{"id":"https://openalex.org/W4415522909","doi":"https://doi.org/10.70504/ijepe.v2i5.15552","title":"Optimization and Prediction of Petrochemical Process Performance by Machine Learning: A Case Study on Catalytic Cracking","display_name":"Optimization and Prediction of Petrochemical Process Performance by Machine Learning: A Case Study on Catalytic Cracking","publication_year":2025,"publication_date":"2025-10-24","ids":{"openalex":"https://openalex.org/W4415522909","doi":"https://doi.org/10.70504/ijepe.v2i5.15552"},"language":"en","primary_location":{"id":"doi:10.70504/ijepe.v2i5.15552","doi":"https://doi.org/10.70504/ijepe.v2i5.15552","is_oa":true,"landing_page_url":"https://doi.org/10.70504/ijepe.v2i5.15552","pdf_url":"https://honexus.org/ijepe/index.php/ijepe/article/download/15552/11232","source":{"id":"https://openalex.org/S4404660669","display_name":"International Journal of Educational Practices and Engineering.","issn_l":"3028-9882","issn":["3028-9882"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Educational Practices and Engineering(IJEPE)","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://honexus.org/ijepe/index.php/ijepe/article/download/15552/11232","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117280244","display_name":"Kelvin Cabrera","orcid":"https://orcid.org/0009-0006-4162-9077","observed_orcids":["https://orcid.org/0009-0006-4162-9077"]},"institutions":[{"id":"https://openalex.org/I868289433","display_name":"Universidad Nacional Experimental Polit\u00e9cnica de la Fuerza Armada Bolivariana","ror":"https://ror.org/01w860d88","country_code":"VE","type":"education","lineage":["https://openalex.org/I868289433"]}],"countries":["VE"],"is_corresponding":false,"raw_author_name":"Kelvin Cabrera","raw_affiliation_strings":["UNEFA, Valencia, Venezuela"],"raw_orcid":"https://orcid.org/0009-0006-4162-9077","affiliations":[{"raw_affiliation_string":"UNEFA, Valencia, Venezuela","institution_ids":["https://openalex.org/I868289433"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5120125836","display_name":"Wakefield Cabrera","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I868289433","display_name":"Universidad Nacional Experimental Polit\u00e9cnica de la Fuerza Armada Bolivariana","ror":"https://ror.org/01w860d88","country_code":"VE","type":"education","lineage":["https://openalex.org/I868289433"]}],"countries":["VE"],"is_corresponding":false,"raw_author_name":"Wakefield Cabrera","raw_affiliation_strings":["UNEFA, Caracas, Venezuela"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UNEFA, Caracas, Venezuela","institution_ids":["https://openalex.org/I868289433"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I868289433"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":"5","first_page":"60","last_page":"74"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.7217000126838684,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.7217000126838684,"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/T11630","display_name":"Petroleum Processing and Analysis","score":0.07620000094175339,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.06260000169277191,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/fluid-catalytic-cracking","display_name":"fluid catalytic cracking","score":0.9990000128746033},{"id":"https://openalex.org/keywords/process-optimization","display_name":"process optimization","score":0.7039999961853027},{"id":"https://openalex.org/keywords/catalyst-to-oil-ratio","display_name":"catalyst-to-oil ratio","score":0.7590000033378601},{"id":"https://openalex.org/keywords/unsupervised-pattern-discovery","display_name":"unsupervised pattern discovery","score":0.6510000228881836},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.7900000214576721},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"principal component analysis","score":0.7870000004768372},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.7139999866485596},{"id":"https://openalex.org/keywords/sobol-indices","display_name":"Sobol indices","score":0.7680000066757202},{"id":"https://openalex.org/keywords/real-time-decision-making","display_name":"real-time decision making","score":0.5220000147819519}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5856999754905701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5853999853134155},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5410000085830688},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5397999882698059},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.503600001335144},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49900001287460327},{"id":"https://openalex.org/C31052017","wikidata":"https://www.wikidata.org/wiki/Q900812","display_name":"Fluid catalytic cracking","level":3,"score":0.489300012588501},{"id":"https://openalex.org/C170573351","wikidata":"https://www.wikidata.org/wiki/Q1364904","display_name":"Petrochemical","level":2,"score":0.48030000925064087},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4180999994277954},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.39160001277923584},{"id":"https://openalex.org/C103697071","wikidata":"https://www.wikidata.org/wiki/Q39558","display_name":"Gasoline","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.388700008392334},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.3865000009536743},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3504999876022339},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3449999988079071},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3447999954223633},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3418000042438507},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.3276999890804291},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C70452415","wikidata":"https://www.wikidata.org/wiki/Q3182448","display_name":"Predictive