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Soft Measurement of Refrigerant Leakage Based on Key Features
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Minbin Ling1, Yuting Yang1, Hua Han1, Ling Xu2, Xiaoyu Cui1
Journal of Refrigeration | 2025, 46(2) : 145 - 154
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Journal of Refrigeration | 2025, 46(2): 145-154
Soft Measurement of Refrigerant Leakage Based on Key Features
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Minbin Ling1, Yuting Yang1, Hua Han1, Ling Xu2, Xiaoyu Cui1
Affiliations
  • 1.School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
  • 2.Carrier Air Conditioning & Refrigeration R&D Management (Shanghai) Co., Ltd., Shanghai, 200436, China
Published: 2025-04-16 doi: 10.12465/j.issn.0253-4339.2025.02.145
Outline
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Refrigerant leakage is a frequent and costly fault that deteriorates the normal operation of a chiller; however, it is difficult to measure directly. This study proposes a data mining- and key-feature-based approach for the soft measurement of refrigerant leakage. Random forest importance ranking and distance correlation coefficients were used to select the characteristic features, and a support vector regression (SVR) soft measurement model was established to measure leakage quantitatively. The proposed model was validated through a leakage experiment conducted on a screw chiller with a rated cooling capacity of 1 440 kW and a refrigerant charge of 330 kg. The results showed that the SVR soft measurement model established on the three selected key features achieved significantly improved performance. The model had a root mean square error (RMSE) of 0.844 kg and a mean absolute error (MAE) of 0.734 kg, outperforming the other three feature subsets.

凌敏彬, 杨钰婷, 韩华, 徐玲, 崔晓钰. 基于关键特征的制冷剂泄漏故障软测量研究. 制冷学报, 2025 , 46 (2) : 145 -154 . DOI: 10.12465/j.issn.0253-4339.2025.02.145
Minbin Ling, Yuting Yang, Hua Han, Ling Xu, Xiaoyu Cui. Soft Measurement of Refrigerant Leakage Based on Key Features[J]. Journal of Refrigeration, 2025 , 46 (2) : 145 -154 . DOI: 10.12465/j.issn.0253-4339.2025.02.145
Funding
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  • National Natural Science Foundation of China(51506125)
Appendix
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Year 2025 volume 46 Issue 2
678
346
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Article Info
doi: 10.12465/j.issn.0253-4339.2025.02.145
  • Receive Date：2023-09-10
  • Online Date：2026-03-13
  • Published：2025-04-16
Article Data
Affiliations
History
  • Received：2023-09-10
  • Revised：2023-12-27
  • Accepted：2024-01-18
Funding
National Natural Science Foundation of China(51506125)
Affiliations
    1.School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
    2.Carrier Air Conditioning & Refrigeration R&D Management (Shanghai) Co., Ltd., Shanghai, 200436, China

Corresponding:

Han Hua, female, associate professor, School of Energy and Power Engineering, University of Shanghai for Science and Technology, 86-13611880360, E-mail: . Research fields: fault diagnosis and optimization of refrigeration and air conditioning system, application of AI in refrigeration system, new refrigeration methods.
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Citations
表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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