Application of response surface methodology in optimization of automotive air-conditioning performance operating with SiO_{2}/PAG nanolubricant
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Abstract
The effect of compressor speed, initial refrigerant charge and volume concentrations of SiO_{2}/PAG nanolubricant on the performance of automotive air-conditioning (AAC) system are investigated in this study. Response surface method (RSM) was used in designing the experimental work and is based on face composite design. The developed quadratic models from RSM were helpful to envisage the response parameters namely heat absorbs, compressor works, and coefficient of performance (COP) to identify the significant relations between the input factors and the responses. The results depicted that adding SiO_{2} nanoparticle into PAG lubricant will enhance the COP of AAC. Optimization of independent variables was performed using the desirability approach of the RSM with the goal of maximizing the heat absorb and COP, consequently, minimizing the compressor work. The results revealed that the optimal condition with a high desirability of 73.4% for the compressor speed of 900 rpm, refrigerant charge of 95 g and volume concentration of 0.07%. At this condition, the AAC system operated with 193.99, 23.28 kJ kg^{−1} and 8.27, respectively, for heat absorb, compressor work and COP. DoE based on RSM was capable of optimizing the significant parameters which affect AAC performance.
Keywords
Nanolubricant Heat absorb Compressor work COP Response surface methodList of symbols
- AAC
Automotive air-conditioning
- ANOVA
Analysis of variance
- CCD
Central composite design
- COP
Coefficient of performance
- EER
Energy efficiency ratio
- FCD
Face-centered design
- PAG
Polyalkylene glycol
- Q_{L}
Heat absorb (kJ kg^{−1})
- RAC
Resident air-conditioning
- rpm
Revolution per minute
- RSM
Response surface method
- W_{in}
Compressor work (kJ kg^{−1})
Greek symbols
- ϕ
Volume concentration (%)
- ρ
Density (kg m^{−1})
Introduction
Although air-conditioning is one of the auxiliary components in an automotive system, it has become a necessary part of providing thermal relieve inside car passengers’ compartment, particularly, in nations experiencing hot and humid atmospheres. On the other hand, the compressor of air-conditioning becomes a singular major auxiliary load on an automotive engine. The additional load engaged by compressor will cause a reduction in efficiency, increase in fuel consumption and release of greenhouse gases. The consequences of employing air-conditioning systems in terms of energy reduction and other refrigerant restraint have forced scholars to consider new ways and technologies to improve its efficiency [1]. One of the recent techniques to increase the efficiency of automotive air-conditioning (AAC) is by introducing nanoparticles into the system. The nanoparticle was dispersed directly into a refrigerant base to form nanorefrigerant or into a refrigerant lubricant to create nanolubricants. Redhwan et al. [2] thoroughly reviewed the development of nanorefrigerant and nanolubricant for different refrigerant bases and performance improvement. In another paper, Azmi et al. [3] summarized the potential of nanorefrigerant and nanolubricant on energy saving in a refrigeration system.
Nanorefrigerant researchers have focused their effort on improving the refrigeration performance in domestic refrigerators [4, 5] resident air-conditioning (RAC) system [6] and vapor compression refrigeration (VCR) system [7]. The researchers concluded that the addition of nanoparticles in the refrigeration system reduces the energy consumption up to 26% [5], increase its coefficient of performance (COP) up to 33% [8] and cooling of energy efficiency ratio (EER) up to 6% [6]. In addition, Nair et al. [9] emphasized that employing nanoparticle into the refrigerant base will enhance the efficiency of compressor power, hence, reduce the energy consumption of refrigeration system.
