{"id":"W3044544879","doi":"10.1016/j.enconman.2020.113166","title":"Sustainable energy design of cruise ships through dynamic simulations: Multi-objective optimization for waste heat recovery","year":2020,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"TRNSYS; Cruise; Engineering; Energy consumption; Payback period; Liquefied natural gas; Waste heat; Waste heat recovery unit; Marine engineering; Automotive engineering; Energy (signal processing); Natural gas; Waste management; Mechanical engineering; Heat exchanger; Production (economics)","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":[0.0007757919,0.0009870605,0.001308397,0.0008825277,0.0008915479,0.001544934,0.0009684717,0.002192605,0.003498011],"category_scores_gemma":[0.002266627,0.001011383,0.001649872,0.0006953626,0.0009607244,0.0009686993,0.00111974,0.001331383,0.0002695128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001332378,"about_ca_system_score_gemma":0.002160542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03892862,"about_ca_topic_score_gemma":0.02300682,"domain_scores_codex":[0.9997825,0.00009394521,0.000007145541,0.0000265151,0.00003783705,0.00005209656],"domain_scores_gemma":[0.9992576,0.0005131103,0.00005210885,0.00003265407,0.00008973163,0.00005485504],"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":[0.000009434453,0.000007423551,0.00006158018,0.00000487656,0.000004470815,0.000006631488,0.000003686402,0.9992869,0.00005465607,0.0002166,0.00002947329,0.0003143003],"study_design_scores_gemma":[0.000005651416,0.000008362963,0.00003850376,0.00000159648,0.000002259563,8.034207e-7,0.000006012912,0.9996448,0.00005542158,0.0001764064,0.00005856986,0.000001571161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6607029,0.000800074,0.2901239,0.001457901,0.0001793313,0.0002743577,0.000757246,0.0004561189,0.04524807],"genre_scores_gemma":[0.9739017,0.0002080738,0.01963341,0.0000896444,0.00002388196,0.0002090183,0.0002522134,0.0000881255,0.005593836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03892862,"threshold_uncertainty_score":0.07740408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465377391584037,"score_gpt":0.2160461285674315,"score_spread":0.2013923546515911,"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."}}