{"id":"W2947808108","doi":"10.1002/cjce.23527","title":"Temperature Inferential Control of Heat‐Integrated Distillation Column Based on Variable Sensitive Stage Temperature Set‐point","year":2019,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Fractionating column; Temperature control; Estimator; Control theory (sociology); Distillation; Set point; Computer science; Set (abstract data type); Mathematics; Control (management); Engineering; Control engineering; Statistics; Chemistry; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005274063,0.0005358983,0.000487249,0.0002612055,0.000456976,0.0007691163,0.000973952,0.0003133337,0.0006221742],"category_scores_gemma":[0.0008724768,0.0003167665,0.0003274738,0.0003055912,0.0005855965,0.0005224501,0.0005399045,0.0005575956,0.00008408965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005598947,"about_ca_system_score_gemma":0.0007731616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004076858,"about_ca_topic_score_gemma":0.004099814,"domain_scores_codex":[0.9995535,0.00006318936,0.0000222236,0.0001427238,0.0001758294,0.00004241078],"domain_scores_gemma":[0.9996135,0.0001483781,0.0000899533,0.00002803521,0.0001011319,0.0000190099],"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":[0.0009371587,0.0003332777,0.002763779,0.000474457,0.0001087498,0.0002210928,0.0003646115,0.5952114,0.1853075,0.008405204,0.001531612,0.2043412],"study_design_scores_gemma":[0.00003425743,0.0001448604,0.0008892564,0.000004502631,0.00001696011,0.00002035614,0.000008199693,0.9803587,0.01769237,0.0003981942,0.0004167595,0.00001563123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1101104,0.0004558493,0.885642,0.0001147605,0.00009448815,0.0000981317,0.0000489124,0.0006892819,0.002746157],"genre_scores_gemma":[0.9812238,0.0001067432,0.01785277,0.00002377951,0.00001428145,0.00004506435,0.00002333622,0.00001016486,0.0007000248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004076858,"threshold_uncertainty_score":0.008106291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002122824214321503,"score_gpt":0.1582257459905892,"score_spread":0.1561029217762677,"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."}}