Modeling of an RO water desalination unit using neural networks
In this paper, a feedforward neural network (NN) model is developed to predict the performance of a reverse osmosis (RO) experimental setup, which uses a FilmTec SW30 membrane. Sixty-three experimental data were generated for training and testing the network. The considered ranges of operating condi...
Gespeichert in:
| Veröffentlicht in: | Chemical engineering journal (Lausanne, Switzerland : 1996) Switzerland : 1996), 2005-11, Vol.114 (1), p.139-143 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 143 |
|---|---|
| container_issue | 1 |
| container_start_page | 139 |
| container_title | Chemical engineering journal (Lausanne, Switzerland : 1996) |
| container_volume | 114 |
| creator | Abbas, Abderrahim Al-Bastaki, Nader |
| description | In this paper, a feedforward neural network (NN) model is developed to predict the performance of a reverse osmosis (RO) experimental setup, which uses a FilmTec SW30 membrane. Sixty-three experimental data were generated for training and testing the network. The considered ranges of operating conditions were chosen so as to include those encountered in a large number of the worldwide brackish water and seawater RO plants. The NN was fed with three inputs: the feed pressure, temperature and salt concentration to predict the water permeate rate. The fast Levenberg–Marquardt (LM) optimization technique was employed for training the NN. The network learned the input–output mappings with accuracy for interpolation cases, but not for extrapolation. |
| doi_str_mv | 10.1016/j.cej.2005.07.016 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_17467903</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S138589470500313X</els_id><sourcerecordid>17467903</sourcerecordid><originalsourceid>FETCH-LOGICAL-c395t-f6dfde8dad7f867d8be8e86d7a78d9746b227ac3ccf4db59dd8d60efa87746583</originalsourceid><addsrcrecordid>eNp9kE9LAzEQxYMoWKsfwNte9LZrstndZPEgUuofqBREzyFNJpJ1m9Rk1-K3N6UFb57e8OY3M8xD6JLggmDS3HSFgq4oMa4LzIrkHKEJ4YzmtCTlcaopr3PeVuwUncXYYYyblrQTdPfiNfTWfWTeZNJlr8tsKwcImYYoky8H6102OjtkY9xhDsYg-yTD1ofPeI5OjOwjXBx0it4f5m-zp3yxfHye3S9yRdt6yE2jjQaupWaGN0zzFXDgjWaScd2yqlmVJZOKKmUqvapbrbluMBjJWWrWnE7R9X7vJvivEeIg1jYq6HvpwI9RkISxFtMEkj2ogo8xgBGbYNcy_AiCxS4q0YkUldhFJTATyUkzV4flMirZmyCdsvFvkJUVp4wk7nbPQfr020IQUVlwCrQNoAahvf3nyi-gjX8W</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>17467903</pqid></control><display><type>article</type><title>Modeling of an RO water desalination unit using neural networks</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Abbas, Abderrahim ; Al-Bastaki, Nader</creator><creatorcontrib>Abbas, Abderrahim ; Al-Bastaki, Nader</creatorcontrib><description>In this paper, a feedforward neural network (NN) model is developed to predict the performance of a reverse osmosis (RO) experimental setup, which uses a FilmTec SW30 membrane. Sixty-three experimental data were generated for training and testing the network. The considered ranges of operating conditions were chosen so as to include those encountered in a large number of the worldwide brackish water and seawater RO plants. The NN was fed with three inputs: the feed pressure, temperature and salt concentration to predict the water permeate rate. The fast Levenberg–Marquardt (LM) optimization technique was employed for training the NN. The network learned the input–output mappings with accuracy for interpolation cases, but not for extrapolation.</description><identifier>ISSN: 1385-8947</identifier><identifier>EISSN: 1873-3212</identifier><identifier>DOI: 10.1016/j.cej.2005.07.016</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Applied sciences ; Chemical engineering ; Drinking water and swimming-pool water. Desalination ; Exact sciences and technology ; Membrane separation (reverse osmosis, dialysis...) ; Neural networks ; Pollution ; Process modeling ; Reverse osmosis ; Water desalination ; Water treatment and pollution</subject><ispartof>Chemical engineering journal (Lausanne, Switzerland : 1996), 2005-11, Vol.114 (1), p.139-143</ispartof><rights>2005 Elsevier B.V.</rights><rights>2005 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c395t-f6dfde8dad7f867d8be8e86d7a78d9746b227ac3ccf4db59dd8d60efa87746583</citedby><cites>FETCH-LOGICAL-c395t-f6dfde8dad7f867d8be8e86d7a78d9746b227ac3ccf4db59dd8d60efa87746583</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S138589470500313X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17248371$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Abbas, Abderrahim</creatorcontrib><creatorcontrib>Al-Bastaki, Nader</creatorcontrib><title>Modeling of an RO water desalination unit using neural networks</title><title>Chemical engineering journal (Lausanne, Switzerland : 1996)</title><description>In this paper, a feedforward neural network (NN) model is developed to predict the performance of a reverse osmosis (RO) experimental setup, which uses a FilmTec SW30 membrane. Sixty-three experimental data were generated for training and testing the network. The considered ranges of operating conditions were chosen so as to include those encountered in a large number of the worldwide brackish water and seawater RO plants. The NN was fed with three inputs: the feed pressure, temperature and salt concentration to predict the water permeate rate. The fast Levenberg–Marquardt (LM) optimization technique was employed for training the NN. The network learned the input–output mappings with accuracy for interpolation cases, but not for extrapolation.