{"id":"W2064549002","doi":"10.1021/ie0704694","title":"Assessing Model Prediction Control (MPC) Performance. 2. Bayesian Approach for Constraint Tuning","year":2007,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Syncrude (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Model predictive control; Constraint (computer-aided design); Computer science; Bayesian probability; Mathematical optimization; Reduction (mathematics); Constraint satisfaction; Fractionating column; Bayesian optimization; Control theory (sociology); Control (management); Distillation; Mathematics; Machine learning; Artificial intelligence; Probabilistic logic","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006760889,0.001305495,0.001177144,0.001743077,0.0004907879,0.002205928,0.001285956,0.001836635,0.002978136],"category_scores_gemma":[0.02611382,0.0006691942,0.0007827541,0.001320972,0.001164359,0.002127867,0.001396557,0.001317566,0.0006764579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001626362,"about_ca_system_score_gemma":0.002115164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004653974,"about_ca_topic_score_gemma":0.003832472,"domain_scores_codex":[0.9930612,0.003373791,0.0002571063,0.0005682025,0.002523007,0.0002166932],"domain_scores_gemma":[0.9893265,0.007683125,0.001229216,0.0005113742,0.001120379,0.0001294073],"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":[0.0001832175,0.0001151961,0.003702234,0.0003711265,0.0001560857,0.00007046205,0.00009159396,0.8000512,0.003694688,0.0568588,0.001775674,0.1329298],"study_design_scores_gemma":[0.00002157884,0.00008985867,0.00212212,0.00008411764,0.00002806541,0.00007574808,0.00002418869,0.954161,0.0025498,0.03886708,0.001925995,0.00005043409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007754768,0.000772107,0.983623,0.0004330938,0.00002093303,0.00007331264,0.0001767924,0.0001870815,0.006958887],"genre_scores_gemma":[0.5995324,0.001499101,0.3943653,0.0003386221,0.0001214598,0.0003974753,0.000630585,0.0001539698,0.002961115],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006760889,"threshold_uncertainty_score":0.03575546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07390999639384843,"score_gpt":0.3118989831351422,"score_spread":0.2379889867412938,"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."}}