maintenance","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.2655999958515167}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.70504/ijepe.v2i5.15552","doi":"https://doi.org/10.70504/ijepe.v2i5.15552","is_oa":true,"landing_page_url":"https://doi.org/10.70504/ijepe.v2i5.15552","pdf_url":"https://honexus.org/ijepe/index.php/ijepe/article/download/15552/11232","source":{"id":"https://openalex.org/S4404660669","display_name":"International Journal of Educational Practices and Engineering.","issn_l":"3028-9882","issn":["3028-9882"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Educational Practices and Engineering(IJEPE)","raw_type":"journal-article"},{"id":"pmh:oai:ojs.honexus.org:article/15552","doi":null,"is_oa":true,"landing_page_url":"https://honexus.org/ijepe/index.php/ijepe/article/view/15552","pdf_url":null,"source":{"id":"https://openalex.org/S4404660669","display_name":"International Journal of Educational Practices and Engineering.","issn_l":"3028-9882","issn":["3028-9882"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"3028-9882","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.70504/ijepe.v2i5.15552","doi":"https://doi.org/10.70504/ijepe.v2i5.15552","is_oa":true,"landing_page_url":"https://doi.org/10.70504/ijepe.v2i5.15552","pdf_url":"https://honexus.org/ijepe/index.php/ijepe/article/download/15552/11232","source":{"id":"https://openalex.org/S4404660669","display_name":"International Journal of Educational Practices and Engineering.","issn_l":"3028-9882","issn":["3028-9882"],"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":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Educational Practices and Engineering(IJEPE)","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.6773999929428101,"display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4415522909.pdf","grobid_xml":"https://content.openalex.org/works/W4415522909.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4404124598","https://openalex.org/W6946108178","https://openalex.org/W7093079779","https://openalex.org/W7116661436","https://openalex.org/W3002323418","https://openalex.org/W3026103152","https://openalex.org/W4417222309","https://openalex.org/W4239225631","https://openalex.org/W4415395535","https://openalex.org/W7206711211"],"abstract_inverted_index":{"Background:":[0],"The":[1,162,229],"fluid":[2],"catalytic":[3],"cracking":[4],"(FCC)":[5],"process":[6,98],"is":[7,45],"critical":[8,47,265],"in":[9,49,225,247],"modern":[10],"refineries,":[11],"as":[12,22,268],"it":[13],"converts":[14],"heavy":[15],"petroleum":[16],"fractions":[17],"into":[18],"high-value":[19],"products":[20],"such":[21,267],"gasoline":[23,172],"and":[24,31,42,61,69,78,88,97,112,119,132,146,158,206,243,258,271],"olefins.":[25],"However,":[26],"its":[27],"highly":[28],"nonlinear":[29],"behavior":[30],"sensitivity":[32],"to":[33,76,117,200,251],"multiple":[34],"operating":[35],"variables":[36,266],"make":[37],"traditional":[38],"optimization":[39],"methods":[40],"inefficient":[41],"inflexible.":[43],"There":[44],"a":[46,80],"gap":[48],"the":[50,167,181,194,204,226,252],"literature":[51],"on":[52],"integrated":[53,230],"machine":[54,231],"learning":[55,232],"(ML)":[56],"frameworks":[57],"that":[58,263],"combine":[59],"supervised":[60,121,256],"unsupervised":[62,138],"techniques":[63,139,260],"for":[64,84,128,143,171,184,237],"holistic":[65],"FCC":[66,86,239],"performance":[67,87,150],"prediction":[68],"operational":[70,91,102,185],"analysis.":[71],"Objective:":[72],"This":[73],"study":[74],"seeks":[75],"develop":[77],"validate":[79],"comprehensive":[81],"ML-based":[82],"framework":[83,233],"predicting":[85],"uncovering":[89],"hidden":[90],"patterns,":[92],"enabling":[93],"real-time":[94,238],"decision":[95],"making":[96],"optimization.":[99],"Methods:":[100],"Historical":[101],"data":[103],"(12":[104],"500":[105],"records,":[106],"15":[107],"variables,":[108],"including":[109],"temperature,":[110],"pressure":[111],"catalyst-oil":[113],"ratio)":[114],"were":[115],"used":[116],"train":[118],"evaluate":[120],"models":[122,257],"(Random":[123],"Forest,":[124],"Neural":[125],"Networks,":[126],"SVM)":[127],"regression":[129],"(gasoline":[130],"yield)":[131],"classification":[133],"(operational":[134],"stability),":[135],"along":[136],"with":[137],"(K-means":[140],"clustering,":[141],"PCA)":[142],"pattern":[144],"discovery":[145],"dimensionality":[147],"reduction.":[148],"Model":[149],"was":[151,180],"evaluated":[152],"by":[153],"R\u00b2,":[154],"RMSE,":[155],"F1-score,":[156],"AUC-ROC":[157],"5-step":[159],"cross-validation.":[160],"Results:":[161],"MLP":[163],"Regressor":[164],"model":[165],"achieved":[166],"highest":[168],"predictive":[169],"accuracy":[170,254],"yield":[173],"(R\u00b2":[174],"=":[175,188],"0.8771),":[176],"while":[177],"Random":[178],"Forest":[179],"best":[182],"classifier":[183],"stability":[186],"(F1-Score":[187],"0.9815).":[189],"Unsupervised":[190],"analysis":[191],"revealed":[192],"limitations:":[193],"PCA":[195],"required":[196],"16":[197],"principal":[198],"components":[199],"retain":[201],"95%":[202],"of":[203,216,221,255],"variance,":[205],"K-means":[207],"clustering":[208,224],"(with":[209],"k=4)":[210],"obtained":[211],"an":[212,219],"average":[213],"silhouette":[214],"score":[215],"0.0440,":[217],"indicating":[218],"absence":[220],"significant":[222],"natural":[223],"data.":[227],"Conclusions:":[228],"demonstrates":[234],"great":[235],"potential":[236],"optimization,":[240],"cost":[241],"reduction":[242],"energy":[244],"efficiency":[245],"improvement":[246],"petrochemical":[248],"operations,":[249],"thanks":[250],"high":[253],"interpretability":[259],"(SHAP,":[261],"Sobol)":[262],"identify":[264],"regenerator":[269],"temperature":[270],"catalyst/oil":[272],"ratio.":[273]},"counts_by_year":[],"updated_date":"2026-10-27T15:51:10.671819","created_date":"2025-10-24T00:00:00"}