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Knowledge extraction from a nitrification denitrification wastewater treatment plant using SOM-NG algorithm
SOM-NG is a hybrid algorithm that is able to carry out visualization of process data, nonlinear function approximation, classification and clustering. The supervised version of SOM-NG produces a new type of 2D lattices called gradient planes which are useful to determine the dynamics of a target var...
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| Published in: | Environmental technology 2017-06, Vol.38 (12), p.1548-1553 |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
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| cites | cdi_FETCH-LOGICAL-c427t-346099fee33556aaa8449a12b6c788d8d771920a0bcf3e18105cb2f686c5a8063 |
| container_end_page | 1553 |
| container_issue | 12 |
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| container_title | Environmental technology |
| container_volume | 38 |
| creator | Machón-González, Iván Rodríguez-Iglesias, Jesús López-García, Hilario Castrillón-Peláez, Leonor Marañón-Maison, Elena |
| description | SOM-NG is a hybrid algorithm that is able to carry out visualization of process data, nonlinear function approximation, classification and clustering. The supervised version of SOM-NG produces a new type of 2D lattices called gradient planes which are useful to determine the dynamics of a target variable according to the remaining training variables. In this way, it is an interesting tool for data mining in order to extract knowledge from databases for nonlinear systems. The main objective of this work is to analyze data from an industrial wastewater treatment plant using SOM-NG algorithm in order to investigate relationships between the process variables. The data used proceeds from a biological wastewater treatment plant. This plant is based on an activated sludge treatment including nitrification and denitrification processes. A direct relation between the nitrification efficiency and the operating temperature was found, and also between the ammonia loading rate and the nitrification denitrification efficiency. |
| doi_str_mv | 10.1080/09593330.2016.1237551 |
| format | article |
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The supervised version of SOM-NG produces a new type of 2D lattices called gradient planes which are useful to determine the dynamics of a target variable according to the remaining training variables. In this way, it is an interesting tool for data mining in order to extract knowledge from databases for nonlinear systems. The main objective of this work is to analyze data from an industrial wastewater treatment plant using SOM-NG algorithm in order to investigate relationships between the process variables. The data used proceeds from a biological wastewater treatment plant. This plant is based on an activated sludge treatment including nitrification and denitrification processes. A direct relation between the nitrification efficiency and the operating temperature was found, and also between the ammonia loading rate and the nitrification denitrification efficiency.</description><identifier>ISSN: 0959-3330</identifier><identifier>ISSN: 1479-487X</identifier><identifier>EISSN: 1479-487X</identifier><identifier>DOI: 10.1080/09593330.2016.1237551</identifier><identifier>PMID: 27681161</identifier><language>eng</language><publisher>England: Taylor & Francis</publisher><subject>Activated sludge ; Algorithms ; Ammonia ; Biological wastewater treatment ; Classification ; Clustering ; coke wastewater ; Data mining ; Data processing ; Denitrification ; Dynamical systems ; environmental technology ; Industrial Waste ; Industrial wastes ; Industrial wastewater ; Industrial wastewater treatment ; Lattices ; Load distribution ; Loading rate ; neural gas ; Nitrification ; Nonlinear dynamics ; Nonlinear systems ; Operating temperature ; Oxygen - metabolism ; Planes ; Plant extracts ; Process variables ; Self organizing map ; Sludge ; Sludge treatment ; temperature ; Temperature effects ; Waste Disposal, Fluid - methods ; Waste Water ; Wastewater treatment ; Wastewater treatment plants ; Water treatment plants</subject><ispartof>Environmental technology, 2017-06, Vol.38 (12), p.1548-1553</ispartof><rights>2016 