{"id":"https://openalex.org/W2947353082","doi":"https://doi.org/10.1109/access.2019.2919110","title":"Gold Recovery Modeling Based on Interval Prediction for a Gold Cyanidation Leaching Plant","display_name":"Gold Recovery Modeling Based on Interval Prediction for a Gold Cyanidation Leaching Plant","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2947353082","doi":"https://doi.org/10.1109/access.2019.2919110","mag":"2947353082"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2919110","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2919110","pdf_url":null,"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":null,"license_id":null,"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://doi.org/10.1109/access.2019.2919110","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100771100","display_name":"Jun Zhang","orcid":"https://orcid.org/0000-0002-4265-0082"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhang Jun","raw_affiliation_strings":["College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102005263","display_name":"Hua Yan","orcid":"https://orcid.org/0000-0003-1524-1555"},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Hua","raw_affiliation_strings":["College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101152099","display_name":"Hongxia Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Hongxia","raw_affiliation_strings":["College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048133364","display_name":"Zhongda Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I157507598","display_name":"Shenyang University of Technology","ror":"https://ror.org/00d7f8730","country_code":"CN","type":"education","lineage":["https://openalex.org/I157507598"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tian Zhongda","raw_affiliation_strings":["College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Shenyang University of Technology, Shenyang, China","institution_ids":["https://openalex.org/I157507598"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035211904","display_name":"Runda Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Runda","raw_affiliation_strings":["College of Information Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"College of Information Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100771100"],"corresponding_institution_ids":["https://openalex.org/I157507598"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.5787,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.75161198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"71511","last_page":"71528"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9896000027656555,"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/T10320","display_name":"Neural Networks and Applications","score":0.9896000027656555,"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/T12282","display_name":"Mineral Processing and Grinding","score":0.9861000180244446,"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/T11063","display_name":"Rough Sets and Fuzzy Logic","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/gold-cyanidation","display_name":"Gold cyanidation","score":0.6481416821479797},{"id":"https://openalex.org/keywords/prediction-interval","display_name":"Prediction interval","score":0.5917243957519531},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.5649746060371399},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5102152824401855},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49860095977783203},{"id":"https://openalex.org/keywords/process-control","display_name":"Process control","score":0.44074639678001404},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4006103575229645},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.33033955097198486},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22439754009246826},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.20321771502494812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18867802619934082},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16811087727546692}],"concepts":[{"id":"https://openalex.org/C18535130","wikidata":"https://www.wikidata.org/wiki/Q775155","display_name":"Gold cyanidation","level":3,"score":0.6481416821479797},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.5917243957519531},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.5649746060371399},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5102152824401855},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49860095977783203},{"id":"https://openalex.org/C155386361","wikidata":"https://www.wikidata.org/wiki/Q1649571","display_name":"Process control","level":3,"score":0.44074639678001404},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4006103575229645},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.33033955097198486},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22439754009246826},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.20321771502494812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18867802619934082},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16811087727546692},{"id":"https://openalex.org/C2778455166","wikidata":"https://www.wikidata.org/wiki/Q55076544","display_name":"Cyanide","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2919110","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2919110","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:dc18e9b82019442287fbdf26d75a9e7c","is_oa":true,"landing_page_url":"https://doaj.org/article/dc18e9b82019442287fbdf26d75a9e7c","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 7, Pp 71511-71528 