{"id":"W2629820564","doi":"10.1002/cjce.22927","title":"The utilization of closed‐loop prediction for dynamic real‐time optimization","year":2017,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Ministry of Higher Education, Malaysia","keywords":"Karush–Kuhn–Tucker conditions; Mathematical optimization; Process (computing); Dynamic programming; Computer science; Control theory (sociology); Quadratic programming; Optimization problem; Dimension (graph theory); Model predictive control; Set (abstract data type); Controller (irrigation); Complementarity (molecular biology); Mathematics; Control (management)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0002983398,0.0001005686,0.0001594019,0.00006486983,0.000202855,0.00007882625,0.0003484076,0.00008108569,0.00000322661],"category_scores_gemma":[0.0004826332,0.00007433737,0.00007003153,0.00005900496,0.00005237103,0.0002144692,0.000006710739,0.0001293501,4.118831e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002284399,"about_ca_system_score_gemma":0.00008617706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000761363,"about_ca_topic_score_gemma":0.00007682289,"domain_scores_codex":[0.9992636,0.000008234204,0.0003704556,0.00005411329,0.0001130603,0.0001905527],"domain_scores_gemma":[0.9991028,0.0001133366,0.0002183833,0.0002489729,0.000202714,0.0001137692],"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":[0.000008189193,8.874155e-7,0.00001395652,0.00002365764,0.00003875217,4.08167e-7,0.00004409618,0.9723398,0.02586916,0.0002248912,0.00005907582,0.001377095],"study_design_scores_gemma":[0.0003116518,0.00001587403,0.00009226943,0.00007128325,0.00003310329,0.00001158379,0.000004869697,0.9919931,0.007075249,0.00009204889,0.0002340303,0.00006487911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06733966,0.0008990247,0.9287266,0.0005073707,0.001358325,0.0005881606,0.00005473957,0.0000776928,0.0004484006],"genre_scores_gemma":[0.9966642,0.00004585721,0.003050487,0.000002467735,0.0001507754,0.000009571544,0.000008389947,0.00003554667,0.00003269572],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9293246,"threshold_uncertainty_score":0.303139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007422341783387181,"score_gpt":0.205446334591726,"score_spread":0.1980239928083388,"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."}}