Optimization of Air Conditioning Performance with Al2O3-SiO2/PAG Composite Nanolubricants Using the Response Surface Method
Abstract
:1. Introduction
2. Materials and Methods
2.1. Preparation of Al2O3-SiO2 Composite Nanolubricants
2.2. Design of Experiment with RSM
2.3. Data Analysis Using RSM
3. Results and Discussion
3.1. ANOVA Analysis
3.2. Regression Analysis
3.3. Residual and Response Surface Plots
3.4. Optimization and Validation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Bouajila, W.; Shimoda, M.; Riccius, J. Design optimization of a rocket engine’s inner liner with improved response surface methodology. Eng. Optim. 2021, 54, 1143–1159. [Google Scholar] [CrossRef]
- Eshghi, A.T.; Lee, S. Adaptive improved response surface method for reliability-based design optimization. Eng. Optim. 2019, 51, 2011–2029. [Google Scholar] [CrossRef]
- Lin, C.-H. Mended grey wolf optimization and Taguchi method with multi-goal optimization for six-phase copper rotor induction motor design. Eng. Optim. 2020, 1–20. [Google Scholar] [CrossRef]
- Kuo, Y.; Yang, T.; Huang, G.-W. The use of a grey-based Taguchi method for optimizing multi-response simulation problems. Eng. Optim. 2008, 40, 517–528. [Google Scholar] [CrossRef]
- Darvishvand, L.; Kamkari, B.; Kowsary, F. Optimal design approach for heating irregular-shaped objects in three-dimensional radiant furnaces using a hybrid genetic algorithm–artificial neural network method. Eng. Optim. 2018, 50, 452–470. [Google Scholar] [CrossRef]
- Khoshbin, F.; Bonakdari, H.; Ashraf Talesh, S.H.; Ebtehaj, I.; Zaji, A.H.; Azimi, H. Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs. Eng. Optim. 2016, 48, 933–948. [Google Scholar] [CrossRef]
- Costa, N.; Garcia, J. Using a multiple response optimization approach to optimize the coefficient of performance. Appl. Therm. Eng. 2016, 96, 137–143. [Google Scholar] [CrossRef]
- Ahmed, R.; Mahadzir, S.; Rozali, N.E.B.; Biswas, K.; Matovu, F.; Ahmed, K. Artificial intelligence techniques in refrigeration system modelling and optimization: A multi-disciplinary review. Sustain. Energy Technol. Assess. 2021, 47, 101488. [Google Scholar] [CrossRef]
- Baş, D.; Boyacı, İ.H. Modeling and optimization I: Usability of response surface methodology. J. Food Eng. 2007, 78, 836–845. [Google Scholar]
- Myers, R.H.; Montgomery, D.C.; Vining, G.G.; Borror, C.M.; Kowalski, S.M. Response surface methodology: A retrospective and literature survey. J. Qual. Technol. 2004, 36, 53–77. [Google Scholar]
- Elfghi, F.M. A hybrid statistical approach for modeling and optimization of RON: A comparative study and combined application of response surface methodology (RSM) and artificial neural network (ANN) based on design of experiment (DOE). Chem. Eng. Res. Des. 2016, 113, 264–272. [Google Scholar] [CrossRef]
- Abhang, L.B.; Hameedullah, M. Modeling and analysis for surface roughness in machining EN-31 steel using response surface methodology. Int. J. Appl. Res. Mech. Eng. 2011, 1, 33–38. [Google Scholar] [CrossRef]
- Makadia, A.J.; Nanavati, J. Optimisation of machining parameters for turning operations based on response surface methodology. Measurement 2013, 46, 1521–1529. [Google Scholar] [CrossRef]
- Gangil, M.; Pradhan, M.K. Modeling and optimization of electrical discharge machining process using RSM: A review. Mater. Today: Proc. 2017, 4, 1752–1761. [Google Scholar] [CrossRef]
