{"id":"W4281387511","doi":"10.1016/j.energy.2022.124288","title":"Intensification of steam reforming process for off-gas upgrading and energy optimization using evolutionary algorithm","year":2022,"lang":"en","type":"article","venue":"Energy","topic":"Catalysts for Methane Reforming","field":"Chemical Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Syngas; Exergy efficiency; Exergy; Pinch analysis; Process engineering; Natural gas; Coal; Environmental science; Process integration; Coal gasification; Chemistry; Waste management; Engineering; Hydrogen","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":[0.0002248752,0.0003929435,0.0005206319,0.0003675971,0.0002947508,0.0004666345,0.0004389265,0.0004430675,0.00181288],"category_scores_gemma":[0.0002513715,0.0002179368,0.0005386526,0.0003035569,0.0001667793,0.0003358721,0.0003335639,0.0003423924,0.0001272123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002674137,"about_ca_system_score_gemma":0.0003264153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001176502,"about_ca_topic_score_gemma":0.001295792,"domain_scores_codex":[0.9999249,0.00001422786,0.000003744777,0.00001400446,0.00002811206,0.00001507344],"domain_scores_gemma":[0.999952,0.00001934576,0.000006186119,0.000005735188,0.00001354288,0.000003160989],"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":[0.0002766481,0.0003665948,0.001683956,0.0002848955,0.00006757709,0.0001392817,0.00006164535,0.8115829,0.07762524,0.007169622,0.0006583768,0.1000831],"study_design_scores_gemma":[0.00002643054,0.0001438295,0.0005599494,0.000004853781,0.00002219285,0.0000190471,0.00001094791,0.9852757,0.0125671,0.0004037411,0.0009613834,0.000004776252],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6638769,0.00100748,0.3067693,0.000201863,0.0001136213,0.0001505949,0.00007252922,0.0003542323,0.02745353],"genre_scores_gemma":[0.9558105,0.0002602025,0.04064744,0.00002388439,0.0000134932,0.0000781781,0.00006050186,0.00002606231,0.003079687],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00181288,"threshold_uncertainty_score":0.006064653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668409188212993,"score_gpt":0.2471493699500045,"score_spread":0.2304652780678746,"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."}}