{"id":"W2384196355","doi":"","title":"Margin optimal design of heat exchanger network with bypasses based on life cycle energy saving","year":2012,"lang":"en","type":"article","venue":"Huagong xuebao","topic":"Process Optimization and Integration","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Heat exchanger; Refinery; Margin (machine learning); Distillation; Energy consumption; Fouling; Process engineering; Engineering; Energy conservation; Energy (signal processing); Computer science; Waste management; Mathematics; Mechanical engineering; Chemistry","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":[0.0001284458,0.0001489332,0.0001593845,0.00008193585,0.00004939738,0.00002714795,0.00008005753,0.00007027284,0.0002659836],"category_scores_gemma":[0.00002417051,0.0001271306,0.00002806479,0.0002287457,0.00002026393,0.0002692637,0.000007884804,0.00007941055,0.00001013845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003794487,"about_ca_system_score_gemma":0.00002596419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001807651,"about_ca_topic_score_gemma":0.000007103717,"domain_scores_codex":[0.9992642,0.00003353732,0.0001602584,0.0001038619,0.0001548393,0.0002832747],"domain_scores_gemma":[0.9995936,0.00006853801,0.00002953393,0.0001542824,0.00004861471,0.0001054721],"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":[0.00005420651,0.00003242122,0.0002812445,0.00003240197,0.00001612473,4.829992e-7,0.00008782675,0.9952109,0.0004490312,0.0003105575,0.002910318,0.0006144968],"study_design_scores_gemma":[0.0002805925,0.00008700311,0.000503514,0.0001081151,0.00001556364,0.000001270364,0.00003727062,0.9907332,0.006582984,0.000005890734,0.001470797,0.0001738227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00642928,0.0007490003,0.9852192,0.00004895208,0.0001959358,0.0001000285,0.000002502649,0.0002147578,0.007040364],"genre_scores_gemma":[0.9739449,0.00004554116,0.02532704,0.0002279126,0.0002172349,0.0000372059,0.00001910848,0.00004273913,0.0001382979],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9675156,"threshold_uncertainty_score":0.5184236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465986946843475,"score_gpt":0.2042292955169925,"score_spread":0.1895694260485577,"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."}}