{"id":"https://openalex.org/W7213985369","doi":"https://doi.org/10.26434/chemrxiv.15009199/v1","title":"Computational Fluid Dynamics-Informed Machine Learning-Enabled Digital Twin for Vapor Maldistribution Prediction in Structured Packed Distillation Columns","display_name":"Computational Fluid Dynamics-Informed Machine Learning-Enabled Digital Twin for Vapor Maldistribution Prediction in Structured Packed Distillation Columns","publication_year":2026,"publication_date":"2026-09-21","ids":{"openalex":"https://openalex.org/W7213985369","doi":"https://doi.org/10.26434/chemrxiv.15009199/v1"},"language":"en","primary_location":{"id":"doi:10.26434/chemrxiv.15009199/v1","doi":"https://doi.org/10.26434/chemrxiv.15009199/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15009199/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009199/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009199/v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5152437894","display_name":"Alek Nino","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I115475287","display_name":"Oklahoma State University","ror":"https://ror.org/01g9vbr38","country_code":"US","type":"education","lineage":["https://openalex.org/I115475287"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alek Nino","raw_affiliation_strings":["Oklahoma State University","School of Chemical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oklahoma State University","institution_ids":["https://openalex.org/I115475287"]},{"raw_affiliation_string":"School of Chemical Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101849683","display_name":"Hang Yi","orcid":"https://orcid.org/0000-0002-6683-3246","observed_orcids":["https://orcid.org/0000-0002-6683-3246"]},"institutions":[{"id":"https://openalex.org/I115475287","display_name":"Oklahoma State University","ror":"https://ror.org/01g9vbr38","country_code":"US","type":"education","lineage":["https://openalex.org/I115475287"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hang Yi","raw_affiliation_strings":["Oklahoma State University","School of Chemical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oklahoma State University","institution_ids":["https://openalex.org/I115475287"]},{"raw_affiliation_string":"School of Chemical Engineering","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5152433562","display_name":"Tony Cai","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tony Cai","raw_affiliation_strings":["Fractionation Research Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fractionation Research Institute","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067460085","display_name":"Izak Nieuwoudt","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Izak Nieuwoudt","raw_affiliation_strings":["Fractionation Research Institute"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fractionation Research Institute","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015963016","display_name":"Yu Feng","orcid":"https://orcid.org/0000-0003-2908-0625","observed_orcids":["https://orcid.org/0000-0003-2908-0625"]},"institutions":[{"id":"https://openalex.org/I115475287","display_name":"Oklahoma State University","ror":"https://ror.org/01g9vbr38","country_code":"US","type":"education","lineage":["https://openalex.org/I115475287"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Feng","raw_affiliation_strings":["Oklahoma State University","School of Chemical Engineering"],"raw_orcid":"https://orcid.org/0000-0003-2908-0625","affiliations":[{"raw_affiliation_string":"Oklahoma State University","institution_ids":["https://openalex.org/I115475287"]},{"raw_affiliation_string":"School of Chemical Engineering","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11053","display_name":"Process Optimization and Integration","score":0.6952999830245972,"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"}},"topics":[{"id":"https://openalex.org/T11053","display_name":"Process Optimization and Integration","score":0.6952999830245972,"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/T12063","display_name":"Heat and Mass Transfer in Porous Media","score":0.12630000710487366,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.11460000276565552,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/digital-twin","display_name":"digital twin","score":0.6790000200271606},{"id":"https://openalex.org/keywords/model-reduction","display_name":"model