Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters
In this study two approaches to predict the total alkalinity (expressed as mgL−1HCO3-) of an anaerobic digester are examined: firstly, software sensors based on multiple linear regression algorithms using data from pH, redox potential and electrical conductivity and secondly, near infrared reflectan...
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| Veröffentlicht in: | Bioresource technology 2011-03, Vol.102 (5), p.4083-4090 |
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| creator | Ward, Alastair J. Hobbs, Philip J. Holliman, Peter J. Jones, David L. |
| description | In this study two approaches to predict the total alkalinity (expressed as mgL−1HCO3-) of an anaerobic digester are examined: firstly, software sensors based on multiple linear regression algorithms using data from pH, redox potential and electrical conductivity and secondly, near infrared reflectance spectroscopy (NIRS). Of the software sensors, the model using data from all three probes but a smaller dataset using total alkalinity values below 6000mgL−1HCO3- produced the best calibration model (R2=0.76 and root mean square error of prediction (RMSEP) of 969mgL−1HCO3-). When validated with new data, the NIRS method produced the best model (R2=0.87 RMSEP=1230mgL−1HCO3-). The NIRS sensor correlated better with new data (R2=0.54). In conclusion, this study has developed new and improved algorithms for monitoring total alkalinity within anaerobic digestion systems which will facilitate real-time optimisation of methane production. |
| doi_str_mv | 10.1016/j.biortech.2010.12.046 |
| format | Article |
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Of the software sensors, the model using data from all three probes but a smaller dataset using total alkalinity values below 6000mgL−1HCO3- produced the best calibration model (R2=0.76 and root mean square error of prediction (RMSEP) of 969mgL−1HCO3-). When validated with new data, the NIRS method produced the best model (R2=0.87 RMSEP=1230mgL−1HCO3-). The NIRS sensor correlated better with new data (R2=0.54). In conclusion, this study has developed new and improved algorithms for monitoring total alkalinity within anaerobic digestion systems which will facilitate real-time optimisation of methane production.</description><identifier>ISSN: 0960-8524</identifier><identifier>EISSN: 1873-2976</identifier><identifier>DOI: 10.1016/j.biortech.2010.12.046</identifier><identifier>PMID: 21227685</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Algorithms ; Alkalinity ; Anaerobic digestion ; Bacteria, Anaerobic - metabolism ; Bicarbonates - analysis ; Biofuel production ; Biogas ; Biological and medical sciences ; Biological treatment of sewage sludges and wastes ; Biotechnology ; Computer programs ; Energy ; Environment and pollution ; Fundamental and applied biological sciences. Psychology ; Hydrogen-Ion Concentration ; Industrial applications and implications. Economical aspects ; Linear Models ; Mathematical models ; Methane - biosynthesis ; Models, Theoretical ; Monitoring ; NIRS ; Refuse Disposal - methods ; Sensors ; Software ; Software sensor ; Spectrophotometry, Infrared - methods</subject><ispartof>Bioresource technology, 2011-03, Vol.102 (5), p.4083-4090</ispartof><rights>2010 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><rights>Copyright © 2010 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c486t-675138ecc0a0a7aae6bad0d5987604aab732660ca2db75b9f778d2902ed570cf3</citedby><cites>FETCH-LOGICAL-c486t-675138ecc0a0a7aae6bad0d5987604aab732660ca2db75b9f778d2902ed570cf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.biortech.2010.12.046$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23870812$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21227685$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ward, Alastair J.</creatorcontrib><creatorcontrib>Hobbs, Philip J.</creatorcontrib><creatorcontrib>Holliman, Peter J.</creatorcontrib><creatorcontrib>Jones, David L.</creatorcontrib><title>Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters</title><title>Bioresource technology</title><addtitle>Bioresour Technol</addtitle><description>In this study two approaches to predict the total alkalinity (expressed as mgL−1HCO3-) of an anaerobic digester are examined: firstly, software sensors based on multiple linear regression algorithms using data from pH, redox potential and electrical conductivity and secondly, near infrared reflectance spectroscopy (NIRS). Of the software sensors, the model using data from all three probes but a smaller dataset using total alkalinity values below 6000mgL−1HCO3- produced the best calibration model (R2=0.76 and root mean square error of prediction (RMSEP) of 969mgL−1HCO3-). When validated with new data, the NIRS method produced the best model (R2=0.87 RMSEP=1230mgL−1HCO3-). The NIRS sensor correlated better with new data (R2=0.54). In conclusion, this study has developed new and improved algorithms for monitoring total alkalinity within anaerobic digestion systems which will facilitate real-time optimisation of methane production.