</description><subject>Applied sciences</subject><subject>Chemical engineering</subject><subject>Drinking water and swimming-pool water. Desalination</subject><subject>Exact sciences and technology</subject><subject>Membrane separation (reverse osmosis, dialysis...)</subject><subject>Neural networks</subject><subject>Pollution</subject><subject>Process modeling</subject><subject>Reverse osmosis</subject><subject>Water desalination</subject><subject>Water treatment and pollution</subject><issn>1385-8947</issn><issn>1873-3212</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><recordid>eNp9kE9LAzEQxYMoWKsfwNte9LZrstndZPEgUuofqBREzyFNJpJ1m9Rk1-K3N6UFb57e8OY3M8xD6JLggmDS3HSFgq4oMa4LzIrkHKEJ4YzmtCTlcaopr3PeVuwUncXYYYyblrQTdPfiNfTWfWTeZNJlr8tsKwcImYYoky8H6102OjtkY9xhDsYg-yTD1ofPeI5OjOwjXBx0it4f5m-zp3yxfHye3S9yRdt6yE2jjQaupWaGN0zzFXDgjWaScd2yqlmVJZOKKmUqvapbrbluMBjJWWrWnE7R9X7vJvivEeIg1jYq6HvpwI9RkISxFtMEkj2ogo8xgBGbYNcy_AiCxS4q0YkUldhFJTATyUkzV4flMirZmyCdsvFvkJUVp4wk7nbPQfr020IQUVlwCrQNoAahvf3nyi-gjX8W</recordid><startdate>20051115</startdate><enddate>20051115</enddate><creator>Abbas, Abderrahim</creator><creator>Al-Bastaki, Nader</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>20051115</creationdate><title>Modeling of an RO water desalination unit using neural networks</title><author>Abbas, Abderrahim ; Al-Bastaki, Nader</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-f6dfde8dad7f867d8be8e86d7a78d9746b227ac3ccf4db59dd8d60efa87746583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Applied sciences</topic><topic>Chemical engineering</topic><topic>Drinking water and swimming-pool water. Desalination</topic><topic>Exact sciences and technology</topic><topic>Membrane separation (reverse osmosis, dialysis...)</topic><topic>Neural networks</topic><topic>Pollution</topic><topic>Process modeling</topic><topic>Reverse osmosis</topic><topic>Water desalination</topic><topic>Water treatment and pollution</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abbas, Abderrahim</creatorcontrib><creatorcontrib>Al-Bastaki, Nader</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Chemical engineering journal (Lausanne, Switzerland : 1996)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abbas, Abderrahim</au><au>Al-Bastaki, Nader</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling of an RO water desalination unit using neural networks</atitle><jtitle>Chemical engineering journal (Lausanne, Switzerland : 1996)</jtitle><date>2005-11-15</date><risdate>2005</risdate><volume>114</volume><issue>1</issue><spage>139</spage><epage>143</epage><pages>139-143</pages><issn>1385-8947</issn><eissn>1873-3212</eissn><abstract>In this paper, a feedforward neural network (NN) model is developed to predict the performance of a reverse osmosis (RO) experimental setup, which uses a FilmTec SW30 membrane. Sixty-three experimental data were generated for training and testing the network. The considered ranges of operating conditions were chosen so as to include those encountered in a large number of the worldwide brackish water and seawater RO plants. The NN was fed with three inputs: the feed pressure, temperature and salt concentration to predict the water permeate rate. The fast Levenberg–Marquardt (LM) optimization technique was employed for training the NN. The network learned the input–output mappings with accuracy for interpolation cases, but not for extrapolation.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.cej.2005.07.016</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1385-8947 |
| ispartof | Chemical engineering journal (Lausanne, Switzerland : 1996), 2005-11, Vol.114 (1), p.139-143 |
| issn | 1385-8947 1873-3212 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_17467903 |
| source | Elsevier ScienceDirect Journals Complete |
| subjects | Applied sciences Chemical engineering Drinking water and swimming-pool water. Desalination Exact sciences and technology Membrane separation (reverse osmosis, dialysis...) Neural networks Pollution Process modeling Reverse osmosis Water desalination Water treatment and pollution |
| title | Modeling of an RO water desalination unit using neural networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T06%3A35%3A07IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modeling%20of%20an%20RO%20water%20desalination%20unit%20using%20neural%20networks&rft.jtitle=Chemical%20engineering%20journal%20(Lausanne,%20Switzerland%20:%201996)&rft.au=Abbas,%20Abderrahim&rft.date=2005-11-15&rft.volume=114&rft.issue=1&rft.spage=139&rft.epage=143&rft.pages=139-143&rft.issn=1385-8947&rft.eissn=1873-3212&rft_id=info:doi/10.1016/j.cej.2005.07.016&rft_dat=%3Cproquest_cross%3E17467903%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=17467903&rft_id=info:pmid/&rft_els_id=S138589470500313X&rfr_iscdi=true |