reduction","score":0.8270000219345093},{"id":"https://openalex.org/keywords/gaussian-process-regression","display_name":"Gaussian process regression","score":0.9380000233650208},{"id":"https://openalex.org/keywords/column-hydrodynamics","display_name":"column hydrodynamics","score":0.8360000252723694},{"id":"https://openalex.org/keywords/inlet-diameter","display_name":"inlet diameter","score":0.7820000052452087},{"id":"https://openalex.org/keywords/design-optimization","display_name":"design optimization","score":0.49799999594688416}],"concepts":[{"id":"https://openalex.org/C1633027","wikidata":"https://www.wikidata.org/wiki/Q815820","display_name":"Computational fluid dynamics","level":2,"score":0.7602999806404114},{"id":"https://openalex.org/C201289731","wikidata":"https://www.wikidata.org/wiki/Q1172599","display_name":"Inlet","level":2,"score":0.7114999890327454},{"id":"https://openalex.org/C2776501888","wikidata":"https://www.wikidata.org/wiki/Q258264","display_name":"Structured packing","level":3,"score":0.6051999926567078},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.5971999764442444},{"id":"https://openalex.org/C154030694","wikidata":"https://www.wikidata.org/wiki/Q1436074","display_name":"Fractionating column","level":3,"score":0.5965999960899353},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5913000106811523},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.5637999773025513},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.4878000020980835},{"id":"https://openalex.org/C172331833","wikidata":"https://www.wikidata.org/wiki/Q751029","display_name":"Packed bed","level":2,"score":0.4851999878883362},{"id":"https://openalex.org/C172120300","wikidata":"https://www.wikidata.org/wiki/Q1134348","display_name":"Volumetric flow rate","level":2,"score":0.4392000138759613},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4194999933242798},{"id":"https://openalex.org/C2779356388","wikidata":"https://www.wikidata.org/wiki/Q7643340","display_name":"Superficial velocity","level":3,"score":0.3522000014781952},{"id":"https://openalex.org/C147534773","wikidata":"https://www.wikidata.org/wiki/Q190120","display_name":"Water vapor","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C75892298","wikidata":"https://www.wikidata.org/wiki/Q1366187","display_name":"Mass flow rate","level":2,"score":0.3208000063896179},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C2780843468","wikidata":"https://www.wikidata.org/wiki/Q3079309","display_name":"Reactive distillation","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.30309998989105225},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.30140000581741333},{"id":"https://openalex.org/C51038369","wikidata":"https://www.wikidata.org/wiki/Q909534","display_name":"Mass transfer","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C196558001","wikidata":"https://www.wikidata.org/wiki/Q190132","display_name":"Turbulence","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.257099986076355},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.26434/chemrxiv.15009199/v1","doi":"https://doi.org/10.26434/chemrxiv.15009199/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15009199/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009199/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.26434/chemrxiv.15009199/v1","doi":"https://doi.org/10.26434/chemrxiv.15009199/v1","is_oa":true,"landing_page_url":"https://doi.org/10.26434/chemrxiv.15009199/v1","pdf_url":"https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15009199/v1","source":{"id":"https://openalex.org/S4393918830","display_name":"ChemRxiv","issn_l":"2573-2293","issn":["2573-2293"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[{"score":0.651199996471405,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G2843917029","display_name":null,"funder_award_id":"AR24-093","funder_id":"https://openalex.org/F4320310627","funder_display_name":"Oklahoma Center for the Advancement of Science and Technology"}],"funders":[{"id":"https://openalex.org/F4320310627","display_name":"Oklahoma Center for the Advancement of Science and Technology","ror":"https://ror.org/02q578v20"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7213985369.pdf","grobid_xml":"https://content.openalex.org/works/W7213985369.