Anbar Journal for Engineering Sciences

Abstract

Materials selection is a multi-criteria decision-making (MCDM) problems because the large numberof factors affecting on decision making. The best choice of available material is critical to thecompetitiveness and success of the manufacturing organisation. The analytical hierarchy process(AHP) is an important tool to solve MCDM problems. The choosing process of suitable material(such as a refrigerant fluid) for the Air Condition System (ACS) is faced with challenges such aslack of a systematic approach in setting the optimal performance in terms of its impact on theenvironment and operation. Selecting process for the one refrigerant from a range suitable ofsuitable refrigerant is complex process. The study presents a comparative performance analysisof ACS for using four alternative refrigerants R290, R410, R404 and R22. Then, one of these suitablerefrigerant is selected. The comparison is based on three criteria system operation, environmentand maintenance.Novels ACS performance assessment model is proposed based on an analytical hierarchy process(AHP). The model is based on two main criteria of ACS, quantitative criteria, cooling capacity(CC), coefficient of performance (COP), etc.).And qualitative criteria (Ozone Depletion Potential (ODP), Global Warming Potential (GWP) andmaintenance cost (MC)). It is necessary to look for new technique help decision making to selectalternative refrigerants, to fulfill the goals of the international protocols (Montreal and Kyoto)and optimum operation, to satisfy the growing worldwide demand, in addition the increase outdoortemperature in some countries.This study provides a developed methodology for evaluating ACS performance. Moreover, it helpsto select a robust decision. The results obtained from AHP process that the best rank of the suitablerefrigerant was R404 (0.3763) followed by R22 (0.3657) and so on for the other. Therefore,the proposed methodology can help the decision maker to select the best alternative for bothcriteria (qualitative and quantitative) in complex selecting process.