- Barik, C.R.; Mandel, N.K. Parametric effect and optimization of surface roughness of EN 31 in CNC dry turning. Int. J. Lean Think. 2012, 3, 54–66. [Google Scholar]
- Krishankant, J.T.; Bector, M.; Kumar, R. Application of Taguchi method for optimizing turning process by the effects of machining parameters. Int. J. Eng. Adv. Technol. 2012, 2, 263–274. [Google Scholar]
- Rao, C.M.; Venkatasubbaiah, K. Optimization of surface roughness in CNC turning using Taguchi Method and ANOVA. Int. J. Adv. Sci. Technol. 2016, 93, 1–14. [Google Scholar] [CrossRef]
- Li, C.; Xiao, Q.; Tang, Y.; Li, L. A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving. J. Clean. Prod. 2016, 135, 263–275. [Google Scholar] [CrossRef]
- Parpas, D.; Amaris, C.; Sun, J.; Tsamos, K.M.; Tassou, S.A. Numerical study of air temperature distribution and refrigeration systems coupling for chilled food processing facilities. Energy Procedia 2017, 123, 156–163. [Google Scholar] [CrossRef]
- Belman-Flores, J.M.; Mota-Babiloni, A.; Ledesma, S.; Makhnatch, P. Using ANNs to approach to the energy performance for a small refrigeration system working with R134a and two alternative lower GWP mixtures. Appl. Therm. Eng. 2017, 127, 996–1004. [Google Scholar] [CrossRef]
- Nataraj, M.; Balasubramanian, K.; Palanisamy, D. Optimization of machining parameters for CNC turning of Al/Al2O3 MMC using RSM approach. Mater. Today Proc. 2018, 5, 14265–14272. [Google Scholar] [CrossRef]
- Ocholi, O.; Menkiti, M.; Auta, M.; Ezemagu, I. Optimization of the operating parameters for the extractive synthesis of biolubricant from sesame seed oil via response surface methodology. Egypt. J. Pet. 2018, 27, 265–275. [Google Scholar] [CrossRef]
- Mao, N.; Song, M.; Pan, D.; Deng, S. Comparative studies on using RSM and TOPSIS methods to optimize residential air conditioning systems. Energy 2018, 144, 98–109. [Google Scholar] [CrossRef]
- Redhwan, A.A.M.; Azmi, W.H.; Najafi, G.; Sharif, M.Z.; Zawawi, N.N.M. Application of response surface methodology in optimization of automotive air-conditioning performance operating with SiO2/PAG nanolubricant. J. Therm. Anal. Calorim. 2018, 135, 1269–1283. [Google Scholar] [CrossRef]
- Qader, B.S.; Supeni, E.E.; Ariffin, M.K.A.; Talib, A.R.A. RSM approach for modelling and optimization of designing parameters for inclined fins of solar air heater. Renew. Energy 2018, 136, 48–68. [Google Scholar] [CrossRef]
- Zendehboudi, A.; Mota-Babiloni, A.; Makhnatch, P.; Saidur, R.; Sait, S.M. Modeling and multi-objective optimization of an R450A vapor compression refrigeration system. Int. J. Refrig. 2019, 100, 141–155. [Google Scholar]
- Canbolat, A.S.; Bademlioglu, A.H.; Arslanoglu, N.; Kaynakli, O. Performance optimization of absorption refrigeration systems using Taguchi, ANOVA and Grey Relational Analysis methods. J. Clean. Prod. 2019, 229, 874–885. [Google Scholar] [CrossRef]
- Zaman, M.A. Photonic radiative cooler optimization using Taguchi’s method. Int. J. Therm. Sci. 2019, 144, 21–26. [Google Scholar] [CrossRef]
- Vyas, M.; Jain, M.; Pareek, K.; Garg, A. Multivariate Optimization for Maximum Capacity of Lead Acid Battery Through Taguchi Method. Measurement 2019, 148, 106904. [Google Scholar] [CrossRef]
