Application of ANFIS and LSSVM strategies for estimating thermal conductivity enhancement of metal and metal oxide based nanofluids

Razieh Razavi,Aida Sabaghmoghadam,Amin Bemani,Alireza Baghban,Kwok-Wing Chau,Ely Salwana

doi:10.1080/19942060.2019.1620130

Abstract

An extensive variety of chemical engineering processes include the transfer of heat energy. Since increasing the effective contact surface is known as one of the popular manners to improve the efficiency of heat transfer, the attention to the nanofluids has been attracted. Due to the difficulty and high cost of an experimental study, researchers have been attracted to fast computational methods. In this work, Adaptive neuro-fuzzy inference system and least square support vector machine algorithms have been applied as a comprehensive predictive tool to forecast the nanofluids thermal conductivity in terms of diameter, temperature, the thermal conductivity of the base fluid, the thermal conductivity of nanoparticle and volume fraction. To this end, a large and comprehensive experimental databank contains 1109 data points have been collected from reliable sources. The particle swarm optimization is utilized to reach the best structures of the proposed algorithms. A comprehensive statistical and graphical investigations are carried out to prove the accuracy and ability of proposed models. In addition, the comparisons outputs indicate that the least square support vector machine algorithm has the best performance among the existing correlations and Adaptive neuro-fuzzy inference system algorithms for forecasting thermal conductivity of different nanofluids.

Highlights

Owning to extensive applications of heat transfer phenomenon in different industrial instruments such as the cooling systems, compressor, evaporators, and boilers, it is well-known as a critical point
The Root Mean squared error (RMSE) values for least square support vector machine (LSSVM) and Adaptive neuro-fuzzy inference system (ANFIS) were determined as 0.02 and 0.034 respectively. These analyses confirm the consistency and applicability of suggested models in order to predict the thermal conductivity of nanofluids. These analyses show that the LSSVM algorithm has better performance respect to the ANFIS algorithm
In order to show the acceptable performance of LSSVM and ANFIS algorithms, a comprehensive dataset from existing experimental studies in literature has been gathered

Summary

Introduction

Owning to extensive applications of heat transfer phenomenon in different industrial instruments such as the cooling systems, compressor, evaporators, and boilers, it is well-known as a critical point. The thermal conductivity property of some base fluids such as ethylene glycol (EG), water (H2O) and different types of oil, some addition of nanoparticles to these fluids can increase their thermal conductivity (Eiamsa-ard & Wongcharee, 2018; Zeeshan, Shehzad, Ellahi, & Alamri, 2017). These nanoparticles give us the advantage of having a simple fluidized process and they solve some major problems, for example, the precipitation of particles, the blockage of channels, and erosion problems because of their nanosize structures

Methods

Results

Conclusion