Advertisement

An insight into the prediction of TiO2/water nanofluid viscosity through intelligence schemes

  • Mohammad Hossein AhmadiEmail author
  • Alireza Baghban
  • Mahyar Ghazvini
  • Masoud Hadipoor
  • Roghayeh GhasempourEmail author
  • Mohammad Reza Nazemzadegan
Article
  • 8 Downloads

Abstract

Viscosity can be mentioned as one of the most crucial properties of nanofluids due to its ability to describe the fluid resistance to flow, and as the result it affects other phenomena. The effects of nanofluids’ viscosity on different parameters can be enumerated as pressure drop, pumping power, feasibility of the nanofluid, and its convective heat transfer coefficient. In this investigation, the viscosity of TiO2/water nanofluid was compared and analyzed with experimental data. The primary goal of this investigation was to introduce a combination of experimental and modeling approaches to predict viscosity values using four different neural networks. Between MLP-ANN, ANFIS, LSSVM, and RBF-ANN methods, it was found that the LSSVM produced better results with the lowest deviation factor and reflected the most accurate responses between the proposed models. The regression diagram of experimental and estimated values shows an R2 coefficient of 0.995 and 0.993 for training and testing sections of the ANFIS model. These values for MLP-ANN, RBF-ANN, and LSSVM models were 0.998 and 0.999, 0.996 and 0.997, and 0.997 and 1.000 for their training and testing parts, respectively. Furthermore, the effect of different parameters was investigated using a sensitivity analysis which demonstrates that the average diameter can be considered as the most affecting parameter on the viscosity TiO2–water nanofluid with a relevancy factor of 0.992123.

Keywords

Viscosity Neural networks LSSVM ANFIS TiO2–water nanofluids 

Notes

References

  1. 1.
    Ahmadi MH, Tatar A, Seifaddini P, Ghazvini M, Ghasempour R, Sheremet MA. Thermal conductivity and dynamic viscosity modeling of Fe2O3/water nanofluid by applying various connectionist approaches. Numer Heat Transf A Appl. 2018;74:1301–22.  https://doi.org/10.1080/10407782.2018.1505092.CrossRefGoogle Scholar
  2. 2.
    Hemmat Esfe M, Kamyab MH, Afrand M, Amiri MK. Using artificial neural network for investigating of concurrent effects of multi-walled carbon nanotubes and alumina nanoparticles on the viscosity of 10 W-40 engine oil. Phys A Stat Mech Appl. 2018;510:610–24.  https://doi.org/10.1016/j.physa.2018.06.029.CrossRefGoogle Scholar
  3. 3.
    Hemmat Esfe M, Rostamian H, Esfandeh S, Afrand M. Modeling and prediction of rheological behavior of Al2O3-MWCNT/5W50 hybrid nano-lubricant by artificial neural network using experimental data. Phys A Stat Mech Appl. 2018;510:625–34.  https://doi.org/10.1016/j.physa.2018.06.041.CrossRefGoogle Scholar
  4. 4.
    Hemmat Esfe M, Nadooshan AA, Arshi A, Alirezaie A. Convective heat transfer and pressure drop of aqua based TiO2 nanofluids at different diameters of nanoparticles: data analysis and modeling with artificial neural network. Phys E Low-Dimens Syst Nanostruct. 2018;97:155–61.  https://doi.org/10.1016/j.physe.2017.10.002.CrossRefGoogle Scholar
  5. 5.
    Hemmat Esfe M, Tatar A, Ahangar MRH, Rostamian H. A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant. Phys E Low-Dimens Syst Nanostruct. 2018;96:85–93.  https://doi.org/10.1016/j.physe.2017.08.019.CrossRefGoogle Scholar
  6. 6.
    Hemmat Esfe M, Rostamian H, Reza Sarlak M, Rejvani M, Alirezaie A. Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: an experimental study and a neural network simulating. Phys E Low-Dimens Syst Nanostruct. 2017;94:231–40.  https://doi.org/10.1016/j.physe.2017.07.012.CrossRefGoogle Scholar
  7. 7.
    Afrand M, Hemmat Esfe M, Abedini E, Teimouri H. Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data. Phys E Low-Dimens Syst Nanostruct. 2017;87:242–7.  https://doi.org/10.1016/j.physe.2016.10.020.CrossRefGoogle Scholar
  8. 8.
