{"id":"W4323665884","doi":"10.1016/j.jprocont.2023.02.013","title":"Economic model predictive control based on lattice trajectory piecewise linear model for wastewater treatment plants","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Nonlinear system; Mathematical optimization; Model predictive control; Benchmark (surveying); Trajectory; Computer science; Control theory (sociology); Piecewise linear function; Optimization problem; Nonlinear programming; Mathematics; Artificial intelligence; Control (management)","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":[0.0004326274,0.0005127227,0.0009981956,0.0003419246,0.0004180223,0.000973868,0.000792473,0.0007963728,0.001960179],"category_scores_gemma":[0.001183216,0.0004018787,0.0005100816,0.0006647472,0.0006300344,0.000705612,0.000559935,0.001031183,0.0001882569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009751032,"about_ca_system_score_gemma":0.0009036586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02325055,"about_ca_topic_score_gemma":0.01029006,"domain_scores_codex":[0.9997985,0.00007501008,0.000007162384,0.0000342669,0.00005078012,0.00003436115],"domain_scores_gemma":[0.9995375,0.0002489057,0.00006982414,0.000021762,0.0001024871,0.00001956867],"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":[0.00001422865,0.000005427488,0.00006292404,0.00001180454,0.000006506939,0.00001284059,0.00000654363,0.99603,0.0001719089,0.002178433,0.0000931824,0.001406185],"study_design_scores_gemma":[0.000001760691,0.000004669318,0.0000309168,7.785367e-7,0.000001319273,0.000001028427,0.000001467749,0.999258,0.00003613864,0.0006189451,0.00004364306,0.000001342292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1520396,0.0007339689,0.831134,0.0008000833,0.0001577639,0.0000575299,0.0003025448,0.0004304877,0.0143439],"genre_scores_gemma":[0.9913597,0.0001876419,0.005198,0.00002155174,0.00001387608,0.00004536814,0.00007675712,0.00001886815,0.003078239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02325055,"threshold_uncertainty_score":0.04623044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01457959006821316,"score_gpt":0.247154799225613,"score_spread":0.2325752091573998,"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."}}