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

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.

An intelligent approach to predicting the effect of nanoparticle mixture ratio, concentration and temperature on thermal conductivity of hybrid nanofluids

“…MLP-ANN, ANFIS, LSSVM and RBF-ANN methods were used to forecast the thermal conductivity of TiO 2 water nanofluids [45]. Similar to Razavi et al [43] study, this study also observed that LSSVM produced the best predictive results showing the least deviation factor. It is important to note that in the sensitivity analysis, the volume fraction of nanoparticles had a direct impact on the results.…”

“…The ANN was more accurate than the proposed correlation model, with an R 2 of 0.9997. Razavi et al [43] used particle swarm-optimised LSSVM and ANFIS to predict the thermal conductivity of nanofluids. In the study, 1109 data points were collected from experimental studies and were used to train and test the neural network.…”

Use of adsorption-influencing parameters for designing the batch adsorber and neural network–based prediction modelling for the aqueous arsenate removal using combustion synthesised nano-alumina

“…ANN is characterized by its structure representing the pattern of connection between nodes, connection weights, and activation function. This technique has been widely used for the prediction of nanomaterial properties (Alizadeh et al, 2017;Ahmadi et al, 2019;Razavi et al, 2019). In the present study, neural network was developed using the tool box of Visual Gene Developer software (1.9).…”

Predictive Modeling of Henry’s Law Constant in Chemical Structures Using LSSVM and ANFIS Algorithms

“…In this method, the configuration have 5 different layers. The Gaussian membership function is optimized to reach most accurate answers [20][21][22][23][24]:…”