{"id":"W4414400225","doi":"10.1016/j.compchemeng.2025.109405","title":"Reinforcement learning-based autonomous control of bench-scale primary separation vessel","year":2025,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta","keywords":"Reinforcement learning; Process (computing); Automation; Controller (irrigation); Component (thermodynamics); Model predictive control; Key (lock); Process control; Control (management); Control system","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":[0.000386265,0.000423492,0.0003412985,0.0001055573,0.0002193513,0.0003583929,0.0005469788,0.0003072019,0.0007886363],"category_scores_gemma":[0.0006767155,0.0001333405,0.0001987246,0.00007402377,0.0005438282,0.0001958477,0.0004371404,0.0005364151,0.0001196287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003801863,"about_ca_system_score_gemma":0.0006669311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004387712,"about_ca_topic_score_gemma":0.002840471,"domain_scores_codex":[0.9998492,0.00002671598,0.000005339018,0.00004323621,0.00004905467,0.00002630803],"domain_scores_gemma":[0.999713,0.0001142803,0.00005632477,0.00001926173,0.00006974775,0.00002729536],"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":[0.0001736715,0.0001305846,0.0006089519,0.00009201333,0.00002116751,0.0001336463,0.00006502714,0.9274378,0.03667003,0.002030477,0.0005129866,0.03212377],"study_design_scores_gemma":[0.000008569597,0.0000864414,0.0001152811,0.000001413068,0.000002289813,0.000005440227,0.000002347689,0.9967733,0.002543627,0.000244416,0.0002138533,0.0000029279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.263471,0.0002706423,0.7288396,0.0002446995,0.0001306856,0.0001144272,0.0000418224,0.001008611,0.005878604],"genre_scores_gemma":[0.9914915,0.00002358329,0.007813486,0.00001435363,0.000005155593,0.00002119504,0.00001168692,0.000005242968,0.0006136793],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004387712,"threshold_uncertainty_score":0.008724332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002631237959786357,"score_gpt":0.1920844970129055,"score_spread":0.1894532590531192,"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."}}