    Karimipour A, Hemmat Esfe M, Safaei MR, Semiromi D, Jafari S, Kazi SN. Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method. Phys A Stat Mech Appl. 2014;402:150–68.  https://doi.org/10.1016/j.physa.2014.01.057.CrossRefGoogle Scholar
  9. 9.
    Hemmat Esfe M, Saedodin S, Mahian O, Wongwises S. Efficiency of ferromagnetic nanoparticles suspended in ethylene glycol for applications in energy devices: effects of particle size, temperature, and concentration. Int Commun Heat Mass Transf. 2014;58:138–46.  https://doi.org/10.1016/j.icheatmasstransfer.2014.08.035.CrossRefGoogle Scholar
  10. 10.
    Nafchi PM, Karimipour A, Afrand M. The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties. Phys A Stat Mech Appl. 2019;516:1–18.  https://doi.org/10.1016/J.PHYSA.2018.10.015.CrossRefGoogle Scholar
  11. 11.
    Vafaei M, Afrand M, Sina N, Kalbasi R, Sourani F, Teimouri H. Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks. Phys E Low-Dimens Syst Nanostruct. 2017;85:90–6.  https://doi.org/10.1016/J.PHYSE.2016.08.020.CrossRefGoogle Scholar
  12. 12.
    Ahmadi MH, Nazari MA, Ghasempour R, Madah H, Shafii MB, Ahmadi MA. Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods. Colloids Surf A Physicochem Eng Asp. 2018.  https://doi.org/10.1016/j.colsurfa.2018.01.030.Google Scholar
  13. 13.
    Ahmadi MH, Ahmadi MA, Nazari MA, Mahian O, Ghasempour R. A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach. J Therm Anal Calorim. 2019;135:271–81.  https://doi.org/10.1007/s10973-018-7035-z.CrossRefGoogle Scholar
  14. 14.
    Kahani M, Ahmadi MH, Tatar A, Sadeghzadeh M. Development of multilayer perceptron artificial neural network (MLP-ANN) and least square support vector machine (LSSVM) models to predict Nusselt number and pressure drop of TiO2/water nanofluid flows through non-straight pathways. Numer Heat Transf A Appl. 2018.  https://doi.org/10.1080/10407782.2018.1523597.Google Scholar
  15. 15.
    Hemmat Esfe M, Goodarzi M, Reiszadeh M, Afrand M. Evaluation of MWCNTs-ZnO/5W50 nanolubricant by design of an artificial neural network for predicting viscosity and its optimization. J Mol Liq. 2019;277:921–31.  https://doi.org/10.1016/j.molliq.2018.08.047.CrossRefGoogle Scholar
  16. 16.
    Hemmat Esfe M, Abbasian Arani AA, Esfandeh S. Experimental study on rheological behavior of monograde heavy-duty engine oil containing CNTs and oxide nanoparticles with focus on viscosity analysis. J Mol Liq. 2018;272:319–29.  https://doi.org/10.1016/j.molliq.2018.09.004.CrossRefGoogle Scholar
  17. 17.
    Hemmat Esfe M, Esfandeh S, Alirezaie A. A novel experimental investigation on the effect of nanoparticles composition on the rheological behavior of nano-hybrids. J Mol Liq. 2018;269:933–9.  https://doi.org/10.1016/j.molliq.2017.11.147.CrossRefGoogle Scholar
  18. 18.
    Alipour H, Karimipour A, Safaei MR, Semiromi DT, Akbari OA. Influence of T-semi attached rib on turbulent flow and heat transfer parameters of a silver-water nanofluid with different volume fractions in a three-dimensional trapezoidal microchannel. Phys E Low-Dimens Syst Nanostruct. 2017;88:60–76.  https://doi.org/10.1016/J.PHYSE.2016.11.021.CrossRefGoogle Scholar
  19. 19.
    Nojoomizadeh M, D’Orazio A, Karimipour A, Afrand M, Goodarzi M. Investigation of permeability effect on slip velocity and temperature jump boundary conditions for FMWNT/water nanofluid flow and heat transfer inside a microchannel filled by a porous media. Phys E Low-Dimens Syst Nanostruct. 2018;97:226–38.  https://doi.org/10.1016/J.PHYSE.2017.11.008.CrossRefGoogle Scholar
  20. 20.
    Anoop KB, Kabelac S, Sundararajan T, Das SK. Rheological and flow characteristics of nanofluids: Influence of electroviscous effects and particle agglomeration. J Appl Phys. 2009;106:034909.  https://doi.org/10.1063/1.3182807.CrossRefGoogle Scholar
  21. 21.