- Huirem, B.; Sahoo, P.K. Thermodynamic Modeling and Performance Optimization of a Solar-Assisted Vapor Absorption Refrigeration System (SAVARS). Int. J. Air-Cond. Refrig. 2020, 28, 2050006. [Google Scholar] [CrossRef]
- Zawawi, N.N.M.; Azmi, W.H.; Ghazali, M.F.; Ramadhan, A.I. Performance Optimization of Automotive Air-Conditioning System Operating with Al2O3-SiO2/PAG Composite Nanolubricants using Taguchi Method. Automot. Exp. 2022, 5, 121–136. [Google Scholar] [CrossRef]
- Esfe, M.H.; Afrand, M.; Yan, W.-M.; Yarmand, H.; Toghraie, D.; Dahari, M. Effects of temperature and concentration on rheological behavior of MWCNTs/SiO2 (20–80)-SAE40 hybrid nano-lubricant. Int. Commun. Heat Mass Transf. 2016, 76, 133–138. [Google Scholar] [CrossRef]
- Esfe, M.H.; Yan, W.-M.; Afrand, M.; Sarraf, M.; Toghraie, D.; Dahari, M. Estimation of thermal conductivity of Al2O3/water (40%)–ethylene glycol (60%) by artificial neural network and correlation using experimental data. Int. Commun. Heat Mass Transf. 2016, 74, 125–128. [Google Scholar] [CrossRef]
- Afrand, M.; Nadooshan, A.A.; Hassani, M.; Yarmand, H.; Dahari, M. Predicting the viscosity of multi-walled carbon nanotubes/water nanofluid by developing an optimal artificial neural network based on experimental data. Int. Commun. Heat Mass Transf. 2016, 77, 49–53. [Google Scholar] [CrossRef]
- Atik, K.; Aktaş, A.; Deniz, E. Performance parameters estimation of MAC by using artificial neural network. Expert Syst. Appl. 2010, 37, 5436–5442. [Google Scholar] [CrossRef]
- Kamar, H.M.; Ahmad, R.; Kamsah, N.; Mustafa, A.F.M. Artificial neural networks for automotive air-conditioning systems performance prediction. Appl. Therm. Eng. 2013, 50, 63–70. [Google Scholar] [CrossRef]
- Tian, Z.; Qian, C.; Gu, B.; Yang, L.; Liu, F. Electric vehicle air conditioning system performance prediction based on artificial neural network. Appl. Therm. Eng. 2015, 89, 101–114. [Google Scholar] [CrossRef]
- Roy, R.; Bhowal, A.J.; Mandal, B.K. Exergy and Cost Optimization of a Two-Stage Refrigeration System Using Refrigerant R32 and R410A. J. Therm. Sci. Eng. Appl. 2020, 12, 031024. [Google Scholar] [CrossRef]
- Deymi-Dashtebayaz, M.; Maddah, S.; Fallahi, E. Thermo-economic-environmental optimization of injection mass flow rate in the two-stage compression refrigeration cycle (Case study: Mobarakeh steel company in Isfahan, Iran). Int. J. Refrig. 2019, 106, 7–17. [Google Scholar] [CrossRef]
- Redhwan, A.A.M.; Azmi, W.H.; Sharif, M.Z.; Zawawi, N.N.M.; Ariffin, S.Z. Utilization of Response Surface Method (RSM) in Optimizing Automotive Air Conditioning (AAC) Performance Exerting Al2O3/PAG Nanolubricant. Proc. J. Phys. Conf. Ser. 2020, 1691, 012003. [Google Scholar] [CrossRef]
- Redhwan, A.A.M.; Azmi, W.H.; Sharif, M.Z.; Mamat, R.; Samykano, M.; Najafi, G. Performance improvement in mobile air conditioning system using Al2O3/PAG nanolubricant. J. Therm. Anal. Calorim. 2019, 135, 1299–1310. [Google Scholar] [CrossRef]
- Bhiradi, I.; Hiremath, S.S. Energy efficient and cost effective method for generation of in-situ silver nanofluids: Formation, morphology and thermal properties. Adv. Powder Technol. 2020, 31, 4031–4044. [Google Scholar] [CrossRef]
- Ying, Z.; He, B.; He, D.; Kuang, Y.; Ren, J.; Song, B. Comparisons of single-phase and two-phase models for numerical predictions of Al2O3/water nanofluids convective heat transfer. Adv. Powder Technol. 2020, 31, 3050–3061. [Google Scholar] [CrossRef]
