Chalak Qazani, Mohammad Reza, Kadirgama, Ganesan, Aslfattahi, Navid, Ramasamy, Devarajan, Samylingam, Lingenthiran, Kadirgama, Kumaran, Kok, Chee Kuang, and Ghazali, Mohd Fairusham (2025) Sustainable Viscosity Prediction of Hybrid Alumina-Copper Oxide Nanofluid for Green Heat Transfer Applications Using Adaptive Neuro-Fuzzy Inference System (ANFIS). The Nexus of Sustainability and Energy Technology Journal, 1 (1). pp. 40-55.

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Abstract

Developing more energy-efficient, environmentally friendly heat transfer solutions is necessary. As a result, the application of nanofluids has attracted an increasing amount of attention. This paper shows a new method for predicting the viscosity of hybrid alumina-copper oxide nanofluid. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to predict the abovementioned physical property of the fluid dispersed in Therminol 55; a medium-temperature medium. The concentration of nanoparticles, temperature, and shear rate were found to be the major input factors. In comparison to Decision Trees (DT) and Support Vector Regression (SVR), the ANFIS method model was more effective, yielding a Root Mean Square Error (RMSE) of 1.0278. The Mean Squared Errors (MSE) for the DT and SVR models were 96.67% and 97.75% higher, respectively, compared to the ANFIS model. It was also discovered that the viscosity is higher at lower temperatures of 32.63° C and a maximal nanoparticle concentration of 1%. It reached 34.34 mPa.s. It may also be emphasized that hybrid nanofluids, such as those incorporating alumina-copper oxide nanoparticles, exhibit lower viscosities at elevated temperatures compared to single-nanoparticle fluids, indicating their potential for optimizing heat transfer applications. It implies that they can be used to optimize the heat transfer application described in this paper. In conclusion, ANFIS is a powerful tool for predicting viscosity. The use of such instruments allows designers to create more effective, as well as environmentally friendly heat transfer systems. This study is, therefore, another example of "green engineering" solutions.

Item ID:	86697
Item Type:	Article (Research - C1)
ISSN:	3080-1915
Keywords:	Hybrid nanofluid, Viscosity, Adaptive neuro-fuzzy system
Copyright Information:	© 2025 The Author(s). This is an open-access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/)
Date Deposited:	13 Nov 2026 01:29
FoR Codes:	40 ENGINEERING > 4016 Materials engineering > 401602 Composite and hybrid materials @ 60% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460204 Fuzzy computation @ 40%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 70% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 30%
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