    Nguyen CT, Desgranges F, Roy G, Galanis N, Maré T, Boucher S, Angue Mintsa H. Temperature and particle-size dependent viscosity data for water-based nanofluids—hysteresis phenomenon. Int J Heat Fluid Flow. 2007;28:1492–506.  https://doi.org/10.1016/j.ijheatfluidflow.2007.02.004.CrossRefGoogle Scholar
  22. 22.
    Pastoriza-Gallego MJ, Casanova C, Legido JL, Piñeiro MM. CuO in water nanofluid: Influence of particle size and polydispersity on volumetric behaviour and viscosity. Fluid Phase Equilib. 2011;300:188–96.  https://doi.org/10.1016/J.FLUID.2010.10.015.CrossRefGoogle Scholar
  23. 23.
    Pak BC, Cho YI. Hydrodynamic and heat transfer study of dispersed fluids with submicron metallic oxide particles. Exp Heat Transf. 1998;11:151–70.  https://doi.org/10.1080/08916159808946559.CrossRefGoogle Scholar
  24. 24.
    Kwek D, Crivoi A, Duan F. Effects of temperature and particle size on the thermal property measurements of Al2O3–water Nanofluids. J Chem Eng Data. 2010;55:5690–5.  https://doi.org/10.1021/je1006407.CrossRefGoogle Scholar
  25. 25.
    Hemmat Esfe M, Saedodin S, Sina N, Afrand M, Rostami S. Designing an artificial neural network to predict thermal conductivity and dynamic viscosity of ferromagnetic nanofluid. Int Commun Heat Mass Transf. 2015;68:50–7.  https://doi.org/10.1016/j.icheatmasstransfer.2015.06.013.CrossRefGoogle Scholar
  26. 26.
    Hemmat Esfe M, Saedodin S. An experimental investigation and new correlation of viscosity of ZnO–EG nanofluid at various temperatures and different solid volume fractions. Exp Therm Fluid Sci. 2014;55:1–5.  https://doi.org/10.1016/j.expthermflusci.2014.02.011.CrossRefGoogle Scholar
  27. 27.
    Prasher R, Song D, Wang J, Phelan P. Measurements of nanofluid viscosity and its implications for thermal applications. Appl Phys Lett. 2006;89:133108.  https://doi.org/10.1063/1.2356113.CrossRefGoogle Scholar
  28. 28.
    Duangthongsuk W, Wongwises S. Measurement of temperature-dependent thermal conductivity and viscosity of TiO2-water nanofluids. Exp. Therm. Fluid Sci. 2009;33:706–14.  https://doi.org/10.1016/J.EXPTHERMFLUSCI.2009.01.005.CrossRefGoogle Scholar
  29. 29.
    Tavman I, Turgut A, Chirtoc M, Schuchmann HP, Tavman S. Archives of materials science and engineering international scientific journal published monthly as the organ of the Committee of Materials Science of the Polish Academy of Sciences. Cheltenham: International OCSCO World Press; 2007.Google Scholar
  30. 30.
    Meybodi MK, Daryasafar A, Koochi MM, Moghadasi J, Meybodi RB, Ghahfarokhi AK. A novel correlation approach for viscosity prediction of water based nanofluids of Al2O3, TiO2, SiO2 and CuO. J Taiwan Inst Chem Eng. 2016;58:19–27.  https://doi.org/10.1016/J.JTICE.2015.05.032.CrossRefGoogle Scholar
  31. 31.
    Yiamsawas T, Mahian O, Dalkilic AS, Kaewnai S, Wongwises S. Experimental studies on the viscosity of TiO2 and Al2O3 nanoparticles suspended in a mixture of ethylene glycol and water for high temperature applications. Appl Energy. 2013;111:40–5.  https://doi.org/10.1016/J.APENERGY.2013.04.068.CrossRefGoogle Scholar
  32. 32.
    Hemmat Esfe M, Saedodin S, Asadi A, Karimipour A. Thermal conductivity and viscosity of Mg(OH)2-ethylene glycol nanofluids. J Therm Anal Calorim. 2015;120:1145–9.  https://doi.org/10.1007/s10973-015-4417-3.CrossRefGoogle Scholar
  33. 33.