- Singh, S.K.; Sarkar, J. Improving hydrothermal performance of hybrid nanofluid in double tube heat exchanger using tapered wire coil turbulator. Adv. Powder Technol. 2020, 31, 2092–2100. [Google Scholar] [CrossRef]
- Anitha, S.; Thomas, T.; Parthiban, V.; Pichumani, M. What dominates heat transfer performance of hybrid nanofluid in single pass shell and tube heat exchanger? Adv. Powder Technol. 2019, 30, 3107–3117. [Google Scholar] [CrossRef]
- Sharif, M.Z.; Azmi, W.H.; Redhwan, A.A.M.; Mamat, R. Investigation of thermal conductivity and viscosity of Al2O3/PAG nanolubricant for application in automotive air conditioning system. Int. J. Refrig. 2016, 70, 93–102. [Google Scholar] [CrossRef]
- Redhwan, A.A.M.; Azmi, W.H.; Sharif, M.Z.; Mamat, R. Comparative study of thermo-physical properties of SiO2 and Al2O3 nanoparticles dispersed in PAG lubricant. Appl. Therm. Eng. 2017, 116, 823–832. [Google Scholar] [CrossRef]
- Redhwan, A.; Azmi, W.; Sharif, M. Thermal conductivity enhancement of Al2O3 and SiO2 nanolubricants for application in automotive air conditioning (AAC) system. MATEC Web Conf. 2017, 90, 01051. [Google Scholar] [CrossRef]
- Sanukrishna, S.S.; Prakash, M.J. Experimental studies on thermal and rheological behaviour of TiO2-PAG nanolubricant for refrigeration system. Int. J. Refrig. 2018, 86, 356–372. [Google Scholar] [CrossRef]
- Sanukrishna, S.; Vishnu, S.; Prakash, M.J. Experimental investigation on thermal and rheological behaviour of PAG lubricant modified with SiO2 nanoparticles. J. Mol. Liq. 2018, 261, 411–422. [Google Scholar] [CrossRef]
- Zawawi, N.N.M.; Azmi, W.H.; Redhwan, A.A.M.; Sharif, M.Z.; Samykano, M. Experimental investigation on thermo-physical properties of metal oxide composite nanolubricants. Int. J. Refrig. 2018, 89, 11–21. [Google Scholar] [CrossRef]
- Sanukrishna, S.S.; Prakash, M.J. Thermal and rheological characteristics of refrigerant compressor oil with alumina nanoparticles—an experimental investigation. Powder Technol. 2018, 339, 119–129. [Google Scholar] [CrossRef]
- Sharif, M.Z.; Azmi, W.H.; Redhwan, A.A.M.; Mamat, R.; Yusof, T.M. Performance analysis of SiO2/PAG nanolubricant in automotive air conditioning system. Int. J. Refrig. 2017, 75, 204–216. [Google Scholar] [CrossRef]
- Zawawi, N.N.M.; Azmi, W.H.; Ghazali, M.F. Performance of Al2O3-SiO2/PAG composite nanolubricants in automotive air-conditioning system. Appl. Therm. Eng. 2022, 204, 117998. [Google Scholar] [CrossRef]
- Zawawi, N.N.M.; Azmi, W.H.; Sharif, M.Z.; Shaiful, A.I.M. Composite nanolubricants in automotive air conditioning system: An investigation on its performance. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Pahang, Malaysia, 31 October 2018; IOP Publishing: Bristol, UK, 2019; p. 012078. [Google Scholar]
- Zakaria, I.; Azmi, W.H.; Mohamed, W.A.N.W.; Mamat, R.; Najafi, G. Experimental investigation of thermal conductivity and electrical conductivity of Al2O3 nanofluid in water-ethylene glycol mixture for proton exchange membrane fuel cell application. Int. Commun. Heat Mass Transf. 2015, 61, 61–68. [Google Scholar] [CrossRef] [Green Version]
- Sharif, M.Z.; Azmi, W.H.; Redhwan, A.A.M.; Zawawi, N.N.M. Preparation and stability of silicone dioxide dispersed in polyalkylene glycol based nanolubricants. MATEC Web Conf. 2017, 90, 01049. [Google Scholar] [CrossRef]
