{"id":"W3181940128","doi":"10.1016/j.applthermaleng.2021.117339","title":"Multi-objective bat optimization for a biomass gasifier integrated energy system based on 4E analyses","year":2021,"lang":"en","type":"article","venue":"Applied Thermal Engineering","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wood gas generator; Process engineering; Exergy; Power station; Multi-objective optimization; Cost of electricity by source; Sensitivity (control systems); Engineering; Software; Renewable energy; Environmental science; Electricity generation; Computer science; Power (physics); Waste management; Mathematical optimization; Mathematics; Coal","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":[0.0006599202,0.0008434858,0.001424438,0.0005698055,0.0007989781,0.001363931,0.0005247726,0.001379914,0.003644704],"category_scores_gemma":[0.000568574,0.0005757827,0.0009681931,0.000513514,0.0004220254,0.0005575763,0.0006526569,0.0009296038,0.0002918477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008609892,"about_ca_system_score_gemma":0.001333266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01365888,"about_ca_topic_score_gemma":0.01317227,"domain_scores_codex":[0.9998043,0.00006477452,0.000006893722,0.00002616561,0.00005405073,0.00004382416],"domain_scores_gemma":[0.999757,0.0001380437,0.00002098888,0.000007605875,0.00006112498,0.00001525645],"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":[0.00003100019,0.00001558517,0.000138859,0.00002688741,0.00001713442,0.00002189384,0.000007277587,0.9964588,0.0008596358,0.000327364,0.00006601666,0.002029578],"study_design_scores_gemma":[0.0000063199,0.00002996309,0.0001457327,0.000002681217,0.000007446534,0.000003321586,0.000008062934,0.9992873,0.0002598066,0.0001289624,0.0001180905,0.000002325973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5664774,0.001405245,0.3886457,0.0006354036,0.0001460119,0.000274949,0.0004538951,0.0005959159,0.04136544],"genre_scores_gemma":[0.9744179,0.0001486622,0.01863485,0.00004923569,0.00001499614,0.0001735031,0.0001073504,0.00004258735,0.006410867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01365888,"threshold_uncertainty_score":0.02715874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01109790537607298,"score_gpt":0.2174571490180869,"score_spread":0.2063592436420139,"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."}}