{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T09:36:04Z","timestamp":1772098564997,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,8]],"date-time":"2020-02-08T00:00:00Z","timestamp":1581120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg\u2013Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.<\/jats:p>","DOI":"10.3390\/sym12020259","type":"journal-article","created":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T11:48:51Z","timestamp":1581335331000},"page":"259","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery"],"prefix":"10.3390","volume":"12","author":[{"given":"Kunal Sandip","family":"Garud","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Dong-A University, 37 Nakdong-Daero 550, Saha-gu, Busan 49315, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0277-4571","authenticated-orcid":false,"given":"Jae-Hyeong","family":"Seo","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dong-A University, 37 Nakdong-Daero 550, Saha-gu, Busan 49315, Korea"}]},{"given":"Chong-Pyo","family":"Cho","sequence":"additional","affiliation":[{"name":"Energy Saving Technologies Laboratory, Korea Institute of Energy Research,152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8857-4444","authenticated-orcid":false,"given":"Moo-Yeon","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dong-A University, 37 Nakdong-Daero 550, Saha-gu, Busan 49315, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Demirel, Y. (2012). Energy: Production, Conversion, Storage, Conservation, and Coupling, Springer Science & Business Media.","DOI":"10.1007\/978-1-4471-2372-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1016\/S1359-4311(03)00012-7","article-title":"Thermoelectrics: A review of present and potential applications","volume":"23","author":"Riffat","year":"2003","journal-title":"Appl. Therm. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.apenergy.2018.02.176","article-title":"Performance evaluation of an automotive thermoelectric generator with inserted fins or dimpled-surface hot heat exchanger","volume":"218","author":"Wang","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.enconman.2014.05.061","article-title":"Investigation and design optimization of exhaust-based thermoelectric generator system for internal combustion engine","volume":"85","author":"Niu","year":"2014","journal-title":"Energy Convers. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.applthermaleng.2014.07.022","article-title":"Experiments and simulations on heat exchangers in thermoelectric generator for automotive application","volume":"71","author":"Liu","year":"2014","journal-title":"Appl. Therm. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Quan, R., Liu, G., Wang, C., Zhou, W., Huang, L., and Deng, Y. (2018). Performance investigation of an exhaust thermoelectric generator for military SUV application. Coatings, 8.","DOI":"10.3390\/coatings8010045"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1016\/j.applthermaleng.2019.03.060","article-title":"Modelling and simulation study of a converging thermoelectric generator for engine waste heat recovery","volume":"153","author":"Luo","year":"2019","journal-title":"Appl. Therm. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/j.ijheatmasstransfer.2018.02.029","article-title":"Evaluation of metal foam based thermoelectric generators for automobile waste heat recovery","volume":"122","author":"Nithyanandam","year":"2018","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1016\/j.applthermaleng.2017.09.134","article-title":"Performance enhancement of heat pipes assisted thermoelectric generator for automobile exhaust heat recovery","volume":"130","author":"Cao","year":"2018","journal-title":"Appl. Therm. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1800012","DOI":"10.1002\/adts.201800012","article-title":"Performance Comparison of Different Exhaust Exchanger Types Considering Peak Net Power and Optimal Dimension in a Thermoelectric Generator System","volume":"1","author":"He","year":"2018","journal-title":"Adv. Theory Simul."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.enconman.2017.08.030","article-title":"Experimental investigation on thermoelectric generator with non-uniform hot-side heat exchanger for waste heat recovery","volume":"150","author":"Lu","year":"2017","journal-title":"Energy Convers. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1016\/j.egypro.2019.02.187","article-title":"Optimization model for power generation using thermoelectric generator","volume":"160","author":"Rana","year":"2019","journal-title":"Energy Procedia"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.apenergy.2012.05.033","article-title":"A 1 kWe thermoelectric stack for geothermal power generation\u2013Modeling and geometrical optimization","volume":"99","author":"Suter","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.enconman.2018.06.006","article-title":"Performance analysis of automobile exhaust thermoelectric generator system with media fluid","volume":"171","author":"Zhao","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.apenergy.2019.01.233","article-title":"Performance investigation of an intermediate fluid thermoelectric generator for automobile exhaust waste heat recovery","volume":"239","author":"Zhao","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.energy.2013.02.067","article-title":"Experiment on thermal uniformity and pressure drop of exhaust heat exchanger for automotive thermoelectric generator","volume":"54","author":"Lu","year":"2013","journal-title":"Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.fuel.2018.11.034","article-title":"In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger","volume":"239","author":"Dheenamma","year":"2019","journal-title":"Fuel"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.18186\/journal-of-thermal-engineering.433806","article-title":"Power generation from combusted \u201cSyngas\u201d using hybrid thermoelectric generator and forecasting the performance with ANN technique","volume":"4","author":"Angeline","year":"2018","journal-title":"J. Therm. Eng."