{"id":"https://openalex.org/W7134199033","doi":"https://doi.org/10.1002/cpe.70623","title":"Multi\u2010Step Temperature Prediction for a <scp>TGAL</scp> Regenerative Aluminum Smelting Furnace","display_name":"Multi\u2010Step Temperature Prediction for a <scp>TGAL</scp> Regenerative Aluminum Smelting Furnace","publication_year":2026,"publication_date":"2026-02-23","ids":{"openalex":"https://openalex.org/W7134199033","doi":"https://doi.org/10.1002/cpe.70623"},"language":"en","primary_location":{"id":"doi:10.1002/cpe.70623","is_oa":false,"landing_page_url":"https://doi.org/10.1002/cpe.70623","pdf_url":null,"source":{"id":"https://openalex.org/S11065456","display_name":"Concurrency and Computation Practice and Experience","issn_l":"1532-0626","issn":["1532-0626","1532-0634"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Concurrency and Computation: Practice and Experience","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/A5122091881","display_name":"Hao Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Jiang","raw_affiliation_strings":["School of Electrical and Information Engineering Zhengzhou University of Light Industry  Zhengzhou China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering Zhengzhou University of Light Industry  Zhengzhou China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lingfei Shen","orcid":"https://orcid.org/0009-0006-3747-4623"},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingfei Shen","raw_affiliation_strings":["School of Electrical and Information Engineering Zhengzhou University of Light Industry  Zhengzhou China"],"raw_orcid":"https://orcid.org/0009-0006-3747-4623","affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering Zhengzhou University of Light Industry  Zhengzhou China","institution_ids":["https://openalex.org/I23171815"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100608157","display_name":"Ningbo Li","orcid":"https://orcid.org/0000-0001-7333-9230"},"institutions":[{"id":"https://openalex.org/I23171815","display_name":"Zhengzhou University of Light Industry","ror":"https://ror.org/05fwr8z16","country_code":"CN","type":"education","lineage":["https://openalex.org/I23171815"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ningbo Li","raw_affiliation_strings":["School of Electrical and Information Engineering Zhengzhou University of Light Industry  Zhengzhou China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Information Engineering Zhengzhou University of Light Industry  Zhengzhou China","institution_ids":["https://openalex.org/I23171815"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5122091881"],"corresponding_institution_ids":["https://openalex.org/I23171815"],"apc_list":{"value":4740,"currency":"USD","value_usd":4740},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.35548439,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"5","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11947","display_name":"Molten salt chemistry and electrochemical processes","score":0.5932999849319458,"subfield":{"id":"https://openalex.org/subfields/1507","display_name":"Fluid Flow and Transfer Processes"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11947","display_name":"Molten salt chemistry and electrochemical processes","score":0.5932999849319458,"subfield":{"id":"https://openalex.org/subfields/1507","display_name":"Fluid Flow and Transfer Processes"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.05719999969005585,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11126","display_name":"Metallurgical Processes and Thermodynamics","score":0.03920000046491623,"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/weighting","display_name":"Weighting","score":0.49889999628067017},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.39959999918937683},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.3905999958515167},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.3763999938964844},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3693999946117401},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.3547999858856201},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.3481999933719635},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.3433000147342682},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.34209999442100525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7678999900817871},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.49889999628067017},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4081000089645386},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3905999958515167},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.3230000138282776},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.31299999356269836},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31119999289512634},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3005000054836273},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2800999879837036},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C157247726","wikidata":"https://www.wikidata.org/wiki/Q2748405","display_name":"Smelting","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.25270000100135803},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1002/cpe.70623","is_oa":false,"landing_page_url":"https://doi.org/10.1002/cpe.70623","pdf_url":null,"source":{"id":"https://openalex.org/S11065456","display_name":"Concurrency