Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler
To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least square...
Gespeichert in:
| Veröffentlicht in: | Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-9 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 9 |
|---|---|
| container_issue | 2017 |
| container_start_page | 1 |
| container_title | Mathematical problems in engineering |
| container_volume | 2017 |
| creator | Cao, Shengxian Che, Ping Zhang, Haiyang Tang, Zhenhao Zhao, Zhiyong |
| description | To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process. |
| doi_str_mv | 10.1155/2017/8048962 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1884125282</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>4317642911</sourcerecordid><originalsourceid>FETCH-LOGICAL-c459t-1a9d3965d5c5c244468c687f65414864e04e0fe051716560b53ea234af1b3ace3</originalsourceid><addsrcrecordid>eNqF0UtLAzEQB_BFFKyPm2cJeBF0NZPXZo99-ALFHhS8rWl2FiPbpiZbpX56UysIXoRAZoYfSfgnyw6AngFIec4oFOeaCl0qtpH1QCqeSxDFZqopEzkw_rSd7cT4SikDCbqXPY9MZ0h_Ztpl52wkAxOxJqOFafP7eeem7jO1_dqk-h3Jna-xJeOAtbPfg6GfdcG3pPGBdC9Ixv4DAxm3ZtaRgXcthr1sqzFtxP2ffTd7vLx4GF7nt_dXN8P-bW6FLLscTFnzUslaWmmZEEJpq3TRKClAaCWQptUglVCAkopOJEfDuDANTLixyHez4_W58-DfFhi7auqixTY9Bf0iVqC1ACaZZoke_aGvfhFSBCtVsCLdyXhSp2tlg48xYFPNg5uasKyAVqu4q1Xc1U_ciZ-s-Yub1ebD_acP1xqTwcb8aihZ-ib-Bdc3hxU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1872765423</pqid></control><display><type>article</type><title>Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler</title><source>Wiley Online Library Open Access</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Alma/SFX Local Collection</source><creator>Cao, Shengxian ; Che, Ping ; Zhang, Haiyang ; Tang, Zhenhao ; Zhao, Zhiyong</creator><contributor>Kavun, Sergii V.</contributor><creatorcontrib>Cao, Shengxian ; Che, Ping ; Zhang, Haiyang ; Tang, Zhenhao ; Zhao, Zhiyong ; Kavun, Sergii V.</creatorcontrib><description>To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2017/8048962</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Adaptive control ; Adaptive control systems ; Algorithms ; Analytics ; Boiler furnaces ; Data analysis ; Data mining ; Electric power ; Electric power generation ; Electric power plants ; Evolutionary algorithms ; Evolutionary computation ; Mathematical models ; Mathematical problems ; Methods ; Optimization ; Power plants ; Prediction models ; Predictive control ; Support vector machines ; Variables</subject><ispartof>Mathematical problems in engineering, 2017-01, Vol.2017 (2017), p.1-9</ispartof><rights>Copyright © 2017 Zhenhao Tang et al.</rights><rights>Copyright © 2017 Zhenhao Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c459t-1a9d3965d5c5c244468c687f65414864e04e0fe051716560b53ea234af1b3ace3</citedby><cites>FETCH-LOGICAL-c459t-1a9d3965d5c5c244468c687f65414864e04e0fe051716560b53ea234af1b3ace3</cites><orcidid>0000-0002-4650-6870</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><contributor>Kavun, Sergii V.</contributor><creatorcontrib>Cao, Shengxian</creatorcontrib><creatorcontrib>Che, Ping</creatorcontrib><creatorcontrib>Zhang, Haiyang</creatorcontrib><creatorcontrib>Tang, Zhenhao</creatorcontrib><creatorcontrib>Zhao, Zhiyong</creatorcontrib><title>Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler</title><title>Mathematical problems in engineering</title><description>To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.</description><subject>Accuracy</subject><subject>Adaptive control</subject><subject>Adaptive control systems</subject><subject>Algorithms</subject><subject>Analytics</subject><subject>Boiler furnaces</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Electric power</subject><subject>Electric power generation</subject><subject>Electric power plants</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Mathematical models</subject><subject>Mathematical problems</subject><subject>Methods</subject><subject>Optimization</subject><subject>Power plants</subject><subject>Prediction models</subject><subject>Predictive control</subject><subject>Support vector