Oil sludge depository assessment using multivariate data analysis
Oil-containing industrial wastes tend to accumulate and present a growing environmental danger. This is of particular concern in certain areas of Russia. For effective processing of depositories, the wastes' physico-chemical properties and depository characteristics should both be taken into ac...
Gespeichert in:
| Veröffentlicht in: | Journal of environmental management 2012-08, Vol.105, p.144-151 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 151 |
|---|---|
| container_issue | |
| container_start_page | 144 |
| container_title | Journal of environmental management |
| container_volume | 105 |
| creator | Ermakov, V.V. Bogomolov, A. Bykov, D.E. |
| description | Oil-containing industrial wastes tend to accumulate and present a growing environmental danger. This is of particular concern in certain areas of Russia. For effective processing of depositories, the wastes' physico-chemical properties and depository characteristics should both be taken into account.
Representative sample sets were collected from fifty four depositories of different age, origin, and location in Samara region and analyzed using multivariate data analysis: Principal Component Analysis (PCA) and Partial Least-Squares (PLS) regression. PCA results provide a better understanding of the internal data structure, i.e. variable correlations and groupings. Based on the PCA results, a new approach to the classification of oil sludge depositories has been suggested. Another practically important task of site assessment has been solved by PLS regression modeling. The method has been successfully applied to the accurate estimation of the depository processing profitability for a specific site.
► We examined 54 oil sludge storage sites of various type, origin, age and composition. ► We suggest using both sludge and depository attributes for the site assessment. ► The PCA model reveals data structure that aids in the waste treatment selection. ► PLS regression on the suggested descriptors is well-suited for storage evaluation. |
| doi_str_mv | 10.1016/j.jenvman.2012.03.041 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1027674231</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0301479712001600</els_id><sourcerecordid>1027674231</sourcerecordid><originalsourceid>FETCH-LOGICAL-c483t-1e8e7501c149406985f0f783e506ca88bfdafba15fded4cf52ba0573298cdc303</originalsourceid><addsrcrecordid>eNqN0U1v1DAQBmALgei28BOASFx6SRh_xc4JVRVfUqUeoGfL60xWjvKxeJKV9t9jugsHLvQ0l2dejeZl7A2HigOvP_RVj9Nh9FMlgIsKZAWKP2MbDo0ubS3hOduABF4q05gLdknUA4AU3LxkF0LoWimpNuzmPg4FDWu7w6LF_UxxmdOx8ERINOK0FCvFaVeM67DEg0_RLxn6xRd-8sORIr1iLzo_EL4-zyv28PnTj9uv5d39l2-3N3dlUFYuJUeLRgMPXDUK6sbqDjpjJWqog7d227W-23quuxZbFTotth60kaKxoQ0S5BW7PuXu0_xzRVrcGCngMPgJ55UcB2Fqo4TkT6FgtW7sU1K5FkrbWmT6_h_az2vKX3hUtRGQv5qVPqmQZqKEndunOPp0zOjRud6dm3O_m3MgXW4u7709p6_bEdu_W3-qyuDdCXR-dn6XIrmH7zlBAXCja2uz-HgSmGs4REyOQsQpYBsThsW1c_zPEb8Asny0NA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1016720256</pqid></control><display><type>article</type><title>Oil sludge depository assessment using multivariate data analysis</title><source>MEDLINE</source><source>ScienceDirect Freedom Collection (Elsevier)</source><creator>Ermakov, V.V. ; Bogomolov, A. ; Bykov, D.E.</creator><creatorcontrib>Ermakov, V.V. ; Bogomolov, A. ; Bykov, D.E.</creatorcontrib><description>Oil-containing industrial wastes tend to accumulate and present a growing environmental danger. This is of particular concern in certain areas of Russia. For effective processing of depositories, the wastes' physico-chemical properties and depository characteristics should both be taken into account.
Representative sample sets were collected from fifty four depositories of different age, origin, and location in Samara region and analyzed using multivariate data analysis: Principal Component Analysis (PCA) and Partial Least-Squares (PLS) regression. PCA results provide a better understanding of the internal data structure, i.e. variable correlations and groupings. Based on the PCA results, a new approach to the classification of oil sludge depositories has been suggested. Another practically important task of site assessment has been solved by PLS regression modeling. The method has been successfully applied to the accurate estimation of the depository processing profitability for a specific site.