- Zawawi, N.N.M.; Azmi, W.H.; Sharif, M.Z.; Najafi, G. Experimental investigation on stability and thermo-physical properties of Al2O3–SiO2/PAG nanolubricants with different nanoparticle ratios. J. Therm. Anal. Calorim. 2019, 135, 1243–1255. [Google Scholar] [CrossRef]
- Zawawi, N.; Azmi, W.; Ghazali, M. Tribological performance of Al2O3–SiO2/PAG composite nanolubricants for application in air-conditioning compressor. Wear 2022, 492, 204238. [Google Scholar] [CrossRef]
- Zawawi, N.N.M.; Azmi, W.H.; Redhwan, A.A.M.; Sharif, M.Z.; Sharma, K.V. Thermo-physical properties of Al2O3-SiO2/PAG composite nanolubricant for refrigeration system. Int. J. Refrig. 2017, 80, 1–10. [Google Scholar] [CrossRef]
- Zawawi, N.N.M.; Azmi, W.H. Performance of Al2O3-SiO2/PAG employed composite nanolubricant in automotive air conditioning (AAC) system. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Kuantan, Malaysia, 1–2 October 2019; IOP Publishing: Bristol, UK, 2020; p. 012052. [Google Scholar]
- Zawawi, N.N.M.; Azmi, W.H.; Redhwan, A.A.M.; Sharif, M.Z. Thermo-physical properties of metal oxides composite Nanolubricants. J. Mech. Eng. 2018, 15, 28–38. [Google Scholar]
- Lee, J.H.; Hwang, K.S.; Jang, S.P.; Lee, B.H.; Kim, J.H.; Choi, S.U.S.; Choi, C.J. Effective viscosities and thermal conductivities of aqueous nanofluids containing low volume concentrations of Al2O3 nanoparticles. Int. J. Heat Mass Transf. 2008, 51, 2651–2656. [Google Scholar] [CrossRef]
- Raval, N.; Maheshwari, R.; Kalyane, D.; Youngren-Ortiz, S.R.; Chougule, M.B.; Tekade, R.K. Importance of physicochemical characterization of nanoparticles in pharmaceutical product development. In Basic Fundamentals of Drug Delivery; Elsevier: Amsterdam, The Netherlands, 2019; pp. 369–400. [Google Scholar]
- Sadeghi, R.; Etemad, S.G.; Keshavarzi, E.; Haghshenasfard, M. Investigation of alumina nanofluid stability by UV–vis spectrum. Microfluid. Nanofluid. 2015, 18, 1023–1030. [Google Scholar] [CrossRef]
- Tan, Y.H.; Abdullah, M.O.; Nolasco-Hipolito, C.; Zauzi, N.S.A. Application of RSM and Taguchi methods for optimizing the transesterification of waste cooking oil catalyzed by solid ostrich and chicken-eggshell derived CaO. Renew. Energy 2017, 114, 437–447. [Google Scholar] [CrossRef]
- Prabhu, M.V.; Karthikeyan, R. Comparative studies on modelling and optimization of hydrodynamic parameters on inverse fluidized bed reactor using ANN-GA and RSM. Alex. Eng. J. 2018, 57, 3019–3032. [Google Scholar] [CrossRef]
- Choi, J.M.; Kim, Y.C. The effects of improper refrigerant charge on the performance of a heat pump with an electronic expansion valve and capillary tube. Energy 2002, 27, 391–404. [Google Scholar] [CrossRef]
- Kıvak, T. Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Measurement 2014, 50, 19–28. [Google Scholar] [CrossRef]
- Mandal, N.; Doloi, B.; Mondal, B.; Das, R. Optimization of flank wear using Zirconia Toughened Alumina (ZTA) cutting tool: Taguchi method and Regression analysis. Measurement 2011, 44, 2149–2155. [Google Scholar] [CrossRef]
- Derdour, F.Z.; Kezzar, M.; Khochemane, L. Optimization of penetration rate in rotary percussive drilling using two techniques: Taguchi analysis and response surface methodology (RMS). Powder Technol. 2018, 339, 846–853. [Google Scholar] [CrossRef]
