{"id":"W4317668796","doi":"10.1016/j.apenergy.2022.120633","title":"Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Hyperparameter; Scheduling (production processes); Reinforcement learning; Schedule; Computer science; Process engineering; Engineering; Real-time computing; Simulation; Artificial intelligence; Operations 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":[0.0006473952,0.000857626,0.0007822064,0.0003505681,0.0003247912,0.0006052543,0.0008261695,0.0008182715,0.003064974],"category_scores_gemma":[0.001597621,0.0003946979,0.0003493876,0.0002056567,0.0003499627,0.0005491679,0.0005879494,0.001060709,0.0002719635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009672992,"about_ca_system_score_gemma":0.001666405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01797851,"about_ca_topic_score_gemma":0.01932917,"domain_scores_codex":[0.9997646,0.00003843709,0.00001029134,0.00006605204,0.00004446063,0.0000762149],"domain_scores_gemma":[0.9993948,0.0003126662,0.00006003048,0.00002787845,0.0001485467,0.00005604502],"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":[0.000122807,0.0000878049,0.00118181,0.00004182054,0.0000261569,0.00005611459,0.00002587451,0.9593711,0.001400838,0.001164994,0.0008753243,0.03564531],"study_design_scores_gemma":[0.000003467197,0.000008793915,0.00005902253,9.37152e-7,0.00000176582,0.000001900581,0.000001487133,0.9996038,0.0001055746,0.0001847219,0.00002774283,8.34848e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2406681,0.0008384834,0.7466789,0.0006843567,0.000194894,0.0001038967,0.0002014165,0.001934435,0.008695478],"genre_scores_gemma":[0.9875147,0.00003429408,0.01118571,0.0000628571,0.00001291841,0.00002869542,0.00005790657,0.00001954806,0.00108326],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01797851,"threshold_uncertainty_score":0.03574777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00641186629434925,"score_gpt":0.1885714595783966,"score_spread":0.1821595932840473,"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."}}