► We examined 54 oil sludge storage sites of various type, origin, age and composition. ► We suggest using both sludge and depository attributes for the site assessment. ► The PCA model reveals data structure that aids in the waste treatment selection. ► PLS regression on the suggested descriptors is well-suited for storage evaluation.</description><identifier>ISSN: 0301-4797</identifier><identifier>EISSN: 1095-8630</identifier><identifier>DOI: 10.1016/j.jenvman.2012.03.041</identifier><identifier>PMID: 22564434</identifier><identifier>CODEN: JEVMAW</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Assessments ; Data processing ; Data structures ; Deposition ; Ecological engineering ; Ecology ; Environmental effects ; Environmental Monitoring ; fruits ; Industrial pollution ; Industrial Waste ; Industrial wastes ; least squares ; Models, Theoretical ; Multivariate analysis ; Oil products ; Oil sludge ; oils ; Partial Least-Squares regression ; Petroleum - analysis ; Physical properties ; physicochemical properties ; Position (location) ; Principal Component Analysis ; Principal components analysis ; Principle component analysis ; profitability ; Regression ; Regression analysis ; Russia ; Russian Federation ; Sewage ; Sludge ; Time Factors ; Waste management ; Waste processing</subject><ispartof>Journal of environmental management, 2012-08, Vol.105, p.144-151</ispartof><rights>2012 Elsevier Ltd</rights><rights>Copyright © 2012 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Academic Press Ltd. Aug 30, 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c483t-1e8e7501c149406985f0f783e506ca88bfdafba15fded4cf52ba0573298cdc303</citedby><cites>FETCH-LOGICAL-c483t-1e8e7501c149406985f0f783e506ca88bfdafba15fded4cf52ba0573298cdc303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jenvman.2012.03.041$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22564434$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ermakov, V.V.</creatorcontrib><creatorcontrib>Bogomolov, A.</creatorcontrib><creatorcontrib>Bykov, D.E.</creatorcontrib><title>Oil sludge depository assessment using multivariate data analysis</title><title>Journal of environmental management</title><addtitle>J Environ Manage</addtitle><description>Oil-containing industrial wastes tend to accumulate and present a growing environmental danger. This is of particular concern in certain areas of Russia. For effective processing of depositories, the wastes' physico-chemical properties and depository characteristics should both be taken into account.
Representative sample sets were collected from fifty four depositories of different age, origin, and location in Samara region and analyzed using multivariate data analysis: Principal Component Analysis (PCA) and Partial Least-Squares (PLS) regression. PCA results provide a better understanding of the internal data structure, i.e. variable correlations and groupings. Based on the PCA results, a new approach to the classification of oil sludge depositories has been suggested. Another practically important task of site assessment has been solved by PLS regression modeling. The method has been successfully applied to the accurate estimation of the depository processing profitability for a specific site.
► We examined 54 oil sludge storage sites of various type, origin, age and composition. ► We suggest using both sludge and depository attributes for the site assessment. ► The PCA model reveals data structure that aids in the waste treatment selection. ► PLS regression on the suggested descriptors is well-suited for storage evaluation.</description><subject>Assessments</subject><subject>Data processing</subject><subject>Data structures</subject><subject>Deposition</subject><subject>Ecological engineering</subject><subject>Ecology</subject><subject>Environmental effects</subject><subject>Environmental Monitoring</subject><subject>fruits</subject><subject>Industrial pollution</subject><subject>Industrial Waste</subject><subject>Industrial wastes</subject><subject>least squares</subject><subject>Models, Theoretical</subject><subject>Multivariate analysis</subject><subject>Oil products</subject><subject>Oil sludge</subject><subject>oils</subject><subject>Partial Least-Squares regression</subject><subject>Petroleum - analysis</subject><subject>Physical properties</subject><subject>physicochemical properties</subject><subject>Position (location)</subject><subject>Principal Component Analysis</subject><subject>Principal components analysis</subject><subject>Principle component analysis</subject><subject>profitability</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Russia</subject><subject>Russian Federation</subject><subject>Sewage</subject><subject>Sludge</subject><subject>Time Factors</subject><subject>Waste management</subject><subject>Waste processing</subject><issn>0301-4797</issn><issn>1095-8630</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqN0U1v1DAQBmALgei28BOASFx6SRh_xc4JVRVfUqUeoGfL60xWjvKxeJKV9t9jugsHLvQ0l2dejeZl7A2HigOvP_RVj9Nh9FMlgIsKZAWKP2MbDo0ubS3hOduABF4q05gLdknUA4AU3LxkF0LoWimpNuzmPg4FDWu7w6LF_UxxmdOx8ERINOK0FCvFaVeM67DEg0_RLxn6xRd-8sORIr1iLzo_EL4-zyv28PnTj9uv5d39l2-3N3dlUFYuJUeLRgMPXDUK6sbqDjpjJWqog7d227W-23quuxZbFTotth60kaKxoQ0S5BW7PuXu0_xzRVrcGCngMPgJ55UcB2Fqo4TkT6FgtW7sU1K5FkrbWmT6_h_az2vKX3hUtRGQv5qVPqmQZqKEndunOPp0zOjRud6dm3O_m3MgXW4u7709p6_bEdu_W3-qyuDdCXR-dn6XIrmH7zlBAXCja2uz-HgSmGs4REyOQsQpYBsThsW1c_zPEb8Asny0NA</recordid><startdate>20120830</startdate><enddate>20120830</enddate><creator>Ermakov, V.V.</creator><creator>Bogomolov, A.</creator><creator>Bykov, D.E.