{"id":"W4386480897","doi":"10.1002/cjce.25082","title":"Multi‐model predictive control of<scp>SCR</scp>flue gas denitrification system in coal‐fired power plant based on kernel fuzzy c‐means clustering and integrated model","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Wastewater Treatment and Nitrogen Removal","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Model predictive control; Particle swarm optimization; Control theory (sociology); Cluster analysis; Support vector machine; Genetic algorithm; Artificial neural network; Engineering; Computer science; Control engineering; Mathematical optimization; Mathematics; Artificial intelligence; Algorithm; Control (management)","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":[0.0005875612,0.0006838809,0.0009175888,0.0003754461,0.000720723,0.0009685687,0.00078597,0.0005468146,0.0007165252],"category_scores_gemma":[0.0004825987,0.0003654571,0.000678971,0.0003880093,0.0004024501,0.0004913658,0.0004891649,0.000564813,0.0001145613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008670602,"about_ca_system_score_gemma":0.001128661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03437544,"about_ca_topic_score_gemma":0.02003155,"domain_scores_codex":[0.9996696,0.00004891385,0.00001931678,0.00008447018,0.0001325083,0.00004529576],"domain_scores_gemma":[0.9997337,0.00006519115,0.00004521442,0.00001805541,0.000122573,0.00001529376],"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":[0.00006798013,0.0000415486,0.0004706832,0.00004647014,0.00003928307,0.00004482769,0.00004398178,0.9766462,0.004092531,0.0007149607,0.0002950313,0.01749636],"study_design_scores_gemma":[0.000002873097,0.00001398305,0.0001262236,9.928012e-7,0.000003699061,0.000002307379,0.000003087099,0.9993542,0.0003748116,0.00007175829,0.00004371345,0.000002375627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2458367,0.0005768906,0.7443826,0.0003020571,0.0001156765,0.0000957893,0.00008396274,0.0008459631,0.007760361],"genre_scores_gemma":[0.9920782,0.00006862834,0.006898498,0.0000139699,0.000006868434,0.00003192707,0.00002905585,0.000008971971,0.0008638122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03437544,"threshold_uncertainty_score":0.06835067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009379388119954517,"score_gpt":0.1747471367580056,"score_spread":0.1653677486380511,"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."}}