{"id":"W4353051709","doi":"10.1016/j.compchemeng.2023.108232","title":"A practical Reinforcement Learning implementation approach for continuous process control","year":2023,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Saudi Aramco; Korea Advanced Institute of Science and Technology; University of British Columbia","keywords":"Reinforcement learning; Computer science; Process (computing); Curse of dimensionality; Artificial intelligence; Multivariable calculus; Machine learning; Process control; Domain (mathematical analysis); Fractionating column; Control engineering; Distillation; Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001545372,0.0002086016,0.0002781611,0.0001091069,0.00004314061,0.00004968238,0.0001083287,0.00008501625,0.000002945894],"category_scores_gemma":[0.00008967401,0.0002378616,0.00007436747,0.0002744862,0.000009313945,0.0002065547,0.00002228414,0.0001894348,0.000006448965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001278713,"about_ca_system_score_gemma":0.00001255894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.307672e-7,"about_ca_topic_score_gemma":2.693061e-8,"domain_scores_codex":[0.9987996,0.000007667606,0.0003485904,0.0002352648,0.0001721793,0.0004367517],"domain_scores_gemma":[0.9994799,0.0001790852,0.00005150527,0.00011982,0.00006844681,0.0001012026],"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":[0.0000122687,0.000004949443,0.00001812448,0.000228119,0.00006605543,0.000001874346,0.0001309056,0.9751334,0.02233751,0.0002330002,0.0004026674,0.001431139],"study_design_scores_gemma":[0.001556226,0.00002881635,0.000007876354,0.00002749291,0.00002470144,0.000008295506,0.00008432125,0.991347,0.006033371,0.00001027501,0.0006247804,0.0002468017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00514972,0.00002494731,0.9920329,0.0000427741,0.0002413877,0.0007534426,0.000003062895,0.001670548,0.00008123107],"genre_scores_gemma":[0.9492594,0.000003899236,0.04970451,0.00001974699,0.000236184,0.0004942862,0.0001948478,0.00007387564,0.00001324128],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9441097,"threshold_uncertainty_score":0.9699716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009611337962457792,"score_gpt":0.2609737076976339,"score_spread":0.2513623697351762,"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."}}