- Cetin, M.H.; Ozcelik, B.; Kuram, E.; Demirbas, E. Evaluation of vegetable based cutting fluids with extreme pressure and cutting parameters in turning of AISI 304L by Taguchi method. J. Clean. Prod. 2011, 19, 2049–2056. [Google Scholar] [CrossRef]
Author (s) | Year | Fields/Applications/Systems | Optimization Methods |
---|---|---|---|
Abhang and Hameedullah [12] | 2011 | EN31 steel turning process | RSM |
Barik and Mandel [15] | 2012 | EN31 steel turning process | RSM |
Krishankant et al. [16] | 2012 | EN34 steel turning process | Taguchi Method |
Makadia and Nanavati [13] | 2013 | EN31 steel turning process | RSM |
Rao and Venkatasubbaiah [17] | 2016 | Surface roughness in CNC turning | Taguchi and ANOVA |
Li et al. [18] | 2016 | CNC machining | Taguchi, RSM, and MOPSO |
Costa and Garcia [7] | 2016 | Refrigeration systems | RSM |
Parpas et al. [19] | 2017 | Refrigeration systems | RSM |
Gangil and Pradhan [14] | 2017 | Electrical discharge machining (EDM) process | RSM |
Parpas et al. [19] | 2017 | Air distribution and refrigeration systems | CFD/EES model |
Belman-Flores et al. [20] | 2017 | Refrigeration systems | ANN |
Nataraj et al. [21] | 2018 | CNC turning | RSM |
Ocholi et al. [22] | 2018 | Sesame biolubricant pilot plant | RSM |
Mao et al. [23] | 2018 | Resident air-conditioning (TAC) systems | RSM |
Redhwan et al. [24] | 2018 | AAC systems | RSM |
Qader et al. [25] | 2018 | Solar air heaters | RSM |
Zendehboudi et al. [26] | 2019 | VCRS | RSM |
Canbolat et al. [27] | 2019 | Absorption refrigeration systems | Taguchi and ANOVA |
Zaman [28] | 2019 | Photonic radiative coolers | Taguchi |
Vyas et al. [29] | 2019 | Capacity of lead acid battery | Taguchi |
Huirem and Sahoo [30] | 2020 | Solar-Assisted Vapor Absorption Refrigeration Systems (SAVARS) | RSM |
Ahmed et al. [8] | 2021 | Refrigeration systems | Multiple Methods |
Zawawi et al. [31] | 2022 | Automotive air-conditioning Systems | Taguchi |
Properties | Al2O3 | SiO2 |
---|---|---|
Molecular mass (g/mol) | 101.96 | 60.08 |
Average particle diameter (nm) | 13 | 30 |
Density (kg/m3) | 4000 | 2220 |
Thermal Conductivity (W/m.k) | 36 | 1.4 |
Specific heat (J/kg·K) | 773 | 745 |
Properties | PAG 46 |
---|---|
Density, g/cm3 @ 20 °C | 0.9954 |
Flash Point, °C | 174 |
Kinematic viscosity, cSt @ 40 °C | 41.4–50.6 |
Pour point, °C | −51 |
Level | A-Volume Concentration, φ (%) | B-Compressor Speed (rpm) | C-Refrigerant Charge (g) |
---|---|---|---|
−1 | 0.005 | 900 | 95 |
0 | 0.025 | 1500 | 125 |
1 | 0.045 | 2100 | 155 |
φ (%) | Speed (rpm) | Refrigerant Charge (g) | Cooling Capacity (kW) | Compressor Work (kJ/kg) | COP | Power Consumption (kW) |
---|---|---|---|---|---|---|
0.005 | 900 | 95 | 0.665 | 23.10 | 8.13 | 0.61 |
0.045 | 900 | 95 | 0.477 | 24.80 | 7.65 | 0.59 |
0.005 | 2100 | 95 | 0.860 | 39.20 | 4.72 | 1.07 |
0.045 | 2100 | 95 | 0.568 | 43.10 | 4.31 | 1.06 |
0.005 | 900 | 155 | 0.777 | 19.70 | 9.16 | 0.68 |
0.045 | 900 | 155 | 0.873 | 20.20 | 8.66 | 0.73 |
0.005 | 2100 | 155 | 1.452 | 32.20 | 5.15 | 1.42 |
0.045 | 2100 | 155 | 0.954 | 34.50 | 4.87 | 1.34 |
0.005 | 1500 | 125 | 0.956 | 32.80 | 6.06 | 0.94 |
0.045 | 1500 | 125 | 0.770 | 33.30 | 5.62 | 0.89 |
0.025 | 900 | 125 | 0.797 | 21.90 | 8.52 | 0.60 |
0.025 | 2100 | 125 | 0.891 | 37.35 | 4.81 | 1.08 |
