{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001759958,0.001838618,0.001480881,0.000898205,0.0003678248,0.001146727,0.001052287,0.001151495,0.002877823],"category_scores_gemma":[0.00175129,0.0004736646,0.001122935,0.001080272,0.001087479,0.0008448207,0.00140157,0.001802454,0.001174247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001174607,"about_ca_system_score_gemma":0.001242967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056441,"about_ca_topic_score_gemma":0.001565797,"domain_scores_codex":[0.9990871,0.0003528238,0.00003821272,0.0001612591,0.0003141919,0.00004640198],"domain_scores_gemma":[0.9996135,0.0001674764,0.00006743761,0.0000451714,0.00009433951,0.00001213342],"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":[0.00003198842,0.00002923025,0.0001004817,0.0002612532,0.0000820377,0.00006784406,0.00004029142,0.7145529,0.003846481,0.2123265,0.0024848,0.06617626],"study_design_scores_gemma":[0.000008374559,0.0000235074,0.0000369823,0.0000148602,0.00001205486,0.00001595803,0.000003670918,0.9550164,0.0005895662,0.03813916,0.00612918,0.00001030402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003503059,0.0007089258,0.9963694,0.00009208926,0.00003406337,0.00001775244,0.00001938577,0.00007122414,0.002336944],"genre_scores_gemma":[0.187769,0.004941384,0.792528,0.0002655498,0.0005231287,0.0008461211,0.0003317028,0.000213244,0.01258193],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002877823,"threshold_uncertainty_score":0.009627283,"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."}}