{"id":"W4378549525","doi":"10.1016/j.renene.2023.05.113","title":"Techno-economic assessment of an efficient liquid air energy storage with ejector refrigeration cycle for peak shaving of renewable energies","year":2023,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Refrigeration; Renewable energy; Process engineering; Energy storage; Exergy efficiency; Electricity generation; Exergy; Environmental science; Payback period; Organic Rankine cycle; Waste management; Electricity; Liquid air; Waste heat; Engineering; Power (physics); Mechanical engineering; Electrical engineering; Production (economics); Chemistry; Thermodynamics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000244467,0.0002405818,0.0004743882,0.000300124,0.00009634728,0.00001927511,0.0002505308,0.0001289249,0.00002350492],"category_scores_gemma":[0.000005485463,0.0002218833,0.0001268013,0.0003024823,0.00004725976,0.0001137947,0.00003814174,0.00004029699,6.472489e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001636065,"about_ca_system_score_gemma":0.0001191943,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01911139,"about_ca_topic_score_gemma":0.002705404,"domain_scores_codex":[0.9986049,0.00003087496,0.0004905321,0.0003070512,0.0002059116,0.0003607325],"domain_scores_gemma":[0.9991622,0.00005993194,0.0001604098,0.0004565661,0.0000779605,0.00008293684],"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":[0.00005283417,0.00003452651,0.000009209391,0.0001072378,0.00009638821,0.00000149551,0.00006954831,0.8745463,0.123045,0.001660321,0.0001586331,0.0002184266],"study_design_scores_gemma":[0.0003684818,0.0003243923,0.00004521899,0.0001060496,0.00004714903,0.0000025586,0.0004127584,0.8754487,0.1214773,0.0001297837,0.001395386,0.0002422487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6903587,0.0003016078,0.3048615,0.000009769205,0.0003902848,0.00009330405,0.00006442039,0.0004209745,0.003499393],"genre_scores_gemma":[0.9968513,0.0001101944,0.001155202,0.000005485851,0.0001299089,0.000101411,0.0001648193,0.00007363834,0.001407998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3064927,"threshold_uncertainty_score":0.9874204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005338904396721029,"score_gpt":0.2230115819164918,"score_spread":0.2176726775197707,"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."}}