Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy
The volumetric heat transfer coefficient (VHTC) of direct-contact evaporator (DCE) in an organic Rankine cycle (ORC) system was one of the key indicators to reflect the heat transfer efficiency and energy utilization. This research proposed an interpretable data-driven hybrid strategy for evaluating...
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Elsevier
2025-10-01
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| Series: | Case Studies in Thermal Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25011402 |
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| author | Yinzhen Tan Bo Yu Jianxin Pan Wuliang Yin Min Wang Hua Wang Kai Yang Qingtai Xiao |
| author_facet | Yinzhen Tan Bo Yu Jianxin Pan Wuliang Yin Min Wang Hua Wang Kai Yang Qingtai Xiao |
| author_sort | Yinzhen Tan |
| collection | DOAJ |
| description | The volumetric heat transfer coefficient (VHTC) of direct-contact evaporator (DCE) in an organic Rankine cycle (ORC) system was one of the key indicators to reflect the heat transfer efficiency and energy utilization. This research proposed an interpretable data-driven hybrid strategy for evaluating heat transfer enhancement performance in the low-temperature waste heat recovery systems. Firstly, the collected experimental operating conditions were preprocessed to obtain a series of highly reliable datasets. Subsequently, the augmented Dickey-Fuller test was used to reveal that whether the steam flow data has non-linear and non-stationary characteristics or not. Then, the steam flow rate was decomposed into a series of intrinsic mode functions by intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and the intrinsic mode functions were divided into high-complexity and low-complexity components by fuzzy entropy (FE). Next, the reconstructed values of the low-complexity steam flow subsequence were substituted into the least squares support vector machine (LSSVM) for training and testing, which obtains the forecasted value of VHTC. Compared to eleven comparative model, ICEEMDAN-FE-LSSVM-VHTC owns excellent accuracy since average absolute error decreases by 21.4 %–70.9 %, mean square error decreases by 62.5 %–94.5 %, root mean square error decreases by 16.7 %–67.9 %, and coefficient of determination increases by 1.9 %–58.0 %. In addition, the Shapley additive explanations values offer both local and global explanations of the proposed hybrid forecasting model. Overall, this study presents an in-depth analysis of complex heat transfer datasets to provide valuable insights into evaporator design to ensure efficient and consistent performance in real-world operation. Hence, the research on the forecasting of VHTC inside DCE of ORC can help promote the efficient use of energy and the cause of environmental protection. |
| format | Article |
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| institution | Kabale University |
| issn | 2214-157X |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj-art-72654b29b779470da3c1fece6f0b3a4d2025-08-24T05:12:43ZengElsevierCase Studies in Thermal Engineering2214-157X2025-10-017410688010.1016/j.csite.2025.106880Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategyYinzhen Tan0Bo Yu1Jianxin Pan2Wuliang Yin3Min Wang4Hua Wang5Kai Yang6Qingtai Xiao7State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; Department of Energy and Power Engineering, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR ChinaState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR ChinaResearch Center for Mathematics, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, Guangdong 519087, PR China; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Faculty of Science and Technology, Beijing Normal - Hong Kong Baptist University, Zhuhai, Guangdong 519087, PR ChinaSchool of Electrical and Electronic Engineering, The University of Manchester, Manchester, M13 9PL, United KingdomDepartment of Statistics and Data Science, The University of Texas at San Antonio, San Antonio, TX, 78249-0634, USAState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; Department of Energy and Power Engineering, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR ChinaState Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; Department of Energy and Power Engineering, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; Corresponding author. State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, 650093, PR China.State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; Department of Energy and Power Engineering, School of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, PR China; Corresponding author. State Key Laboratory of Complex Nonferrous Metal Resources Clean Utilization, Kunming University of Science and Technology, Kunming, 650093, PR China.The volumetric heat transfer coefficient (VHTC) of direct-contact evaporator (DCE) in an organic Rankine cycle (ORC) system was one of the key indicators to reflect the heat transfer efficiency and energy utilization. This research proposed an interpretable data-driven hybrid strategy for evaluating heat transfer enhancement performance in the low-temperature waste heat recovery systems. Firstly, the collected experimental operating conditions were preprocessed to obtain a series of highly reliable datasets. Subsequently, the augmented Dickey-Fuller test was used to reveal that whether the steam flow data has non-linear and non-stationary characteristics or not. Then, the steam flow rate was decomposed into a series of intrinsic mode functions by intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and the intrinsic mode functions were divided into high-complexity and low-complexity components by fuzzy entropy (FE). Next, the reconstructed values of the low-complexity steam flow subsequence were substituted into the least squares support vector machine (LSSVM) for training and testing, which obtains the forecasted value of VHTC. Compared to eleven comparative model, ICEEMDAN-FE-LSSVM-VHTC owns excellent accuracy since average absolute error decreases by 21.4 %–70.9 %, mean square error decreases by 62.5 %–94.5 %, root mean square error decreases by 16.7 %–67.9 %, and coefficient of determination increases by 1.9 %–58.0 %. In addition, the Shapley additive explanations values offer both local and global explanations of the proposed hybrid forecasting model. Overall, this study presents an in-depth analysis of complex heat transfer datasets to provide valuable insights into evaporator design to ensure efficient and consistent performance in real-world operation. Hence, the research on the forecasting of VHTC inside DCE of ORC can help promote the efficient use of energy and the cause of environmental protection.http://www.sciencedirect.com/science/article/pii/S2214157X25011402Volumetric heat transfer coefficientDirect-contact evaporatorInterpretabilityMachine learningHybrid strategy |
| spellingShingle | Yinzhen Tan Bo Yu Jianxin Pan Wuliang Yin Min Wang Hua Wang Kai Yang Qingtai Xiao Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy Case Studies in Thermal Engineering Volumetric heat transfer coefficient Direct-contact evaporator Interpretability Machine learning Hybrid strategy |
| title | Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy |
| title_full | Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy |
| title_fullStr | Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy |
| title_full_unstemmed | Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy |
| title_short | Augmenting insights into heat transfer performance of direct-contact evaporator: An interpretable data-driven hybrid strategy |
| title_sort | augmenting insights into heat transfer performance of direct contact evaporator an interpretable data driven hybrid strategy |
| topic | Volumetric heat transfer coefficient Direct-contact evaporator Interpretability Machine learning Hybrid strategy |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X25011402 |
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