Open AccessUtilization of Response Surface Method (RSM) in Optimizing Automotive Air Conditioning (AAC) Performance Exerting Al2O3/PAG Nanolubricant
Author(s) -
A. A. M. Redhwan,
W.H. Azmi,
M.Z. Sharif,
N.N.M. Zawawi,
S. Zainal Ariffin
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1532/1/012003
Subject(s) - response surface methodology , gas compressor , cooling capacity , refrigerant , work (physics) , automotive engineering , design of experiments , air compressor , air conditioning , computer science , engineering , mathematics , mechanical engineering , machine learning , statistics
This manuscript examines the performance of automotive air conditioning (AAC) with the variation of the concentration of Al 2 O 3 /PAG nanolubricant, initial refrigerant charges, and compressor speed. Today, the response surface methodology (RSM) is one of the most commonly used optimization techniques for designing experimental work and for optimizing variables for a system. In this study, RSM was used to predict response parameters such as cooling capacity and compressor work. Besides, critical relationships between input and response factors will be identified using RSM. Independent variable optimization is carried out using a desirability approach to maximize cooling capacity and minimize the compressor. The results of the RSM analysis found that the optimum conditions with high desirability of 100% were at a concentration of 0.010%, cooling charge of 168 grams and compressive speed of 1160 rpm. At this optimum condition, the AAC system produces a cooling capacity of 1314 kW and a compressor work of 14.19 kJ/kg. The model predicted by RSM is accurate and has been validated in experiments with a deviation of less than 3.4%. Therefore, it can be concluded that RSM can predict optimization parameters that affect AAC performance.