**7. Conclusions**

The coupled numerical approach is investigated to predict the current, power and thermal efficiency of the thermoelectric generator system for waste heat recovery. Six ANN and seven ANFIS models are developed to predict the current, power and thermal efficiency of the thermoelectric generator system for waste heat recovery using the hot gas inlet temperatures and the voltage conditions as the inputs. Six ANN models with combinations of three training variants of Levenberg–Marquardt (LM), Scaled Conjugate Gradient (SCG) and Pola–Ribiere Conjugate Gradient (CGP), two transfer functions of Tan-Sigmodal and Log-Sigmoidal and the number of hidden neurons of 10, 15, 20 and 25 are compared. Seven ANFIS models are compared with seven types of the membership functions of triangular, trapezoidal, gauss, gauss2, gbell, pi and dsig, and the number of the membership functions of 2, 3, 4 and 5. The optimum ANN and ANFIS models are proposed from the comparison with experimental data using three statistical parameters of the coefficient of determination (R<sup>2</sup> ), root mean square error (RMSE) and coefficient of variance (COV). The ANN model with back-propagation algorithm, Levenberg–Marquardt training variant, Tan-Sigmoidal transfer function, and 25 hidden neurons is suggested as the optimum model based on optimum values of statistical parameters for the prediction of the current, power and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANFIS model with gbell membership function in a number of sets of 3 is suggested as the optimum model based on optimum values of statistical parameters to predict the current, power and thermal efficiency of the thermoelectric generator system for waste heat recovery with low prediction cost and acceptable prediction accuracy. The ANFIS model with pi or gauss membership function in the number of sets of 5 is suggested as the optimum model based on optimum values of statistical parameters to predict the current, power and thermal efficiency of the thermoelectric generator system for waste heat recovery with higher prediction accuracy. The optimum ANN and ANFIS models show better prediction of the current, power and thermal efficiency of the thermoelectric generator system for waste heat recovery with low computational time and cost than the coupled numerical approach.

**Author Contributions:** Conceptualization, K.S.G.; J.-H.S., and M.-Y.L.; methodology, K.S.G.; J.-H.S.; C.-P.C., and M.-Y.L.; software, K.S.G. and J.-H.S.; validation, K.S.G. and J.-H.S.; formal analysis, K.S.G.; J.-H.S.; C.-P.C., and M.-Y.L.; investigation, K.S.G.; J.-H.S.; C.-P.C., and M.-Y.L.; resources, K.S.G. and J.-H.S.; data curation, K.S.G.; writing—original draft preparation, K.S.G.; J.-H.S. and M.-Y.L.; writing—review and editing, K.S.G.; J.-H.S. and M.-Y.L.; visualization, K.S.G.; supervision, M.-Y.L.; project administration, M.-Y.L.; funding acquisition, M.-Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** This work was supported by the Dong-A University research fund.

**Conflicts of Interest:** The authors declare no conflict of interest.