    Nabeel Rashin M, Hemalatha J. Viscosity studies on novel copper oxide–coconut oil nanofluid. Exp Therm Fluid Sci. 2013;48:67–72.  https://doi.org/10.1016/j.expthermflusci.2013.02.009.CrossRefGoogle Scholar
  34. 34.
    Zhao N, Wen X, Yang J, Li S, Wang Z. Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks. Powder Technol. 2015;281:173–83.  https://doi.org/10.1016/J.POWTEC.2015.04.058.CrossRefGoogle Scholar
  35. 35.
    Vajjha RS, Das DK, Ray DR. Development of new correlations for the Nusselt number and the friction factor under turbulent flow of nanofluids in flat tubes. Int J Heat Mass Transf. 2015;80:353–67.  https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2014.09.018.CrossRefGoogle Scholar
  36. 36.
    Maddah H, Ghazvini M, Ahmadi MH. Predicting the efficiency of CuO/water nanofluid in heat pipe heat exchanger using neural network. Int Commun Heat Mass Transf. 2019;104:33–40.  https://doi.org/10.1016/J.ICHEATMASSTRANSFER.2019.02.002.CrossRefGoogle Scholar
  37. 37.
    Hemmat Esfe M, Saedodin S, Mahmoodi M. Experimental studies on the convective heat transfer performance and thermophysical properties of MgO–water nanofluid under turbulent flow. Exp Therm Fluid Sci. 2014;52:68–78.  https://doi.org/10.1016/j.expthermflusci.2013.08.023.CrossRefGoogle Scholar
  38. 38.
    Abdellahoum C, Mataoui A, Oztop HF. Comparison of viscosity variation formulations for turbulent flow of Al2O3–water nanofluid over a heated cavity in a duct. Adv Powder Technol. 2015;26:1210–8.  https://doi.org/10.1016/J.APT.2015.06.002.CrossRefGoogle Scholar
  39. 39.
    Mehrabi M, Sharifpur M, Meyer JP. Viscosity of nanofluids based on an artificial intelligence model. Int Commun Heat Mass Transf. 2013;43:16–21.  https://doi.org/10.1016/J.ICHEATMASSTRANSFER.2013.02.008.CrossRefGoogle Scholar
  40. 40.
    Hemmat Esfe M, Ahangar MR, Rejvani M, Toghraie D, Hajmohammad MH. Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data. Int Commun Heat Mass Transf. 2016;75:192–6.  https://doi.org/10.1016/j.icheatmasstransfer.2016.04.002.CrossRefGoogle Scholar
  41. 41.
    Jia-Fei Z, Zhong-Yang L, Ming-Jiang N, Ke-Fa C. Dependence of nanofluid viscosity on particle size and pH value. Chin Phys Lett. 2009;26:066202.  https://doi.org/10.1088/0256-307X/26/6/066202.CrossRefGoogle Scholar
  42. 42.
    Hari M, Joseph SA, Mathew S, Nithyaja B, Nampoori VPN, Radhakrishnan P. Thermal diffusivity of nanofluids composed of rod-shaped silver nanoparticles. Int J Therm Sci. 2013;64:188–94.  https://doi.org/10.1016/J.IJTHERMALSCI.2012.08.011.CrossRefGoogle Scholar
  43. 43.
    Kole M, Dey TK. Role of interfacial layer and clustering on the effective thermal conductivity of CuO–gear oil nanofluids. Exp Therm Fluid Sci. 2011;35:1490–5.  https://doi.org/10.1016/J.EXPTHERMFLUSCI.2011.06.010.CrossRefGoogle Scholar
  44. 44.
    Tso CY, Fu SC, Chao CYH. A semi-analytical model for the thermal conductivity of nanofluids and determination of the nanolayer thickness. Int J Heat Mass Transf. 2014;70:202–14.  https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2013.10.077.CrossRefGoogle Scholar
  45. 45.
    Chen H, Ding Y, Tan C. Rheological behaviour of nanofluids. New J Phys. 2007;9:367.  https://doi.org/10.1088/1367-2630/9/10/367.CrossRefGoogle Scholar
  46. 46.
    He Y, Jin Y, Chen H, Ding Y, Cang D, Lu H. Heat transfer and flow behaviour of aqueous suspensions of TiO2 nanoparticles (nanofluids) flowing upward through a vertical pipe. Int J Heat Mass Transf. 2007;50:2272–81.  https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2006.10.024.CrossRefGoogle Scholar
  47. 47.