machines</subject><subject>Variables</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNqF0UtLAzEQB_BFFKyPm2cJeBF0NZPXZo99-ALFHhS8rWl2FiPbpiZbpX56UysIXoRAZoYfSfgnyw6AngFIec4oFOeaCl0qtpH1QCqeSxDFZqopEzkw_rSd7cT4SikDCbqXPY9MZ0h_Ztpl52wkAxOxJqOFafP7eeem7jO1_dqk-h3Jna-xJeOAtbPfg6GfdcG3pPGBdC9Ixv4DAxm3ZtaRgXcthr1sqzFtxP2ffTd7vLx4GF7nt_dXN8P-bW6FLLscTFnzUslaWmmZEEJpq3TRKClAaCWQptUglVCAkopOJEfDuDANTLixyHez4_W58-DfFhi7auqixTY9Bf0iVqC1ACaZZoke_aGvfhFSBCtVsCLdyXhSp2tlg48xYFPNg5uasKyAVqu4q1Xc1U_ciZ-s-Yub1ebD_acP1xqTwcb8aihZ-ib-Bdc3hxU</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Cao, Shengxian</creator><creator>Che, Ping</creator><creator>Zhang, Haiyang</creator><creator>Tang, Zhenhao</creator><creator>Zhao, Zhiyong</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-4650-6870</orcidid></search><sort><creationdate>20170101</creationdate><title>Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler</title><author>Cao, Shengxian ; Che, Ping ; Zhang, Haiyang ; Tang, Zhenhao ; Zhao, Zhiyong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c459t-1a9d3965d5c5c244468c687f65414864e04e0fe051716560b53ea234af1b3ace3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Adaptive control</topic><topic>Adaptive control systems</topic><topic>Algorithms</topic><topic>Analytics</topic><topic>Boiler furnaces</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Electric power</topic><topic>Electric power generation</topic><topic>Electric power plants</topic><topic>Evolutionary algorithms</topic><topic>Evolutionary computation</topic><topic>Mathematical models</topic><topic>Mathematical problems</topic><topic>Methods</topic><topic>Optimization</topic><topic>Power plants</topic><topic>Prediction models</topic><topic>Predictive control</topic><topic>Support vector machines</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cao, Shengxian</creatorcontrib><creatorcontrib>Che, Ping</creatorcontrib><creatorcontrib>Zhang, Haiyang</creatorcontrib><creatorcontrib>Tang, Zhenhao</creatorcontrib><creatorcontrib>Zhao, Zhiyong</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cao, Shengxian</au><au>Che, Ping</au><au>Zhang, Haiyang</au><au>Tang, Zhenhao</au><au>Zhao, Zhiyong</au><au>Kavun, Sergii V.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2017-01-01</date><risdate>2017</risdate><volume>2017</volume><issue>2017</issue><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>To control the furnace temperature of a power plant boiler precisely, a dual-optimized adaptive model predictive control (DoAMPC) method is designed based on the data analytics. In the proposed DoAMPC, an accurate predictive model is constructed adaptively by the hybrid algorithm of the least squares support vector machine and differential evolution method. Then, an optimization problem is constructed based on the predictive model and many constraint conditions. To control the boiler furnace temperature, the differential evolution method is utilized to decide the control variables by solving the optimization problem. The proposed method can adapt to the time-varying situation by updating the sample data. The experimental results based on practical data illustrate that the DoAMPC can control the boiler furnace temperature with errors of less than 1.5% which can meet the requirements of the real production process.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2017/8048962</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-4650-6870</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1024-123X |
| ispartof | Mathematical problems in engineering, 2017-01, Vol.2017 (2017), p.1-9 |
| issn | 1024-123X 1563-5147 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1884125282 |
| source | Wiley Online Library Open Access; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Accuracy Adaptive control Adaptive control systems Algorithms Analytics Boiler furnaces Data analysis Data mining Electric power Electric power generation Electric power plants Evolutionary algorithms Evolutionary computation Mathematical models Mathematical problems Methods Optimization Power plants Prediction models Predictive control Support vector machines Variables |
| title | Data Analytics Based Dual-Optimized Adaptive Model Predictive Control for the Power Plant Boiler |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T06%3A49%3A03IST&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=Data%20Analytics%20Based%20Dual-Optimized%20Adaptive%20Model%20Predictive%20Control%20for%20the%20Power%20Plant%20Boiler&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Cao,%20Shengxian&rft.date=2017-01-01&rft.volume=2017&rft.issue=2017&rft.spage=1&rft.epage=9&rft.pages=1-9&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2017/8048962&rft_dat=%3Cproquest_cross%3E4317642911%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=1872765423&rft_id=info:pmid/&rfr_iscdi=true |