| Type of Material: | Thesis |
| Title: | Modelling and Analysis of Polymer Electrolyte Membrane Fuel Cell using Artificial Neural Networks |
| Researcher: | Bhoopal, N. |
| Guide: | Pathapati, V V N R Prasad Raju | Amaranath, J. |
| Department: | Faculty of Energy System |
| Publisher: | Jawaharlal Nehru Technological University, Hyderabad |
| Place: | Hyderabad |
| Year: | 2013 |
| Language: | English |
| Subject: | Analysis | Electrolyte | Membrane | Modelling | Neura | Polymer | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Faculty of Energy System, Jawaharlal Nehru Technological University, Hyderabad, Hyderabad; 2013 |
| Fulltext: | Shodhganga |
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| 001 | 453859 | |
| 003 | IN-AhILN | |
| 005 | 2024-07-01 16:05:16 | |
| 008 | __ | 240701t2013||||ii#||||g|m||||||||||eng|| |
| 035 | __ | |a(IN-AhILN)th_453859 |
| 040 | __ | |aJNTU_500028|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aBhoopal, N.|eResearcher |
| 110 | __ | |aFaculty of Energy System|bJawaharlal Nehru Technological University, Hyderabad|dHyderabad|ein|0U-0017 |
| 245 | __ | |aModelling and Analysis of Polymer Electrolyte Membrane Fuel Cell using Artificial Neural Networks |
| 260 | __ | |aHyderabad|bJawaharlal Nehru Technological University, Hyderabad|c2013 |
| 300 | __ | |c-|dNone|a180 p. |
| 500 | __ | |aReferences p. 170-180 |
| 502 | __ | |bPhD|cFaculty of Energy System, Jawaharlal Nehru Technological University, Hyderabad, Hyderabad|d2013 |
| 520 | __ | |aTo avoid extensive and costly experiments, the fuel cells developers use detailed newlinecell and stack models for economic ssessments and development purposes. From the results of the Pure Mathematical model simulations, conducted for a broad range of operating conditions, performance charts can be constructed. However, these models are rather detailed descriptions of the physical newlineprocesses occurring in the fuel cells and hence they are intricately complex and newlinecumbersome, especially in operating point analysis and optimization. In the proposed work, an alternative approach to mathematical models based on statistical data-driven artificial neural networks (ANNs) is introduced. Applications of ANNs include a large variety of engineering applications like pattern recognition (protein analysis, spectroscopy and fingerprint identification), as well as behavior prediction and function approximation (stock market forecasting, energy demand forecasting and process control newlinesystems). All these m |
| 650 | __ | |aEngineering and Technology|2AIU |
| 653 | __ | |aAnalysis |
| 653 | __ | |aElectrolyte |
| 653 | __ | |aMembrane |
| 653 | __ | |aModelling |
| 653 | __ | |aNeura |
| 653 | __ | |aPolymer |
| 700 | __ | |aPathapati, V V N R Prasad Raju|eGuide |
| 700 | __ | |eGuide|aAmaranath, J. |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/19047|yShodhganga |
| 905 | __ | |afromsg |
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