{"id":"W3092187641","doi":"10.3390/pr8101277","title":"Thermodynamic Optimization of a Geothermal Power Plant with a Genetic Algorithm in Two Stages","year":2020,"lang":"en","type":"article","venue":"Processes","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Organic Rankine cycle; Geothermal gradient; Geothermal energy; Renewable energy; Geothermal power; Environmental science; Mass flow rate; Electricity generation; Heat exchanger; Process engineering; Power station; Petroleum engineering; Power (physics); Engineering; Geology; Mechanics; Thermodynamics; Mechanical engineering; Physics; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002782053,0.0001235568,0.0001963679,0.00004816003,0.00001321497,0.00001291215,0.000119005,0.00002581437,0.00005827008],"category_scores_gemma":[0.000006787714,0.00009893714,0.00002053628,0.0002413014,0.00002552441,0.00006215188,0.00000968743,0.0000637384,0.00000344549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001215294,"about_ca_system_score_gemma":0.00003558067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006096991,"about_ca_topic_score_gemma":0.00002576958,"domain_scores_codex":[0.999405,0.000009637994,0.0001891834,0.0001282824,0.0001161657,0.0001517647],"domain_scores_gemma":[0.9997864,0.00001606858,0.00003844564,0.00007578116,0.0000390119,0.00004433217],"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":[0.00003206762,0.00002160258,0.001261242,0.0005385959,0.00004928632,0.00001235556,0.001657889,0.9945901,0.0002964719,0.00001390935,0.000008089642,0.001518408],"study_design_scores_gemma":[0.0004341669,0.00006461325,0.0005152962,0.0000981169,0.00001991569,0.000006777498,0.0004194965,0.9979795,0.0002344906,0.00002020628,0.00004752981,0.0001598574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2240345,0.002933456,0.7711363,0.000034719,0.00004022916,0.0001481275,0.00004138917,0.0001495486,0.001481661],"genre_scores_gemma":[0.9959762,0.0001872021,0.003722615,0.00002410143,0.00002416282,0.0000139653,0.00001035249,0.0000296909,0.00001175522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7719417,"threshold_uncertainty_score":0.4034539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005239958233575949,"score_gpt":0.2004872367421153,"score_spread":0.1952472785085393,"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."}}