Sabareesh et al. [10] have studied the effect of operating parameters by utilizing an individual approach. The previous research by Frey and Wang [11] of altering a control factor for one time which fit well only in specific conditions should not be considered in the present study. This is due to the performance of AAC since it is influenced by the collective effect of various parameters such as compressor speed, refrigerant charge, concentration of nanolubricant and other factors. Hence, an organized multifactor analysis could offer a comprehensible and detailed understanding of the performance characteristics of the AAC system in comparison with an individual approach. In such multifactor problems, the employment of nonlinear method such as Design of Experiments (DoE) is appropriate to investigate the interaction effects of experiment variables. DoE is regarded as one of the most efficient and cost-effective methods to assess the individual and collective effects of experiment factors on output responses [12]. Several techniques, for instance, factorial design, Taguchi method, and response surface method could be employed for planning the experiments.
In the present paper, response surface methodology (RSM) is employed to investigate the influence of input factors on the response parameters. RSM is a compilation of mathematical and statistical techniques which was employed to ascertain a mathematical representation between factors and responses, and identify the cause of factors affecting a response in a specific process [13]. RSM is also renowned as a practical technique to analyze engineering problems based on both modeling and optimizing the response surface which is affected by the experiments inputs [14]. As the key benefit, the use of RSM in designing the experiment entailed lesser tests and less time-consuming as compared to the full factorial design experimentation. Much of the computation resources are reduced. Consequently, the time needed to resolve the objective problem could be minimized by using RSM. While Taguchi’s method is tool for robust design, it offers a simple and systematic approach to optimize design for performance, quality and cost. Taguchi designs help determining, the parameter settings for experiments that give optimal settings. However, this optimal setting value depends on the number of experiment, since experimentation itself involves great cost. RSM on the other hand/could achieve closer to global optimum with lesser of experiment.
The works related to the application of RSM in the evaluation of the performance of refrigeration system were limited in the literature. One of the recent papers was presented by Costa and Garcia [15]. They used RSM in optimizing the efficiency of a refrigeration cycle demonstration unit using a multi-response optimization method. In their work, Costa and Garcia [15] considered experimental variable parameters such as evaporator temperature, condenser temperature, condenser mass flow rate and evaporator mass flow rate. Statistically designed experiments were carried out to concurrently maximize the refrigeration effect and minimize energy consumption of a compression refrigeration cycle (CR). However, the employment of RSM techniques in AAC system by using nanolubricant is yet to be explored further.
Hence, the present study utilizes RSM approach to investigate the influence of compressor speed, refrigerant charge and volume concentration on the AAC performance operated with SiO_{2}/PAG nanolubricants. Design Expert software is used in the present analysis and the experiments are planned using face-centered design (FCD) procedure. Based on the FCD, 20 experiments were conducted. The heat absorb (Q_{L}), compressor work (W_{in}) and coefficient of performance (COP) are used as the response factors in the RSM evaluation.
Methodology
The response surface method (RSM) and desirability approach in achieving the optimal performance are deliberately explained in this section. Prior to that, the preparation and stability of SiO_{2}/PAG nanolubricants is briefly discussed. The experimental setup of AAC system was designed and developed for the performance analysis of SiO_{2}/PAG nanolubricants in the previous study [16, 17]. For further evaluation, the experimental data presented by Sharif et al. [16] are used in the present study.
