Fast quality control of natural gas for commercial supply and transport utilities

•A quality procedure to determine the composition of natural gas was developed.•Production samples instead of synthetic mixtures yielded accurate predictions.•Selectivity ratio and iPLS were selected among five variable selection procedures.•The prediction errors and limits of detection halved those...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Fuel (Guildford) 2021-12, Vol.305, p.121500, Article 121500
Hauptverfasser: Ferreiro, Borja, Andrade, José, López-Mahía, Purificación, Muniategui, Soledad, Vázquez, Cristina, Pérez, Andrés, Rey, María, Vales, Carlos
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page 121500
container_title Fuel (Guildford)
container_volume 305
creator Ferreiro, Borja
Andrade, José
López-Mahía, Purificación
Muniategui, Soledad
Vázquez, Cristina
Pérez, Andrés
Rey, María
Vales, Carlos
description •A quality procedure to determine the composition of natural gas was developed.•Production samples instead of synthetic mixtures yielded accurate predictions.•Selectivity ratio and iPLS were selected among five variable selection procedures.•The prediction errors and limits of detection halved those previously published. Quality control of natural gas frequently relies on off-line slow standardized chromatographic techniques. Previous implementations of new measurement approaches focused of synthetic mixtures without extensive industrial validation. Here, a fast alternative based on infrared spectra is presented to predict the gas constituents and a physical parameter, the Wobbe index. Commercial samples instead of synthetic mixtures were used to develop predictive models. Method performance parameters were calculated and ca. 100 % of the sample-specific confidence intervals for the predictions overlapped with those of the reference values and the approach was unbiased and precise. The limits of detection and quantification (classical and considering errors of type I and II) outperformed other approaches. Validation included commercial samples and primary mixtures. Furthermore, prediction models considering reduced sets of variables were sought for using Markov-chain Monte Carlo guided searches (uninformative variable elimination and random frog) and common (iPLS, UVE and SR) approaches. The prediction errors and limits of detection of these ‘reduced’ models outperformed those from other approaches. The methodology takes only minutes to analyse a sample, requires few sample and no reagents (only some argon), making this approach cost-effective and environmentally-friendly.
doi_str_mv 10.1016/j.fuel.2021.121500
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2581871912</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S001623612101379X</els_id><sourcerecordid>2581871912</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-d099d93753f8757dff87bed04e767b4fb32dc662a17d4ff211deb5440a43ae363</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhoMouK7-AU8Bz635aJsWvMjiFyyIoOeQJhNJ6TbdJBX235tlPXsamHnfd2YehG4pKSmhzf1Q2gXGkhFGS8poTcgZWtFW8ELQmp-jFcmqgvGGXqKrGAdCiGjraoU-nlVMeL-o0aUD1n5KwY_YWzyptAQ14m8VsfUhj3Y7CNrlVlzmeTxgNRmcgpri7EPCS3I5wkG8RhdWjRFu_uoafT0_fW5ei-37y9vmcVtoLlgqDOk603FRc9uKWhibSw-GVCAa0Ve258zopmGKClNZyyg10NdVRVTFFfCGr9HdKXcOfr9ATHLwS5jySsnqNv9OO8qyip1UOvgYA1g5B7dT4SApkUd0cpBHdPKITp7QZdPDyQT5_h8HQUbtYNJgXACdpPHuP_svLFd4Bw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2581871912</pqid></control><display><type>article</type><title>Fast quality control of natural gas for commercial supply and transport utilities</title><source>Access via ScienceDirect (Elsevier)</source><creator>Ferreiro, Borja ; Andrade, José ; López-Mahía, Purificación ; Muniategui, Soledad ; Vázquez, Cristina ; Pérez, Andrés ; Rey, María ; Vales, Carlos</creator><creatorcontrib>Ferreiro, Borja ; Andrade, José ; López-Mahía, Purificación ; Muniategui, Soledad ; Vázquez, Cristina ; Pérez, Andrés ; Rey, María ; Vales, Carlos</creatorcontrib><description>•A quality procedure to determine the composition of natural gas was developed.•Production samples instead of synthetic mixtures yielded accurate predictions.•Selectivity ratio and iPLS were selected among five variable selection procedures.•The prediction errors and limits of detection halved those previously published. Quality control of natural gas frequently relies on off-line slow standardized chromatographic techniques. Previous implementations of new measurement approaches focused of synthetic mixtures without extensive industrial validation. Here, a fast alternative based on infrared spectra is presented to predict the gas constituents and a physical parameter, the Wobbe index. Commercial samples instead of synthetic mixtures were used to develop predictive models. Method performance parameters were calculated and ca. 100 % of the sample-specific confidence intervals for the predictions overlapped with those of the reference values and the approach was unbiased and precise. The limits of detection and quantification (classical and considering errors of type I and II) outperformed other approaches. Validation included commercial samples and primary mixtures. Furthermore, prediction models considering reduced sets of variables were sought for using Markov-chain Monte Carlo guided searches (uninformative variable elimination and random frog) and common (iPLS, UVE and SR) approaches. The prediction errors and limits of detection of these ‘reduced’ models outperformed those from other approaches. The methodology takes only minutes to analyse a sample, requires few sample and no reagents (only some argon), making this approach cost-effective and environmentally-friendly.