    Ansari HR, Zarei MJ, Sabbaghi S, Keshavarz P. A new comprehensive model for relative viscosity of various nanofluids using feed-forward back-propagation MLP neural networks. Int Commun Heat Mass Transf. 2018;91:158–64.  https://doi.org/10.1016/J.ICHEATMASSTRANSFER.2017.12.012.CrossRefGoogle Scholar
  48. 48.
    Baghban A, Kardani MN, Habibzadeh S. Prediction viscosity of ionic liquids using a hybrid LSSVM and group contribution method. J Mol Liq. 2017;236:452–64.  https://doi.org/10.1016/J.MOLLIQ.2017.04.019.CrossRefGoogle Scholar
  49. 49.
    Bahadori A, Baghban A, Bahadori M, Lee M, Ahmad Z, Zare M, Abdollahi E. Computational intelligent strategies to predict energy conservation benefits in excess air controlled gas-fired systems. Appl Therm Eng. 2016;102:432–46.  https://doi.org/10.1016/J.APPLTHERMALENG.2016.04.005.CrossRefGoogle Scholar
  50. 50.
    Baghban A, Mohammadi AH, Taleghani MS. Rigorous modeling of CO2 equilibrium absorption in ionic liquids. Int J Greenh Gas Control. 2017;58:19–41.  https://doi.org/10.1016/J.IJGGC.2016.12.009.CrossRefGoogle Scholar
  51. 51.
    Baghban A, Bahadori M, Rozyn J, Lee M, Abbas A, Bahadori A, Rahimali A. Estimation of air dew point temperature using computational intelligence schemes. Appl Therm Eng. 2016;93:1043–52.CrossRefGoogle Scholar
  52. 52.
    Baghban A, Bahadori A, Mohammadi AH, Behbahaninia A. Prediction of CO2 loading capacities of aqueous solutions of absorbents using different computational schemes. Int J Greenh Gas Control. 2017;57:143–61.CrossRefGoogle Scholar
  53. 53.
    Baghban A, Ahmadi MA, Shahraki BH. Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. J Supercrit Fluids. 2015;98:50–64.CrossRefGoogle Scholar
  54. 54.
    Atashrouz S, Pazuki G, Alimoradi Y. Estimation of the viscosity of nine nanofluids using a hybrid GMDH-type neural network system. Fluid Phase Equilib. 2014;372:43–8.  https://doi.org/10.1016/J.FLUID.2014.03.031.CrossRefGoogle Scholar
  55. 55.
    Derakhshanfard F, Mehralizadeh A. Application of artificial neural networks for viscosity of crude oil-based nanofluids containing oxides nanoparticles. J Pet Sci Eng. 2018;168:263–72.CrossRefGoogle Scholar
  56. 56.
    Meybodi MK, Naseri S, Shokrollahi A, Daryasafar A. Prediction of viscosity of water-based Al2O3, TiO2, SiO2, and CuO nanofluids using a reliable approach. Chemom Intell Lab Syst. 2015;149:60–9.  https://doi.org/10.1016/J.CHEMOLAB.2015.10.001.CrossRefGoogle Scholar
  57. 57.
    Baghban A, Habibzadeh S, Ashtiani FZ. Toward a modeling study of thermal conductivity of nanofluids using LSSVM strategy. J Therm Anal Calorim. 2018;10:1–10.  https://doi.org/10.1007/s10973-018-7074-5.Google Scholar
  58. 58.
    Atashrouz S, Mozaffarian M, Pazuki G. Viscosity and rheological properties of ethylene glycol + water + Fe3O4 nanofluids at various temperatures: Experimental and thermodynamics modeling. Korean J Chem Eng. 2016;33:2522–9.  https://doi.org/10.1007/s11814-016-0169-4.CrossRefGoogle Scholar
  59. 59.
    Heidari E, Sobati MA, Movahedirad S. Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemom Intell Lab Syst. 2016;155:73–85.  https://doi.org/10.1016/j.chemolab.2016.03.031.CrossRefGoogle Scholar
  60. 60.
    Barati-Harooni A, Najafi-Marghmaleki A. An accurate RBF-NN model for estimation of viscosity of nanofluids. J Mol Liq. 2016;224:580–8.  https://doi.org/10.1016/J.MOLLIQ.2016.10.049.CrossRefGoogle Scholar
  61. 61.
