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Abstract
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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.
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