Dynamic modeling and leak detection of ammonia leakage in food cold storage system

Abstract

AbstractWithin the domain of food processing and storage, cold storage facilities are crucial for maintaining product quality. The primary source of refrigerant leakage in these systems typically originates from slight defects at the welding joints. Such leakage elevates the internal temperature of the facility, subsequently degrading the quality of the stored food products. This research presents a versatile, dynamic model that harmonizes the structural parameters with the thermophysical parameters of disparate components within the cold storage system designed to analyze leakage in ammonia refrigeration systems by employing interchangeable modules, an approach that overcomes the challenge associated with gathering leakage data from conventional ammonia systems. The inherent flexibility of this model facilitates module substitution in alignment with various cold storage parameters, thus ensuring adaptability to diverse conditions. The “Refrigeration System Leak Detection Cloud Service” (RSLDCS) and an associated leakage detection procedure are proposed, with validation against pertinent datasets. The empirical results demonstrate that the deviation between the model's output and the actual engineering data, including the extant calculation methodologies under 20% (the average fitting of the trend of is 96.43%). This degree of precision confirming the model's accuracy and reliability. Through the verification of the leak detection procedure, the complete state of leakage can be discerned within the appropriate temporal data set, and the site of the leak can be pinpointed with a margin of deviation ranging from −19% to +13%. The model provide new solutions for food storage safety.Practical applicationsCold storage facilities often experience subtle leaks that are challenging to detect. These minor leaks adversely affect the efficiency of the refrigeration system, leading to temperature variations that can degrade the quality of stored food. Additionally, they pose a risk to the safe operation of the facility. The presented results enable companies to effectively monitor and control leaks in cold storage systems of identical design. For different cold storage configurations, modules can be adjusted accordingly. Utilizing this model provides critical data and assessment criteria for ammonia leakage, leading to a comprehensive understanding of leakage scenarios. By implementing the RSLDCS‐based detection and localization methodology, leaks within the ammonia refrigeration system can be swiftly and accurately identified. This provides a robust and reliable safeguard for the safe operation of ammonia refrigeration systems, enhancing food production safety.