{"id":"W2891289570","doi":"10.1016/j.compchemeng.2018.09.013","title":"Utilizing big data for batch process modeling and control","year":2018,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Model predictive control; Process (computing); Subspace topology; Computer science; Process control; Variety (cybernetics); Process analytical technology; Linear model; Control (management); Product (mathematics); Control theory (sociology); Engineering; Mathematics; Work in process; Artificial intelligence; Machine learning","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":[0.001771051,0.001712395,0.001401512,0.001051127,0.0006637151,0.001798128,0.001724959,0.001028272,0.001339005],"category_scores_gemma":[0.00691657,0.001128882,0.001015731,0.001220402,0.0007702248,0.003261614,0.001252254,0.002209292,0.0004614387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009163967,"about_ca_system_score_gemma":0.001579612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008705843,"about_ca_topic_score_gemma":0.01000818,"domain_scores_codex":[0.9991837,0.000272166,0.00007754357,0.0001593197,0.0002478299,0.00005938655],"domain_scores_gemma":[0.9957943,0.002118591,0.0002696219,0.00116233,0.0005366087,0.0001186529],"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":[0.0003649826,0.000266459,0.004354124,0.0001923017,0.0001863515,0.0001170439,0.00003392298,0.9280608,0.002390753,0.005142607,0.002586542,0.05630412],"study_design_scores_gemma":[0.000006877762,0.0000164194,0.0003143387,0.000003683676,0.00000933108,0.000004576328,0.000007064391,0.9935998,0.00100437,0.004624644,0.0004029768,0.000006040344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1015147,0.001786332,0.8829265,0.001528533,0.0006660043,0.0001628798,0.003929064,0.004592214,0.002893737],"genre_scores_gemma":[0.8713877,0.00113893,0.1200914,0.0002479106,0.0002212419,0.0002146904,0.004868114,0.000248355,0.001581771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008705843,"threshold_uncertainty_score":0.01731038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335948325206711,"score_gpt":0.2314347963424408,"score_spread":0.2080753130903737,"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."}}