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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.

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
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  • National Natural Science Foundation of China(51506125)
Appendix
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Year 2025 volume 46 Issue 2
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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
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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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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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