</description><subject>Algorithms</subject><subject>Alkalinity</subject><subject>Anaerobic digestion</subject><subject>Bacteria, Anaerobic - metabolism</subject><subject>Bicarbonates - analysis</subject><subject>Biofuel production</subject><subject>Biogas</subject><subject>Biological and medical sciences</subject><subject>Biological treatment of sewage sludges and wastes</subject><subject>Biotechnology</subject><subject>Computer programs</subject><subject>Energy</subject><subject>Environment and pollution</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Hydrogen-Ion Concentration</subject><subject>Industrial applications and implications. Economical aspects</subject><subject>Linear Models</subject><subject>Mathematical models</subject><subject>Methane - biosynthesis</subject><subject>Models, Theoretical</subject><subject>Monitoring</subject><subject>NIRS</subject><subject>Refuse Disposal - methods</subject><subject>Sensors</subject><subject>Software</subject><subject>Software sensor</subject><subject>Spectrophotometry, Infrared - methods</subject><issn>0960-8524</issn><issn>1873-2976</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkkFv1DAQhSMEokvhL5RcEFyyjO2N7dxAVWmRKnGAnq2JPWm9ZOPF9hbtkX-Oo92WGz3ZGn_z3tjPVXXGYMmAyY_rZe9DzGTvlhzmIl_CSj6rFkwr0fBOyefVAjoJjW756qR6ldIaAART_GV1whnnSup2Uf25uMdxh9mHqQ5DPRHG2k9DxEiuTluyOYZkw3Zf41QKYci_y1GdaEoh1hvKd8Gleih7R5nixk-PWjlkHGscf-LoJ5_3RbeIIMXQe1s7f0updKTX1YsBx0RvjutpdfPl4sf5VXP97fLr-efrxq60zI1ULROarAUEVIgke3Tg2k4rCSvEXgkuJVjkrldt3w1Kacc74ORaBXYQp9X7g-42hl-74m02PlkaR5wo7JLREoRqlYKnyTKQlqwVhfzwX5IppZiQjM2oPKC2PGiKNJht9BuMe8PAzJGatXmI1MyRGsZNibQ0nh09dv2G3GPbQ4YFeHcEMFkcS3aT9ekfJ7QCzXjh3h64AYPB21iYm-_FSQDrRLn3THw6EFRyuPcUTbKeJkvOx_ITjAv-qWn_AivoznM</recordid><startdate>20110301</startdate><enddate>20110301</enddate><creator>Ward, Alastair J.</creator><creator>Hobbs, Philip J.</creator><creator>Holliman, Peter J.</creator><creator>Jones, David L.</creator><general>Elsevier Ltd</general><general>[New York, NY]: Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</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>7SU</scope><scope>7TB</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>KR7</scope><scope>7X8</scope><scope>7QO</scope><scope>P64</scope></search><sort><creationdate>20110301</creationdate><title>Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters</title><author>Ward, Alastair J. ; Hobbs, Philip J. ; Holliman, Peter J. ; Jones, David L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c486t-675138ecc0a0a7aae6bad0d5987604aab732660ca2db75b9f778d2902ed570cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Alkalinity</topic><topic>Anaerobic digestion</topic><topic>Bacteria, Anaerobic - metabolism</topic><topic>Bicarbonates - analysis</topic><topic>Biofuel production</topic><topic>Biogas</topic><topic>Biological and medical sciences</topic><topic>Biological treatment of sewage sludges and wastes</topic><topic>Biotechnology</topic><topic>Computer programs</topic><topic>Energy</topic><topic>Environment and pollution</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Hydrogen-Ion Concentration</topic><topic>Industrial applications and implications. Economical aspects</topic><topic>Linear Models</topic><topic>Mathematical models</topic><topic>Methane - biosynthesis</topic><topic>Models, Theoretical</topic><topic>Monitoring</topic><topic>NIRS</topic><topic>Refuse Disposal - methods</topic><topic>Sensors</topic><topic>Software</topic><topic>Software sensor</topic><topic>Spectrophotometry, Infrared - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ward, Alastair J.</creatorcontrib><creatorcontrib>Hobbs, Philip J.</creatorcontrib><creatorcontrib>Holliman, Peter J.</creatorcontrib><creatorcontrib>Jones, David L.</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Environmental Engineering Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Bioresource technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ward, Alastair J.</au><au>Hobbs, Philip J.</au><au>Holliman, Peter J.</au><au>Jones, David L.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters</atitle><jtitle>Bioresource technology</jtitle><addtitle>Bioresour Technol</addtitle><date>2011-03-01</date><risdate>2011</risdate><volume>102</volume><issue>5</issue><spage>4083</spage><epage>4090</epage><pages>4083-4090</pages><issn>0960-8524</issn><eissn>1873-2976</eissn><abstract>In this study two approaches to predict the total alkalinity (expressed as mgL−1HCO3-) of an anaerobic digester are examined: firstly, software sensors based on multiple linear regression algorithms using data from pH, redox potential and electrical conductivity and secondly, near infrared reflectance spectroscopy (NIRS). Of the software sensors, the model using data from all three probes but a smaller dataset using total alkalinity values below 6000mgL−1HCO3- produced the best calibration model (R2=0.76 and root mean square error of prediction (RMSEP) of 969mgL−1HCO3-). When validated with new data, the NIRS method produced the best model (R2=0.87 RMSEP=1230mgL−1HCO3-). The NIRS sensor correlated better with new data (R2=0.54). In conclusion, this study has developed new and improved algorithms for monitoring total alkalinity within anaerobic digestion systems which will facilitate real-time optimisation of methane production.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><pmid>21227685</pmid><doi>10.1016/j.biortech.2010.12.046</doi><tpages>8</tpages></addata></record> |
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| subjects | Algorithms Alkalinity Anaerobic digestion Bacteria, Anaerobic - metabolism Bicarbonates - analysis Biofuel production Biogas Biological and medical sciences Biological treatment of sewage sludges and wastes Biotechnology Computer programs Energy Environment and pollution Fundamental and applied biological sciences. Psychology Hydrogen-Ion Concentration Industrial applications and implications. Economical aspects Linear Models Mathematical models Methane - biosynthesis Models, Theoretical Monitoring NIRS Refuse Disposal - methods Sensors Software Software sensor Spectrophotometry, Infrared - methods |
| title | Evaluation of near infrared spectroscopy and software sensor methods for determination of total alkalinity in anaerobic digesters |
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