{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003365727,0.0005095027,0.0005314519,0.0003055971,0.0002495112,0.0005845958,0.0005000386,0.0003678269,0.001253141],"category_scores_gemma":[0.0004371089,0.0003265759,0.0003331638,0.0002277306,0.0003671,0.0006359057,0.0003513294,0.0002907889,0.0001042081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005268247,"about_ca_system_score_gemma":0.0008198559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001885688,"about_ca_topic_score_gemma":0.001800286,"domain_scores_codex":[0.9998482,0.000044489,0.000006269513,0.00004197321,0.00003279496,0.00002620426],"domain_scores_gemma":[0.9998945,0.00003453062,0.00002676215,0.000006198413,0.00002758095,0.00001035095],"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":[0.0001659551,0.00003007456,0.0005484608,0.00009253481,0.00002185975,0.00004301935,0.00004866013,0.9537256,0.02357012,0.005734502,0.0002562047,0.01576309],"study_design_scores_gemma":[0.00001443978,0.0001005527,0.0002127684,0.000003743453,0.00001196375,0.000009423435,0.00001280943,0.9952815,0.002642588,0.001160403,0.0005438383,0.000006013756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.131472,0.0003879907,0.8620254,0.00008309502,0.00002956606,0.00006283873,0.00004131398,0.0002119018,0.005685941],"genre_scores_gemma":[0.9666075,0.0001511841,0.03159316,0.00001404474,0.000007140054,0.0000801286,0.00003089698,0.00001861975,0.001497359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001885688,"threshold_uncertainty_score":0.004192114,"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."}}