{"id":"W2750710924","doi":"10.1002/cjce.23007","title":"Computational fluid dynamic model for the estimation of coke formation and gas generation inside petrochemical furnace pipes with the use of a kinetic net","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Petrobras","keywords":"Petrochemical; Cracking; Coke; Naphtha; Kerosene; Fuel oil; Thermal; Petroleum coke; Diesel fuel; Petroleum engineering; Heat transfer; Materials science; Waste management; Chemistry; Metallurgy; Thermodynamics; Composite material; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038791,0.0008497147,0.0009383185,0.0005169629,0.0007557404,0.001223191,0.0008163848,0.001825216,0.002094024],"category_scores_gemma":[0.001127972,0.000631677,0.0009080116,0.0003580055,0.0007315985,0.0004808004,0.0007067648,0.001128426,0.000298491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001022717,"about_ca_system_score_gemma":0.001642706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03966434,"about_ca_topic_score_gemma":0.01369686,"domain_scores_codex":[0.9998715,0.00003529003,0.000008545638,0.0000265459,0.00003307108,0.00002508777],"domain_scores_gemma":[0.9995366,0.0002881973,0.00004287754,0.0000173006,0.000081306,0.00003379317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001884284,0.00001512959,0.0003093719,0.000009263406,0.000005894432,0.000019776,0.000006757523,0.9981533,0.0003930649,0.0002539028,0.00004838256,0.0007663053],"study_design_scores_gemma":[0.00000126686,0.000002938024,0.0000326043,5.994984e-7,5.486705e-7,6.350634e-7,0.000001257409,0.9998606,0.0000509573,0.00002138087,0.00002641772,8.778605e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4786237,0.0005937556,0.5033728,0.0006660883,0.0002800662,0.0002539339,0.0009110948,0.0008288369,0.01446974],"genre_scores_gemma":[0.97388,0.0001417771,0.02077922,0.0000365414,0.00002153199,0.0002403292,0.000308497,0.00003749831,0.004554604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03966434,"threshold_uncertainty_score":0.0788669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687727230762563,"score_gpt":0.2113727425635806,"score_spread":0.194495470255955,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}