{"id":"W4387190718","doi":"10.1016/j.tsep.2023.102161","title":"A Cogeneration-Coupled energy storage system utilizing hydrogen and methane-fueled CAES and ORC with ambient temperature consideration enhanced by artificial neural Network, and Multi-Objective optimization","year":2023,"lang":"en","type":"article","venue":"Thermal Science and Engineering Progress","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning","keywords":"Process engineering; Organic Rankine cycle; Environmental science; Greenhouse gas; Waste management; Fossil fuel; Exergy; Engineering; Waste heat; Mechanical engineering; Heat exchanger","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":[0.0002845011,0.0005552748,0.001218896,0.000283605,0.001376333,0.0006475991,0.00128086,0.0006250365,0.002461098],"category_scores_gemma":[0.0001167144,0.000297018,0.0004812085,0.0004829496,0.0004551835,0.0006783045,0.0005568088,0.0003732823,0.0002886037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005630141,"about_ca_system_score_gemma":0.001061554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007269092,"about_ca_topic_score_gemma":0.01429045,"domain_scores_codex":[0.9998821,0.00001694506,0.00001220986,0.00003642752,0.0000321634,0.00002013485],"domain_scores_gemma":[0.9999008,0.00002523736,0.00001216178,0.000009217048,0.00003356426,0.00001892908],"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":[0.003978875,0.000949331,0.01078445,0.001836288,0.000526984,0.002380826,0.0003426689,0.6187637,0.2543599,0.004405061,0.005081159,0.09659068],"study_design_scores_gemma":[0.0002045258,0.0006598317,0.002328055,0.00002016086,0.0001527894,0.0001153154,0.00007521629,0.9659033,0.02860055,0.0005801856,0.001313637,0.00004622686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9344811,0.0006589436,0.05486865,0.0003442742,0.0002143488,0.0001756776,0.000356428,0.001054762,0.007845765],"genre_scores_gemma":[0.9949628,0.0000719503,0.003030607,0.00003397046,0.00001168346,0.00003158228,0.00006506171,0.000007482914,0.001784855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007269092,"threshold_uncertainty_score":0.01445353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00702757930575296,"score_gpt":0.2060950107235862,"score_spread":0.1990674314178332,"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."}}