and Computation Practice and Experience","issn_l":"1532-0626","issn":["1532-0626","1532-0634"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Concurrency and Computation: Practice and Experience","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2070402767","https://openalex.org/W2074300736","https://openalex.org/W2614182302","https://openalex.org/W2736119585","https://openalex.org/W2774189697","https://openalex.org/W2889104334","https://openalex.org/W2890050757","https://openalex.org/W2922065427","https://openalex.org/W2991236192","https://openalex.org/W3083595256","https://openalex.org/W3137613462","https://openalex.org/W3200459379","https://openalex.org/W4283391949","https://openalex.org/W4285044591","https://openalex.org/W4292671038","https://openalex.org/W4293548748","https://openalex.org/W4365421368","https://openalex.org/W4381149256","https://openalex.org/W4388333441","https://openalex.org/W4388816926","https://openalex.org/W4391023033","https://openalex.org/W4391242333","https://openalex.org/W4400688073","https://openalex.org/W4402563014","https://openalex.org/W4408687456","https://openalex.org/W4409013577","https://openalex.org/W4413326002"],"related_works":[],"abstract_inverted_index":{"ABSTRACT":[0],"This":[1],"study":[2],"addresses":[3],"the":[4,8,67,72,77,88,91,99,115,143,167,189,200,204,220,224,245,253],"industrial":[5,68],"challenge":[6],"that":[7,188,198],"temperature":[9],"inside":[10],"regenerative":[11],"aluminum":[12],"smelting":[13,225],"furnaces":[14],"cannot":[15],"be":[16,230],"directly":[17],"or":[18],"accurately":[19],"measured.":[20],"To":[21,232],"overcome":[22],"this":[23],"issue,":[24],"a":[25,30],"TGAL":[26,96,190],"hybrid":[27],"model":[28,97,145,205],"combining":[29],"Temporal":[31],"Convolutional":[32,36],"Network":[33,37],"(TCN),":[34],"Graph":[35],"(GCN),":[38],"Multi\u2010Head":[39],"Attention":[40],"mechanism,":[41],"and":[42,75,122,156,179,216,219,244,257],"Long":[43],"Short\u2010Term":[44],"Memory":[45],"(LSTM)":[46],"network":[47],"is":[48,194],"proposed":[49,95,144],"for":[50,160],"multi\u2010step":[51],"accurate":[52],"prediction":[53],"of":[54,90,101,149,199,223,247],"furnace":[55],"temperature.":[56],"The":[57,94],"method":[58],"first":[59],"applies":[60],"wavelet":[61],"denoising":[62],"to":[63,81,197,229,250],"suppress":[64],"noise":[65,215],"in":[66,103,109,118,124,151,154,158,174,177,181],"data":[69,132,214],"collected":[70],"by":[71,213],"SCADA":[73],"system":[74],"employs":[76],"Pearson":[78],"correlation":[79],"coefficient":[80],"select":[82],"highly":[83],"correlated":[84],"features,":[85,121],"thereby":[86],"improving":[87],"quality":[89],"input":[92],"data.":[93],"exploits":[98],"synergy":[100],"TCN":[102],"capturing":[104],"long\u2010term":[105],"temporal":[106,125],"dependencies,":[107],"GCN":[108],"uncovering":[110],"spatial":[111],"correlations":[112],"among":[113],"variables,":[114],"attention":[116],"mechanism":[117],"dynamically":[119],"weighting":[120],"LSTM":[123],"dynamic":[126,241],"modeling.":[127],"Validation":[128],"on":[129,240],"44,640":[130],"one\u2010minute":[131],"samples":[133],"from":[134],"actual":[135],"production":[136],"shows":[137],"that,":[138],"compared":[139],"with":[140],"traditional":[141],"models,":[142],"achieves":[146],"maximum":[147],"improvements":[148],"7.44%":[150],"RMSE,":[152,175],"24.85%":[153],"MAE,":[155,178],"25.27%":[157],"MAPE":[159],"2\u2010step":[161],"prediction,":[162,166],"respectively.":[163],"For":[164],"10\u2010step":[165],"improvement":[168],"rates":[169],"remain":[170],"at":[171],"least":[172],"4.23%":[173],"6.91%":[176],"6.31%":[180],"MAPE.":[182],"Moreover,":[183],"Diebold\u2013Mariano":[184],"statistical":[185],"tests":[186],"confirm":[187],"model's":[191,254],"predictive":[192],"accuracy":[193],"significantly":[195],"superior":[196],"comparison":[201],"models.":[202],"Nevertheless,":[203],"performance":[206],"under":[207],"extreme":[208],"operating":[209],"conditions":[210],"remains":[211],"limited":[212],"nonlinear":[217],"dynamics,":[218],"physical":[221,248,258],"mechanisms":[222],"process":[226],"have":[227],"yet":[228],"incorporated.":[231],"address":[233],"these":[234],"limitations,":[235],"future":[236],"work":[237],"will":[238],"focus":[239],"coupling":[242],"modeling":[243],"embedding":[246],"information":[249],"further":[251],"enhance":[252],"generalization":[255],"capability":[256],"consistency.":[259]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-09T00:00:00"}
