{"id":"https://openalex.org/W2316030966","doi":"https://doi.org/10.1109/tase.2016.2538560","title":"Multistep Forecasting Models of the Liquid Level in a Blast Furnace Hearth","display_name":"Multistep Forecasting Models of the Liquid Level in a Blast Furnace Hearth","publication_year":2016,"publication_date":"2016-03-28","ids":{"openalex":"https://openalex.org/W2316030966","doi":"https://doi.org/10.1109/tase.2016.2538560","mag":"2316030966"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2016.2538560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2016.2538560","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Automation Science and Engineering","raw_type":"journal-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/A5058862179","display_name":"Fl\u00e1vio S. V. Gomes","orcid":null},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Flavio S. V. Gomes","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, E.S., Brazil"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, E.S., Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084947537","display_name":"Klaus Fabian C\u00f4co","orcid":"https://orcid.org/0000-0001-7793-0693"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Klaus F. Coco","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, E.S., Brazil"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, E.S., Brazil","institution_ids":["https://openalex.org/I51235708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054773028","display_name":"Jos\u00e9 Leandro Felix Salles","orcid":"https://orcid.org/0000-0002-3417-6544"},"institutions":[{"id":"https://openalex.org/I51235708","display_name":"Universidade Federal do Esp\u00edrito Santo","ror":"https://ror.org/05sxf4h28","country_code":"BR","type":"education","lineage":["https://openalex.org/I51235708"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Jose Leandro Felix Salles","raw_affiliation_strings":["Electrical Engineering Department, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, E.S., Brazil"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering Department, Federal University of Esp\u00edrito Santo, Vit\u00f3ria, E.S., Brazil","institution_ids":["https://openalex.org/I51235708"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5058862179"],"corresponding_institution_ids":["https://openalex.org/I51235708"],"apc_list":null,"apc_paid":null,"fwci":2.6017,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.88342314,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"14","issue":"2","first_page":"1286","last_page":"1296"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11837","display_name":"Iron and Steelmaking Processes","score":0.9656000137329102,"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/T11837","display_name":"Iron and Steelmaking Processes","score":0.9656000137329102,"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":0.9635999798774719,"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"}},{"id":"https://openalex.org/T11126","display_name":"Metallurgical Processes and Thermodynamics","score":0.9617000222206116,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.7052502632141113},{"id":"https://openalex.org/keywords/blast-furnace","display_name":"Blast furnace","score":0.6799066066741943},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6385326385498047},{"id":"https://openalex.org/keywords/hearth","display_name":"Hearth","score":0.6165133714675903},{"id":"https://openalex.org/keywords/autoregressive\u2013moving-average-model","display_name":"Autoregressive\u2013moving-average model","score":0.5028976798057556},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.47705331444740295},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.465624064207077},{"id":"https://openalex.org/keywords/nonlinear-autoregressive-exogenous-model","display_name":"Nonlinear autoregressive exogenous model","score":0.44052284955978394},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.41946470737457275},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.4174298644065857},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.41033509373664856},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.35754871368408203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25021690130233765},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20240086317062378},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17394274473190308},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.15470746159553528},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1536191999912262},{"id":"https://openalex.org/keywords/metallurgy","display_name":"Metallurgy","score":0.11673194169998169},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.08349061012268066}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.7052502632141113},{"id":"https://openalex.org/C2780269488","wikidata":"https://www.wikidata.org/wiki/Q181485","display_name":"Blast