{"id":"W2170586925","doi":"10.1002/cjce.20539","title":"Adaptive generalised predictive control of high purity internal thermally coupled distillation column","year":2011,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariable calculus; Fractionating column; Model predictive control; Distillation; Sensitivity (control systems); Control theory (sociology); Process (computing); Inverse; PID controller; Process engineering; Process control; Nonlinear system; Computer science; Chromatography; Temperature control; Control (management); Chemistry; Mathematics; Engineering; Control engineering; Artificial intelligence; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.0006305757,0.0005929707,0.0005996387,0.0002570417,0.0004326511,0.0009431222,0.0008263568,0.0005108173,0.000766879],"category_scores_gemma":[0.000806517,0.0003021116,0.0003359096,0.0002809788,0.0006398436,0.0002945415,0.00059475,0.0006555342,0.0001224522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006785399,"about_ca_system_score_gemma":0.0007251031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01256611,"about_ca_topic_score_gemma":0.007736673,"domain_scores_codex":[0.9996399,0.00008989454,0.00001067803,0.00006862859,0.0001377452,0.00005305751],"domain_scores_gemma":[0.9995545,0.0001797961,0.00007771022,0.00002361029,0.0001423553,0.00002202739],"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":[0.0002100456,0.00005480928,0.0005881254,0.0001064099,0.00004451393,0.0001278329,0.000068519,0.9568533,0.01772015,0.00176372,0.0004718032,0.02199074],"study_design_scores_gemma":[0.0000138949,0.00006091594,0.0001679781,0.000001718879,0.000007009693,0.000004999136,0.000002815137,0.9979146,0.001529605,0.0001687241,0.0001237737,0.000003932802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2807607,0.0006086728,0.7057437,0.000386552,0.0001657619,0.00008901489,0.00006546835,0.0009644983,0.01121562],"genre_scores_gemma":[0.9937822,0.00004986158,0.005277366,0.00002161041,0.000008823615,0.00001867738,0.00001286292,0.000006316667,0.0008224498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01256611,"threshold_uncertainty_score":0.02498597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006554835425500077,"score_gpt":0.1553147624756927,"score_spread":0.1487599270501926,"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."}}