{"id":"W2085602695","doi":"10.1002/htj.21135","title":"Exergoeconomic Based Optimization of a Gas Fired Steam Power Plant Using Genetic Algorithm","year":2014,"lang":"en","type":"article","venue":"Heat Transfer-Asian Research","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Boiler (water heating); Exergy; Process engineering; Power station; Steam-electric power station; Genetic algorithm; Engineering; Waste management; Environmental science; Mathematical optimization; Mechanical engineering; Mathematics; Combined cycle; Gas turbines","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":[0.0004620115,0.0004859306,0.0005873733,0.0006632025,0.0002964533,0.0006838838,0.0004845929,0.0008007972,0.001477723],"category_scores_gemma":[0.000784989,0.0003498843,0.0004831098,0.0005645025,0.0004786806,0.0003276237,0.0003090571,0.0004162955,0.0001188138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000953952,"about_ca_system_score_gemma":0.001324562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008834436,"about_ca_topic_score_gemma":0.00791058,"domain_scores_codex":[0.999908,0.00003812551,0.000002689723,0.0000107855,0.00002261972,0.00001770596],"domain_scores_gemma":[0.9998117,0.0001273865,0.00001734485,0.000005481862,0.00002809436,0.000009940873],"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":[0.00001104151,0.000008304669,0.0001227503,0.000008243263,0.000004814531,0.00001092682,0.000003078726,0.997626,0.0002099201,0.0004662039,0.00002945002,0.001499301],"study_design_scores_gemma":[0.000006549719,0.00001578661,0.00008912839,0.000001702898,0.000002697697,0.000002393909,0.000003902707,0.9993748,0.0001273135,0.0003006127,0.00007414096,0.000001090273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4654129,0.0006743592,0.5079021,0.0004388282,0.00006099999,0.0002535063,0.0001985049,0.0003018939,0.02475699],"genre_scores_gemma":[0.9595931,0.0001719216,0.03712787,0.00003359992,0.000008352075,0.0001613336,0.00008580212,0.00002260448,0.002795392],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008834436,"threshold_uncertainty_score":0.01756603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02094361381388558,"score_gpt":0.2587375920312284,"score_spread":0.2377939782173428,"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."}}