Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery
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 considerin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/2/259 |
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| author | Kunal Sandip Garud Jae-Hyeong Seo Chong-Pyo Cho Moo-Yeon Lee |
| author_facet | Kunal Sandip Garud Jae-Hyeong Seo Chong-Pyo Cho Moo-Yeon Lee |
| author_sort | Kunal Sandip Garud |
| collection | DOAJ |
| description | 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−Marquardt 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. |
| first_indexed | 2024-04-11T13:43:29Z |
| format | Article |
| id | doaj.art-e36a34d3c64749a2ad6ca2d0c490fb98 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T13:43:29Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-e36a34d3c64749a2ad6ca2d0c490fb982022-12-22T04:21:10ZengMDPI AGSymmetry2073-89942020-02-0112225910.3390/sym12020259sym12020259Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat RecoveryKunal Sandip Garud0Jae-Hyeong Seo1Chong-Pyo Cho2Moo-Yeon Lee3School of Mechanical Engineering, Dong-A University, 37 Nakdong-Daero 550, Saha-gu, Busan 49315, KoreaSchool of Mechanical Engineering, Dong-A University, 37 Nakdong-Daero 550, Saha-gu, Busan 49315, KoreaEnergy Saving Technologies Laboratory, Korea Institute of Energy Research,152 Gajeong-ro, Yuseong-gu, Daejeon 34129, KoreaSchool of Mechanical Engineering, Dong-A University, 37 Nakdong-Daero 550, Saha-gu, Busan 49315, KoreaThe 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−Marquardt 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.https://www.mdpi.com/2073-8994/12/2/259adaptive neuro-fuzzy interface systemartificial neural networkcurrentpowerthermal efficiencythermoelectric generator systemwaste heat recovery |
| spellingShingle | Kunal Sandip Garud Jae-Hyeong Seo Chong-Pyo Cho Moo-Yeon Lee Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery Symmetry adaptive neuro-fuzzy interface system artificial neural network current power thermal efficiency thermoelectric generator system waste heat recovery |
| title | Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery |
| title_full | Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery |
| title_fullStr | Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery |
| title_full_unstemmed | Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery |
| title_short | Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery |
| title_sort | artificial neural network and adaptive neuro fuzzy interface system modelling to predict thermal performances of thermoelectric generator for waste heat recovery |
| topic | adaptive neuro-fuzzy interface system artificial neural network current power thermal efficiency thermoelectric generator system waste heat recovery |
| url | https://www.mdpi.com/2073-8994/12/2/259 |
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