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The impact of man-made chemicals on the environment, and in particular, the ozone layer, has been investigated over the last 20 years. The phaseout of CFCs under European Regulations and the Montreal Protocol has already taken place and HCFCs are set to follow. A wide range of alternative refrigerants with zero ozone depletion potential have already been developed as replacements for the refrigeration industry. Mathematical modelling of refrigeration systems enables the performance of these alternatives to be evaluated across a broad range of operating conditions. This paper investigates the simulated performance of a liquid chiller retrofitted with a range of alternative refrigerants. The mathematical model of the system is briefly outlined and the properties of the alternative refrigerants discussed. The performance of the system is determined in terms of cooling capacity, power consumption and coefficient of performance for a range of different operating conditions. The relative performance of each refrigerant is discussed and the preferred alternative identified for typical applications.

Automated fault detection and diagnosis of refrigeration equipment is important in maintaining efficient performance, reducing energy consumption, and increasing the reliability and availability of these systems. The reducing costs of microprocessor technology and the incorporation of more sophisticated monitoring equipment on to even fairly small refrigeration plant, now makes the introduction of on-line fault detection and diagnosis on refrigeration equipment feasible and cost effective. This paper reports on the development of a fault detection and diagnosis (FDD) system for liquid chillers based on artificial intelligence techniques. The system was designed to monitor plant performance and to detect and diagnose faults through comparison with expected behaviour and previous experience of fault characteristics. The system operates on line in real time on a Java 2 platform and was initially used to detect refrigerant charge conditions. The results indicate that the FDD system developed is able to detect and diagnose fault conditions arising from low or high refrigerant charge correctly, using two parameters as detectors: condenser refrigerant outlet temperature and discharge pressure.