Utilization of response surface method (RSM) in optimizing automotive air conditioning (AAC) performance exerting Al2O3/PAG nanolubricant
. This manuscript examines the performance of automotive air conditioning (AAC) with the variation of the concentration of Al2O3/PAG nanolubricant, initial refrigerant charges, and compressor speed. Today, the response surface methodology (RSM) is one of the most commonly used optimization technique...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/29376/1/37.%20Utilization%20of%20Response%20Surface%20Method%20%28RSM%29%20in%20Optimizing%20Automotive%20Air%20Conditioning%20%28AAC%29.pdf http://umpir.ump.edu.my/id/eprint/29376/ |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | . This manuscript examines the performance of automotive air conditioning (AAC) with the variation of the concentration of Al2O3/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. |
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