{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003603172,0.001345843,0.001560334,0.0007181246,0.0005630079,0.001770124,0.001138361,0.001190948,0.006721996],"category_scores_gemma":[0.00701056,0.0005363643,0.001231022,0.0009291114,0.001538542,0.001916001,0.001703498,0.002944324,0.001081962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009727394,"about_ca_system_score_gemma":0.00154178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004920937,"about_ca_topic_score_gemma":0.002230684,"domain_scores_codex":[0.9982773,0.000995926,0.00009015208,0.000180629,0.0003180992,0.000137852],"domain_scores_gemma":[0.9976797,0.001691458,0.0001258877,0.0001318513,0.0003414135,0.00002974681],"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":[0.00005611861,0.00008143,0.0001955979,0.0005290912,0.00003854023,0.00021335,0.000200693,0.5436869,0.00109378,0.3814444,0.004618295,0.06784172],"study_design_scores_gemma":[0.00003619035,0.00005292792,0.00007328767,0.00009288199,0.0000149069,0.00004120325,0.00004225588,0.8718255,0.0008344649,0.1192756,0.007695108,0.00001567001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001332536,0.000400077,0.9924494,0.0002665795,0.00005671093,0.00007566121,0.00004051081,0.00004717302,0.005331367],"genre_scores_gemma":[0.2001074,0.001975718,0.7859917,0.0005134033,0.0004726539,0.001408727,0.0003299461,0.0002409905,0.008959619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006721996,"threshold_uncertainty_score":0.02248734,"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."}}