</creator><general>Elsevier Ltd</general><general>Academic Press Ltd</general><scope>FBQ</scope><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>7QH</scope><scope>7SN</scope><scope>7ST</scope><scope>7UA</scope><scope>8BJ</scope><scope>C1K</scope><scope>F1W</scope><scope>FQK</scope><scope>H97</scope><scope>JBE</scope><scope>L.G</scope><scope>SOI</scope><scope>7X8</scope><scope>7SU</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20120830</creationdate><title>Oil sludge depository assessment using multivariate data analysis</title><author>Ermakov, V.V. ; Bogomolov, A. ; Bykov, D.E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c483t-1e8e7501c149406985f0f783e506ca88bfdafba15fded4cf52ba0573298cdc303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Assessments</topic><topic>Data processing</topic><topic>Data structures</topic><topic>Deposition</topic><topic>Ecological engineering</topic><topic>Ecology</topic><topic>Environmental effects</topic><topic>Environmental Monitoring</topic><topic>fruits</topic><topic>Industrial pollution</topic><topic>Industrial Waste</topic><topic>Industrial wastes</topic><topic>least squares</topic><topic>Models, Theoretical</topic><topic>Multivariate analysis</topic><topic>Oil products</topic><topic>Oil sludge</topic><topic>oils</topic><topic>Partial Least-Squares regression</topic><topic>Petroleum - analysis</topic><topic>Physical properties</topic><topic>physicochemical properties</topic><topic>Position (location)</topic><topic>Principal Component Analysis</topic><topic>Principal components analysis</topic><topic>Principle component analysis</topic><topic>profitability</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Russia</topic><topic>Russian Federation</topic><topic>Sewage</topic><topic>Sludge</topic><topic>Time Factors</topic><topic>Waste management</topic><topic>Waste processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ermakov, V.V.</creatorcontrib><creatorcontrib>Bogomolov, A.</creatorcontrib><creatorcontrib>Bykov, D.E.</creatorcontrib><collection>AGRIS</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aqualine</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>International Bibliography of the Social Sciences</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>International Bibliography of the Social Sciences</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Environmental Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of environmental management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ermakov, V.V.</au><au>Bogomolov, A.</au><au>Bykov, D.E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Oil sludge depository assessment using multivariate data analysis</atitle><jtitle>Journal of environmental management</jtitle><addtitle>J Environ Manage</addtitle><date>2012-08-30</date><risdate>2012</risdate><volume>105</volume><spage>144</spage><epage>151</epage><pages>144-151</pages><issn>0301-4797</issn><eissn>1095-8630</eissn><coden>JEVMAW</coden><abstract>Oil-containing industrial wastes tend to accumulate and present a growing environmental danger. This is of particular concern in certain areas of Russia. For effective processing of depositories, the wastes' physico-chemical properties and depository characteristics should both be taken into account.
Representative sample sets were collected from fifty four depositories of different age, origin, and location in Samara region and analyzed using multivariate data analysis: Principal Component Analysis (PCA) and Partial Least-Squares (PLS) regression. PCA results provide a better understanding of the internal data structure, i.e. variable correlations and groupings. Based on the PCA results, a new approach to the classification of oil sludge depositories has been suggested. Another practically important task of site assessment has been solved by PLS regression modeling. The method has been successfully applied to the accurate estimation of the depository processing profitability for a specific site.
► We examined 54 oil sludge storage sites of various type, origin, age and composition. ► We suggest using both sludge and depository attributes for the site assessment. ► The PCA model reveals data structure that aids in the waste treatment selection. ► PLS regression on the suggested descriptors is well-suited for storage evaluation.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>22564434</pmid><doi>10.1016/j.jenvman.2012.03.041</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0301-4797 |
| ispartof | Journal of environmental management, 2012-08, Vol.105, p.144-151 |
| issn | 0301-4797 1095-8630 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1027674231 |
| source | MEDLINE; ScienceDirect Freedom Collection (Elsevier) |
| subjects | Assessments Data processing Data structures Deposition Ecological engineering Ecology Environmental effects Environmental Monitoring fruits Industrial pollution Industrial Waste Industrial wastes least squares Models, Theoretical Multivariate analysis Oil products Oil sludge oils Partial Least-Squares regression Petroleum - analysis Physical properties physicochemical properties Position (location) Principal Component Analysis Principal components analysis Principle component analysis profitability Regression Regression analysis Russia Russian Federation Sewage Sludge Time Factors Waste management Waste processing |
| title | Oil sludge depository assessment using multivariate data analysis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T05%3A33%3A16IST&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=Oil%20sludge%20depository%20assessment%20using%20multivariate%20data%20analysis&rft.jtitle=Journal%20of%20environmental%20management&rft.au=Ermakov,%20V.V.&rft.date=2012-08-30&rft.volume=105&rft.spage=144&rft.epage=151&rft.pages=144-151&rft.issn=0301-4797&rft.eissn=1095-8630&rft.coden=JEVMAW&rft_id=info:doi/10.1016/j.jenvman.2012.03.041&rft_dat=%3Cproquest_cross%3E1027674231%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=1016720256&rft_id=info:pmid/22564434&rft_els_id=S0301479712001600&rfr_iscdi=true |