{"id":"W4383172212","doi":"10.1016/j.ref.2023.06.008","title":"A new analysis for a concentrated solar power-based cogeneration system with molten salt energy storage and heat recovery steam generator – Case study – (USA, France, Canada)","year":2023,"lang":"en","type":"article","venue":"Renewable energy focus","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; Yeungnam University","keywords":"Exergy; Exergy efficiency; Engineering; Solar energy; Environmental science; Steam turbine; Renewable energy; Thermal energy storage; Process engineering; Solar power; Cogeneration; Automotive engineering; Electricity generation; Power (physics); Mechanical engineering; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001787162,0.0003904208,0.00069669,0.000302474,0.0002769007,0.0001107224,0.0001427417,0.0001362985,0.00001982679],"category_scores_gemma":[0.000005542663,0.0003532073,0.0001359107,0.0011991,0.00001882039,0.0001009687,0.00001832456,0.0000646037,8.16423e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002792687,"about_ca_system_score_gemma":0.0005088108,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8062353,"about_ca_topic_score_gemma":0.8458188,"domain_scores_codex":[0.9980682,0.00009092461,0.0004522791,0.0004988043,0.0003547161,0.000535079],"domain_scores_gemma":[0.998984,0.00007855388,0.00008056489,0.0004178452,0.0001255247,0.0003135031],"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":[0.00007567302,0.00002494048,0.0006875203,0.00005083101,0.001605926,0.000721734,0.00008619037,0.9922032,0.00106665,0.00007234899,0.002678384,0.000726603],"study_design_scores_gemma":[0.001590456,0.0003136298,0.00006031405,0.00005046938,0.0007577192,0.00008292103,0.001504749,0.989489,0.00184699,0.000004811195,0.003765642,0.000533306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3232245,0.002067792,0.6733653,0.00001269355,0.0004194131,0.0002038309,0.0001204894,0.0002905944,0.000295459],"genre_scores_gemma":[0.9970218,0.00004666023,0.0001790666,0.00003138806,0.0001755637,0.0001478527,0.0001359205,0.00008339182,0.002178357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6737974,"threshold_uncertainty_score":0.999892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005218440287194271,"score_gpt":0.1845495659067123,"score_spread":0.179331125619518,"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."}}