{"id":"W2615790402","doi":"10.1016/j.compchemeng.2017.05.007","title":"A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty","year":2017,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Flexibility (engineering); Scheduling (production processes); Mathematical optimization; Continuous stirred-tank reactor; Computer science; Realization (probability); Dynamic priority scheduling; Control theory (sociology); Engineering; Control (management); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.00007635858,0.0001533456,0.0002433403,0.0000488914,0.00003731558,0.00004971448,0.0001281247,0.0000959361,6.222548e-7],"category_scores_gemma":[0.0006530096,0.0001517718,0.00002620524,0.00006783228,0.00006807837,0.0002160059,0.00002456225,0.0001138378,3.641852e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003487366,"about_ca_system_score_gemma":0.00001496928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001533547,"about_ca_topic_score_gemma":1.848326e-7,"domain_scores_codex":[0.9993665,0.000003540538,0.0002495709,0.0001864481,0.00007052303,0.0001234586],"domain_scores_gemma":[0.999392,0.0001522879,0.00009889189,0.0001520093,0.0001557022,0.00004905759],"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":[0.00002052445,0.00001275816,0.00001273983,0.0004766816,0.00002516005,5.310657e-8,0.00009714507,0.8680204,0.1292008,0.0003296394,0.000002098661,0.001802039],"study_design_scores_gemma":[0.0004912883,0.00001305937,0.00003734548,0.0003247324,0.00002058734,0.000001548015,0.00001163631,0.8927845,0.1060695,0.0001157708,0.000001443181,0.0001286157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03323008,0.0007157627,0.9655768,0.00005254894,0.0000953122,0.0002381075,0.000008434332,0.0000809629,0.000001979389],"genre_scores_gemma":[0.5752515,0.00005916084,0.4246236,0.000005331674,0.00001628566,0.00001747517,0.00001181375,0.00001434306,4.867457e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5420215,"threshold_uncertainty_score":0.6189074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378339496361687,"score_gpt":0.2417861481768547,"score_spread":0.2280027532132378,"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."}}