{"id":"https://openalex.org/W2015676885","doi":"https://doi.org/10.1109/ijcnn.2014.6889873","title":"Modeling of vertical mill raw meal grinding process and optimal setting of operating parameters based on wavelet neural network","display_name":"Modeling of vertical mill raw meal grinding process and optimal setting of operating parameters based on wavelet neural network","publication_year":2014,"publication_date":"2014-07-01","ids":{"openalex":"https://openalex.org/W2015676885","doi":"https://doi.org/10.1109/ijcnn.2014.6889873","mag":"2015676885"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2014.6889873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108581987","display_name":"Xiaofeng Lin","orcid":"https://orcid.org/0009-0008-7460-1569"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaofeng Lin","raw_affiliation_strings":["The School of Electrical Engineering, Guangxi University, Nanning, China","Sch. of Electr. Eng., Guangxi Univ., Nanning, China"],"affiliations":[{"raw_affiliation_string":"The School of Electrical Engineering, Guangxi University, Nanning, China","institution_ids":["https://openalex.org/I150807315"]},{"raw_affiliation_string":"Sch. of Electr. Eng., Guangxi Univ., Nanning, China","institution_ids":["https://openalex.org/I150807315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050387886","display_name":"Zhe Qian","orcid":"https://orcid.org/0000-0002-3273-2359"},"institutions":[{"id":"https://openalex.org/I150807315","display_name":"Guangxi University","ror":"https://ror.org/02c9qn167","country_code":"CN","type":"education","lineage":["https://openalex.org/I150807315"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Qian","raw_affiliation_strings":["The School of Electrical Engineering, Guangxi University, Nanning, China","Sch. of Electr. Eng., Guangxi Univ., Nanning, China"],"affiliations":[{"raw_affiliation_string":"The School of Electrical Engineering, Guangxi University, Nanning, China","institution_ids":["https://openalex.org/I150807315"]},{"raw_affiliation_string":"Sch. of Electr. Eng., Guangxi Univ., Nanning, China","institution_ids":["https://openalex.org/I150807315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108581987"],"corresponding_institution_ids":["https://openalex.org/I150807315"],"apc_list":null,"apc_paid":null,"fwci":0.7262,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.73075863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"28","issue":null,"first_page":"3015","last_page":"3020"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10188","display_name":"Advanced machining processes and optimization","score":0.9592000246047974,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9426000118255615,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.6004668474197388},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.59876549243927},{"id":"https://openalex.org/keywords/grinding","display_name":"Grinding","score":0.56855708360672},{"id":"https://openalex.org/keywords/parametric-programming","display_name":"Parametric programming","score":0.499298095703125},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.49269917607307434},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4884697496891022},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.4498632252216339},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.41519707441329956},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.37737908959388733},{"id":"https://openalex.org/keywords/process-engineering","display_name":"Process engineering","score":0.3578488826751709},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3546554446220398},{"id":"https://openalex.org/keywords/control-theory","display_name":"Control theory (sociology)","score":0.3313027024269104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21308183670043945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1774713695049286},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16893929243087769},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.10964959859848022},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.09748542308807373}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.6004668474197388},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.59876549243927},{"id":"https://openalex.org/C2777571299","wikidata":"https://www.wikidata.org/wiki/Q3680646","display_name":"Grinding","level":2,"score":0.56855708360672},{"id":"https://openalex.org/C2776045410","wikidata":"https://www.wikidata.org/wiki/Q25111241","display_name":"Parametric programming","level":3,"score":0.499298095703125},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.49269917607307434},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4884697496891022},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.4498632252216339},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.41519707441329956},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.37737908959388733},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.3578488826751709},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3546554446220398},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3313027024269104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21308183670043945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1774713695049286},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16893929243087769},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.10964959859848022},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.09748542308807373},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2014.6889873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2014.6889873","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1532093370","https://openalex.org/W1858943496","https://openalex.org/W2005316203","https://openalex.org/W2085555526","https://openalex.org/W2106799144","https://openalex.org/W2167898351","https://openalex.org/W2356474141","https://openalex.org/W2391928621","https://openalex.org/W6631816981","https://openalex.org/W7061417956"],"related_works":["https://openalex.org/W2060520329","https://openalex.org/W2033258130","https://openalex.org/W2268772487","https://openalex.org/W4235273712","https://openalex.org/W2026597543","https://openalex.org/W1976005168","https://openalex.org/W155178367","https://openalex.org/W2075671866","https://openalex.org/W2064551423","https://openalex.org/W2020784316"],"abstract_inverted_index":{"The":[0,164],"stability":[1],"of":[2,14,21,26,32,53,77,101,186],"vertical":[3,54,78],"mill":[4,55,79],"raw":[5,80],"meal":[6,81],"grinding":[7,22,82],"process":[8,83,187],"affect":[9],"the":[10,19,33,49,63,69,94,99,102,110,117,124,149,154,159,169,176,184,197,204],"yield":[11],"and":[12,29,91,98,113,141,152,175,189],"quality":[13],"cement":[15],"clinker.":[16],"Due":[17],"to":[18,40,183,195],"nonlinear":[20],"process,":[23],"random":[24],"variation":[25],"working":[27,131],"conditions,":[28,116],"large":[30],"lag":[31],"offline":[34],"index":[35,74],"test,":[36],"it":[37],"is":[38],"difficult":[39],"establish":[41],"an":[42,142],"accurate":[43],"mathematics":[44],"model,":[45,97],"thus":[46],"cannot":[47],"collect":[48],"optimizing":[50,178,205],"operating":[51,126],"parameters":[52],"in":[56],"time.":[57],"In":[58],"this":[59],"paper,":[60],"based":[61,108],"on":[62,109],"principal":[64],"component":[65],"analysis":[66],"(PCA)":[67],"for":[68],"related":[70,114],"variables,":[71],"a":[72],"production":[73,198],"prediction":[75,111],"model":[76,104,112,120,174],"was":[84,105,121,133,146,162],"established":[85],"using":[86,136],"wavelet":[87,171],"neural":[88,172],"network":[89,96,173],"(WNN)":[90],"compared":[92],"with":[93],"BP":[95],"validity":[100],"novel":[103,170],"verified.":[106],"Then,":[107],"constraint":[115],"parametric":[118],"optimization":[119,139],"established,":[122],"wherein,":[123],"optimal":[125,143,155,192],"setting":[127,179],"value":[128,194],"under":[129,158],"typical":[130],"conditions":[132,161],"obtained":[134],"by":[135],"particle":[137],"swarm":[138],"algorithm,":[140],"case":[144,150],"base":[145],"established;":[147],"through":[148],"inquiry":[151],"revision,":[153],"set":[156],"points":[157],"current":[160],"obtained.":[163],"simulation":[165],"results":[166],"showed":[167],"that,":[168],"parameter":[177,193],"method":[180],"could":[181,190],"adapt":[182],"changing":[185],"indicators,":[188],"provide":[191],"make":[196],"performance":[199],"meet":[200],"expectations,":[201],"meanwhile":[202],"achieved":[203],"goal.":[206]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