</description><identifier>ISSN: 0016-2361</identifier><identifier>EISSN: 1873-7153</identifier><identifier>DOI: 10.1016/j.fuel.2021.121500</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Argon ; Chromatography ; Confidence intervals ; Errors ; Infrared gas measurement ; Infrared spectra ; Markov chains ; Mathematical models ; Natural gas ; Natural gas composition ; Parameters ; Performance prediction ; Physical properties ; PLS prediction ; Prediction models ; Quality control ; Reagents ; Spectral variable Selection ; Wobbe index</subject><ispartof>Fuel (Guildford), 2021-12, Vol.305, p.121500, Article 121500</ispartof><rights>2021 The Author(s)</rights><rights>Copyright Elsevier BV Dec 1, 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-d099d93753f8757dff87bed04e767b4fb32dc662a17d4ff211deb5440a43ae363</citedby><cites>FETCH-LOGICAL-c372t-d099d93753f8757dff87bed04e767b4fb32dc662a17d4ff211deb5440a43ae363</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.fuel.2021.121500$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Ferreiro, Borja</creatorcontrib><creatorcontrib>Andrade, José</creatorcontrib><creatorcontrib>López-Mahía, Purificación</creatorcontrib><creatorcontrib>Muniategui, Soledad</creatorcontrib><creatorcontrib>Vázquez, Cristina</creatorcontrib><creatorcontrib>Pérez, Andrés</creatorcontrib><creatorcontrib>Rey, María</creatorcontrib><creatorcontrib>Vales, Carlos</creatorcontrib><title>Fast quality control of natural gas for commercial supply and transport utilities</title><title>Fuel (Guildford)</title><description>•A quality procedure to determine the composition of natural gas was developed.•Production samples instead of synthetic mixtures yielded accurate predictions.•Selectivity ratio and iPLS were selected among five variable selection procedures.•The prediction errors and limits of detection halved those previously published. Quality control of natural gas frequently relies on off-line slow standardized chromatographic techniques. Previous implementations of new measurement approaches focused of synthetic mixtures without extensive industrial validation. Here, a fast alternative based on infrared spectra is presented to predict the gas constituents and a physical parameter, the Wobbe index. Commercial samples instead of synthetic mixtures were used to develop predictive models. Method performance parameters were calculated and ca. 100 % of the sample-specific confidence intervals for the predictions overlapped with those of the reference values and the approach was unbiased and precise. The limits of detection and quantification (classical and considering errors of type I and II) outperformed other approaches. Validation included commercial samples and primary mixtures. Furthermore, prediction models considering reduced sets of variables were sought for using Markov-chain Monte Carlo guided searches (uninformative variable elimination and random frog) and common (iPLS, UVE and SR) approaches. The prediction errors and limits of detection of these ‘reduced’ models outperformed those from other approaches. The methodology takes only minutes to analyse a sample, requires few sample and no reagents (only some argon), making this approach cost-effective and environmentally-friendly.</description><subject>Argon</subject><subject>Chromatography</subject><subject>Confidence intervals</subject><subject>Errors</subject><subject>Infrared gas measurement</subject><subject>Infrared spectra</subject><subject>Markov chains</subject><subject>Mathematical models</subject><subject>Natural gas</subject><subject>Natural gas composition</subject><subject>Parameters</subject><subject>Performance prediction</subject><subject>Physical properties</subject><subject>PLS prediction</subject><subject>Prediction models</subject><subject>Quality control</subject><subject>Reagents</subject><subject>Spectral variable Selection</subject><subject>Wobbe index</subject><issn>0016-2361</issn><issn>1873-7153</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AU8Bz635aJsWvMjiFyyIoOeQJhNJ6TbdJBX235tlPXsamHnfd2YehG4pKSmhzf1Q2gXGkhFGS8poTcgZWtFW8ELQmp-jFcmqgvGGXqKrGAdCiGjraoU-nlVMeL-o0aUD1n5KwY_YWzyptAQ14m8VsfUhj3Y7CNrlVlzmeTxgNRmcgpri7EPCS3I5wkG8RhdWjRFu_uoafT0_fW5ei-37y9vmcVtoLlgqDOk603FRc9uKWhibSw-GVCAa0Ve258zopmGKClNZyyg10NdVRVTFFfCGr9HdKXcOfr9ATHLwS5jySsnqNv9OO8qyip1UOvgYA1g5B7dT4SApkUd0cpBHdPKITp7QZdPDyQT5_h8HQUbtYNJgXACdpPHuP_svLFd4Bw</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Ferreiro, Borja</creator><creator>Andrade, José</creator><creator>López-Mahía, Purificación</creator><creator>Muniategui, Soledad</creator><creator>Vázquez, Cristina</creator><creator>Pérez, Andrés</creator><creator>Rey, María</creator><creator>Vales, Carlos</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</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>P64</scope></search><sort><creationdate>20211201</creationdate><title>Fast