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2052204136","https://openalex.org/W2370973550","https://openalex.org/W2019165907","https://openalex.org/W2334799462","https://openalex.org/W2015121803","https://openalex.org/W1515465453","https://openalex.org/W2094497230","https://openalex.org/W1999794353","https://openalex.org/W2347443108","https://openalex.org/W2061263077"],"abstract_inverted_index":{"Vapor":[0,133],"maldistribution":[1,39,134,156,180,313],"in":[2,93,181,211,220,278,314],"structured-packed":[3,94],"distillation":[4,95,334],"columns":[5],"reduces":[6],"separation":[7],"performance.":[8],"While":[9],"experimental":[10],"measurements":[11],"are":[12],"essential":[13],"for":[14,102,126,310],"characterizing":[15],"column":[16,32,68,104,108,193,214,317],"hydrodynamics,":[17],"they":[18],"often":[19],"lack":[20],"the":[21,41,63,138,161,168,182,186,200,204,212,221,234,250,279,284,290,321],"resolution":[22],"to":[23,28,87,209,218],"capture":[24],"velocity":[25,146],"distributions":[26],"or":[27],"systematically":[29],"quantify":[30],"how":[31],"geometry":[33,194,327],"and":[34,70,114,129,164,174,195,215,237,246,266,293,329],"operating":[35,71],"conditions":[36],"affect":[37],"vapor":[38,90,115,155,171,312],"throughout":[40],"packed":[42],"bed.":[43],"High-fidelity,":[44],"first-principles":[45],"computational":[46,60],"fluid":[47],"dynamics":[48],"(CFD)":[49],"simulations":[50,99],"can":[51],"provide":[52],"detailed":[53],"spatial":[54],"flow":[55,91,117,172,285],"information,":[56],"but":[57],"their":[58],"high":[59],"cost":[61],"limits":[62],"rapid":[64,326],"evaluation":[65],"of":[66,122,141,144,188,257,264,269,271,332],"alternative":[67],"designs":[69],"conditions.":[72],"To":[73],"address":[74],"this":[75],"limitation,":[76],"a":[77,120,253,259,267,304],"CFD-informed":[78,300],"machine-learning":[79],"(ML)":[80],"reduced-order":[81],"model":[82,131],"(ROM)":[83],"framework":[84,302],"was":[85,135,157,230,286],"developed":[86],"rapidly":[88],"predict":[89],"uniformity":[92],"columns.":[96,335],"3D":[97],"CFD":[98,151,235],"were":[100,275],"performed":[101],"13":[103,148],"geometries":[105,318],"by":[106,289],"varying":[107],"diameter,":[109,111],"inlet":[110,176,201,291],"nozzle-to-packing":[112,189],"clearance,":[113],"mass":[116],"rate,":[118],"yielding":[119],"dataset":[121,236],"65":[123,243],"simulation":[124],"cases":[125,245],"parametric":[127],"analysis":[128],"ML":[130,301],"training.":[132],"quantified":[136],"using":[137,233],"area-weighted":[139],"coefficient":[140,268],"variation":[142],"(CoV)":[143],"streamwise":[145],"across":[147],"packing":[149,281],"layers.":[150],"results":[152],"indicated":[153],"that":[154],"most":[158],"pronounced":[159],"near":[160],"packed-bed":[162],"entrance":[163],"progressively":[165],"decreased":[166,294],"through":[167,239],"packing.":[169],"Higher":[170],"rates":[173],"smaller":[175],"diameters":[177],"generally":[178],"increased":[179],"inletdominated":[183],"region,":[184],"while":[185],"effect":[187],"clearance":[190],"depended":[191],"on":[192],"scale.":[196],"For":[197],"instance,":[198],"increasing":[199],"diameter":[202],"reduced":[203],"Layer-1":[205],"CoV":[206],"from":[207,216],"51.01%":[208],"30.73%":[210],"47.75-in.":[213],"48.82%":[217],"22.32%":[219],"120in.":[222],"column.":[223],"A":[224],"Gaussian":[225],"process":[226],"regression":[227],"(GPR)-based":[228],"ROM":[229,251],"subsequently":[231],"trained":[232],"evaluated":[238],"leave-one-geometry-out":[240],"cross-validation.":[241],"Across":[242],"held-out":[244],"845":[247],"packed-layer":[248],"predictions,":[249],"achieved":[252],"mean":[254,261],"absolute":[255],"error":[256,263],"0.851,":[258],"root":[260],"squared":[262],"1.524,":[265],"determination":[270],"0.980.":[272],"Prediction":[273],"errors":[274],"primarily":[276],"concentrated":[277],"lower":[280],"layers,":[282],"where":[283],"strongly":[287],"influenced":[288],"geometry,":[292],"substantially":[295],"farther":[296],"downstream.":[297],"The":[298],"resulting":[299],"offers":[303],"computationally":[305],"efficient":[306],"digital-twin":[307],"development":[308],"workflow":[309],"predicting":[311],"previously":[315],"untested":[316],"and,":[319],"within":[320],"investigated":[322],"design":[323,330],"space,":[324],"enables":[325],"screening":[328],"optimization":[331],"structuredpacked":[333]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-09-22T00:00:00"}
