The Data Mining Technique Using RapidMiner Software for New Zeotropic Refrigerant
Keywords:Refrigerant, refrigeration system, energy efficiency, environmentally friendly, data mining
This research presents the development of environmentally-friendly and energy efficient refrigerant for medium temperature refrigeration systems that new azeotropic refrigerant mixture of hydrofluorocarbons and hydrocarbon that can retrofit in the refrigeration system using R404A. The medium back pressure refrigeration testing standard that follow CAN/ANSI/AHRI540 standard air-conditioning, heating, and refrigeration institute (AHRI) and The properties of refrigerants and refrigeration simulation system that used national institute of standards and technology (NIST) reference fluid thermodynamic and transport properties database (REFPROP) software and NIST vapor compression cycle model accounting for refrigerant thermodynamic and transport properties (CYCLE_D-HX) software. The methodology uses decision tree function in datamining by rapid minor software that first of KDnuggets annual software poll that showed new azeotropic refrigerant mixture had cooling capacity, refrigerant effect, GWP and boiling point were lower than R404A but work and pressure for medium temperature refrigeration system of azeotropic refrigerant mixture were higher than R404A. The artificial intelligence (AI) by data mining technic can predictive environmentally-friendly and energy efficient refrigerant for medium temperature refrigeration. The result of refrigerant mixed by R134A, R32, R125 and R1270 and is consistent with the evolution of fourth-generation refrigerants that contain a mixture of HFCs and HCs which are required to produce a low-GWP, zero-ozone-depletion-potential (ODP), high-capacity, low-operating-pressure, and nontoxic refrigerant.