Material and Formulation of SiO_{2}/PAG Nanolubricant
Property | Nanoparticle SiO_{2} | Lubricant PAG 46 |
---|---|---|
Purity/% | 99.9 | – |
Molecular mass/g mol^{−1} | 60.08 | – |
Average particle diameter/nm | 30 | – |
Density/kg m^{−3} | 2220 | 995.4 |
Thermal conductivity/W (m K)^{−1} | 1.4 | – |
Specific heat/J (kg K)^{−1} K^{−1} | 745 | – |
Flash point/°C | – | 174 |
Kinematic viscosity, cSt @ 40 °C | – | 41.4–50.6 |
Pour point/°C | – | − 51 |
Experimental design employing RSM
Independent variables and levels for central composite design
Independent variables | Code | Variable levels | ||
---|---|---|---|---|
− 1 | 0 | 1 | ||
Speed | A | 900 | 1500 | 2100 |
Refrigerant charge | B | 95 | 110 | 125 |
Volume concentration | C | 0 | 0.05 | 0.1 |
Desirability approach
Results, analysis, and discussion
Experimental results
The experimental design, result and prediction based on RSM
Run | Process parameter settings | Response | |||||||
---|---|---|---|---|---|---|---|---|---|
Speed/rpm | Refrigerant charge/g | Concentration/% | Experimental | Predicted | |||||
Q_{L}/kJ kg^{−1} | W_{in}/kJ kg^{−1} | COP | Q_{L}/kJ kg^{−1} | W_{in}/kJ kg^{−1} | COP | ||||
1 | 1500 | 110 | 0.05 | 189.69 | 31.20 | 6.08 | 189.40 | 31.34 | 6.03 |
2 | 900 | 125 | 0.00 | 188.81 | 25.85 | 7.30 | 188.91 | 25.94 | 7.39 |
3 | 2100 | 110 | 0.05 | 188.40 | 38.90 | 4.84 | 187.83 | 40.48 | 4.96 |
4 | 2100 | 125 | 0.10 | 188.60 | 38.70 | 4.87 | 188.38 | 38.25 | 4.97 |
5 | 1500 | 95 | 0.05 | 193.23 | 34.30 | 5.63 | 192.33 | 32.55 | 5.86 |
6 | 1500 | 125 | 0.05 | 188.06 | 30.60 | 6.15 | 188.65 | 30.13 | 6.20 |
7 | 1500 | 110 | 0.05 | 188.69 | 30.95 | 6.10 | 189.40 | 31.34 | 6.03 |
8 | 1500 | 110 | 0.00 | 188.98 | 38.15 | 4.95 | 188.62 | 37.67 | 4.91 |
9 | 2100 | 95 | 0.00 | 190.13 | 49.81 | 3.82 | 190.52 | 49.41 | 3.67 |
10 | 1500 | 110 | 0.05 | 189.19 | 32.00 | 5.91 | 189.40 | 31.34 | 6.03 |
11 | 900 | 110 | 0.05 | 191.36 | 22.50 | 8.50 | 190.96 | 22.20 | 8.35 |
12 | 2100 | 125 | 0.00 | 185.92 | 46.83 | 3.97 | 185.77 | 47.00 | 4.00 |
13 | 900 | 95 | 0.10 | 194.17 | 24.50 | 7.93 | 194.15 | 25.19 | 8.02 |
14 | 900 | 125 | 0.10 | 191.84 | 22.70 | 8.45 | 191.52 | 22.77 | 8.35 |
15 | 2100 | 95 | 0.10 | 190.79 | 40.40 | 4.72 | 191.01 | 40.67 | 4.63 |
16 | 1500 | 110 | 0.05 | 188.90 | 30.40 | 6.21 | 189.40 | 31.34 | 6.03 |
17 | 1500 | 110 | 0.05 | 190.01 | 31.30 | 6.07 | 189.40 | 31.34 | 6.03 |
18 | 900 | 95 | 0.00 | 193.35 | 27.74 | 6.97 | 193.66 | 28.35 | 7.05 |
19 | 1500 | 110 | 0.10 | 189.53 | 32.30 | 5.87 | 190.17 | 31.72 | 5.87 |
20 | 1500 | 110 | 0.05 | 189.20 | 31.25 | 6.05 | 189.40 | 31.34 | 6.03 |
Analysis of data
Heat absorb (Q _{L})
Model summary and ANOVA for heat absorb response surface quadratic model
Source | Model summary | Sum of squares | df | Mean squares | f value | p value prob > f | Remarks |
---|---|---|---|---|---|---|---|
R ^{2} | 0.9488 | – | – | – | – | – | – |
Adjusted R^{2} | 0.9305 | – | – | – | – | – | – |
Predicted R^{2} | 0.9076 | – | – | – | – | – | Closed to Adj. R^{2} |
