiencyoptimizationinindustrialprocesses |