ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants

Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascadin...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on visualization and computer graphics Jg. 29; H. 1; S. 1 - 11
Hauptverfasser:	Liu, Shuhan, Weng, Di, Tian, Yuan, Deng, Zikun, Xu, Haoran, Zhu, Xiangyu, Yin, Honglei, Zhan, Xianyuan, Wu, Yingcai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Coal-fired power plants Complexity Data visualization Efficiency energy data visualization Impact analysis Industries Interactive control Interactive systems Positive feedback Power generation Power plant visual analytics Sensor systems Sensors smart factory spatiotemporal visualization Time series analysis Visual analytics
ISSN:	1077-2626, 1941-0506, 1941-0506
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.
AbstractList	Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts. Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However, due to the complex and dynamic environment inside the power plants, it is hard to extract and evaluate control strategies and their cascading impact across massive sensors. Existing manual and data-driven approaches cannot well support the analysis of control strategies because these approaches are time-consuming and do not scale with the complexity of the power plant systems. Three challenges were identified: a) interactive extraction of control strategies from large-scale dynamic sensor data, b) intuitive visual representation of cascading impact among the sensors in a complex power plant system, and c) time-lag-aware analysis of the impact of control strategies on electricity generation efficiency. By collaborating with energy domain experts, we addressed these challenges with ECoalVis, a novel interactive system for experts to visually analyze the control strategies of coal-fired power plants extracted from historical sensor data. The effectiveness of the proposed system is evaluated with two usage scenarios on a real-world historical dataset and received positive feedback from experts.
Author	Weng, Di Zhan, Xianyuan Xu, Haoran Tian, Yuan Wu, Yingcai Liu, Shuhan Deng, Zikun Zhu, Xiangyu Yin, Honglei
Author_xml	– sequence: 1 givenname: Shuhan surname: Liu fullname: Liu, Shuhan organization: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Di surname: Weng fullname: Weng, Di organization: Microsoft Research Asia, Beijing, China – sequence: 3 givenname: Yuan surname: Tian fullname: Tian, Yuan organization: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China – sequence: 4 givenname: Zikun orcidid: 0000-0002-4477-5292 surname: Deng fullname: Deng, Zikun organization: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China – sequence: 5 givenname: Haoran surname: Xu fullname: Xu, Haoran organization: JD iCity, JD Technology, Beijing, China – sequence: 6 givenname: Xiangyu surname: Zhu fullname: Zhu, Xiangyu organization: Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China – sequence: 7 givenname: Honglei surname: Yin fullname: Yin, Honglei organization: JD iCity, JD Technology, Beijing, China – sequence: 8 givenname: Xianyuan orcidid: 0000-0002-3683-0554 surname: Zhan fullname: Zhan, Xianyuan organization: Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China – sequence: 9 givenname: Yingcai orcidid: 0000-0002-1119-3237 surname: Wu fullname: Wu, Yingcai organization: State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36191102$$D View this record in MEDLINE/PubMed
BookMark	eNp9kU1LHTEUhkOx1M8fUAQJuOlmricfk5m4k8HaFkHBj23IzT0pkbkTTWYQ_70Z7rULF90kWTzPy8l598nOEAck5DuDBWOgz-4fu6sFB84XgoOWAr6QPaYlq6AGtVPe0DQVV1ztkv2cnwCYlK3-RnaFYrok8D3y57KLtn8M-ZyWY7I9vRhs_5ZDptHTLg5jij29G5Md8W_ATMNAZ6PyIeGK3sZXTPS2t8OYD8lXb_uMR9v7gDz8vLzvflXXN1e_u4vrynElx8pyob0EidAiU156AMm0rR3jwmEjHECtBbZLaKV3qwZrBLYSzHml7dIJcUB-bHKfU3yZMI9mHbLDvgyBccqGN5xxBUrJgp5-Qp_ilMoHZ6qWum55MweebKlpucaVeU5hbdOb-dhSAZoN4FLMOaE3Lox2DPN2bOgNAzP3YeY-zNyH2fZRTPbJ_Aj_n3O8cQIi_uO1hrYu074De4GS7g
CODEN	ITVGEA
CitedBy_id	crossref_primary_10_1007_s12650_022_00884_1 crossref_primary_10_1109_TVCG_2022_3229953 crossref_primary_10_1109_TVCG_2025_3538768 crossref_primary_10_1109_TVCG_2024_3397554 crossref_primary_10_1109_TVCG_2025_3537115
Cites_doi	10.1007/978-1-4612-6185-8_2 10.1109/TVCG.2021.3114877 10.1109/TVCG.2009.117 10.1016/j.energy.2018.06.157 10.1109/PacificVis.2013.6596144 10.1007/s12650-021-00778-8 10.1109/APPEEC.2009.4918261 10.1016/j.enpol.2013.05.047 10.1109/TVCG.2021.3114875 10.1609/aaai.v36i4.20393 10.1007/s12650-020-00717-z 10.1109/TVCG.2021.3114792 10.1109/PACIFICVIS.2011.5742373 10.1109/TVCG.2009.200 10.1016/j.visinf.2020.12.002 10.1016/j.rinp.2018.04.045 10.1109/TVCG.2015.2467851 10.1109/TCST.2005.854319 10.1109/TVCG.2014.2346454 10.1137/0110015 10.1016/j.jvlc.2017.11.004 10.1177/104973239300300403 10.1007/3-540-44541-2_17 10.1109/TVCG.2021.3071387 10.1073/pnas.2017936118 10.1016/j.visinf.2021.09.001 10.1109/PACIFICVIS.2016.7465252 10.1109/VAST.2009.5332595 10.1007/s12650-022-00884-1 10.1016/j.energy.2021.121228 10.1007/s12650-018-0530-2 10.1016/j.visinf.2021.12.004 10.1109/PEITS.2008.103 10.1109/PacificVis.2018.00026 10.1016/j.applthermaleng.2020.115706 10.1109/TVCG.2019.2934275 10.1109/TVCG.2015.2467622 10.1109/TVCG.2021.3114878 10.1007/s41095-022-0275-7 10.3390/en13153775 10.1109/TVCG.2016.2640960 10.1016/j.jclepro.2020.122837 10.1201/b17511 10.1016/j.visinf.2022.05.003 10.1109/TVCG.2012.213 10.1007/s11704-020-0088-8 10.1016/j.apenergy.2011.06.029 10.1109/TVCG.2018.2864886 10.1109/ICIEA.2008.4582615 10.1109/SPIRE.2000.878178 10.1016/j.visinf.2022.03.001 10.1109/VAST.2006.261421 10.1109/CCDC.2008.4598004 10.1109/TVCG.2013.173 10.17775/CSEEJPES.2015.00009 10.1080/14786440109462720 10.1109/TVCG.2021.3100413 10.1103/PhysRevLett.100.084102 10.1109/ACCESS.2020.2985233 10.1109/TVCG.2016.2598664 10.1007/s12650-021-00818-3 10.1109/TVCG.2017.2745083 10.1111/cgf.12901 10.1109/TVCG.2022.3209360 10.1109/TVCG.2017.2745105 10.1016/j.energy.2021.120077
ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
Copyright_xml	– notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023
DBID	97E RIA RIE AAYXX CITATION NPM 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8
DOI	10.1109/TVCG.2022.3209430
DatabaseName	IEEE All-Society Periodicals Package (ASPP) 2005-present IEEE All-Society Periodicals Package (ASPP) 1998-Present IEEE Electronic Library Online CrossRef PubMed Computer and Information Systems Abstracts Electronics & Communications Abstracts Technology Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitle	CrossRef PubMed Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic PubMed Technology Research Database
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1941-0506
EndPage	11
ExternalDocumentID	36191102 10_1109_TVCG_2022_3209430 9908527
Genre	orig-research Journal Article
GroupedDBID	--- -~X .DC 0R~ 29I 4.4 53G 5GY 5VS 6IK 97E AAJGR AARMG AASAJ AAWTH ABAZT ABQJQ ABVLG ACGFO ACIWK AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CS3 DU5 EBS EJD F5P HZ~ H~9 IEDLZ IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RNI RNS RZB TN5 VH1 AAYXX CITATION AAYOK NPM RIG 7SC 7SP 8FD JQ2 L7M L~C L~D 7X8
ID	FETCH-LOGICAL-c264t-a239f404e08e16f4f00419a5c123ce73c00593e8b084fcd7e5e01d31cf69abc33
IEDL.DBID	RIE
ISSN	1077-2626 1941-0506
IngestDate	Sun Sep 28 06:41:16 EDT 2025 Sun Nov 09 07:57:21 EST 2025 Thu Apr 03 07:12:28 EDT 2025 Tue Nov 18 21:59:35 EST 2025 Sat Nov 29 03:31:42 EST 2025 Wed Aug 27 02:29:14 EDT 2025
IsPeerReviewed	true
IsScholarly	true
Issue	1
Language	English
License	https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c264t-a239f404e08e16f4f00419a5c123ce73c00593e8b084fcd7e5e01d31cf69abc33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0002-4477-5292 0000-0002-1119-3237 0000-0002-3683-0554
PMID	36191102
PQID	2754958273
PQPubID	75741
PageCount	11
ParticipantIDs	crossref_primary_10_1109_TVCG_2022_3209430 proquest_miscellaneous_2721260664 ieee_primary_9908527 crossref_citationtrail_10_1109_TVCG_2022_3209430 proquest_journals_2754958273 pubmed_primary_36191102
PublicationCentury	2000
PublicationDate	2023-01-01
PublicationDateYYYYMMDD	2023-01-01
PublicationDate_xml	– month: 01 year: 2023 text: 2023-01-01 day: 01
PublicationDecade	2020
PublicationPlace	United States
PublicationPlace_xml	– name: United States – name: New York
PublicationTitle	IEEE transactions on visualization and computer graphics
PublicationTitleAbbrev	TVCG
PublicationTitleAlternate	IEEE Trans Vis Comput Graph
PublicationYear	2023
Publisher	IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml	– name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References	ref57 ref13 ref56 ref12 ref59 ref14 ref53 ref52 ref55 ref54 (ref58) 2005 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref48 ref42 ref41 ref44 ref43 deng (ref15) 2023; 26 ref49 ref8 ref7 ritchie (ref45) 2020 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 sengupta (ref47) 2018 ref1 ref39 ref38 (ref11) 2018 ref71 ref70 ref68 ref24 ref67 ref23 ref26 ref69 ref25 ref64 ref20 ref63 ref66 ref22 ref65 ref21 ref28 ref27 ref29 ref60 ref62 ref61 cormen (ref10) 2022
References_xml	– year: 2020 ident: ref45 publication-title: Electricity Mix – ident: ref41 doi: 10.1007/978-1-4612-6185-8_2 – ident: ref66 doi: 10.1109/TVCG.2021.3114877 – ident: ref52 doi: 10.1109/TVCG.2009.117 – ident: ref6 doi: 10.1016/j.energy.2018.06.157 – ident: ref29 doi: 10.1109/PacificVis.2013.6596144 – ident: ref56 doi: 10.1007/s12650-021-00778-8 – ident: ref19 doi: 10.1109/APPEEC.2009.4918261 – ident: ref64 doi: 10.1016/j.enpol.2013.05.047 – ident: ref16 doi: 10.1109/TVCG.2021.3114875 – ident: ref67 doi: 10.1609/aaai.v36i4.20393 – ident: ref57 doi: 10.1007/s12650-020-00717-z – ident: ref4 doi: 10.1109/TVCG.2021.3114792 – ident: ref28 doi: 10.1109/PACIFICVIS.2011.5742373 – ident: ref34 doi: 10.1109/TVCG.2009.200 – ident: ref1 doi: 10.1016/j.visinf.2020.12.002 – ident: ref25 doi: 10.1016/j.rinp.2018.04.045 – ident: ref2 doi: 10.1109/TVCG.2015.2467851 – ident: ref35 doi: 10.1109/TCST.2005.854319 – ident: ref31 doi: 10.1109/TVCG.2014.2346454 – ident: ref27 doi: 10.1137/0110015 – ident: ref71 doi: 10.1016/j.jvlc.2017.11.004 – ident: ref21 doi: 10.1177/104973239300300403 – ident: ref53 doi: 10.1007/3-540-44541-2_17 – ident: ref13 doi: 10.1109/TVCG.2021.3071387 – ident: ref12 doi: 10.1073/pnas.2017936118 – ident: ref55 doi: 10.1016/j.visinf.2021.09.001 – ident: ref38 doi: 10.1109/PACIFICVIS.2016.7465252 – ident: ref59 doi: 10.1109/VAST.2009.5332595 – volume: 26 year: 2023 ident: ref15 article-title: You are experienced: Interactive tour planning with crowdsourcing tour data from web publication-title: Journal of Visualization doi: 10.1007/s12650-022-00884-1 – ident: ref48 doi: 10.1016/j.energy.2021.121228 – ident: ref70 doi: 10.1007/s12650-018-0530-2 – year: 2022 ident: ref10 publication-title: Introduction to Algorithms – ident: ref8 doi: 10.1016/j.visinf.2021.12.004 – ident: ref36 doi: 10.1109/PEITS.2008.103 – ident: ref61 doi: 10.1109/PacificVis.2018.00026 – ident: ref40 doi: 10.1016/j.applthermaleng.2020.115706 – ident: ref49 doi: 10.1109/TVCG.2019.2934275 – ident: ref32 doi: 10.1109/TVCG.2015.2467622 – ident: ref51 doi: 10.1109/TVCG.2021.3114878 – ident: ref14 doi: 10.1007/s41095-022-0275-7 – ident: ref54 doi: 10.3390/en13153775 – ident: ref37 doi: 10.1109/TVCG.2016.2640960 – year: 2018 ident: ref11 publication-title: Automation of 30MW Power Plant using PCS 7 – ident: ref68 doi: 10.1016/j.jclepro.2020.122837 – ident: ref39 doi: 10.1201/b17511 – ident: ref7 doi: 10.1016/j.visinf.2022.05.003 – ident: ref46 doi: 10.1109/TVCG.2012.213 – ident: ref62 doi: 10.1007/s11704-020-0088-8 – ident: ref50 doi: 10.1016/j.apenergy.2011.06.029 – ident: ref33 doi: 10.1109/TVCG.2018.2864886 – ident: ref20 doi: 10.1109/ICIEA.2008.4582615 – year: 2018 ident: ref47 article-title: The World Needs to Quit Coal publication-title: Why Is It So Hard? – ident: ref3 doi: 10.1109/SPIRE.2000.878178 – ident: ref23 doi: 10.1016/j.visinf.2022.03.001 – ident: ref18 doi: 10.1109/VAST.2006.261421 – ident: ref69 doi: 10.1109/CCDC.2008.4598004 – ident: ref24 doi: 10.1109/TVCG.2013.173 – ident: ref17 doi: 10.17775/CSEEJPES.2015.00009 – ident: ref42 doi: 10.1080/14786440109462720 – ident: ref26 doi: 10.1109/TVCG.2021.3100413 – ident: ref43 doi: 10.1103/PhysRevLett.100.084102 – ident: ref22 doi: 10.1109/ACCESS.2020.2985233 – ident: ref63 doi: 10.1109/TVCG.2016.2598664 – ident: ref5 doi: 10.1007/s12650-021-00818-3 – ident: ref9 doi: 10.1109/TVCG.2017.2745083 – ident: ref44 doi: 10.1111/cgf.12901 – ident: ref60 doi: 10.1109/TVCG.2022.3209360 – year: 2005 ident: ref58 article-title: What Is publication-title: The definition of fuzzy search – ident: ref30 doi: 10.1109/TVCG.2017.2745105 – ident: ref65 doi: 10.1016/j.energy.2021.120077
SSID	ssj0014489
Score	2.433173
Snippet	Improving the efficiency of coal-fired power plants has numerous benefits. The control strategy is one of the major factors affecting such efficiency. However,...
SourceID	proquest pubmed crossref ieee
SourceType	Aggregation Database Index Database Enrichment Source Publisher
StartPage	1
SubjectTerms	Coal-fired power plants Complexity Data visualization Efficiency energy data visualization Impact analysis Industries Interactive control Interactive systems Positive feedback Power generation Power plant visual analytics Sensor systems Sensors smart factory spatiotemporal visualization Time series analysis Visual analytics
Title	ECoalVis: Visual Analysis of Control Strategies in Coal-fired Power Plants
URI	https://ieeexplore.ieee.org/document/9908527 https://www.ncbi.nlm.nih.gov/pubmed/36191102 https://www.proquest.com/docview/2754958273 https://www.proquest.com/docview/2721260664
Volume	29
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVIEE databaseName: IEEE Electronic Library (IEL) customDbUrl: eissn: 1941-0506 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014489 issn: 1077-2626 databaseCode: RIE dateStart: 19950101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8QwEB5UPOjB92N9EcGTWM32lcSbLD4QEQ8qeyttOoGFpRW76-93pu0WBRW8lEImbchMkvkyL4CTWOc6U2HsqcxIL1SEWTOVGs9J6zhbjAnjOlD4QT0-6uHQPM3BWRcLg4i18xme82tty89LO-WrsgvaOXXkq3mYV0o1sVqdxYBghmn8C5Xnk5beWjD70lw8vw5uCQn6_nngsyMdV38LCDhQo__tOKrrq_yuatZHzs3q_wa7BiutaimuGllYhzksNmD5S8LBTbi_HpTp-HVUXQp6TJm6TUoiSicGjdu6mGWsxUqMCsE9PEc7Yy6euKSa4DpHk2oLXm6unwd3XltNwbOk9Ey81A-MC2WIUmM_dqHjVFsmjSydXRZVYOvqfqgzqUNnc4URyn4e9K2LTZrZINiGhaIscBeEpS3UKSZGRXDSag5wjXJGXzE6G_ZAziY1sW2qca54MU5qyCFNwixJmCVJy5IenHZd3po8G38Rb_J8d4TtVPfgYMa5pF2JVeIrQsCRJi2tB8ddM60hNoykBZZTpqEDnJEcjXyn4Xj37Zmg7P38z31Y4gL0zaXMASxM3qd4CIv2YzKq3o9IUIf6qBbUTwwq3vQ
linkProvider	IEEE
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lb9QwEB6VgkQ5lEcfbClgJE6ItI7jxDY3tGopsKx6WKreosQZSytVSdXs8vuZSbIRSIDEJYrkcWJ5bM98nhfA28xWtjQ6i0zpZKQNYdbSFC4K0gfOFuN01gUKz8x8bq-v3eUWvB9jYRCxcz7DE37tbPlV49d8VXZKJ6dNlbkH91OtVdxHa402AwIarvcwNJEiPX2wYcbSnS6upp8ICyp1kih2peP6bwlBB2pUvwmkrsLK35XNTuicP_6_4T6B3UG5FB_71fAUtrB-Bo9-STm4B1_Opk1xc7VsPwh6rJl6SEsimiCmveO62OSsxVYsa8E9okBnYyUuuaia4EpHq3Yfvp-fLaYX0VBPIfKk9qyiQiUuaKlRWoyzoAMn23JF6kl6eTSJ7-r7oS2l1cFXBlOUcZXEPmSuKH2SHMB23dT4HISnQzQYJkZDgNJbDnFNK8ZfGQavJyA3k5r7Idk417y4yTvQIV3OLMmZJfnAkgm8G7vc9pk2_kW8x_M9Eg5TPYHjDefyYS-2uTKEgVNLetoE3ozNtIvYNFLU2KyZhkQ4Yzka-WHP8fHbm4Vy9Od_voaHF4tvs3z2ef71BexwOfr-iuYYtld3a3wJD_yP1bK9e9Ut15-N4uFT
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=ECoalVis%3A+Visual+Analysis+of+Control+Strategies+in+Coal-fired+Power+Plants&rft.jtitle=IEEE+transactions+on+visualization+and+computer+graphics&rft.au=Liu%2C+Shuhan&rft.au=Weng%2C+Di&rft.au=Tian%2C+Yuan&rft.au=Deng%2C+Zikun&rft.date=2023-01-01&rft.issn=1941-0506&rft.eissn=1941-0506&rft.volume=29&rft.issue=1&rft.spage=1091&rft_id=info:doi/10.1109%2FTVCG.2022.3209430&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1077-2626&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1077-2626&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1077-2626&client=summon