0.025 | 1500 | 95 | 0.667 | 33.00 | 5.49 | 0.71 |
0.025 | 1500 | 155 | 1.168 | 26.60 | 6.27 | 0.89 |
0.025 | 1500 | 125 | 0.832 | 31.00 | 5.85 | 0.85 |
0.025 | 1500 | 125 | 0.832 | 31.00 | 5.85 | 0.85 |
0.025 | 1500 | 125 | 0.832 | 31.00 | 5.85 | 0.85 |
0.025 | 1500 | 125 | 0.832 | 31.00 | 5.85 | 0.85 |
0.025 | 1500 | 125 | 0.832 | 31.00 | 5.85 | 0.85 |
0.025 | 1500 | 125 | 0.832 | 31.00 | 5.85 | 0.85 |
Source | p-Value | Std. Dev | R2 | Adj R2 (%) | Pred R2 (%) | Remark |
---|---|---|---|---|---|---|
Linear | <0.0001 | 0.097 | 0.8075 | 0.7714 | 0.5784 | suggested |
2FI | 0.0076 | 0.069 | 0.9207 | 0.8841 | 0.3576 | suggested |
Quadratic | 0.7544 | 0.075 | 0.9293 | 0.8656 | −0.1099 | not suggested |
Qubic | 0.0088 | 0.035 | 0.9905 | 0.9698 | −10.7188 | aliased |
Source | Sum of Squares | df | Mean Square | F Value | p-Value | |
---|---|---|---|---|---|---|
Model | 0.70 | 6 | 0.17 | 25.16 | <0.0001 | significant |
A | 0.11 | 1 | 0.11 | 23.62 | 0.0003 | |
B | 0.13 | 1 | 0.13 | 26.84 | 0.0002 | |
C | 0.39 | 1 | 0.39 | 81.94 | <0.0001 | |
AB | 0.061 | 1 | 0.061 | 12.66 | 0.0035 | |
AC | 7.849 × 10−4 | 1 | 7.849 × 10−4 | 0.16 | 0.6931 | |
BC | 0.028 | 1 | 0.028 | 5.7 | 0.0325 | |
Residual | 0.063 | 13 | 4.819 × 10−3 | |||
Lack of fit | 0.063 | 8 | 7.830 × 10−3 | |||
Pure error | 0.000 | 5 | 0.000 | |||
R2 | 0.9207 | |||||
Adj R2 | 0.8841 | |||||
Pred R2 | 0.3576 | |||||
Adeq Precision | 21.986 |
Source | p-Value | Std. Dev | R2 | Adj R2 (%) | Pred R2 (%) | Remark |
---|---|---|---|---|---|---|
Linear | <0.0001 | 1.47 | 0.9520 | 0.9429 | 0.9133 | not suggested |
2FI | 0.1947 | 1.37 | 0.9661 | 0.9505 | 0.8683 | not suggested |
Quadratic | <0.0001 | 0.39 | 0.9979 | 0.9961 | 0.9852 | suggested |
Qubic | 0.0377 | 0.24 | 0.9995 | 0.9985 | 0.4299 | aliased |
Source | Sum of Squares | df | Mean Square | F Value | p-Value | |
---|---|---|---|---|---|---|
Model | 718.55 | 9 | 79.84 | 536.88 | <0.0001 | significant |
A | 7.92 | 1 | 7.92 | 53.27 | <0.0001 | |
B | 587.52 | 1 | 587.52 | 3950.82 | <0.0001 | |
C | 90.00 | 1 | 90.00 | 605.21 | <0.0001 | |
AB | 2.00 | 1 | 2.00 | 13.45 | 0.0043 | |
AC | 0.98 | 1 | 0.98 | 6.59 | 0.0280 | |
BC | 7.22 | 1 | 7.22 | 48.55 | <0.0001 | |
A2 | 8.25 | 1 | 8.25 | 55.46 | <0.0001 | |
B2 | 7.88 | 1 | 7.88 | 53.02 | <0.0001 | |
C2 | 6.34 | 1 | 6.34 | 42.62 | <0.0001 | |
Residual | 1.49 | 10 | 0.15 | |||
Lack of fit | 1.49 | 5 | 0.30 | |||
Pure error | 0.000 | 5 | 0.000 | |||
R2 | 0.9979 | |||||
Adj R2 | 0.9961 | |||||
Pred R2 | 0.9822 | |||||
Adeq Precision | 84.751 |
Source | p-Value | Std. Dev | R2 | Adj R2 (%) | Pred R2 (%) | Remark |
---|---|---|---|---|---|---|
Linear | <0.0001 | 0.43 | 0.9236 | 0.9093 | 0.8706 | not suggested |
2FI | 0.8700 | 0.46 | 0.9276 | 0.8941 | 0.5939 | not suggested |
Quadratic | <0.0001 | 0.030 | 0.9998 | 0.9995 | 0.9974 | suggested |
Qubic | 0.1951 | 0.025 | 0.9999 | 0.9997 | 0.8774 | aliased |
Source | Sum of Squares | df | Mean Square | F Value | p-Value | |
---|---|---|---|---|---|---|
Model | 38.21 | 9 | 4.25 | 4604.92 | <0.0001 | significant |
A | 0.45 | 1 | 0.45 | 489.34 | <0.0001 | |
B | 33.40 | 1 | 33.40 | 36228.04 | <0.0001 | |
C | 1.45 | 1 | 1.45 | 1570.68 | <0.0001 | |
AB | 0.010 | 1 | 0.010 | 11.22 | 0.0074 | |
AC | 1.886 × 10−3 | 1 | 1.886 × 10−3 | 2.05 | 0.1831 | |
BC | 0.14 | 1 | 0.14 | 149.91 | <0.0001 | |
