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Modeling of reverse osmosis plants using system identification and neural networks
| Title: | Modeling of reverse osmosis plants using system identification and neural networks. |
 74 views 35 downloads |
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| Name(s): |
Saengrung, Anucha Florida Atlantic University, Degree grantor Zilouchian, Ali, Thesis advisor College of Engineering and Computer Science Department of Computer and Electrical Engineering and Computer Science |
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| Type of Resource: | text | |
| Genre: | Electronic Thesis Or Dissertation | |
| Issuance: | monographic | |
| Date Issued: | 2002 | |
| Publisher: | Florida Atlantic University | |
| Place of Publication: | Boca Raton, Fla. | |
| Physical Form: | application/pdf | |
| Extent: | 235 p. | |
| Language(s): | English | |
| Summary: | Modeling of two reverse osmosis plants at FAU Gumbo Limbo facility and at the city of Boca Raton are investigated. System identification as well as artificial neural networks are utilized to carried out the tasks. The data for a six months operational period of both plants are utilized. The prediction error method and subspace method are utilized to estimate state-space model while the auto regression with extra input (ARX) model is estimated by using the least square method and the approximately optimal four-stage instrumental variable method. The training algorithms for artificial neural networks are based on backpropagation and radial basis network function (RBNF). The implementation of each methodology is performed step by step and finally, the results from both methodologies are analyzed and discussed. The results of the proposed study indicate that both system identification and neural networks algorithms can predict the outputs of both RO plants with the acceptable accuracy. Among all methodologies utilized in the thesis, the least square method for the auto regression with the extra input (ARX) model, can provide the best prediction for both RO plants. | |
| Identifier: | 9780493913452 (isbn), 12963 (digitool), FADT12963 (IID), fau:9831 (fedora) | |
| Collection: | FAU Electronic Theses and Dissertations Collection | |
| Note(s): |
College of Engineering and Computer Science Thesis (M.S.)--Florida Atlantic University, 2002. |
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| Subject(s): |
Saline water conversion--Reverse osmosis process System identification Neural networks (Computer science) |
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| Held by: | Florida Atlantic University Libraries | |
| Persistent Link to This Record: | http://purl.flvc.org/fcla/dt/12963 | |
| Sublocation: | Digital Library | |
| Use and Reproduction: | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
| Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
| Host Institution: | FAU | |
| Is Part of Series: | Florida Atlantic University Digital Library Collections. |
