Daneshfar, RezaBemani, AminHadipoor, MasoudSharifpur, MohsenAli, Hafiz MuhammadMahariq, IbrahimAbdeljawad, Thabet2021-02-202021-02-202020-09Daneshfar, R., Bemani. A., Hadipoor, M. et al. 2020, 'Estimating the heat capacity of non-Newtonian ionanofluid systems using ANN, ANFIS, and SGB tree algorithms', Applied Sciences (Switzerland), vol. 10, no. 18, art. 6432, pp. 1-20.2076-3417 (online)10.3390/app10186432http://hdl.handle.net/2263/78780This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R 2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.en© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Ionic liquidNanofluidHeat capacitySoft computing modelsParticle swarm optimizationStochastic gradient boosting (SGB)Radial basis function artificial neural network (RBF–ANN)Adaptive neuro-fuzzy inference system (ANFIS)Non-Newtonian ionanofluid systemsEngineering, built environment and information technology articles SDG-04SDG-04: Quality educationEngineering, built environment and information technology articles SDG-07SDG-07: Affordable and clean energyEngineering, built environment and information technology articles SDG-09SDG-09: Industry, innovation and infrastructureEngineering, built environment and information technology articles SDG-12SDG-12: Responsible consumption and productionEngineering, built environment and information technology articles SDG-13SDG-13: Climate actionArticle