Modeling and optimization of thermal conductivity and viscosity of water-based hybrid nanofluids containing graphene oxide combined with silicon dioxide (Go-SiO2, 50:50) using MLP & GA

Sabouni, Hamid Reza; Mokhtarian, Ali; Rahimi, Mojtaba

doi:10.22034/cnj.2026.733628

Modeling and optimization of thermal conductivity and viscosity of water-based hybrid nanofluids containing graphene oxide combined with silicon dioxide (Go-SiO2, 50:50) using MLP & GA

Document Type : Original Article

Authors

Hamid Reza Sabouni ¹

Ali Mokhtarian ¹

Mojtaba Rahimi ²

¹ Department of Mechanical Engineering, Kho.C., Islamic Azad University, Khomeinishahr, Iran

² Department of Petroleum Engineering, Kho.C., Islamic Azad University, Khomeinishahr, Iran; Stone Research Center, Kho.C., Islamic Azad University, Khomeinishahr, Iran

10.22034/cnj.2026.733628

Abstract

In this study, architecture and training of two artificial neural networks (ANNs) were designed to predict the thermal conductivity (TC) and viscosity properties of nanofluids GO-SiO2 (50:50, Graphene Oxide and Silicon Dioxide)-based nanofluids. The nanofluid samples investigated comprised various weight fractions (0.1–1%) of a 50:50 mass-ratio mixture of graphene oxide (GO) and silicon dioxide (SiO₂) nanoparticles dispersed in the base fluid; their viscosity and thermal conductivity were measured at temperatures ranging from 30 to 60°C. For each ANN developed to predict either nanofluid viscosity or TC, the regression plots corresponding to the training, validation, and test datasets provided a clear demonstration of the network’s optimal performance. Specifically, the mean and maximum relative errors obtained for the test dataset were 0.3425% and 0.8359%, respectively, for viscosity prediction, and 0.2465% and 0.4069%, respectively, for TC prediction. Furthermore, following a sensitivity analysis of the networks, we found that the weight fraction exerts a more pronounced influence on nanofluid viscosity and TC than temperature. Subsequently, a genetic algorithm (GA) applied to the trained ANN models identified the optimal conditions as a weight fraction in the range of 0.1–1% and a temperature of 60°C.

Graphical Abstract

Keywords

Hybrid nanofluids

Weight fraction

Artificial neural network

Optimization

Genetic algorithm

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