{"id":"W3158113070","doi":"10.1016/j.ijhydene.2021.04.044","title":"Seasonal design and multi-objective optimization of a novel biogas-fueled cogeneration application","year":2021,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Cogeneration; Environmental science; Organic Rankine cycle; Biogas; Refrigeration; Process engineering; Degree Rankine; Electricity; Electricity generation; Fossil fuel; Exergy; Waste management; Automotive engineering; Power (physics); Engineering; Mechanical engineering; Thermodynamics","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":[0.0002496137,0.0005737101,0.0005117608,0.0003488081,0.0004294307,0.0006014165,0.0005476307,0.0008206321,0.002111532],"category_scores_gemma":[0.0002625929,0.0003294089,0.0005270193,0.0003450398,0.0002348056,0.0002716414,0.0003471472,0.0004114714,0.0001760681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005096314,"about_ca_system_score_gemma":0.0008896683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005453005,"about_ca_topic_score_gemma":0.008029643,"domain_scores_codex":[0.9999297,0.00001476565,0.000002440659,0.00001603139,0.00001635107,0.00002080742],"domain_scores_gemma":[0.9999106,0.00003630032,0.00001116579,0.000004317737,0.00002367966,0.00001388159],"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":[0.0001111463,0.0001039213,0.0003653013,0.0000590436,0.00001545144,0.00005890424,0.00002017722,0.9784721,0.00989634,0.0004548549,0.000204364,0.01023845],"study_design_scores_gemma":[0.00001374845,0.0001222916,0.0003261775,0.000001624598,0.000008798464,0.000005080887,0.00001175207,0.9974186,0.001806615,0.00007267488,0.0002098278,0.00000281251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.81854,0.0003063694,0.1667927,0.0002490182,0.0000948512,0.0001581889,0.0001901118,0.0002514984,0.01341731],"genre_scores_gemma":[0.979587,0.00006749297,0.01747818,0.00002240337,0.000006728445,0.00008820709,0.00006321986,0.00001924037,0.002667514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005453005,"threshold_uncertainty_score":0.0108425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009723559771283164,"score_gpt":0.2296928057381575,"score_spread":0.2199692459668743,"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."}}