Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation
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| Title: | Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation |
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| Authors: | Entchev, Evgueniy eentchev@nrcan.gc.ca, Yang, Libing1 |
| Source: | Journal of Power Sources. Jun2007, Vol. 170 Issue 1, p122-129. 8p. |
| Subjects: | Fuel cells, Direct energy conversion, Electrochemistry, Estimation theory |
| Abstract: | Abstract: This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5kWel SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc. The study revealed that both ANN and ANFIS models’ predictions agreed well with variety of experimental data sets representing steady-state, start-up and shut-down operations of the SOFC system. The initial data set was subjected to detailed sensitivity analysis and statistically insignificant parameters were excluded from the training set. As a result, significant reduction of computational time was achieved without affecting models’ accuracy. The study showed that adaptive models can be applied with confidence during the design process and for performance optimization of existing and newly developed solid oxide fuel cell systems. It demonstrated that by using ANN and ANFIS techniques SOFC microgeneration system''s performance could be modelled with minimum time demand and with a high degree of accuracy. [Copyright &y& Elsevier] |
| Copyright of Journal of Power Sources is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 25343081 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Entchev%2C+Evgueniy%22">Entchev, Evgueniy</searchLink><i> eentchev@nrcan.gc.ca</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Libing%22">Yang, Libing</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Power+Sources%22">Journal of Power Sources</searchLink>. Jun2007, Vol. 170 Issue 1, p122-129. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fuel+cells%22">Fuel cells</searchLink><br /><searchLink fieldCode="DE" term="%22Direct+energy+conversion%22">Direct energy conversion</searchLink><br /><searchLink fieldCode="DE" term="%22Electrochemistry%22">Electrochemistry</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract: This study applies adaptive neuro-fuzzy inference system (ANFIS) techniques and artificial neural network (ANN) to predict solid oxide fuel cell (SOFC) performance while supplying both heat and power to a residence. A microgeneration 5kWel SOFC system was installed at the Canadian Centre for Housing Technology (CCHT), integrated with existing mechanical systems and connected in parallel to the grid. SOFC performance data were collected during the winter heating season and used for training of both ANN and ANFIS models. The ANN model was built on back propagation algorithm as for ANFIS model a combination of least squares method and back propagation gradient decent method were developed and applied. Both models were trained with experimental data and used to predict selective SOFC performance parameters such as fuel cell stack current, stack voltage, etc. The study revealed that both ANN and ANFIS models’ predictions agreed well with variety of experimental data sets representing steady-state, start-up and shut-down operations of the SOFC system. The initial data set was subjected to detailed sensitivity analysis and statistically insignificant parameters were excluded from the training set. As a result, significant reduction of computational time was achieved without affecting models’ accuracy. The study showed that adaptive models can be applied with confidence during the design process and for performance optimization of existing and newly developed solid oxide fuel cell systems. It demonstrated that by using ANN and ANFIS techniques SOFC microgeneration system''s performance could be modelled with minimum time demand and with a high degree of accuracy. [Copyright &y& Elsevier] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Power Sources is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jpowsour.2007.04.015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 122 Subjects: – SubjectFull: Fuel cells Type: general – SubjectFull: Direct energy conversion Type: general – SubjectFull: Electrochemistry Type: general – SubjectFull: Estimation theory Type: general Titles: – TitleFull: Application of adaptive neuro-fuzzy inference system techniques and artificial neural networks to predict solid oxide fuel cell performance in residential microgeneration installation Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Entchev, Evgueniy – PersonEntity: Name: NameFull: Yang, Libing IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 06 Text: Jun2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 03787753 Numbering: – Type: volume Value: 170 – Type: issue Value: 1 Titles: – TitleFull: Journal of Power Sources Type: main |
| ResultId | 1 |
