{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001298528,0.00006746867,0.00007389474,0.0001237111,0.00005800527,0.00001501948,0.00004319064,0.00005398303,0.0000150319],"category_scores_gemma":[0.00004132564,0.00005178996,0.00001717281,0.0001654913,0.00001383641,0.0003974257,0.000003445821,0.00007131538,0.000002016583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000779589,"about_ca_system_score_gemma":0.00007792448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.067843e-7,"about_ca_topic_score_gemma":0.000006428085,"domain_scores_codex":[0.9995296,0.00001428951,0.0002191137,0.00005851096,0.0001034916,0.00007500943],"domain_scores_gemma":[0.9995353,0.000008543945,0.0000575775,0.00006090738,0.00029502,0.00004264178],"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":[0.000009571477,0.000005146819,0.000009654775,0.000009803195,0.000009219418,5.035921e-8,0.00009842303,0.926387,0.07124784,0.0001020198,0.0001604042,0.001960885],"study_design_scores_gemma":[0.0003312464,0.00001888765,0.00002190653,0.00005192739,0.00001441108,0.00001687282,0.00004660504,0.9874964,0.01127743,0.0006449063,0.00001406973,0.00006537481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.210951,0.0001651001,0.7883234,0.0001938566,0.00007793066,0.00004031797,0.000002651322,0.00003711914,0.0002086939],"genre_scores_gemma":[0.9924559,0.0003693381,0.006963418,0.00001889057,0.00002109457,0.000003636731,0.000005610581,0.0000124294,0.0001496639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.781505,"threshold_uncertainty_score":0.2111933,"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."}}