{"id":"https://openalex.org/W7202299670","doi":"https://doi.org/10.1038/s41598-026-76108-5","title":"Machine learning modeling of the volumetric overall mass transfer coefficient in a novel L-shaped pulsed sieve plate extraction column","display_name":"Machine learning modeling of the volumetric overall mass transfer coefficient in a novel L-shaped pulsed sieve plate extraction column","publication_year":2026,"publication_date":"2026-10-26","ids":{"openalex":"https://openalex.org/W7202299670","doi":"https://doi.org/10.1038/s41598-026-76108-5"},"language":"en","primary_location":{"id":"doi:10.1038/s41598-026-76108-5","doi":"https://doi.org/10.1038/s41598-026-76108-5","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-76108-5","pdf_url":"https://www.nature.com/articles/s41598-026-76108-5_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.nature.com/articles/s41598-026-76108-5_reference.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5036276303","display_name":"Vahid Rafiei","orcid":"https://orcid.org/0000-0002-9532-1514","observed_orcids":["https://orcid.org/0000-0002-9532-1514"]},"institutions":[{"id":"https://openalex.org/I35510649","display_name":"Arak University","ror":"https://ror.org/00ngrq502","country_code":"IR","type":"education","lineage":["https://openalex.org/I35510649"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Vahid Rafiei","raw_affiliation_strings":["Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran","institution_ids":["https://openalex.org/I35510649"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035328565","display_name":"Sadegh Moradi","orcid":"https://orcid.org/0009-0008-7376-2600","observed_orcids":["https://orcid.org/0009-0008-7376-2600"]},"institutions":[{"id":"https://openalex.org/I35510649","display_name":"Arak University","ror":"https://ror.org/00ngrq502","country_code":"IR","type":"education","lineage":["https://openalex.org/I35510649"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Sadegh Moradi","raw_affiliation_strings":["Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Chemical Engineering, Faculty of Engineering, Arak University, Arak, Iran","institution_ids":["https://openalex.org/I35510649"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5036276303"],"corresponding_institution_ids":["https://openalex.org/I35510649"],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.46905067,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T10864","display_name":"Fluid Dynamics and Mixing","score":0.9384999871253967,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T10864","display_name":"Fluid Dynamics and Mixing","score":0.9384999871253967,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/T11091","display_name":"Extraction and Separation Processes","score":0.01489999983459711,"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/T11053","display_name":"Process Optimization and Integration","score":0.009100000374019146,"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/liquid-liquid-extraction","display_name":"liquid-liquid extraction","score":0.8510000109672546},{"id":"https://openalex.org/keywords/pulsed-sieve-plate-column","display_name":"pulsed sieve plate column","score":0.8840000033378601},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.6589999794960022},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9589999914169312},{"id":"https://openalex.org/keywords/support-vector-regression","display_name":"support vector regression","score":1.0},{"id":"https://openalex.org/keywords/dispersed-phase-holdup","display_name":"dispersed phase holdup","score":0.972000002861023},{"id":"https://openalex.org/keywords/droplet-size","display_name":"droplet size","score":0.9100000262260437},{"id":"https://openalex.org/keywords/process-parameter-optimization","display_name":"process parameter optimization","score":0.7409999966621399},{"id":"https://openalex.org/keywords/response-surface-analysis","display_name":"response surface analysis","score":0.5989999771118164},{"id":"https://openalex.org/keywords/sensitivity-analysis","display_name":"sensitivity analysis","score":0.6259999871253967}],"concepts":[{"id":"https://openalex.org/C17732976","wikidata":"https://www.wikidata.org/wiki/Q7511782","display_name":"Sieve (category theory)","level":2,"score":0.6190000176429749},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5430999994277954},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.4867999851703644},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47940000891685486},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C51038369","wikidata":"https://www.wikidata.org/wiki/Q909534","display_name":"Mass transfer","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43630000948905945},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C97947650","wikidata":"https://www.wikidata.org/wiki/Q901870","display_name":"Sieve analysis","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C2781345722","wikidata":"https://www.wikidata.org/wiki/Q5308388","display_name":"Drop (telecommunication)","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C128990827","wikidata":"https://www.wikidata.org/wiki/Q192830","display_name":"Coefficient of determination","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3003999888896942},{"id":"https://openalex.org/C101555633","wikidata":"https://www.wikidata.org/wiki/Q907261","display_name":"Mass transfer coefficient","level":3,"score":0.28540000319480896},{"id":"https://openalex.org/C113196181","wikidata":"https://www.wikidata.org/wiki/Q485223","display_name":"Analytical Chemistry (journal)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2743000090122223},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2732999920845032},{"id":"https://openalex.org/C44280652","wikidata":"https://www.wikidata.org/wiki/Q104837","display_name":"Phase (matter)","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C166811707","wikidata":"https://www.wikidata.org/wiki/Q6468669","display_name":"Lack-of-fit sum of squares","level":4,"score":0.26350000500679016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1038/s41598-026-76108-5","doi":"https://doi.org/10.1038/s41598-026-76108-5","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-76108-5","pdf_url":"https://www.nature.com/articles/s41598-026-76108-5_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},{"id":"doi:10.21203/rs.3.rs-9940716/v1","doi":"https://doi.org/10.21203/rs.3.rs-9940716/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-9940716/v1","pdf_url":"https://www.researchsquare.com/article/rs-9940716/latest.pdf","source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"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":"Research Square","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.1038/s41598-026-76108-5","doi":"https://doi.org/10.1038/s41598-026-76108-5","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-76108-5","pdf_url":"https://www.nature.com/articles/s41598-026-76108-5_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7202299670.pdf","grobid_xml":"https://content.openalex.org/works/W7202299670.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1964148270","https://openalex.org/W1976397848","https://openalex.org/W2026089620","https://openalex.org/W2031900630","https://openalex.org/W2033479545","https://openalex.org/W2059712524","https://openalex.org/W2064649660","https://openalex.org/W2107083327","https://openalex.org/W2523109058","https://openalex.org/W2751381884","https://openalex.org/W2766192139","https://openalex.org/W2884582089","https://openalex.org/W2910390831","https://openalex.org/W3100613483","https://openalex.org/W3185088474","https://openalex.org/W4210321284","https://openalex.org/W4248830832","https://openalex.org/W4391069712","https://openalex.org/W4408950190","https://openalex.org/W4412489039","https://openalex.org/W7117413615","https://openalex.org/W7140115803"],"related_works":["https://openalex.org/W7203613983","https://openalex.org/W4409343037","https://openalex.org/W4311127370","https://openalex.org/W4393178855","https://openalex.org/W7204205487","https://openalex.org/W3210580957","https://openalex.org/W2884582089","https://openalex.org/W2087561904","https://openalex.org/W4413165222","https://openalex.org/W7140115803"],"abstract_inverted_index":{"Abstract":[0],"The":[1,61],"volumetric":[2],"overall":[3],"mass":[4],"transfer":[5],"coefficient":[6],"(K":[7],"oc":[8,37],"a)":[9],"is":[10],"a":[11,38,69],"key":[12],"design":[13],"parameter":[14],"in":[15,39],"liquid-liquid":[16,59],"extraction":[17,45],"columns,":[18],"yet":[19],"reliable":[20],"prediction":[21],"remains":[22],"difficult":[23],"for":[24],"non-standard":[25],"geometries.":[26],"This":[27],"study":[28],"applied":[29],"machine":[30],"learning":[31],"to":[32,34],"predict":[33,35],"K":[36],"an":[40],"L-shaped":[41],"pulsed":[42],"sieve":[43],"plate":[44],"column":[46],"using":[47],"ANN,":[48],"RF,":[49],"and":[50,75,90],"SVR":[51],"models":[52],"trained":[53],"on":[54],"experimental":[55],"data":[56],"from":[57],"two":[58],"systems.":[60],"ANN":[62],"model":[63],"achieved":[64],"the":[65,108,114],"highest":[66],"accuracy,":[67],"with":[68,104],"test":[70],"R\u00b2":[71],"value":[72],"above":[73],"0.98":[74],"AARE":[76],"below":[77],"6%,":[78],"substantially":[79],"outperforming":[80],"published":[81],"empirical":[82],"correlations.":[83],"Sensitivity":[84],"analysis":[85,99],"identified":[86,100],"dispersed":[87],"phase":[88],"holdup":[89],"drop":[91],"size":[92],"as":[93],"dominant":[94],"variables.":[95],"ANN-based":[96],"response":[97],"surface":[98],"optimal":[101],"operating":[102],"conditions,":[103],"residual":[105],"diagnostics":[106],"confirming":[107],"absence":[109],"of":[110,117],"systematic":[111],"bias":[112],"across":[113],"full":[115],"range":[116],"predicted":[118],"values.":[119]},"counts_by_year":[],"updated_date":"2026-11-02T09:45:03.842371","created_date":"2026-08-13T00:00:00"}