Data Optimization Analysis of Integrated Energy System Based on K-Means Algorithm
To learn about the practical application of K-environment algorithms in electronic data analysis. To increase the thermal efficiency of boiler combustion and reduce nitrogen oxide emissions, the paper uses a 300 MW circulating liquid bed boiler for a thermal power plant as a research product. The st...
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| creator | Guo, Haifeng Li, Jianan Sun, Zhenlong Du, Zhongbo Cheng, Xueting |
| description | To learn about the practical application of K-environment algorithms in electronic data analysis. To increase the thermal efficiency of boiler combustion and reduce nitrogen oxide emissions, the paper uses a 300 MW circulating liquid bed boiler for a thermal power plant as a research product. The studied and improved optimization methods have been successfully used to optimize the combustion of circulating liquefied boilers. Based on the advantages and disadvantages of biogeographic optimization algorithm and K-means clustering algorithm, this paper combines the two algorithms into a new improved clustering algorithm k-bbo-cluster. According to the operation mode of circulating fluidized bed boiler, the calculation method of boiler combustion thermal efficiency and the generation mechanism of nitrogen oxides, the boiler thermal efficiency model, nitrogen oxide emission concentration model and its comprehensive model are established by using the least square support vector machine method based on Bayesian structure framework. The learning outcomes of the vector machines that support the minimum squares of the Bayesian structure are less than 0.05 by the difference between MSE, MAE, and MAPE. The study of optimizing the combustion of circulating liquefied bed furnaces in this article can effectively improve the thermal efficiency of circulating liquefied bed furnaces and reduce nitrogen oxide emissions. Protection is important. |
| doi_str_mv | 10.1155/2022/1211515 |
| format | Article |
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To increase the thermal efficiency of boiler combustion and reduce nitrogen oxide emissions, the paper uses a 300 MW circulating liquid bed boiler for a thermal power plant as a research product. The studied and improved optimization methods have been successfully used to optimize the combustion of circulating liquefied boilers. Based on the advantages and disadvantages of biogeographic optimization algorithm and K-means clustering algorithm, this paper combines the two algorithms into a new improved clustering algorithm k-bbo-cluster. According to the operation mode of circulating fluidized bed boiler, the calculation method of boiler combustion thermal efficiency and the generation mechanism of nitrogen oxides, the boiler thermal efficiency model, nitrogen oxide emission concentration model and its comprehensive model are established by using the least square support vector machine method based on Bayesian structure framework. The learning outcomes of the vector machines that support the minimum squares of the Bayesian structure are less than 0.05 by the difference between MSE, MAE, and MAPE. The study of optimizing the combustion of circulating liquefied bed furnaces in this article can effectively improve the thermal efficiency of circulating liquefied bed furnaces and reduce nitrogen oxide emissions. Protection is important.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2022/1211515</identifier><language>eng</language><publisher>Oxford: Hindawi</publisher><subject>Algorithms ; Artificial intelligence ; Bayesian analysis ; Boilers ; Cluster analysis ; Clustering ; Coal ; Combustion ; Control theory ; Data analysis ; Data mining ; Efficiency ; Energy consumption ; Fluidized beds ; Furnaces ; Industrial plant emissions ; Integrated energy systems ; Machine learning ; Nitrogen oxides ; Optimization ; Optimization algorithms ; Pollutants ; Power plants ; Research methodology ; Support vector machines ; Thermal power plants ; Thermodynamic efficiency ; Vector quantization</subject><ispartof>Wireless communications and mobile computing, 2022-05, Vol.2022, p.1-8</ispartof><rights>Copyright © 2022 Haifeng Guo et al.</rights><rights>Copyright © 2022 Haifeng Guo et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-88b0ea3db14f35a0bc1297706cf82d5228fde6dc5e03579a513b3bcc1e6330ad3</citedby><cites>FETCH-LOGICAL-c337t-88b0ea3db14f35a0bc1297706cf82d5228fde6dc5e03579a513b3bcc1e6330ad3</cites><orcidid>0000-0001-5317-2218 ; 0000-0001-7840-0790 ; 0000-0001-6785-0163 ; 0000-0001-8062-4339 ; 0000-0003-1124-9939</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><contributor>Rakkesh R, Ajay</contributor><contributor>Ajay Rakkesh R</contributor><creatorcontrib>Guo, Haifeng</creatorcontrib><creatorcontrib>Li, Jianan</creatorcontrib><creatorcontrib>Sun, Zhenlong</creatorcontrib><creatorcontrib>Du, Zhongbo</creatorcontrib><creatorcontrib>Cheng, Xueting</creatorcontrib><title>Data Optimization Analysis of Integrated Energy System Based on K-Means Algorithm</title><title>Wireless communications and mobile computing</title><description>To learn about the practical application of K-environment algorithms in electronic data analysis. To increase the thermal efficiency of boiler combustion and reduce nitrogen oxide emissions, the paper uses a 300 MW circulating liquid bed boiler for a thermal power plant as a research product. The studied and improved optimization methods have been successfully used to optimize the combustion of circulating liquefied boilers. Based on the advantages and disadvantages of biogeographic optimization algorithm and K-means clustering algorithm, this paper combines the two algorithms into a new improved clustering algorithm k-bbo-cluster. According to the operation mode of circulating fluidized bed boiler, the calculation method of boiler combustion thermal efficiency and the generation mechanism of nitrogen oxides, the boiler thermal efficiency model, nitrogen oxide emission concentration model and its comprehensive model are established by using the least square support vector machine method based on Bayesian structure framework. The learning outcomes of the vector machines that support the minimum squares of the Bayesian structure are less than 0.05 by the difference between MSE, MAE, and MAPE. The study of optimizing the combustion of circulating liquefied bed furnaces in this article can effectively improve the thermal efficiency of circulating liquefied bed furnaces and reduce nitrogen oxide emissions. Protection is important.