},{"key":"ref_19","first-page":"53","article-title":"Performance prediction of hybrid thermoelectric generator with high accuracy using artificial neural networks","volume":"33","author":"Angeline","year":"2019","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.enconman.2014.11.015","article-title":"Performance analysis of a waste heat recovery thermoelectric generation system for automotive application","volume":"90","author":"Liu","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s10973-018-7762-1","article-title":"Performance analysis of a double-pass solar air heater system with asymmetric channel flow passages","volume":"136","author":"Raj","year":"2019","journal-title":"J. Therm. Anal. Calorim."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1021\/acsaem.8b00064","article-title":"High-Power-Density Skutterudite-Based Thermoelectric Modules with Ultralow Contact Resistivity Using Fe\u2013Ni Metallization Layers","volume":"1","author":"Park","year":"2018","journal-title":"ACS Appl. Energy Mater."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ijheatmasstransfer.2017.10.007","article-title":"Heat transfer characteristics of the integrated heating system for cabin and battery of an electric vehicle under cold weather conditions","volume":"117","author":"Seo","year":"2018","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.applthermaleng.2017.08.056","article-title":"Theoretical analysis and design optimization of thermoelectric generator","volume":"127","author":"Ma","year":"2017","journal-title":"Appl. Therm. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yazdani-Chamzini, A., Zavadskas, E.K., Antucheviciene, J., and Bausys, R. (2017). A model for shovel capital cost estimation, using a hybrid model of multivariate regression and neural networks. Symmetry, 9.","DOI":"10.3390\/sym9120298"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1016\/j.apenergy.2009.01.001","article-title":"Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks","volume":"86","author":"Mohanraj","year":"2009","journal-title":"Appl. Energy"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Islam, K.T., Raj, R.G., and Mujtaba, G. (2017). Recognition of traffic sign based on bag-of-words and artificial neural network. Symmetry, 9.","DOI":"10.3390\/sym9080138"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1016\/j.applthermaleng.2018.10.136","article-title":"Characterization of a triple concentric-tube heat exchanger with corrugated tubes using Artificial Neural Networks (ANN)","volume":"147","author":"Molina","year":"2019","journal-title":"Appl. Therm. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ullah, I., Fayaz, M., and Kim, D. (2019). Improving accuracy of the kalman filter algorithm in dynamic conditions using ANN-based learning module. Symmetry, 11.","DOI":"10.3390\/sym11010094"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2747","DOI":"10.1016\/j.renene.2011.03.009","article-title":"Performance analysis of single-stage refrigeration system with internal heat exchanger using neural network and neuro-fuzzy","volume":"36","year":"2011","journal-title":"Renew. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bogiatzis, A., and Papadopoulos, B. (2019). Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures. Symmetry, 11.","DOI":"10.3390\/sym11020286"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Q., Zhang, X., Song, S., Niu, C., and Shangguan, Y. (2019). Using ANFIS and BPNN Methods to Predict the Unfrozen Water Content of Saline Soil in Western Jilin, China. Symmetry, 11.","DOI":"10.3390\/sym11010016"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.icheatmasstransfer.2010.12.025","article-title":"Modeling of heat transfer and fluid flow characteristics of helicoidal double-pipe heat exchangers using adaptive neuro-fuzzy inference system (ANFIS)","volume":"38","author":"Mehrabi","year":"2011","journal-title":"Int. Commun. Heat Mass Transf."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sarkar, M., Julai, S., Wen Tong, C., and Toha, S.F. (2019). Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency. Symmetry, 11.","DOI":"10.3390\/sym11040456"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yeom, C.U., and Kwak, K.C. (2018). Performance Comparison of ANFIS Models by Input Space Partitioning Methods. Symmetry, 10.","DOI":"10.3390\/sym10120700"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Erturun, U., and Mossi, K. (2012, January 19\u201321). A feasibility investigation on improving structural integrity of thermoelectric modules with varying geometry. Proceedings of the ASME 2012 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Stone Mountain, GA, USA.","DOI":"10.1115\/SMASIS2012-8247"},{"key":"ref_37","unstructured":"Phillips, S.S. (2009). Characterizing the Thermal Efficiency of Thermoelectric Modules. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1016\/j.energy.2015.09.078","article-title":"Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps","volume":"93","author":"Gunasekar","year":"2015","journal-title":"Energy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8134","DOI":"10.1016\/j.eswa.2010.05.074","article-title":"ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system","volume":"37","author":"Esen","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.renene.2018.05.008","article-title":"Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification","volume":"127","author":"Belaout","year":"2018","journal-title":"Renew. Energy"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.renene.2015.08.028","article-title":"Adaptive neuro-fuzzy inference system modelling for performance prediction of solar thermal energy system","volume":"86","author":"Entchev","year":"2016","journal-title":"Renew. Energy"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/2\/259\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:52Z","timestamp":1760172952000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/2\/259"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,8]]},"references-count":41,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["sym12020259"],"URL":"https:\/\/doi.org\/10.3390\/sym12020259","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,8]]}}}