{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001720243,0.0001337173,0.0002764541,0.00009333507,0.00002385663,0.00001396826,0.0002317361,0.00008007653,0.00003002274],"category_scores_gemma":[0.0001084929,0.0001132363,0.00008060794,0.0001078311,0.00005117095,0.0001542416,0.000005469687,0.0002587108,7.702297e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002782269,"about_ca_system_score_gemma":0.0000968517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001353717,"about_ca_topic_score_gemma":0.0002138563,"domain_scores_codex":[0.9991398,0.00001781021,0.0004188599,0.00006386013,0.0001510005,0.0002087125],"domain_scores_gemma":[0.9992266,0.00006630769,0.0001572232,0.0001188613,0.0002115697,0.0002194042],"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":[0.00006878952,0.000003349065,0.0001908922,0.00001567147,0.0001746913,0.00001068537,0.0004640674,0.9604089,0.03793167,0.0005782028,0.00001160615,0.0001415131],"study_design_scores_gemma":[0.0009189363,0.00005437914,0.001697914,0.0000950308,0.00006459808,0.00004180679,0.00001350935,0.9785376,0.01823108,0.0002053535,0.00001357937,0.0001261815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4462233,0.0003641463,0.5525483,0.00002363116,0.0003890443,0.00018511,0.00003434809,0.00003155019,0.0002005499],"genre_scores_gemma":[0.998195,0.000002177164,0.001567597,0.000008505628,0.0001844718,0.000005691425,0.00000190351,0.00003091295,0.000003742658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5519717,"threshold_uncertainty_score":0.461764,"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."}}