{"id":"W7106823850","doi":"10.6047/j.issn.1000-8241.2025.11.010","title":"Design and optimization of integrated membrane separation for natural gas decarbonization and light hydrocarbon recovery from LNG","year":2025,"lang":"zh","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Membrane Separation and Gas Transport","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Petro-Canada","funders":"","keywords":"Natural gas; Volume (thermodynamics); Fraction (chemistry); Volume fraction; Methane; Energy consumption; Hydrocarbon; Energy recovery; Process (computing)","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":[0.0006585781,0.0008388998,0.0009752162,0.0006077781,0.0004890602,0.001111498,0.0007432799,0.00100616,0.001390887],"category_scores_gemma":[0.0004671931,0.0005219695,0.00105113,0.0005033684,0.0003450617,0.0003846082,0.0006214763,0.0005497684,0.0002257286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239417,"about_ca_system_score_gemma":0.001650233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004883816,"about_ca_topic_score_gemma":0.006096987,"domain_scores_codex":[0.9996421,0.00004795529,0.0000156185,0.00008122692,0.0001357907,0.0000772428],"domain_scores_gemma":[0.999858,0.00004150907,0.00003530194,0.000008993538,0.0000426003,0.00001366943],"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":[0.0001991131,0.0002501302,0.0009198533,0.0003815055,0.00006266663,0.0001561742,0.00003644716,0.9061751,0.07310105,0.00110113,0.0001927762,0.01742393],"study_design_scores_gemma":[0.00003303609,0.0004535071,0.0007445166,0.00001029552,0.00004274759,0.0000164854,0.00002653785,0.9789251,0.0186237,0.0002181713,0.000893959,0.000011952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6556985,0.001826363,0.3256089,0.0002978299,0.0000974073,0.0005561807,0.000329349,0.0006317901,0.0149536],"genre_scores_gemma":[0.9425772,0.0004764666,0.05392932,0.00003262392,0.000005075168,0.0004137254,0.0001206653,0.00002456049,0.002420484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004883816,"threshold_uncertainty_score":0.009710789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07553129847898146,"score_gpt":0.4352482849128422,"score_spread":0.3597169864338607,"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."}}