A2 | 6.006 × 10−3 | 1 | 6.006 × 10−3 | 6.51 | 0.0288 | |
B2 | 1.66 | 1 | 1.66 | 1798.26 | <0.0001 | |
C2 | 3.415 × 10−4 | 1 | 3.415 × 10−4 | 0.37 | 0.5564 | |
Residual | 9.220 × 10−3 | 10 | 9.220 × 10−4 | |||
Lack of fit | 9.220 × 10−3 | 5 | 1.844 × 10−3 | |||
Pure error | 0.000 | 5 | 0.000 | |||
R2 | 0.9998 | |||||
Adj R2 | 0.9995 | |||||
Pred R2 | 0.9974 | |||||
Adeq Precision | 225.476 |
Source | p-Value | Std. Dev | R2 | Adj R2 (%) | Pred R2 (%) | Remark |
---|---|---|---|---|---|---|
Linear | <0.0001 | 0.024 | 0.9556 | 0.9473 | 0.9230 | suggested |
2FI | 0.5746 | 0.025 | 0.9617 | 0.9440 | 0.8285 | not suggested |
Quadratic | 0.0006 | 0.012 | 0.9927 | 0.9862 | 0.9181 | suggested |
Qubic | 0.3124 | 0.011 | 0.9964 | 0.9885 | −3.4727 | aliased |
Source | Sum of Squares | df | Mean Square | F Value | p-Value | |
---|---|---|---|---|---|---|
Model | 0.21 | 9 | 0.023 | 151.49 | <0.0001 | significant |
A | 6.882 × 10−5 | 1 | 6.882 × 10−5 | 0.45 | 0.005179 | |
B | 0.18 | 1 | 0.18 | 1176.19 | <0.0001 | |
C | 0.021 | 1 | 0.021 | 135.83 | <0.0001 | |
AB | 2.977 × 10−4 | 1 | 2.977 × 10−4 | 1.94 | 0.1935 | |
AC | 5.249 × 10−5 | 1 | 5.249 × 10−5 | 0.34 | 0.5713 | |
BC | 9.312 × 10−4 | 1 | 9.312 × 10−4 | 6.08 | 0.0334 | |
A2 | 5.971 × 10−3 | 1 | 5.971 × 10−3 | 38.97 | <0.0001 | |
B2 | 2.481 × 10−4 | 1 | 2.481 × 10−4 | 1.62 | 0.2320 | |
C2 | 5.313 × 10−4 | 1 | 5.313 × 10−4 | 3.47 | 0.0922 | |
Residual | 1.532 × 10−3 | 10 | 1.532 × 10−4 | 1.532 × 10−3 | ||
Lack of fit | 1.532 × 10−3 | 5 | 3.064 × 10−4 | 1.532 × 10−3 | ||
Pure error | 0.000 | 5 | 0.000 | 0.000 | ||
R2 | 0.9927 | |||||
Adj R2 | 0.9862 | |||||
Pred R2 | 0.9181 | |||||
Adeq precision | 41.908 |
Parameter | Optimum Operating Condition |
---|---|
A—Volume Concentration, φ (%) | 0.019 |
B—Compressor Speed (rpm) | 900 |
C—Initial Refrigerant Charge (g) | 155 |
No. | Responses | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Cooling Capacity | Compressor Work | COP | Power Consumption | |||||||||
Pred. | Exp. | % | Pred. | Exp. | % | Pred. | Exp. | % | Pred. | Exp. | % | |
1 | 0.935 | 0.976 | 4.23 | 19.23 | 19.6 | 1.89 | 9.05 | 9.34 | 3.10 | 0.621 | 0.650 | 4.43 |
2 | 0.897 | 4.23 | 18.6 | 3.39 | 9.87 | 8.31 | 0.655 | 5.23 | ||||
3 | 0.868 | 7.73 | 19.9 | 3.37 | 9.19 | 1.52 | 0.656 | 5.33 | ||||
4 | 0.987 | 5.27 | 19.3 | 0.36 | 9.47 | 4.44 | 0.674 | 7.86 | ||||
5 | 0.874 | 6.93 | 21.3 | 9.72 | 8.55 | 5.85 | 0.671 | 7.38 | ||||
Avg | 5.68 | 3.74 | 4.64 | 6.05 |
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Zawawi, N.N.M.; Azmi, W.H.; Redhwan, A.A.M.; Ramadhan, A.I.; Ali, H.M. Optimization of Air Conditioning Performance with Al2O3-SiO2/PAG Composite Nanolubricants Using the Response Surface Method. Lubricants 2022, 10, 243. https://doi.org/10.3390/lubricants10100243
Zawawi NNM, Azmi WH, Redhwan AAM, Ramadhan AI, Ali HM. Optimization of Air Conditioning Performance with Al2O3-SiO2/PAG Composite Nanolubricants Using the Response Surface Method. Lubricants. 2022; 10(10):243. https://doi.org/10.3390/lubricants10100243
Chicago/Turabian StyleZawawi, Nurul Nadia Mohd, Wan Hamzah Azmi, Abd Aziz Mohamad Redhwan, Anwar Ilmar Ramadhan, and Hafiz Muhammad Ali. 2022. "Optimization of Air Conditioning Performance with Al2O3-SiO2/PAG Composite Nanolubricants Using the Response Surface Method" Lubricants 10, no. 10: 243. https://doi.org/10.3390/lubricants10100243