{"id":"W2006831092","doi":"10.1021/ie801806t","title":"Handling Inequality Constraints in Optimal Control by Problem Reformulation","year":2009,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Optimal control; Piecewise; Interval (graph theory); Mathematical optimization; Control variable; Control theory (sociology); Constraint (computer-aided design); Temperature control; Dynamic programming; Mathematics; Constant (computer programming); Control (management); Optimization problem; Variable (mathematics); Sensitivity (control systems); Computer science; Control engineering; Engineering; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001209222,0.0002344806,0.000334726,0.0001338313,0.00005841176,0.00008687379,0.0002378918,0.0004095501,0.00003387548],"category_scores_gemma":[0.0004667616,0.0002665333,0.00004631947,0.0005929192,0.00004165562,0.0002699615,0.00001888787,0.001142729,0.000006769512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006249857,"about_ca_system_score_gemma":0.0000630877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001794495,"about_ca_topic_score_gemma":6.272018e-7,"domain_scores_codex":[0.9978704,0.00004998381,0.0005772065,0.0003069635,0.0004866104,0.0007088681],"domain_scores_gemma":[0.9992062,0.000183299,0.00004320366,0.00027272,0.0001304751,0.0001640532],"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":[0.00002708314,0.0000152924,0.0001038867,0.00003192026,0.00001144878,0.000005696494,0.00003059501,0.5782251,0.4186015,0.00001980686,0.000106703,0.002820999],"study_design_scores_gemma":[0.004497323,0.00005279817,0.0001125637,0.0002794676,0.000006425183,0.00001116714,0.00004542193,0.8199151,0.1735952,0.00004960363,0.001007289,0.0004276524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9195138,0.0006914691,0.0709172,0.0003876064,0.0003319485,0.00201571,0.0001184329,0.001273001,0.004750822],"genre_scores_gemma":[0.9990625,0.00001150727,0.0003102733,0.00000376495,0.0003763207,0.00006691271,0.00006168719,0.00003851421,0.00006856048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2450062,"threshold_uncertainty_score":0.9999787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0378701568830225,"score_gpt":0.3013277140228648,"score_spread":0.2634575571398423,"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."}}