Optimization of Air Conditioning Performance with Al2O3-SiO2/PAG Composite Nanolubricants Using the Response Surface Method

Abstract

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.

Experimental investigation of usage of POE lubricants with Al2O3, graphene or CNT nanoparticles in a refrigeration compressor

“…The zeta potential values of nanolubricants containing Al 2 O 3 were also measured and given in Figure 9. Accordingly, it can be said that the stability of nanolubricant samples with Al 2 O 3 is excellent and normal for the lowest mass fraction (ω = 0.25%) and highest mass fraction (ω = 1.00%), respectively [33,34]. It is noted that data about the stability of the nanolubricant shown in Figure 9 has also been used in a previous study [21].…”

Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization

“…According to Table 2, the fuzzy model for cooling capacity has RMSE values of 0.0002 and 0.0991 for training and testing data, respectively. Compared to ANOVA [33], the RMSE for all data is reduced from 0.412 (using ANOVA) to 0.0572 (fuzzy). The coefficient of determination (R 2 ) values for training and testing are 1.0 and 0.9653, respectively.…”

“…Permission was obtained from Ref. [33] to use an experimental data set consisting of 15 sets, which reflects the effectiveness of the AAC system. The input parameters of the system include the volume concentration (VC) of nano-lubricants (%), the refrigerant charge (RC) in grams, and compressor speed (CS) in revolutions per minute (RPM).…”

Enhancement of Biolubricant Production from Waste Cooking Oil using Alkali Derived from Oil-Palm empty Fruit Bunches

“…The P-values were used as a tool to check the significance of each of the coefficients, which in turn were necessary to understand the pattern of the mutual interactions between the test variables (Gwise et al, 2021). Hence, the higher the F-test value and the smaller the P-values, the higher the significance of the corresponding coefficient (Zawawi et al, 2022). Adeq Precision measures the signal-to-noise ratio.…”