Adequate precision | 28.839 | – | – | – | – | – | > 4 |
Model | – | 73.30 | 9 | 8.14 | 23.37 | < 0.0001 | Significant |
A-Speed | – | 24.62 | 1 | 24.62 | 70.63 | < 0.0001 | Significant |
B-Ref. charge | – | 34.02 | 1 | 34.02 | 97.60 | < 0.0001 | Significant |
C-Concentration | – | 5.98 | 1 | 5.98 | 17.16 | 0.0020 | Significant |
AB | – | 0.027 | 1 | 0.027 | 0.076 | 0.7879 | Not significant |
AC | – | 0.032 | 1 | 0.032 | 0.093 | 0.7665 | Not significant |
BC | – | 2.24 | 1 | 2.24 | 6.43 | 0.0296 | Significant |
A^{2} | – | 0.28 | 1 | 0.28 | 0.81 | 0.3897 | Not significant |
B^{2} | – | 3.24 | 1 | 3.24 | 9.29 | 0.0123 | Significant |
C^{2} | – | 0.25 | 1 | 0.25 | 0.73 | 0.4139 | Not significant |
Residual | – | 3.49 | 10 | 0.35 | – | – | – |
Lack of fit | – | 2.28 | 5 | 0.46 | 1.89 | 0.2516 | Not significant |
Pure error | – | 1.21 | 5 | 0.24 | – | – | – |
Compressor work (W _{in})
Model summary and ANOVA for compressor work response surface quadratic model
Source | Model summary | Sum of squares | df | Mean squares | f value | p value prob > f | Remarks |
---|---|---|---|---|---|---|---|
R ^{2} | 0.9909 | – | – | – | – | – | – |
Adjusted R^{2} | 0.9877 | – | – | – | – | – | – |
Predicted R^{2} | 0.9826 | – | – | – | – | – | Closed to Adj. R^{2} |
Adequate precision | 61.110 | – | – | – | – | – | > 4 |
Model | – | 1013.18 | 9 | 112.58 | 198.54 | < 0.0001 | Significant |
A-Speed | – | 834.48 | 1 | 834.48 | 1471.73 | < 0.0001 | Significant |
B-Ref. charge | – | 14.57 | 1 | 14.57 | 25.69 | 0.0005 | Significant |
C-Concentration | – | 88.65 | 1 | 88.65 | 156.35 | < 0.0001 | Significant |
AB | – | 0.13 | 1 | 0.13 | 0.22 | 0.6487 | Not significant |
AC | – | 15.54 | 1 | 15.54 | 27.41 | 0.0004 | Significant |
BC | – | 0.23 | 1 | 0.23 | 0.41 | 0.5348 | Not significant |
A^{2} | – | 2.80 | 1 | 2.80 | 4.93 | 0.0506 | Not significant |
B^{2} | – | 1.51 | 1 | 1.51 | 2.67 | 0.1336 | Not significant |
C^{2} | – | 34.03 | 1 | 34.03 | 60.02 | < 0.0001 | Significant |
Residual | – | 5.67 | 10 | 0.57 | – | – | – |
Lack of fit | – | 4.32 | 5 | 0.86 | 3.19 | 0.1144 | Not significant |
Pure error | – | 1.35 | 5 | 0.27 | – | – | – |
Coefficient of performance
Model summary and ANOVA for coefficient of performance (COP) response surface quadratic model
Source | Model summary | Sum of squares | df | Mean squares | f value | p value Prob > f | Remarks |
---|---|---|---|---|---|---|---|
R ^{2} | 0.9932 | – | – | – | – | – | – |
Adjusted R^{2} | 0.9907 | – | – | – | – | – | – |
Predicted R^{2} | 0.9853 | – | – | – | – | – | Closed to Adj. R^{2} |
Adequate precision | 67.386 | – | – | – | – | – | > 4 |
Model | – | 32.96 | 9 | 3.66 | 275.05 | < 0.0001 | Significant |
A-Speed | – | 28.66 | 1 | 28.66 | 2152.63 | < 0.0001 | Significant |
B- Ref. charge | – | 0.28 | 1 | 0.28 | 21.07 | 0.0010 | Significant |
C-Vol. concentration | – | 2.33 | 1 | 2.33 | 174.77 | < 0.0001 | Significant |
AB | – | 0.038 | 1 | 0.038 | 2.88 | 0.1203 | Not significant |
