{"id":"W2752130017","doi":"10.1002/aic.15950","title":"Nonlinear robust optimization for process design","year":2017,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Process Optimization and Integration","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Linearization; Robust optimization; Nonlinear system; Optimization problem; Constraint (computer-aided design); Process (computing); Mathematics; Affine transformation; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002083272,0.00008419166,0.0000848998,0.00005122371,0.0004006575,0.000352021,0.0002185963,0.00006448761,0.0001243227],"category_scores_gemma":[0.0002530399,0.00007425434,0.00003472059,0.00003179966,0.00001506823,0.0006962782,0.000005432209,0.0001296245,0.000006289545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003243099,"about_ca_system_score_gemma":0.00004126581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.413127e-7,"about_ca_topic_score_gemma":9.547032e-7,"domain_scores_codex":[0.9995387,0.000007613152,0.0001655914,0.00006427806,0.00009382724,0.0001300486],"domain_scores_gemma":[0.9994493,0.00001563607,0.00009782653,0.000124872,0.0002506838,0.00006167762],"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":[0.00001178854,0.00001012431,0.00003118299,0.00001865932,0.00001390913,5.158738e-7,0.00008276731,0.9928021,0.0000898842,0.0000213079,0.002946972,0.003970779],"study_design_scores_gemma":[0.0003803206,0.00003597687,0.00002051954,0.00002690744,0.00001377031,0.0000195928,0.00003484423,0.9933603,0.0049695,0.0001766783,0.0008659429,0.00009567945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009063088,0.00008669722,0.9967088,0.0002346054,0.0003477873,0.0001260717,0.000002933338,0.00007424298,0.001512621],"genre_scores_gemma":[0.2678637,0.0004575175,0.7303198,0.0001321157,0.0007556567,0.00003445793,0.00002037331,0.00005934938,0.0003570546],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2669574,"threshold_uncertainty_score":0.3394546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0430311945629967,"score_gpt":0.2813973864244547,"score_spread":0.238366191861458,"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."}}