{"id":"W4380792881","doi":"10.1016/j.compchemeng.2023.108322","title":"Data-driven models of crude distillation units for production planning and for operations monitoring","year":2023,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Project 211; National Key Research and Development Program of China; Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Artificial intelligence; Artificial neural network; Computer science; Transformation (genetics); Lasso (programming language); Residual; Raw data; Data mining; Partial least squares regression; Pattern recognition (psychology); Distillation; Production (economics); Selection (genetic algorithm); Machine learning; Algorithm; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007751623,0.0000969559,0.0001445543,0.00008392043,0.00003575339,0.00002565505,0.00009448277,0.00004984586,8.470239e-8],"category_scores_gemma":[0.00006851624,0.0001098161,0.0000203933,0.0001888509,0.000008313528,0.0001920124,0.0000331672,0.00005415935,2.609333e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002859956,"about_ca_system_score_gemma":0.000005097225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001691788,"about_ca_topic_score_gemma":1.728812e-7,"domain_scores_codex":[0.9994402,0.000002297615,0.0001886859,0.0001661686,0.00006154281,0.0001411375],"domain_scores_gemma":[0.9996554,0.00008705679,0.00001357832,0.0001519765,0.00004944522,0.00004258192],"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":[0.000003818682,0.000001355282,0.000009380566,0.000180218,0.00002462508,9.246003e-8,0.0001359856,0.8142141,0.1838408,0.000061956,0.0001718257,0.001355865],"study_design_scores_gemma":[0.0002414041,0.000008995264,0.00003708368,0.0001146876,0.00001304079,0.000002204217,0.00003548685,0.9720057,0.026934,0.00001304435,0.0004894818,0.0001049269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3546632,0.0001276041,0.6437355,0.00002001649,0.0007504526,0.0002771703,0.00004414317,0.0003784252,0.00000351765],"genre_scores_gemma":[0.9887761,0.000007473523,0.01059607,9.74761e-7,0.0003787744,0.00008127229,0.0001239596,0.00002888428,0.00000642277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.634113,"threshold_uncertainty_score":0.447817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0562445283653617,"score_gpt":0.2702459751999077,"score_spread":0.214001446834546,"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."}}