Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials

A. Melaibari, Ammar and Khetib, Yacine and K. Alanazi, Abdullah and Sajadi, S. Mohammad and Sharifpur, Mohsen and Cheraghian, Goshtasp (2021) Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials. Sustainability, 13 (20): 11505. ISSN 2071-1050

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Abstract

In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The first technique employed the response surface methodology based on the design of experiments, while in the second technique, artificial intelligence algorithms were implemented to estimate the GO-CuO/water-EG hybrid nanofluid viscosity. The nanofluid sample’s behavior at 0.1, 0.2, and 0.4 vol.% is in agreement with the Newtonian behavior of the base fluid, but loading more nanoparticles conforms with the behavior of the fluid with non-Newtonian classification. Considering the possibility of non-Newtonian behavior of nanofluid temperature, shear rate and volume fraction were effective on the target variable and were defined in the implementation of both techniques. Considering two constraints (i.e., the maximum R-square value and the minimum mean square error), the best neural network and suitable polynomial were selected. Finally, a comparison was made between the two techniques to evaluate their potential in viscosity estimation. Statistical considerations proved that the R-squared for ANN and RSM techniques could reach 0.995 and 0.944, respectively, which is an indication of the superiority of the ANN technique to the RSM one.

Item Type: Article
Uncontrolled Keywords: Hybrid Nanofluid, Viscosity, Arterial Neural Network, Response Surface Method
Subjects: Q Science > QD Chemistry
Divisions: Department of Nutrition > Research papers
Depositing User: ePrints Depositor
Date Deposited: 08 Oct 2024 10:08
Last Modified: 08 Oct 2024 10:08
URI: https://eprints.cihanuniversity.edu.iq/id/eprint/1623

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