Informa UK Limited, trading as Taylor & Francis Group 2016</rights><rights>2016 Informa UK Limited, trading as Taylor & Francis Group</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c427t-346099fee33556aaa8449a12b6c788d8d771920a0bcf3e18105cb2f686c5a8063</citedby><cites>FETCH-LOGICAL-c427t-346099fee33556aaa8449a12b6c788d8d771920a0bcf3e18105cb2f686c5a8063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27681161$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Machón-González, Iván</creatorcontrib><creatorcontrib>Rodríguez-Iglesias, Jesús</creatorcontrib><creatorcontrib>López-García, Hilario</creatorcontrib><creatorcontrib>Castrillón-Peláez, Leonor</creatorcontrib><creatorcontrib>Marañón-Maison, Elena</creatorcontrib><title>Knowledge extraction from a nitrification denitrification wastewater treatment plant using SOM-NG algorithm</title><title>Environmental technology</title><addtitle>Environ Technol</addtitle><description>SOM-NG is a hybrid algorithm that is able to carry out visualization of process data, nonlinear function approximation, classification and clustering. The supervised version of SOM-NG produces a new type of 2D lattices called gradient planes which are useful to determine the dynamics of a target variable according to the remaining training variables. In this way, it is an interesting tool for data mining in order to extract knowledge from databases for nonlinear systems. The main objective of this work is to analyze data from an industrial wastewater treatment plant using SOM-NG algorithm in order to investigate relationships between the process variables. The data used proceeds from a biological wastewater treatment plant. This plant is based on an activated sludge treatment including nitrification and denitrification processes. A direct relation between the nitrification efficiency and the operating temperature was found, and also between the ammonia loading rate and the nitrification denitrification efficiency.</description><subject>Activated sludge</subject><subject>Algorithms</subject><subject>Ammonia</subject><subject>Biological wastewater treatment</subject><subject>Classification</subject><subject>Clustering</subject><subject>coke wastewater</subject><subject>Data mining</subject><subject>Data processing</subject><subject>Denitrification</subject><subject>Dynamical systems</subject><subject>environmental technology</subject><subject>Industrial Waste</subject><subject>Industrial wastes</subject><subject>Industrial wastewater</subject><subject>Industrial wastewater treatment</subject><subject>Lattices</subject><subject>Load distribution</subject><subject>Loading rate</subject><subject>neural gas</subject><subject>Nitrification</subject><subject>Nonlinear dynamics</subject><subject>Nonlinear systems</subject><subject>Operating temperature</subject><subject>Oxygen - metabolism</subject><subject>Planes</subject><subject>Plant extracts</subject><subject>Process variables</subject><subject>Self organizing map</subject><subject>Sludge</subject><subject>Sludge treatment</subject><subject>temperature</subject><subject>Temperature effects</subject><subject>Waste Disposal, Fluid - methods</subject><subject>Waste Water</subject><subject>Wastewater treatment</subject><subject>Wastewater treatment plants</subject><subject>Water treatment plants</subject><issn>0959-3330</issn><issn>1479-487X</issn><issn>1479-487X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kU1vFDEMhiMEotvCTwCNxIXLLPn-uIEqaBGFHgCJW-TNJEvKzGSbZLT03zPDbpHgwMWWrcf2K78IPSN4TbDGr7ARhjGG1xQTuSaUKSHIA7QiXJmWa_XtIVotTLtAJ-i0lBuMqRbaPEYnVElNiCQr9OPDmPa977a-8T9rBldjGpuQ09BAM8aaY4gOfjc7_3e9h1L9HqrPTc0e6uDH2ux6mONU4rhtPl9_bD9dNNBvU471-_AEPQrQF__0mM_Q13dvv5xftlfXF-_P31y1jlNVW8YlNiZ4z5gQEgA05wYI3UintO50pxQxFAPeuMA80QQLt6FBaukEaCzZGXp52LvL6XbypdohFuf7WZpPU7EUY8y5oGxBX_yD3qQpj7M6SwwminFBzEyJA-VyKiX7YHc5DpDvLMF2ccPeu2EXN-zRjXnu-XH7tBl892fq_v0z8PoAxDGkPMA-5b6zFe76lEOG0cVi2f9v_AKBl5mX</recordid><startdate>20170618</startdate><enddate>20170618</enddate><creator>Machón-González, Iván</creator><creator>Rodríguez-Iglesias, Jesús</creator><creator>López-García, Hilario</creator><creator>Castrillón-Peláez, Leonor</creator><creator>Marañón-Maison, Elena</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QL</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>7U7</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20170618</creationdate><title>Knowledge