A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype
This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynami...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/13/1535 |
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| author | Hamid Enayatollahi Paul Sapin Chinedu K. Unamba Peter Fussey Christos N. Markides Bao Kha Nguyen |
| author_facet | Hamid Enayatollahi Paul Sapin Chinedu K. Unamba Peter Fussey Christos N. Markides Bao Kha Nguyen |
| author_sort | Hamid Enayatollahi |
| collection | DOAJ |
| description | This paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes. |
| first_indexed | 2024-03-09T04:55:45Z |
| format | Article |
| id | doaj.art-0c53caf104bf4454b15dc6a555c60369 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T04:55:45Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-0c53caf104bf4454b15dc6a555c603692023-12-03T13:05:32ZengMDPI AGElectronics2079-92922021-06-011013153510.3390/electronics10131535A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle PrototypeHamid Enayatollahi0Paul Sapin1Chinedu K. Unamba2Peter Fussey3Christos N. Markides4Bao Kha Nguyen5Department of Engineering and Design, University of Sussex, Brighton BN1 9QT, UKClean Energy Processes (CEP) Laboratory, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UKClean Energy Processes (CEP) Laboratory, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UKDepartment of Engineering and Design, University of Sussex, Brighton BN1 9QT, UKClean Energy Processes (CEP) Laboratory, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UKDepartment of Engineering and Design, University of Sussex, Brighton BN1 9QT, UKThis paper presents a control-oriented neuro-fuzzy model of brazed-plate evaporators for use in organic Rankine cycle (ORC) engines for waste heat recovery from exhaust-gas streams of diesel engines, amongst other applications. Careful modelling of the evaporator is both crucial to assess the dynamic performance of the ORC system and challenging due to the high nonlinearity of its governing equations. The proposed adaptive neuro-fuzzy inference system (ANFIS) model consists of two separate neuro-fuzzy sub-models for predicting the evaporator output temperature and evaporating pressure. Experimental data are collected from a 1-kWe ORC prototype to train, and verify the accuracy of the ANFIS model, which benefits from the feed-forward output calculation and backpropagation capability of the neural network, while keeping the interpretability of fuzzy systems. The effect of training the models using gradient-descent least-square estimate (GD-LSE) and particle swarm optimisation (PSO) techniques is investigated, and the performance of both techniques are compared in terms of RMSEs and correlation coefficients. The simulation results indicate strong learning ability and high generalisation performance for both. Training the ANFIS models using the PSO algorithm improved the obtained test data RMSE values by 29% for the evaporator outlet temperature and by 18% for the evaporator outlet pressure. The accuracy and speed of the model illustrate its potential for real-time control purposes.https://www.mdpi.com/2079-9292/10/13/1535ANFISdynamic modellingevaporatororganic Rankine cyclewaste heat recovery |
| spellingShingle | Hamid Enayatollahi Paul Sapin Chinedu K. Unamba Peter Fussey Christos N. Markides Bao Kha Nguyen A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype Electronics ANFIS dynamic modelling evaporator organic Rankine cycle waste heat recovery |
| title | A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype |
| title_full | A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype |
| title_fullStr | A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype |
| title_full_unstemmed | A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype |
| title_short | A Control-Oriented ANFIS Model of Evaporator in a 1-kWe Organic Rankine Cycle Prototype |
| title_sort | control oriented anfis model of evaporator in a 1 kwe organic rankine cycle prototype |
| topic | ANFIS dynamic modelling evaporator organic Rankine cycle waste heat recovery |
| url | https://www.mdpi.com/2079-9292/10/13/1535 |
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