    Hemmati-Sarapardeh A, Varamesh A, Husein MM, Karan K. On the evaluation of the viscosity of nanofluid systems: Modeling and data assessment. Renew Sustain Energy Rev. 2018;81:313–29.  https://doi.org/10.1016/J.RSER.2017.07.049.CrossRefGoogle Scholar
  62. 62.
    Longo GA, Zilio C, Ceseracciu E, Reggiani M. Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids. Nano Energy. 2012;1:290–6.  https://doi.org/10.1016/J.NANOEN.2011.11.007.CrossRefGoogle Scholar
  63. 63.
    Hemmat Esfe M, 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;74:125–8.  https://doi.org/10.1016/j.icheatmasstransfer.2016.02.002.CrossRefGoogle Scholar
  64. 64.
    Hemmat Esfe M, Afrand M, Wongwises S, Naderi A, Asadi A, Rostami S, Akbari M. Applications of feedforward multilayer perceptron artificial neural networks and empirical correlation for prediction of thermal conductivity of Mg(OH)2–EG using experimental data. Int Commun Heat Mass Transf. 2015;67:46–50.  https://doi.org/10.1016/j.icheatmasstransfer.2015.06.015.CrossRefGoogle Scholar
  65. 65.
    Hemmat Esfe M, Afrand M, Yan W-M, Akbari M. Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data. Int Commun Heat Mass Transf. 2015;66:246–9.  https://doi.org/10.1016/j.icheatmasstransfer.2015.06.002.CrossRefGoogle Scholar
  66. 66.
    Esfe M, Wongwises S, Naderi A, Asadi A, Safaei MR, Rostamian H, Dahari M, Karimipour A. Thermal conductivity of Cu/TiO2–water/EG hybrid nanofluid: experimental data and modeling using artificial neural network and correlation. Int Commun Heat Mass Transf. 2015;66:100–4.  https://doi.org/10.1016/j.icheatmasstransfer.2015.05.014.CrossRefGoogle Scholar
  67. 67.
    Hemmat Esfe M, Rostamian H, Afrand M, Karimipour A, Hassani M. Modeling and estimation of thermal conductivity of MgO–water/EG (60:40) by artificial neural network and correlation. Int Commun Heat Mass Transf. 2015;68:98–103.  https://doi.org/10.1016/j.icheatmasstransfer.2015.08.015.CrossRefGoogle Scholar
  68. 68.
    Aminian A. Predicting the effective viscosity of nanofluids for the augmentation of heat transfer in the process industries. J Mol Liq. 2017;229:300–8.  https://doi.org/10.1016/J.MOLLIQ.2016.12.071.CrossRefGoogle Scholar
  69. 69.
    Tafarroj MM, Daneshazarian R, Kasaeian A. CFD modeling and predicting the performance of direct absorption of nanofluids in trough collector. Appl Therm Eng. 2019;148:256–69.CrossRefGoogle Scholar
  70. 70.
    Manogaran G, Varatharajan R, Priyan MK. Hybrid recommendation system for heart disease diagnosis based on multiple kernel learning with adaptive neuro-fuzzy inference system. Multimed. Tools Appl. 2018;77:4379–99.CrossRefGoogle Scholar
  71. 71.
    Mohaghegh S. Virtual-intelligence applications in petroleum engineering: part 3—fuzzy logic. J Pet Technol. 2000;52:82–7.  https://doi.org/10.2118/62415-JPT.CrossRefGoogle Scholar
  72. 72.
    Mohagheghian E, Zafarian-Rigaki H, Motamedi-Ghahfarrokhi Y, Hemmati-Sarapardeh A. Using an artificial neural network to predict carbon dioxide compressibility factor at high pressure and temperature. Korean J Chem Eng. 2015;32:2087–96.  https://doi.org/10.1007/s11814-015-0025-y.CrossRefGoogle Scholar
  73. 73.
    Lashkarbolooki M, Hezave AZ, Ayatollahi S. Artificial neural network as an applicable tool to predict the binary heat capacity of mixtures containing ionic liquids. Fluid Phase Equilib. 2012;324:102–7.  https://doi.org/10.1016/J.FLUID.2012.03.015.CrossRefGoogle Scholar
  74. 74.
    Hemmati-Sarapardeh A, Ghazanfari M-H, Ayatollahi S, Masihi M. Accurate determination of the CO2-crude oil minimum miscibility pressure of pure and impure CO2 streams: a robust modelling approach. Can J Chem Eng. 2016;94:253–61.  https://doi.org/10.1002/cjce.22387.CrossRefGoogle Scholar
  75. 75.
