Zhang, TaoSalahshour, SoheılPasha, Anahita Manafi KhajehSajadi, S. MohammadJasim, Dheyaa J.Nasajpour-Esfahani, NavidMaleki, HamidBaghaei, Sh.2024-05-252024-05-25202411385-89471873-321210.1016/j.cej.2024.1500592-s2.0-85186334090https://doi.org/10.1016/j.cej.2024.150059https://hdl.handle.net/20.500.14517/1169Zhang, Tao/0000-0002-0134-3094; Manafi Khajeh Pasha, Anahita/0000-0002-6235-3202The rheological and thermal behavior of nanofluids in real-world scenarios is significantly affected by their thermophysical properties (TPPs). Therefore, optimizing TPPs can remarkably improve the performance of nanofluids. In this regard, in the present study, a hybrid strategy is proposed that combines machine learning (ML), multi-objective optimization (MOO), and multi-criteria decision-making (MCDM) to select optimal parameters for water-based multi-walled carbon nanotubes (MWCNTs)-oxide hybrid nanofluids. In the first step, four critical TPPs, including density ratio (DR), viscosity ratio (VR), specific heat capacity ratio (SHCR), and thermal conductivity ratio (TCR), are modeled using two efficient ML techniques, the group method of data handling neural network (GMDH-NN) and combinatorial (COMBI) algorithm. In the next step, the superior models are subjected to a four-objective optimization by the well-known non-dominated sorting genetic algorithm II (NSGA-II), which aims to minimize DR/VR and maximize SHCR/TCR. This study considers volume fraction (VF), oxide nanoparticle (NP) type, and system temperature as optimization variables. In the final step, two prominent MCDM techniques, TOPSIS and VIKOR, were used to identify the desirable optimal points from the Pareto fronts generated by the MOO algorithm. ML results reveal the COMBI algorithm's superior reliability in accurately modeling various TPPs. The pattern of Pareto fronts for all oxide-NPs indicated that over one-third of the optimal points have a VF > 1.5 %. On the other hand, the distribution of optimal points across different temperature ranges varied significantly depending on the type of oxide-NPs. For Al2O3-based nanofluid, around 90 % of the optimal points were within 40-50 degrees C. Conversely, for nanofluids containing CeO2 NPs, only approximately 24 % of the optimal points were found within the same temperature range. Considering diverse scenarios for weighting TPPs in the MCDM process implied that combining CeO2/ZnO oxide-NPs with MWCNTs in water-based nanofluids is highly effective across various real-world applications.eninfo:eu-repo/semantics/closedAccessDynamic viscosityHybrid nanofluidMachine learningMulti -criteria decision -makingMulti -objective optimizationThermal conductivityArticleQ1Q1485WOS:001208917000001