{"id":"https://openalex.org/W3021393192","doi":"https://doi.org/10.1109/access.2020.2992451","title":"The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler","display_name":"The Deep Convolutional Neural Network for NOx Emission Prediction of a Coal-Fired Boiler","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3021393192","doi":"https://doi.org/10.1109/access.2020.2992451","mag":"3021393192"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2992451","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2992451","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09086502.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09086502.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100341057","display_name":"Nan Li","orcid":"https://orcid.org/0000-0002-4189-7921"},"institutions":[{"id":"https://openalex.org/I182707071","display_name":"Ludong University","ror":"https://ror.org/028h95t32","country_code":"CN","type":"education","lineage":["https://openalex.org/I182707071"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Nan Li","raw_affiliation_strings":["School of Information and Electrical Engineering, Lu Dong University, Yantai, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electrical Engineering, Lu Dong University, Yantai, China","institution_ids":["https://openalex.org/I182707071"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086685216","display_name":"Yong Hu","orcid":"https://orcid.org/0000-0002-3837-1750"},"institutions":[{"id":"https://openalex.org/I153473198","display_name":"North China Electric Power University","ror":"https://ror.org/04qr5t414","country_code":"CN","type":"education","lineage":["https://openalex.org/I153473198"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Hu","raw_affiliation_strings":["State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China","institution_ids":["https://openalex.org/I153473198"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100341057"],"corresponding_institution_ids":["https://openalex.org/I182707071"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.9029,"has_fulltext":true,"cited_by_count":32,"citation_normalized_percentile":{"value":0.88753577,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"8","issue":null,"first_page":"85912","last_page":"85922"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9488999843597412,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7787457704544067},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.7291125655174255},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7206616997718811},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7143424153327942},{"id":"https://openalex.org/keywords/boiler","display_name":"Boiler (water heating)","score":0.6683900356292725},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6045384407043457},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5346870422363281},{"id":"https://openalex.org/keywords/nox","display_name":"NOx","score":0.5243192911148071},{"id":"https://openalex.org/keywords/coal","display_name":"Coal","score":0.4964316487312317},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.48136240243911743},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.4374862313270569},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34930509328842163},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32085850834846497},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1366792619228363},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09997603297233582},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0793825089931488},{"id":"https://openalex.org/keywords/waste-management","display_name":"Waste management","score":0.07337784767150879}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7787457704544067},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.7291125655174255},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7206616997718811},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7143424153327942},{"id":"https://openalex.org/C2780013297","wikidata":"https://www.wikidata.org/wiki/Q19596153","display_name":"Boiler (water heating)","level":2,"score":0.6683900356292725},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6045384407043457},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5346870422363281},{"id":"https://openalex.org/C203032635","wikidata":"https://www.wikidata.org/wiki/Q20962970","display_name":"NOx","level":3,"score":0.5243192911148071},{"id":"https://openalex.org/C518851703","wikidata":"https://www.wikidata.org/wiki/Q24489","display_name":"Coal","level":2,"score":0.4964316487312317},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.48136240243911743},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.4374862313270569},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34930509328842163},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32085850834846497},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1366792619228363},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09997603297233582},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0793825089931488},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.07337784767150879},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C105923489","wikidata":"https://www.wikidata.org/wiki/Q133235","display_name":"Combustion","level":2,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2992451","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2992451","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09086502.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:e149230eb34345229398ae08de60dcb3","is_oa":true,"landing_page_url":"https://doaj.org/article/e149230eb34345229398ae08de60dcb3","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 85912-85922 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2992451","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2992451","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09086502.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.49000000953674316,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1231421488","display_name":null,"funder_award_id":"under","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2696157397","display_name":null,"funder_award_id":"ZR201","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6806890942","display_name":null,"funder_award_id":"ZR2018PF003","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G6924028813","display_name":null,"funder_award_id":"ZR2018","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G8905331959","display_name":null,"funder_award_id":"51806063","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3021393192.pdf","grobid_xml":"https://content.openalex.org/works/W3021393192.grobid-xml"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1667652561","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1993352927","https://openalex.org/W2006881475","https://openalex.org/W2030477110","https://openalex.org/W2076063813","https://openalex.org/W2079735306","https://openalex.org/W2112796928","https://openalex.org/W2163922914","https://openalex.org/W2194775991","https://openalex.org/W2214263137","https://openalex.org/W2271840356","https://openalex.org/W2315758390","https://openalex.org/W2495968700","https://openalex.org/W2520224971","https://openalex.org/W2531409750","https://openalex.org/W2546302380","https://openalex.org/W2738180183","https://openalex.org/W2883780447","https://openalex.org/W2888463866","https://openalex.org/W2919115771","https://openalex.org/W2925872868","https://openalex.org/W2949117887","https://openalex.org/W2963125010","https://openalex.org/W2964121744","https://openalex.org/W2986602512","https://openalex.org/W2991232089","https://openalex.org/W2995575179","https://openalex.org/W4288024261","https://openalex.org/W4302296459","https://openalex.org/W6631190155","https://openalex.org/W6637151318","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6694517276","https://openalex.org/W6741655716","https://openalex.org/W6770251742"],"related_works":["https://openalex.org/W4401154840","https://openalex.org/W4295442786","https://openalex.org/W2621951291","https://openalex.org/W4319441652","https://openalex.org/W2974739419","https://openalex.org/W2811187992","https://openalex.org/W803509314","https://openalex.org/W2132491819","https://openalex.org/W3042921537","https://openalex.org/W4360615906"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,10,118],"methodology":[4],"for":[5,35,54],"predicting":[6],"NOx":[7,55],"emissions":[8],"of":[9,83,112,124],"coal-fired":[11],"boiler":[12],"by":[13],"using":[14],"real":[15],"operation":[16],"data,":[17],"coal":[18],"properties":[19],"and":[20,104],"CNN":[21,39],"(Convolutional":[22],"Neural":[23],"Network).":[24],"Two":[25],"building":[26,44],"blocks":[27,45],"are":[28,46],"carefully":[29],"designed":[30],"following":[31],"the":[32,36,43,50,71,94,109,113,122,125],"practical":[33],"guidelines":[34],"light":[37],"weight":[38],"architecture":[40,110],"design.":[41],"Furthermore,":[42],"used":[47],"to":[48],"develop":[49],"deep":[51,73,96],"CNN-based":[52,74,97],"model":[53,76,99,116],"prediction.":[56],"A":[57],"comprehensive":[58],"comparison":[59],"among":[60],"different":[61],"prediction":[62,75,79,98,115,126],"models":[63,80],"based":[64],"on":[65,121],"DL":[66],"(Deep":[67],"Learning)":[68],"shows":[69],"that":[70,93],"proposed":[72],"outperforms":[77],"other":[78],"in":[81],"terms":[82],"RMSE":[84],"(Root":[85],"Mean":[86],"Square":[87],"Error)":[88],"criteria.":[89],"The":[90],"results":[91],"indicate":[92],"developed":[95],"has":[100,117],"more":[101],"excellent":[102],"accuracy":[103],"better":[105],"numerical":[106],"stability.":[107],"Besides,":[108],"design":[111],"DL-based":[114],"significant":[119],"impact":[120],"performance":[123],"model.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-04-13T07:58:08.660418","created_date":"2025-10-10T00:00:00"}