    Fausett L. Fundamentals of neural networks: architectures, algorithms, and applications. Prentice-Hall, Inc., 1994.Google Scholar
  76. 76.
    Alfarhan KA, Mashor MY, Saad AR, Azeez HA, Sabry MM. Effects of the window size and feature extraction approach for arrhythmia classification. J Biomim Biomater Biomed Eng. 2017;30:1–11.  https://doi.org/10.4028/www.scientific.net/JBBBE.30.1.CrossRefGoogle Scholar
  77. 77.
    Wang R, Du H, Zhou F, Deng D, Liu Y. An adaptive neural fuzzy network clothing comfort evaluation model and application in digital home. Multimed Tools Appl. 2014;71:395–410.  https://doi.org/10.1007/s11042-013-1519-4.CrossRefGoogle Scholar
  78. 78.
    Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9:293–300.  https://doi.org/10.1023/A:1018628609742.CrossRefGoogle Scholar
  79. 79.
    Varamesh A, Hemmati-Sarapardeh A, Dabir B, Mohammadi AH. Development of robust generalized models for estimating the normal boiling points of pure chemical compounds. J Mol Liq. 2017;242:59–69.  https://doi.org/10.1016/J.MOLLIQ.2017.06.039.CrossRefGoogle Scholar
  80. 80.
    Panda SS, Chakraborty D, Pal SK. Flank wear prediction in drilling using back propagation neural network and radial basis function network. Appl Soft Comput. 2008;8:858–71.  https://doi.org/10.1016/J.ASOC.2007.07.003.CrossRefGoogle Scholar
  81. 81.
    Turgut A, Tavman I, Chirtoc M, Schuchmann HP, Sauter C, Tavman S. Thermal conductivity and viscosity measurements of water-based TiO2 nanofluids. Int J Thermophys. 2009;30(4):1213–26.CrossRefGoogle Scholar
  82. 82.
    Duangthongsuk Weerapun, Wongwises Somchai. Measurement of temperature-dependent thermal conductivity and viscosity of TiO2–water nanofluids. Exp Therm Fluid Sci. 2009;33(4):706–14.CrossRefGoogle Scholar
  83. 83.
    Murshed SMS, Leong KC, Yang C. Enhanced thermal conductivity of TiO2–water based nanofluids. Int J Therm Sci. 2005;44(4):367–73.CrossRefGoogle Scholar
  84. 84.
    Murshed SMS, Leong KC, Yang C. Investigations of thermal conductivity and viscosity of nanofluids. Int J Therm Sci. 2008;47(5):560–8.CrossRefGoogle Scholar
  85. 85.
    Bobbo Sergio, Fedele Laura, Benetti Anna, Colla Laura, Fabrizio Monica, Pagura Cesare, Barison Simona. Viscosity of water based SWCNH and TiO2 nanofluids. Exp Therm Fluid Sci. 2012;36:65–71.CrossRefGoogle Scholar
  86. 86.
    Gramatica P. Principles of QSAR models validation: internal and external. Mol Inform. 2007;26:694–701.Google Scholar
  87. 87.
    Goodall CR. 13 Computation using the QR decomposition. Handb Stat. 1993;9:467–508.CrossRefGoogle Scholar
  88. 88.
    Thacker BH, Doebling SW, Hemez FM, Anderson MC, Pepin JE, Rodriguez EA. Concepts of model verification and validation. Los Alamos: Los Alamos National Laboratory; 2004.  https://doi.org/10.2172/835920.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  • Mohammad Hossein Ahmadi
    • 1
    Email author
  • Alireza Baghban
    • 2
  • Mahyar Ghazvini
    • 3
  • Masoud Hadipoor
    • 4
  • Roghayeh Ghasempour
    • 3
    Email author
  • Mohammad Reza Nazemzadegan
    • 3
  1. 1.Faculty of Mechanical EngineeringShahrood University of TechnologyShahroodIran
  2. 2.Chemical Engineering DepartmentAmirkabir University of Technology, Mahshahr CampusMahshahrIran
  3. 3.Department of Renewable Energy and Environment, Faculty of New Sciences and TechnologiesUniversity of TehranTehranIran
  4. 4.Department of Petroleum Engineering, Ahwaz Faculty of Petroleum EngineeringPetroleum University of Technology (PUT)AhwazIran

Personalised recommendations