Optimization of air conditioning performance with Al2O3-SiO/PAG composite nanolubricants using the response surface method

• Main Authors: Nurul Nadia, Mohd Zawawi, Wan Hamzah, Azmi, Mohamad Redhwan, Abd Aziz, Ramadhan, Anwar Ilmar, Ali, Hafiz Muhammad
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: T Technology (General); TA Engineering (General). Civil engineering (General); TJ Mechanical engineering and machinery; TL Motor vehicles. Aeronautics. Astronautics
• Online Access: http://umpir.ump.edu.my/id/eprint/40226/1/Optimization%20of%20air%20conditioning%20performance%20with%20Al2O3-SiO2_PAG.pdf; http://umpir.ump.edu.my/id/eprint/40226/; https://doi.org/10.3390/lubricants10100243; https://doi.org/10.3390/lubricants10100243

A variety of operational parameters can influence the operation of an automobile air-conditioning (AAC) system. This issue is solved by using optimization techniques that can recommend the ideal parameters for the best results. To improve the performance of AAC system usings Al2O3-SiO2/PAG composite nanolubricants, the response surface method (RSM) was employed. RSM was used to design the experimental work, which was based on a face composite design (FCD). The RSM quadratic models were helpful in determining the links between the input parameters and the responses. The addition of composite nanolubricants improved the overall performance of AAC systems. The parameters were optimized using the RSM’s desirability approach, with the goal of increasing cooling capacity and the coefficient of performance (COP), while reducing compressor work and power consumption. The ideal parameters for the AAC system were found to be 900 rpm compressor speed, 155 g refrigerant charge, and 0.019% volume concentration, with a high desirability of 81.60%. Test runs based on the optimum circumstances level were used to estimate and validate cooling capacity, compressor work, COP, and power consumption. Both predicted and measured values were in good agreement with each other. A new RSM model was successfully developed to predict the optimal conditions for AAC system performance.