{"id":"W2996297618","doi":"10.1002/asjc.2265","title":"Event‐based neural network predictive controller application for a distillation column","year":2019,"lang":"en","type":"article","venue":"Asian Journal of Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Setpoint; Artificial neural network; Control theory (sociology); Model predictive control; Controller (irrigation); Fractionating column; MIMO; Computer science; Cuckoo search; Nonlinear system; Process (computing); Event (particle physics); Control engineering; Engineering; Distillation; Artificial intelligence; Control (management); Machine learning","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":[0.0003041844,0.0001510431,0.0004220436,0.00007943526,0.00004443836,0.00002974982,0.0001267305,0.0000845906,0.000009806082],"category_scores_gemma":[0.00006400672,0.0001445748,0.0001799142,0.000121259,0.00001298048,0.000280215,0.000002663375,0.000135778,0.000007111933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001243658,"about_ca_system_score_gemma":0.00003515256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.097228e-7,"about_ca_topic_score_gemma":0.000002323883,"domain_scores_codex":[0.9987937,0.0000576088,0.0005955721,0.0001121018,0.000202044,0.0002390001],"domain_scores_gemma":[0.9988596,0.0001788197,0.0004184018,0.0001346814,0.0003177044,0.00009083356],"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":[0.000611635,0.00001063919,0.00235251,0.00002536581,0.000127645,6.527654e-7,0.00003449601,0.9877462,0.001138925,0.0002284012,0.0002195304,0.007503954],"study_design_scores_gemma":[0.009017362,0.0002932413,0.003420675,0.00005071327,0.0000976884,0.000009458719,0.00002809299,0.9842872,0.00001999335,0.0004735661,0.002175761,0.0001262209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003157558,0.0004142315,0.9934022,0.0003078186,0.0006169191,0.001611419,0.00002226386,0.00006197767,0.0004056071],"genre_scores_gemma":[0.9975543,0.000002151056,0.001475487,0.00008356549,0.0007000106,0.0001002484,0.000009495873,0.00004200395,0.00003272462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9943967,"threshold_uncertainty_score":0.5895588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002131242332636342,"score_gpt":0.1943672814524331,"score_spread":0.1922360391197968,"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."}}