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In the present work, the thermal conductivity (TC) of stable water base fly ash and fly ash-Copper (80:20 vol.%) nanofluid was determined experimentally in the temperature range of 30–60 °C for the volume concentration range of 0–4.0%. The two-step method was applied to prepare the nanofluids. The outcomes revealed that the TC of both the nanofluids augmented with the temperature and concentration, and also hybrid nanofluid had greater TC compared to the fly ash nanofluid and water. A new correlation was proposed for the calculation of the TC of these nanofluids based on obtained data. The maximum TC ratio of 1.32 and 1.50 obtained for a concentration of 4 vol.% of fly ash and hybrid nanofluid at 60 °C. In addition, to effectively predict and optimize the TC of water-based fly ash and studied hybrid nanofluid, multi-gene genetic programming  ( MGGP), and artificial neural network (ANN) approaches were applied. The comparative analysis showed the excellent ability of the ANN and MGGP model to predict the TC of fly ash and hybrid nanofluid with the regression coefficient (R) values of 0.9969 and 0.9966, respectively.

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Experimental investigation on thermal conductivity of fly ash nanofluid and fly ash-Cu hybrid nanofluid: prediction and optimization via ANN and MGGP model