{"id":"W4381548879","doi":"10.1016/j.enconman.2023.117320","title":"Comparative energy, exergy, economic, and environmental (4E) analysis and optimization of two high-temperature Kalina cycles integrated with thermoelectric generators for waste heat recovery from a diesel engine","year":2023,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Exergy; Thermoelectric generator; Waste heat recovery unit; Process engineering; Waste heat; Exergy efficiency; Electricity generation; Waste management; Diesel fuel; Thermoelectric effect; Environmental science; Engineering; Nuclear engineering; Power (physics); Thermodynamics; Mechanical engineering; 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":[0.00005222134,0.0001817791,0.0003358317,0.0002625577,0.00007639791,0.00003168083,0.00005164402,0.0000502929,0.00002241191],"category_scores_gemma":[3.531794e-7,0.0001514034,0.0000459727,0.0001991191,0.00004331789,0.00007090569,0.0000397172,0.00003107465,3.264669e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004202106,"about_ca_system_score_gemma":0.00000414234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007413236,"about_ca_topic_score_gemma":0.0001569704,"domain_scores_codex":[0.999352,0.00002459237,0.0001745605,0.0002410943,0.00006817321,0.0001395226],"domain_scores_gemma":[0.9997518,0.00003384964,0.00003841653,0.0001065182,0.000008727534,0.00006069561],"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":[0.0001371863,0.00001625577,0.0001393117,0.00004635576,0.00226757,0.000003665612,0.000222221,0.9893477,0.002358922,0.001026294,0.0005244482,0.003910072],"study_design_scores_gemma":[0.001034036,0.00008358664,0.0002709441,0.00002974519,0.0005760011,5.992671e-7,0.001551455,0.9890046,0.006723064,0.00006623874,0.0004398576,0.0002198395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8928853,0.001240939,0.1053888,0.00002462501,0.0001116697,0.00009831245,0.0001092022,0.00006854895,0.00007258547],"genre_scores_gemma":[0.9909219,0.007355981,0.0006895895,0.00002132321,0.00002856415,0.00002640641,0.0006824902,0.00001759438,0.0002561446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1046992,"threshold_uncertainty_score":0.6174051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003500048451181619,"score_gpt":0.1776176007873855,"score_spread":0.1741175523362039,"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."}}