{"id":"W2048743770","doi":"10.1016/j.jprocont.2012.10.001","title":"Fouling control and optimization of a drinking water membrane filtration process with real-time model parameter adaptation using fluorescence and permeate flux measurements","year":2012,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Else Kröner-Fresenius-Stiftung","keywords":"Membrane fouling; Fouling; Extended Kalman filter; Ultrafiltration (renal); Membrane; Filtration (mathematics); Kalman filter; Permeation; Environmental engineering; Environmental science; Biological system; Process engineering; Engineering; Chemistry; Control theory (sociology); Chromatography; Computer science; Mathematics; Artificial intelligence; Control (management); Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005274108,0.0006905806,0.0007090184,0.0002766098,0.0004459879,0.0007734637,0.0003557071,0.0006722718,0.000196922],"category_scores_gemma":[0.0008100839,0.0002817621,0.0005771489,0.000216459,0.0003204947,0.0004073195,0.000356044,0.0004374412,0.00006096186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007467708,"about_ca_system_score_gemma":0.0005639295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00809293,"about_ca_topic_score_gemma":0.004582454,"domain_scores_codex":[0.999813,0.00003792072,0.00001400755,0.00004852535,0.0000527608,0.0000336421],"domain_scores_gemma":[0.9997419,0.0001201328,0.00004086624,0.00001794836,0.00006385082,0.00001524971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008803278,0.0004945421,0.003669007,0.0001547372,0.00007853567,0.00009827545,0.0001583012,0.4099245,0.5425398,0.0003115026,0.0001594687,0.04153107],"study_design_scores_gemma":[0.00003535527,0.0003630387,0.003446988,0.000003288183,0.00004584086,0.00001987636,0.00002763605,0.8025132,0.1931549,0.0001329652,0.0002286611,0.00002827168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9440904,0.0001033185,0.05497974,0.00008461672,0.00001278999,0.00002902485,0.00003855629,0.0001950606,0.0004663921],"genre_scores_gemma":[0.9947189,0.00002738201,0.004995827,0.000004548015,0.000001180515,0.00001733402,0.00001594115,0.000007440807,0.0002114219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00809293,"threshold_uncertainty_score":0.01609164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02805906770756437,"score_gpt":0.2538458405443955,"score_spread":0.2257867728368311,"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."}}