Enhancing efficiency of large cold store refrigeration systems through automated fault identification and intelligent energy optimization

• Published in: International journal of refrigeration Vol. 168; pp. 411 - 422
• Main Authors: Zhu, Zongsheng, Liu, Xinghua, Wang, Xiaoming, Liu, Bin
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2024
• Subjects: agrégation de cartes auto-organisées; Carbon emission; Cold store; Defrost fault diagnosis; diagnostic de panne de dégivrage; entrepôt frigorifique; fuite de valve; SOM clustering; valve leakage; émission de carbone; Carbon emission; entrepôt frigorifique; Defrost fault diagnosis; SOM clustering; émission de carbone; agrégation de cartes auto-organisées; Cold store; diagnostic de panne de dégivrage; fuite de valve; valve leakage
• ISSN: 0140-7007
• DOI: 10.1016/j.ijrefrig.2024.09.002

•A procedure combined SOM was developed to identify new refrigeration system faults.•Defrosting fault was due to abnormal action of gas-powered suction stop valve.•The diagnostic accuracy for DLL, DML, and DHL were 93.8 %, 91.2 % and 88.6 %, respectively.•Resolution of defrosting issues resulted in up to 18.3 % energy consumption reduction. Refrigeration systems in large cold stores frequently operate suboptimally due to component faults, leading to significant energy wastage and high carbon emissions. This study introduces a novel procedure that leverages data mining to automatically analyze and identify faults, thereby enhancing the intelligence of refrigeration equipment. The research focused on abnormal suction temperatures of compressors during the defrosting of air coolers in a large cold store. Through theoretical analysis and key data acquisition, the root cause of defrosting issues was traced to the abnormal operation of gas-powered suction stop valves, causing leakage of high-pressure hot gas. Clustering methods, Self-Organizing Maps (SOM), were utilized to classify system states and achieved high accuracy rates of 88.6 % to 93.8 % for the three fault modes during the defrosting process, respectively. The resolution of defrosting faults resulted in an energy consumption reduction of up to 18.3 %, aligning with global sustainability initiatives. The study also evaluated the carbon emission reduction, providing a comprehensive approach to improving the efficiency and environmental impact of cold store operations.