furnace","level":2,"score":0.6799066066741943},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6385326385498047},{"id":"https://openalex.org/C200198126","wikidata":"https://www.wikidata.org/wiki/Q585473","display_name":"Hearth","level":2,"score":0.6165133714675903},{"id":"https://openalex.org/C74883015","wikidata":"https://www.wikidata.org/wiki/Q290467","display_name":"Autoregressive\u2013moving-average model","level":3,"score":0.5028976798057556},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.47705331444740295},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.465624064207077},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.44052284955978394},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.41946470737457275},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.4174298644065857},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.41033509373664856},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.35754871368408203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25021690130233765},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20240086317062378},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17394274473190308},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.15470746159553528},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1536191999912262},{"id":"https://openalex.org/C191897082","wikidata":"https://www.wikidata.org/wiki/Q11467","display_name":"Metallurgy","level":1,"score":0.11673194169998169},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.08349061012268066},{"id":"https://openalex.org/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2016.2538560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2016.2538560","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Automation Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1488198768","https://openalex.org/W1519631210","https://openalex.org/W1588163064","https://openalex.org/W1816026943","https://openalex.org/W1973400817","https://openalex.org/W1988115241","https://openalex.org/W1990701202","https://openalex.org/W1998674867","https://openalex.org/W2011227258","https://openalex.org/W2013472039","https://openalex.org/W2029141364","https://openalex.org/W2039108772","https://openalex.org/W2043646229","https://openalex.org/W2056991698","https://openalex.org/W2070178901","https://openalex.org/W2071189485","https://openalex.org/W2073240809","https://openalex.org/W2084068786","https://openalex.org/W2108537597","https://openalex.org/W2123513648","https://openalex.org/W2132592819","https://openalex.org/W2139211383","https://openalex.org/W2139476852","https://openalex.org/W2141278204","https://openalex.org/W2145103790","https://openalex.org/W2149391626","https://openalex.org/W2151029520","https://openalex.org/W2156074302","https://openalex.org/W2313953460","https://openalex.org/W2786121965","https://openalex.org/W3098047727","https://openalex.org/W4232093537","https://openalex.org/W4241115065","https://openalex.org/W4245730203","https://openalex.org/W4247128285","https://openalex.org/W4285719527","https://openalex.org/W4299435087","https://openalex.org/W4301856975","https://openalex.org/W6680837760"],"related_works":["https://openalex.org/W1516407798","https://openalex.org/W2580847106","https://openalex.org/W1973400817","https://openalex.org/W2097161368","https://openalex.org/W2352677674","https://openalex.org/W27982416","https://openalex.org/W1989293139","https://openalex.org/W2373538410","https://openalex.org/W833557560","https://openalex.org/W2735171259"],"abstract_inverted_index":{"The":[0,77],"extraction":[1],"of":[2,15,32],"molten":[3],"iron":[4],"and":[5,27,87,119,127],"slag":[6],"in":[7],"the":[8,12,36,54,64,73,128,132],"liquid":[9,37,56,75],"phase":[10],"from":[11],"lower":[13],"part":[14],"a":[16,29,46,60,85,98,120],"blast":[17,65],"furnace":[18,66],"(hearth)":[19],"is":[20,69,84],"usually":[21],"accomplished":[22],"according":[23],"to":[24,52,140],"operational":[25],"experience":[26],"involves":[28],"high":[30],"degree":[31],"uncertainty,":[33],"mainly":[34],"because":[35],"level":[38,57],"cannot":[39],"be":[40,95],"directly":[41],"measured.":[42],"This":[43],"study":[44],"presents":[45],"methodology":[47],"for":[48],"obtaining":[49],"multistep":[50],"models":[51,111],"forecast":[53],"hearth":[55,74],"by":[58,97,113],"measuring":[59],"voltage":[61],"generated":[62],"on":[63],"shell,":[67],"which":[68],"strongly":[70],"correlated":[71],"with":[72,108],"level.":[76],"results":[78,129],"show":[79],"that":[80,131],"this":[81],"electrical":[82],"signal":[83],"nonstationary":[86],"nonlinear":[88],"time-series":[89,110],"that,":[90],"after":[91],"appropriate":[92],"treatment,":[93],"can":[94],"represented":[96,112],"time-delay":[99],"neural":[100],"network":[101],"(TDNN)":[102],"model.":[103],"Some":[104],"comparisons":[105],"are":[106],"made":[107],"linear":[109],"an":[114],"autoregressive":[115,122],"moving":[116,124],"average":[117,125],"model":[118,134],"seasonal":[121],"integrated":[123],"model,":[126],"indicate":[130],"TDNN":[133],"provides":[135],"better":[136],"forecasting":[137],"performance":[138],"up":[139],"one":[141],"hour":[142],"ahead.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