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Bayesian analysis</subject><subject>Boilers</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Coal</subject><subject>Combustion</subject><subject>Control theory</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Efficiency</subject><subject>Energy consumption</subject><subject>Fluidized beds</subject><subject>Furnaces</subject><subject>Industrial plant emissions</subject><subject>Integrated energy systems</subject><subject>Machine learning</subject><subject>Nitrogen oxides</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Pollutants</subject><subject>Power plants</subject><subject>Research methodology</subject><subject>Support vector machines</subject><subject>Thermal power plants</subject><subject>Thermodynamic efficiency</subject><subject>Vector quantization</subject><issn>1530-8669</issn><issn>1530-8677</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp90E1LAzEQBuAgCtbqzR8Q8KhrJ4nZ7B5rrVqsFFHPIZtk25T9qEmKrL_eLS0ePc3L8DAML0KXBG4J4XxEgdIRoX0m_AgNCGeQZKkQx385zU_RWQhrAGBAyQC9Paio8GITXe1-VHRtg8eNqrrgAm5LPGuiXXoVrcHTxvplh9-7EG2N71Xod71-SV6tagIeV8vWu7iqz9FJqapgLw5ziD4fpx-T52S-eJpNxvNEMyZikmUFWMVMQe5KxhUUmtBcCEh1mVHDKc1KY1OjuQXGRa44YQUrtCY2ZQyUYUN0tb-78e3X1oYo1-3W978HSVPBKM0JiF7d7JX2bQjelnLjXa18JwnIXWlyV5o8lNbz6z1fucaob_e__gWrNWq6</recordid><startdate>20220526</startdate><enddate>20220526</enddate><creator>Guo, Haifeng</creator><creator>Li, Jianan</creator><creator>Sun, Zhenlong</creator><creator>Du, Zhongbo</creator><creator>Cheng, Xueting</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-5317-2218</orcidid><orcidid>https://orcid.org/0000-0001-7840-0790</orcidid><orcidid>https://orcid.org/0000-0001-6785-0163</orcidid><orcidid>https://orcid.org/0000-0001-8062-4339</orcidid><orcidid>https://orcid.org/0000-0003-1124-9939</orcidid></search><sort><creationdate>20220526</creationdate><title>Data Optimization Analysis of Integrated Energy System Based on K-Means Algorithm</title><author>Guo, Haifeng ; Li, Jianan ; Sun, Zhenlong ; Du, Zhongbo ; Cheng, Xueting</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-88b0ea3db14f35a0bc1297706cf82d5228fde6dc5e03579a513b3bcc1e6330ad3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Bayesian analysis</topic><topic>Boilers</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Coal</topic><topic>Combustion</topic><topic>Control theory</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Efficiency</topic><topic>Energy consumption</topic><topic>Fluidized beds</topic><topic>Furnaces</topic><topic>Industrial plant emissions</topic><topic>Integrated energy systems</topic><topic>Machine learning</topic><topic>Nitrogen oxides</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Pollutants</topic><topic>Power plants</topic><topic>Research methodology</topic><topic>Support vector machines</topic><topic>Thermal power plants</topic><topic>Thermodynamic efficiency</topic><topic>Vector quantization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guo, Haifeng</creatorcontrib><creatorcontrib>Li, Jianan</creatorcontrib><creatorcontrib>Sun, Zhenlong</creatorcontrib><creatorcontrib>Du, Zhongbo</creatorcontrib><creatorcontrib>Cheng, Xueting</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</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>Computing 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>ProQuest Central Basic</collection><jtitle>Wireless communications and mobile computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guo, Haifeng</au><au>Li, Jianan</au><au>Sun, Zhenlong</au><au>Du, Zhongbo</au><au>Cheng, Xueting</au><au>Rakkesh R, Ajay</au><au>Ajay Rakkesh R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data Optimization Analysis of Integrated Energy System Based on K-Means Algorithm</atitle><jtitle>Wireless communications and mobile computing</jtitle><date>2022-05-26</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>1530-8669</issn><eissn>1530-8677</eissn><abstract>To learn about the practical application of K-environment algorithms in electronic data analysis. 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The learning outcomes of the vector machines that support the minimum squares of the Bayesian structure are less than 0.05 by the difference between MSE, MAE, and MAPE. The study of optimizing the combustion of circulating liquefied bed furnaces in this article can effectively improve the thermal efficiency of circulating liquefied bed furnaces and reduce nitrogen oxide emissions. Protection is important.</abstract><cop>Oxford</cop><pub>Hindawi</pub><doi>10.1155/2022/1211515</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-5317-2218</orcidid><orcidid>https://orcid.org/0000-0001-7840-0790</orcidid><orcidid>https://orcid.org/0000-0001-6785-0163</orcidid><orcidid>https://orcid.org/0000-0001-8062-4339</orcidid><orcidid>https://orcid.org/0000-0003-1124-9939</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Artificial intelligence Bayesian analysis Boilers Cluster analysis Clustering Coal Combustion Control theory Data analysis Data mining Efficiency Energy consumption Fluidized beds Furnaces Industrial plant emissions Integrated energy systems Machine learning Nitrogen oxides Optimization Optimization algorithms Pollutants Power plants Research methodology Support vector machines Thermal power plants Thermodynamic efficiency Vector quantization |
| title | Data Optimization Analysis of Integrated Energy System Based on K-Means Algorithm |
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