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2919110","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2919110","pdf_url":null,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2398587000","display_name":null,"funder_award_id":"61803273","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4320538203","display_name":null,"funder_award_id":"61603172","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6775011481","display_name":null,"funder_award_id":"61873049","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7820088396","display_name":null,"funder_award_id":"20180550970","funder_id":"https://openalex.org/F4320323086","funder_display_name":"Natural Science Foundation of Liaoning Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323086","display_name":"Natural Science Foundation of Liaoning Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W310337652","https://openalex.org/W645081031","https://openalex.org/W1481167439","https://openalex.org/W1560021816","https://openalex.org/W1874278032","https://openalex.org/W1964128579","https://openalex.org/W1964986814","https://openalex.org/W1970693300","https://openalex.org/W1971241355","https://openalex.org/W1974412007","https://openalex.org/W1982441855","https://openalex.org/W2009797713","https://openalex.org/W2009879168","https://openalex.org/W2011550024","https://openalex.org/W2015164528","https://openalex.org/W2018697886","https://openalex.org/W2024060531","https://openalex.org/W2034544282","https://openalex.org/W2036766258","https://openalex.org/W2037671825","https://openalex.org/W2040769006","https://openalex.org/W2048611916","https://openalex.org/W2049240225","https://openalex.org/W2049857321","https://openalex.org/W2060621271","https://openalex.org/W2067796266","https://openalex.org/W2071467620","https://openalex.org/W2074221952","https://openalex.org/W2075772239","https://openalex.org/W2080617022","https://openalex.org/W2086637119","https://openalex.org/W2088538739","https://openalex.org/W2091899299","https://openalex.org/W2094830408","https://openalex.org/W2114160202","https://openalex.org/W2118277375","https://openalex.org/W2132477882","https://openalex.org/W2133244483","https://openalex.org/W2148710904","https://openalex.org/W2152680387","https://openalex.org/W2155816288","https://openalex.org/W2161278885","https://openalex.org/W2161913203","https://openalex.org/W2171277043","https://openalex.org/W2171666055","https://openalex.org/W2480295938","https://openalex.org/W2488399954","https://openalex.org/W2745690522","https://openalex.org/W2767097949","https://openalex.org/W2790714629","https://openalex.org/W4248887009","https://openalex.org/W4251512373"],"related_works":["https://openalex.org/W2358315604","https://openalex.org/W2373728940","https://openalex.org/W4253852779","https://openalex.org/W4206831428","https://openalex.org/W2075234505","https://openalex.org/W1585599148","https://openalex.org/W2063427286","https://openalex.org/W2383710691","https://openalex.org/W2341350566","https://openalex.org/W3126075123"],"abstract_inverted_index":{"The":[0,113,191],"production":[1,25],"index":[2],"of":[3,16,31,52,141,200,204,226],"gold":[4,101,205],"cyanidation":[5,102],"leaching":[6,24,36,103],"process":[7,37,45,76,108,162,176,182],"has":[8],"an":[9,233],"important":[10,234],"influence":[11],"on":[12,88,125],"the":[13,17,22,29,32,35,42,49,53,73,80,83,118,126,130,135,142,148,156,161,168,179,201,211,222,227],"economic":[14],"benefits":[15],"plant-wide":[18,237],"hydrometallurgical":[19],"process.":[20],"In":[21],"actual":[23],"process,":[26],"due":[27],"to":[28,98,155,178],"fluctuations":[30],"previous":[33],"procedure,":[34],"is":[38,94,122],"often":[39],"affected":[40],"by":[41],"uncertainty":[43,74,106,163],"and":[44,66,96,107,147,164,188,193,213,224,239],"disturbance.":[46,77],"And":[47],"hence,":[48],"prediction":[50,56,85,120,127,143,149,170,229],"accuracy":[51],"traditional":[54,157],"point":[55,158],"models":[57],"(such":[58],"as":[59],"ANN:":[60],"Artificial":[61],"Neural":[62],"Network)":[63],"decreases":[64],"seriously":[65],"cannot":[67],"provide":[68,173],"any":[69],"quantified":[70],"information":[71,177],"about":[72],"or":[75,181],"To":[78],"solve":[79],"above":[81],"problem,":[82],"interval":[84,119,144,150,169,228],"technique":[86],"based":[87,124],"Radial":[89],"Basis":[90],"Function":[91],"(RBF)":[92],"ANN":[93],"proposed":[95],"used":[97],"model":[99,121,171],"a":[100,217],"plant":[104],"encountering":[105],"disturbances":[109,165],"in":[110,117],"this":[111],"paper.":[112],"objective":[114],"function":[115],"trained":[116],"not":[123],"error,":[128],"but":[129],"comprehensive":[131],"measure":[132],"index,":[133],"namely,":[134],"coverage":[136,145],"width":[137],"criteria,":[138],"which":[139,220],"consists":[140],"probability":[146],"normalized":[151],"averaged":[152],"width.":[153],"Compared":[154],"prediction,":[159],"when":[160],"are":[166],"present,":[167],"will":[172],"more":[174],"helpful":[175],"operators":[180],"designers":[183],"for":[184,236],"determining":[185],"further":[186],"optimization":[187,238],"control":[189],"strategies.":[190],"simulation":[192],"practical":[194],"application":[195],"results":[196],"show":[197],"that":[198],"most":[199],"real":[202],"values":[203],"recovery":[206],"can":[207],"be":[208],"covered":[209],"between":[210],"upper":[212],"lower":[214],"bounds":[215],"with":[216],"predefined":[218],"probability,":[219],"indicates":[221],"effectiveness":[223],"reliability":[225],"model,":[230],"thus":[231],"laying":[232],"foundation":[235],"control.":[240]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}