{"id":"W4409933515","doi":"10.3390/pr13051359","title":"A Dual-Loop Modified Active Disturbance Rejection Control Scheme for a High-Purity Distillation Column","year":2025,"lang":"en","type":"article","venue":"Processes","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Fractionating column; Control theory (sociology); Disturbance (geology); Dual (grammatical number); Distillation; Loop (graph theory); Scheme (mathematics); Active disturbance rejection control; Column (typography); Chromatography; Environmental science; Computer science; Control (management); Chemistry; Engineering; Mathematics; Physics; Biology; Artificial intelligence; Connection (principal bundle); Mechanical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004881556,0.0004529009,0.0003635462,0.0002332526,0.0004162538,0.0006255428,0.001065138,0.0005621953,0.00122821],"category_scores_gemma":[0.0005011683,0.0001837741,0.0003328301,0.0001964473,0.0003992357,0.0004127121,0.0004496166,0.0006282605,0.00021352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003872642,"about_ca_system_score_gemma":0.0005016977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001952949,"about_ca_topic_score_gemma":0.001174244,"domain_scores_codex":[0.9995896,0.00006013914,0.00002578779,0.0001148361,0.0001763244,0.00003331417],"domain_scores_gemma":[0.9997618,0.00004491596,0.00006337331,0.00003332419,0.00007822106,0.00001837296],"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":[0.001099563,0.0003051488,0.00106533,0.0004429841,0.00008095552,0.0002935757,0.0002721286,0.429541,0.3313617,0.01763332,0.001726698,0.2161776],"study_design_scores_gemma":[0.00003562224,0.0002645813,0.0001988893,0.000004537661,0.00001330434,0.00003386024,0.000004570487,0.9793134,0.0185721,0.0002979768,0.001247986,0.0000130716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06902651,0.0002685877,0.9255828,0.0001446715,0.0001455144,0.00008265689,0.00002177793,0.000616835,0.004110709],"genre_scores_gemma":[0.9505734,0.0000800198,0.04722089,0.00004034765,0.00002417656,0.00004541105,0.00002158087,0.00001037062,0.001983801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001952949,"threshold_uncertainty_score":0.004108787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006031398821786067,"score_gpt":0.2259043515339818,"score_spread":0.2198729527121957,"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."}}