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Refrigerant Leakage Detection and Diagnosis for a Distributed Refrigeration System
A new technique to detect and diagnose leakage in a distributed refrigeration system is proposed in this paper. Unlike a simple refrigeration system of an HVAC system, a distributed refrigeration system, such as the refrigeration system in a supermarket, consists of several racks of compressors and evaporators, large condenser systems on the roof, meters of refrigerant piping systems, and liquid receivers. There are several potential leak points all over the system, and the presence of liquid receivers alters the equilibrium of the system. Therefore, existing leakage instruments or existing thermodynamic-based measuring techniques for HVAC applications do not guarantee correct detection and diagnosis. The proposed technique is not only based on simple and inexpensive measurements of refrigerant thermodynamic states but also accounts for the complexity of the system and the presence of the liquid receiver. Key disciplines to develop the leakage detection and diagnosis include the belief network technique and the decision theory. The probabilistic approach embedded in these two principles makes the detection and diagnosis more effective and precise. Additionally, a neural network model is constructed to make the technique faster and more practical for field implementation with small microcomputers. By implementing the technique in a real supermarket store in Longmont, Colorado, it is shown that the technique is capable of distinguishing between leak and no-leak systems. The developed technique detected a leakage if the refrigeration system lost more than 1.0%, or 5.3 kg, of the initial charge and misclassified one data set from ten data sets.
Refrigerant Leakage Detection and Diagnosis for a Distributed Refrigeration System
A new technique to detect and diagnose leakage in a distributed refrigeration system is proposed in this paper. Unlike a simple refrigeration system of an HVAC system, a distributed refrigeration system, such as the refrigeration system in a supermarket, consists of several racks of compressors and evaporators, large condenser systems on the roof, meters of refrigerant piping systems, and liquid receivers. There are several potential leak points all over the system, and the presence of liquid receivers alters the equilibrium of the system. Therefore, existing leakage instruments or existing thermodynamic-based measuring techniques for HVAC applications do not guarantee correct detection and diagnosis. The proposed technique is not only based on simple and inexpensive measurements of refrigerant thermodynamic states but also accounts for the complexity of the system and the presence of the liquid receiver. Key disciplines to develop the leakage detection and diagnosis include the belief network technique and the decision theory. The probabilistic approach embedded in these two principles makes the detection and diagnosis more effective and precise. Additionally, a neural network model is constructed to make the technique faster and more practical for field implementation with small microcomputers. By implementing the technique in a real supermarket store in Longmont, Colorado, it is shown that the technique is capable of distinguishing between leak and no-leak systems. The developed technique detected a leakage if the refrigeration system lost more than 1.0%, or 5.3 kg, of the initial charge and misclassified one data set from ten data sets.
Refrigerant Leakage Detection and Diagnosis for a Distributed Refrigeration System
Assawamartbunlue, Kriengkrai (author) / Brandemuehl, Michael J. (author)
HVAC&R Research ; 12 ; 389-405
2006-07-01
17 pages
Article (Journal)
Electronic Resource
Unknown
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