{"id":"W2335572701","doi":"10.1021/ie020124f","title":"Refinery Short-Term Scheduling Using Continuous Time Formulation:  Crude-Oil Operations","year":2003,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Process Optimization and Integration","field":"Engineering","cited_by":183,"is_retracted":false,"has_abstract":true,"ca_institutions":"Honeywell (Canada)","funders":"National Science Foundation","keywords":"Refinery; Scheduling (production processes); Computer science; Oil refinery; Crude oil; Distillation; Integer programming; Binary number; Mathematical optimization; Linear programming; Schedule; Term (time); Representation (politics); Algorithm; Mathematics; Engineering; Chemistry; Waste management; Petroleum engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006168701,0.0006808917,0.0006401754,0.000233958,0.0003090567,0.0009784424,0.000761636,0.0008273129,0.003192078],"category_scores_gemma":[0.00101629,0.0003514113,0.0004383173,0.0006524278,0.0004441868,0.0008949562,0.0004157917,0.0008369352,0.0002887022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009555216,"about_ca_system_score_gemma":0.002122299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01269919,"about_ca_topic_score_gemma":0.01318315,"domain_scores_codex":[0.9996226,0.0001325136,0.00001431539,0.00006091613,0.0001126961,0.00005694102],"domain_scores_gemma":[0.9995825,0.0002277734,0.00006442399,0.00002393485,0.00006459976,0.00003683295],"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":[0.00006098719,0.00003107794,0.00007422832,0.00005156622,0.000008545781,0.00005169839,0.00001880502,0.9787549,0.001837182,0.008091204,0.0005369239,0.01048278],"study_design_scores_gemma":[0.00001658688,0.0000372896,0.00005001086,0.000003667996,0.000004206301,0.000008952526,0.000009858529,0.996155,0.00083603,0.001802922,0.001071285,0.000004223062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02619724,0.0003245118,0.9664755,0.0003269885,0.00005479558,0.00007694229,0.0001374223,0.0002015387,0.006205001],"genre_scores_gemma":[0.8002856,0.0007591738,0.1910305,0.00006763817,0.00008098463,0.000198243,0.0002698093,0.0001086854,0.007199417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01269919,"threshold_uncertainty_score":0.02525055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09038960106167897,"score_gpt":0.324856029746496,"score_spread":0.234466428684817,"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."}}