{"id":"W4282597795","doi":"10.1016/j.compchemeng.2022.107892","title":"Robust simulation-optimization framework for synthesis and design of natural gas downstream Incorporating renewable hydrogen network under uncertainty","year":2022,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Catalysts for Methane Reforming","field":"Chemical Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Process engineering; Natural gas; Renewable energy; Syngas; Hydrogen production; Raw material; Renewable natural gas; Engineering; Environmental science; Waste management; Hydrogen; Chemistry; Fuel gas","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":[0.00149332,0.001377085,0.001810377,0.0006442647,0.0006864841,0.001383101,0.001421641,0.001934579,0.003597873],"category_scores_gemma":[0.002551544,0.001091426,0.001339086,0.0005012534,0.001257724,0.0008096363,0.00169365,0.001419359,0.0004177009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001547986,"about_ca_system_score_gemma":0.002858905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01785881,"about_ca_topic_score_gemma":0.009371663,"domain_scores_codex":[0.9995183,0.0001728136,0.00001793345,0.00008659385,0.0001312608,0.00007299698],"domain_scores_gemma":[0.9990358,0.0005921165,0.000103088,0.00003704885,0.0001837809,0.00004811305],"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":[0.00000930803,0.000004885468,0.00002490378,0.000009403875,0.000006357642,0.000007026143,0.000003640751,0.9975357,0.0001461271,0.001639274,0.00004201005,0.0005713336],"study_design_scores_gemma":[0.000004160848,0.000005319457,0.000008522611,0.000001244065,0.000001944388,8.23795e-7,9.694252e-7,0.9993136,0.0000678208,0.000518027,0.00007649195,0.000001193764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0122515,0.0002841465,0.9786934,0.0003004272,0.00006496835,0.00008414636,0.0001201952,0.000297156,0.007904047],"genre_scores_gemma":[0.9039441,0.0003041039,0.0883069,0.0001709032,0.0000712633,0.0003826206,0.0002448713,0.0001417643,0.00643366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01785881,"threshold_uncertainty_score":0.03550971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01731939353432499,"score_gpt":0.2152483046587362,"score_spread":0.1979289111244112,"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."}}