{"id":"W2237222326","doi":"10.1002/aic.15164","title":"Dynamic modeling and collocation‐based model reduction of cryogenic air separation units","year":2016,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Process Optimization and Integration","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Air separation; Heat exchanger; Distillation; Reduction (mathematics); Orthogonal collocation; Collocation (remote sensing); Process engineering; Process (computing); Process integration; Separation (statistics); Computer science; Mechanical engineering; Engineering; Simulation; Chemistry; Collocation method; Mathematics; Chromatography","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":[0.0003505129,0.0007201044,0.000808523,0.0003080169,0.0005840918,0.0008584535,0.0009719034,0.001041461,0.001485855],"category_scores_gemma":[0.0008028441,0.0006613529,0.0007941649,0.0002539692,0.0005261019,0.0006087918,0.0006192143,0.001142398,0.0002880534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008904528,"about_ca_system_score_gemma":0.001210756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03591601,"about_ca_topic_score_gemma":0.01341329,"domain_scores_codex":[0.9998038,0.00006744518,0.000009338265,0.00004002635,0.00005220211,0.00002722634],"domain_scores_gemma":[0.9996397,0.000168077,0.00005670453,0.00003559385,0.00008147359,0.00001834243],"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":[0.000009785506,0.00000705562,0.0001393465,0.000007226191,0.000004687246,0.00001205537,0.00001372049,0.9974044,0.0007433596,0.0005055869,0.00005281501,0.001099945],"study_design_scores_gemma":[0.000001000615,0.000002774689,0.00003285882,4.827599e-7,8.195468e-7,8.462025e-7,0.000001604536,0.9997146,0.0001080359,0.00007406023,0.00006182141,0.000001039156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2212178,0.0002822644,0.7663178,0.0003246708,0.00005562928,0.0001148853,0.0003595459,0.0006751279,0.01065225],"genre_scores_gemma":[0.967155,0.0001458305,0.02814491,0.00003491789,0.00001286747,0.0001830792,0.0002355512,0.00008891097,0.003998804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03591601,"threshold_uncertainty_score":0.07141387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553274005261338,"score_gpt":0.2489117792764196,"score_spread":0.2333790392238062,"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."}}