extraction from a nitrification denitrification wastewater treatment plant using SOM-NG algorithm</title><author>Machón-González, Iván ; Rodríguez-Iglesias, Jesús ; López-García, Hilario ; Castrillón-Peláez, Leonor ; Marañón-Maison, Elena</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c427t-346099fee33556aaa8449a12b6c788d8d771920a0bcf3e18105cb2f686c5a8063</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Activated sludge</topic><topic>Algorithms</topic><topic>Ammonia</topic><topic>Biological wastewater treatment</topic><topic>Classification</topic><topic>Clustering</topic><topic>coke wastewater</topic><topic>Data mining</topic><topic>Data processing</topic><topic>Denitrification</topic><topic>Dynamical systems</topic><topic>environmental technology</topic><topic>Industrial Waste</topic><topic>Industrial wastes</topic><topic>Industrial wastewater</topic><topic>Industrial wastewater treatment</topic><topic>Lattices</topic><topic>Load distribution</topic><topic>Loading rate</topic><topic>neural gas</topic><topic>Nitrification</topic><topic>Nonlinear dynamics</topic><topic>Nonlinear systems</topic><topic>Operating temperature</topic><topic>Oxygen - 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Academic</collection><jtitle>Environmental technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Machón-González, Iván</au><au>Rodríguez-Iglesias, Jesús</au><au>López-García, Hilario</au><au>Castrillón-Peláez, Leonor</au><au>Marañón-Maison, Elena</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Knowledge extraction from a nitrification denitrification wastewater treatment plant using SOM-NG algorithm</atitle><jtitle>Environmental technology</jtitle><addtitle>Environ Technol</addtitle><date>2017-06-18</date><risdate>2017</risdate><volume>38</volume><issue>12</issue><spage>1548</spage><epage>1553</epage><pages>1548-1553</pages><issn>0959-3330</issn><issn>1479-487X</issn><eissn>1479-487X</eissn><abstract>SOM-NG is a hybrid algorithm that is able to carry out visualization of process data, nonlinear function approximation, classification and clustering. The supervised version of SOM-NG produces a new type of 2D lattices called gradient planes which are useful to determine the dynamics of a target variable according to the remaining training variables. In this way, it is an interesting tool for data mining in order to extract knowledge from databases for nonlinear systems. The main objective of this work is to analyze data from an industrial wastewater treatment plant using SOM-NG algorithm in order to investigate relationships between the process variables. The data used proceeds from a biological wastewater treatment plant. This plant is based on an activated sludge treatment including nitrification and denitrification processes. A direct relation between the nitrification efficiency and the operating temperature was found, and also between the ammonia loading rate and the nitrification denitrification efficiency.</abstract><cop>England</cop><pub>Taylor & Francis</pub><pmid>27681161</pmid><doi>10.1080/09593330.2016.1237551</doi><tpages>6</tpages></addata></record> |
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| ispartof | Environmental technology, 2017-06, Vol.38 (12), p.1548-1553 |
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| source | Taylor and Francis Science and Technology Collection |
| subjects | Activated sludge Algorithms Ammonia Biological wastewater treatment Classification Clustering coke wastewater Data mining Data processing Denitrification Dynamical systems environmental technology Industrial Waste Industrial wastes Industrial wastewater Industrial wastewater treatment Lattices Load distribution Loading rate neural gas Nitrification Nonlinear dynamics Nonlinear systems Operating temperature Oxygen - metabolism Planes Plant extracts Process variables Self organizing map Sludge Sludge treatment temperature Temperature effects Waste Disposal, Fluid - methods Waste Water Wastewater treatment Wastewater treatment plants Water treatment plants |
| title | Knowledge extraction from a nitrification denitrification wastewater treatment plant using SOM-NG algorithm |
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