{"id":"W4402299331","doi":"10.1016/j.ifacol.2024.08.417","title":"State estimation of a carbon capture process through POD model reduction and neural network approximation","year":2024,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Reduction (mathematics); Artificial neural network; Process (computing); Point of delivery; Estimation; State (computer science); Carbon fibers; Computer science; Econometrics; Environmental science; Biological system; Artificial intelligence; Mathematics; Algorithm; Economics; Agronomy; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008155422,0.0001748726,0.0002036356,0.00005678798,0.00003423997,0.0000350453,0.0000478992,0.00008913686,0.000001305127],"category_scores_gemma":[0.00001663989,0.0001696111,0.00003056641,0.0002804261,0.00002577932,0.0004935132,0.000008793514,0.0001518289,4.987984e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006739301,"about_ca_system_score_gemma":0.00001994505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002383351,"about_ca_topic_score_gemma":0.000008564987,"domain_scores_codex":[0.9991273,0.00001739643,0.0002943889,0.0002165888,0.0001611716,0.0001831929],"domain_scores_gemma":[0.9997112,0.00001873764,0.00005880234,0.0001211294,0.0000568827,0.00003322804],"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":[0.00001639333,0.000006483323,0.000006099343,0.0004616761,0.00002900276,0.000001172623,0.002065683,0.9849059,0.003937489,0.0001453358,0.000001794578,0.008422961],"study_design_scores_gemma":[0.0002338377,0.00002105954,0.00001591298,0.0001641343,0.00004012078,0.00002177758,0.0001361133,0.9969985,0.0004899871,0.001724028,0.000003112827,0.0001514251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3049079,0.003548039,0.6892952,0.0002069759,0.0004117479,0.0005360402,0.00002670643,0.0005879742,0.000479434],"genre_scores_gemma":[0.8432621,0.0000775575,0.1563056,0.000007809011,0.0001241421,0.00003799764,0.00007143575,0.0000444192,0.00006892773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5383542,"threshold_uncertainty_score":0.6916538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007582858904943253,"score_gpt":0.233764677293003,"score_spread":0.2261818183880598,"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."}}