quality control of natural gas for commercial supply and transport utilities</title><author>Ferreiro, Borja ; Andrade, José ; López-Mahía, Purificación ; Muniategui, Soledad ; Vázquez, Cristina ; Pérez, Andrés ; Rey, María ; Vales, Carlos</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-d099d93753f8757dff87bed04e767b4fb32dc662a17d4ff211deb5440a43ae363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Argon</topic><topic>Chromatography</topic><topic>Confidence intervals</topic><topic>Errors</topic><topic>Infrared gas measurement</topic><topic>Infrared spectra</topic><topic>Markov chains</topic><topic>Mathematical models</topic><topic>Natural gas</topic><topic>Natural gas composition</topic><topic>Parameters</topic><topic>Performance prediction</topic><topic>Physical properties</topic><topic>PLS prediction</topic><topic>Prediction models</topic><topic>Quality control</topic><topic>Reagents</topic><topic>Spectral variable Selection</topic><topic>Wobbe index</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ferreiro, Borja</creatorcontrib><creatorcontrib>Andrade, José</creatorcontrib><creatorcontrib>López-Mahía, Purificación</creatorcontrib><creatorcontrib>Muniategui, Soledad</creatorcontrib><creatorcontrib>Vázquez, Cristina</creatorcontrib><creatorcontrib>Pérez, Andrés</creatorcontrib><creatorcontrib>Rey, María</creatorcontrib><creatorcontrib>Vales, Carlos</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Fuel (Guildford)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ferreiro, Borja</au><au>Andrade, José</au><au>López-Mahía, Purificación</au><au>Muniategui, Soledad</au><au>Vázquez, Cristina</au><au>Pérez, Andrés</au><au>Rey, María</au><au>Vales, Carlos</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fast quality control of natural gas for commercial supply and transport utilities</atitle><jtitle>Fuel (Guildford)</jtitle><date>2021-12-01</date><risdate>2021</risdate><volume>305</volume><spage>121500</spage><pages>121500-</pages><artnum>121500</artnum><issn>0016-2361</issn><eissn>1873-7153</eissn><abstract>•A quality procedure to determine the composition of natural gas was developed.•Production samples instead of synthetic mixtures yielded accurate predictions.•Selectivity ratio and iPLS were selected among five variable selection procedures.•The prediction errors and limits of detection halved those previously published. Quality control of natural gas frequently relies on off-line slow standardized chromatographic techniques. Previous implementations of new measurement approaches focused of synthetic mixtures without extensive industrial validation. Here, a fast alternative based on infrared spectra is presented to predict the gas constituents and a physical parameter, the Wobbe index. Commercial samples instead of synthetic mixtures were used to develop predictive models. Method performance parameters were calculated and ca. 100 % of the sample-specific confidence intervals for the predictions overlapped with those of the reference values and the approach was unbiased and precise. The limits of detection and quantification (classical and considering errors of type I and II) outperformed other approaches. Validation included commercial samples and primary mixtures. Furthermore, prediction models considering reduced sets of variables were sought for using Markov-chain Monte Carlo guided searches (uninformative variable elimination and random frog) and common (iPLS, UVE and SR) approaches. The prediction errors and limits of detection of these ‘reduced’ models outperformed those from other approaches. The methodology takes only minutes to analyse a sample, requires few sample and no reagents (only some argon), making this approach cost-effective and environmentally-friendly.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.fuel.2021.121500</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0016-2361
ispartof Fuel (Guildford), 2021-12, Vol.305, p.121500, Article 121500
issn 0016-2361
1873-7153
language eng
recordid cdi_proquest_journals_2581871912
source Access via ScienceDirect (Elsevier)
subjects Argon
Chromatography
Confidence intervals
Errors
Infrared gas measurement
Infrared spectra
Markov chains
Mathematical models
Natural gas
Natural gas composition
Parameters
Performance prediction
Physical properties
PLS prediction
Prediction models
Quality control
Reagents
Spectral variable Selection
Wobbe index
title Fast quality control of natural gas for commercial supply and transport utilities
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T21%3A33%3A44IST&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=Fast%20quality%20control%20of%20natural%20gas%20for%20commercial%20supply%20and%20transport%20utilities&rft.jtitle=Fuel%20(Guildford)&rft.au=Ferreiro,%20Borja&rft.date=2021-12-01&rft.volume=305&rft.spage=121500&rft.pages=121500-&rft.artnum=121500&rft.issn=0016-2361&rft.eissn=1873-7153&rft_id=info:doi/10.1016/j.fuel.2021.121500&rft_dat=%3Cproquest_cross%3E2581871912%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=2581871912&rft_id=info:pmid/&rft_els_id=S001623612101379X&rfr_iscdi=true