An Intelligent Approach to Predict the Viscosity of Water/Glycerin Containing Cu Nanoparticles: Neuro-Fuzzy Inference System (ANFIS) Model

Document Type : Research Paper

Author

Department of Chemical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

Abstract

The ability to approximate the nanofluid properties such as viscosity, thermal conductivity, and specific heat capacity will greatly assist in the modeling and design of nanofluidic systems. The purpose of this study was to present an adaptive neuro-fuzzy inference system (ANFIS) model for estimating the viscosity of Water/Glycerin nanofluid-containing Cu nanoparticles. The model inputs consist of two variables of temperature and volume concentration of nanofluids which have a great influence on the nanofluid viscosity. The experimental data were divided into two categories: training (three-quarters) and testing (a quarter of the data). The grid partition and subtractive clustering approaches were employed to determine the ANFIS configuration. The mean value of the relative error of 5.18% and the root mean square error of 0.0794 were obtained by comparing the target and model output values for the testing data. Proper matching of ANFIS prediction results with the test data set indicates the validity of the model. In addition, an empirical correlation was developed based on the form presented in the literature. The constants of the equation were determined by the genetic algorithm (GA) searching technique. The comparison of the prediction accuracy of the two models showed the complete superiority of the ANFIS.

Keywords


Van der Geer J, Hanraads JA, Lupton RA. The art of writing a scientific article. J. Sci. Commun. 2000;163(2):51-9.
[2] Lu H, Xu M, Gong L, Duan X, Chai JC. Effects of surface roughness in microchannel with passive heat transfer enhancement structures. International Journal of Heat and Mass Transfer. 2020 Feb 1;148:119070.
174 Beigzadeh
[3] Kumar B, Srivastava GP, Kumar M, Patil AK. A review of heat transfer and fluid flow mechanism in heat exchanger tube with inserts. Chemical Engineering and Processing-Process Intensification. 2018 Jan 1;123:126-37.
[4] Beigzadeh R, Ozairy R. Developing predictive models for analysis the heat transfer in sinusoidal wavy channels. Thermal Science and Engineering Progress. 2019 Dec 1;14:100425.
[5] Assael MJ, Antoniadis KD, Wakeham WA, Zhang X. Potential applications of nanofluids for heat transfer. International Journal of Heat and Mass Transfer. 2019 Aug 1;138:597-607.
[6] Yıldız Ç, Arıcı M, Karabay H. Comparison of a theoretical and experimental thermal conductivity model on the heat transfer performance of Al2O3-SiO2/water hybrid-nanofluid. International Journal of Heat and Mass Transfer. 2019 Sep 1;140:598-605.
[7] Bardool R, Bakhtyari A, Esmaeilzadeh F, Wang X. Nanofluid viscosity modeling based on the friction theory. Journal of Molecular Liquids. 2019 Jul 15;286:110923.
[8] Einstein A. A new determination of molecular dimensions. Annual Physics. 1906;19: 289–306.
[9] Brinkman HC. The viscosity of concentrated suspensions and solutions. The Journal of Chemical Physics. 1952 Apr;20(4):571-571.
[10] Lundgren TS. Slow flow through stationary random beds and suspensions of spheres. Journal of fluid mechanics. 1972 Jan;51(2):273-299.
[11] Alawi OA, Sidik NA, Xian HW, Kean TH, Kazi SN. Thermal conductivity and viscosity models of metallic oxides nanofluids. International Journal of Heat and Mass Transfer. 2018 Jan 1;116:1314-25.