AC | – | 0.011 | 1 | 0.011 | 0.81 | 0.3907 | Not significant |
BC | – | 4.548 × 10^{−3} | 1 | 4.548 × 10^{−3} | 0.34 | 0.5719 | Not significant |
A^{2} | – | 1.22 | 1 | 1.22 | 91.50 | < 0.0001 | Significant |
B^{2} | – | 0.039 | 1 | 0.039 | 2.92 | 0.1185 | Not significant |
C^{2} | – | 0.98 | 1 | 0.98 | 73.84 | < 0.0001 | Significant |
Residual | – | 0.13 | 10 | 0.013 | – | – | – |
Lack of fit | – | 0.087 | 5 | 0.017 | 1.86 | 0.2569 | Not significant |
Pure error | – | 0.047 | 5 | 9.325 × 10^{−3} | – | – | – |
Optimization
Optimum heat absorb, compressor work and COP under different volume concentration
No. | Factors | Response | Desirability | ||||
---|---|---|---|---|---|---|---|
Speed/rpm | Refrigerant charge/g | Concentration/% | Q_{L}/kJ kg^{−1} | W_{in}/kJ kg^{−1} | COP | ||
1 | 900 | 95 | 0.07 | 193.99 | 23.28 | 8.27 | 0.734 |
2 | 900 | 95 | 0.05 | 193.92 | 23.29 | 8.22 | 0.720 |
Comparison of predicted and experimental result
No. | Factors | Desirability | Q _{L} | W _{in} | COP | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Pred./kJ kg^{−1} | Exp./kJ kg^{−1} | Error/% | Pred./kJ kg^{−1} | Exp./kJ kg^{−1} | Error/% | Pred. | Exp. | Error/% | |||
1 | (Refer Table 7) | 0.734 | 193.99 | 196.15 | 1.11 | 23.28 | 23.98 | 3.01 | 8.27 | 8.18 | 1.09 |
2 | 0.720 | 193.92 | 198.97 | 2.60 | 23.29 | 24.15 | 3.69 | 8.22 | 8.24 | 0.23 |
Conclusions
The optimization of operating parameters for an automotive air-conditioning system (AAC) was performed in the present work by varying the compressor speed, refrigerant charge and volume concentration of SiO_{2}/PAG nanolubricant. The design of experiments (DOE) based on response surface methodology (RSM) was helpful in designing the experiment and the statistical analysis in order to distinguish the significant variables which will contribute to the coefficient of performance of AAC system. This design of experiment drastically reduced the time required by reducing the number of experiments to be carried out and represent statistically proven models for all the responses. Desirability method of the response surface methodology (RSM) was believed to be the most efficient and simplest optimization technique in the present study. A high desirability of 73.4% was achieved at the compressor speed of 900 rpm, refrigerant charge of 95 g and nanolubricant volume concentration of 0.07%. This situation was regarded as the optimum parameter for the AAC system having heat absorb (Q_{L}) of 193.99 kJ kg^{−1}, compressor work (W_{in}) of 23.28 kJ kg^{−1} and COP of 8.27.
Notes
Acknowledgements
The authors are grateful to the Universiti Malaysia Pahang (www.ump.edu.my) for financial supports given under RDU160395 and PGRS170374. The authors also thank to the research team from Automotive Engineering Centre (EAC) and Advanced Automotive Liquids Laboratory (A2LL), who provided insight and expertise that greatly assisted in the present research work.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
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