{"id":"W2207176483","doi":"10.1016/j.energy.2015.12.008","title":"Energy and exergy analyses of a novel power cycle using the cold of LNG (liquefied natural gas) and low-temperature solar energy","year":2016,"lang":"en","type":"article","venue":"Energy","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Liquefied natural gas; Exergy; Organic Rankine cycle; Cogeneration; Rankine cycle; Working fluid; Exergy efficiency; Regenerative heat exchanger; Nuclear engineering; Solar energy; Environmental science; Process engineering; Combined cycle; Energy recovery; Natural gas; Vaporization; Waste management; Turbine; Engineering; Electricity generation; Heat exchanger; Mechanical engineering; Chemistry; Thermodynamics; Waste heat; Power (physics); Energy (signal processing); 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":[0.00009364171,0.0002398807,0.0004281426,0.0001188818,0.00007177985,0.00002177924,0.0001714627,0.0001442824,0.00001692824],"category_scores_gemma":[0.00001115036,0.0001451644,0.0001193232,0.0001813039,0.0001453957,0.0001052156,0.00006336044,0.0000593834,7.669458e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002450175,"about_ca_system_score_gemma":0.00002594098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00166341,"about_ca_topic_score_gemma":0.0002639964,"domain_scores_codex":[0.9989197,0.00004345628,0.0003692849,0.00022252,0.0001837359,0.0002612878],"domain_scores_gemma":[0.9993481,0.00009833723,0.0001084197,0.0002883501,0.00007839603,0.00007842144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003453654,0.0000262702,0.00002060879,0.00003173286,0.0002562885,0.000002896241,0.0001305203,0.005209073,0.9645602,0.02805718,0.00009589686,0.001574778],"study_design_scores_gemma":[0.002231724,0.0001410621,0.0002891399,0.0007895086,0.0003129752,0.0001007343,0.0007256279,0.2170043,0.769573,0.0008845228,0.007004133,0.0009433632],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9284502,0.0109751,0.05930459,0.00005074315,0.0003675863,0.00002714405,0.00003058917,0.00006506042,0.0007289307],"genre_scores_gemma":[0.9984785,0.0008168821,0.0001345115,0.00005339314,0.000082618,0.000005220059,0.00000410714,0.0000377518,0.0003870453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2117952,"threshold_uncertainty_score":0.5919635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007030579567355836,"score_gpt":0.2185029439792943,"score_spread":0.2114723644119385,"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."}}