[12] Akilu S, Baheta AT, Kadirgama K, Padmanabhan E, Sharma KV. Viscosity, electrical and thermal conductivities of ethylene and propylene glycol-based β-SiC nanofluids. Journal of Molecular Liquids. 2019 Jun 15;284:780-92.
[13] Kavitha R, Kumar PM, A Review on nanofluids thermal properties determination using intelligent techniques or soft computing tools. Int. J. Scientific Res. 2015; 463–465.
[14] Akhgar A, Toghraie D, Sina N, Afrand M. Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid. Powder Technology. 2019 Oct 1;355:602-10.
[15] Afrand M, Najafabadi KN, Sina N, Safaei MR, Kherbeet AS, Wongwises S, Dahari M. Prediction of dynamic viscosity of a hybrid nano-lubricant by an optimal artificial neural network. International Communications in Heat and Mass Transfer. 2016 Aug 1;76:209-14.
[16] Jang JS. ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man. Cybern., 1993;23: 665–685.
[17] Beigzadeh R. Estimation of LLE Data for Binary Systems of N-Formylmorpholine with Alkanes Using Artificial Neural Network–Genetic Algorithm (ANN–GA) Model. Chemical Methodologies. 2019 Jan 1;3(1):67-82.
[18] Alarifi IM, Nguyen HM, Naderi Bakhtiyari A, Asadi A. Feasibility of ANFIS-PSO and ANFIS-GA models in predicting thermophysical properties of Al2O3-MWCNT/oil hybrid nanofluid. Materials. 2019 Jan;12(21):3628.
[19] Mehrabi M, Sharifpur M, Meyer JP. Viscosity of nanofluids based on an artificial intelligence model. International Communications in Heat and Mass Transfer. 2013 Apr 1;43:16-21.
[20] Alrashed AA, Gharibdousti MS, Goodarzi M, de Oliveira LR, Safaei MR, Bandarra Filho EP. Effects on thermophysical properties of carbon based nanofluids: experimental data, modelling using regression, ANFIS and ANN. International Journal of Heat and Mass Transfer. 2018 Oct 1;125:920-32.
[21] Toghraie D, Sina N, Jolfaei NA, Hajian M, Afrand M. Designing an Artificial Neural Network (ANN) to predict the viscosity of Silver/Ethylene glycol nanofluid at different temperatures and volume fraction of nanoparticles. Physica A: Statistical Mechanics and its Applications. 2019 Nov 15;534:122142.
[22] Meybodi MK, Naseri S, Shokrollahi A, Daryasafar A. Prediction of viscosity of water-based Al2O3, TiO2, SiO2, and CuO nanofluids using a reliable approach. Chemometrics and Intelligent Laboratory Systems. 2015 Dec 15;149:60-9.
[23] Lahari MC, Sai PS, Narayanaswamy KS, HaseenaBee P, Devaraj S, Sharma KV. Experimental determination of viscosity of Water-Glycerin based Cu nano-fluids. Materials Today: Proceedings. 2019 Jan 1;19:517-20.
Journal of Chemical and Petroleum Engineering 2020, 55(1): 163-175 175
[24] Tong RM. A control engineering review of fuzzy systems. Automatica. 1977 Nov 1;13(6):559-69.
[25] Beigzadeh R, Hajialyani M, Rahimi M. Heat transfer and fluid flow modeling in serpentine microtubes using adaptive neuro-fuzzy approach. Korean Journal of Chemical Engineering. 2016 May 1;33(5):1534-50.
[26] Goldberg DE, Genetic Algorithms in Search, Optimization, and Machine Learning,
Addison-Wesley Longman Inc. 2000.
[27] Alade IO, Abd Rahman MA, Saleh TA. Modeling and prediction of the specific heat capacity of Al2 O3/water nanofluids using hybrid genetic algorithm/support vector regression model. Nano-Structures & Nano-Objects. 2019 Feb 1;17:103-11.
[28] Batchelor GK. The effect of Brownian motion on the bulk stress in a suspension of spherical particles. Journal of fluid mechanics. 1977 Nov;83(1):97-117.
[29] Chen H, Ding Y, Tan C. Rheological behaviour of nanofluids. New journal of physics. 2007 Oct 9;9(10):367
Volume 55, Issue 1
June 2021
Pages 163-175
  • Receive Date: 30 May 2020
  • Revise Date: 20 December 2020
  • Accept Date: 14 February 2021
  • First Publish Date: 10 April 2021