{"id":"W3047853645","doi":"10.1016/j.ijhydene.2020.06.262","title":"Multi-objective optimization of an experimental integrated thermochemical cycle of hydrogen production with an artificial neural network","year":2020,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Chemical Looping and Thermochemical Processes","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland; Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Hydrogen production; Sensitivity (control systems); Genetic algorithm; Exergy; Computer science; Process engineering; Range (aeronautics); Exergy efficiency; Biological system; Hydrogen; Materials science; Chemistry; Engineering; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006572893,0.0008854834,0.0009266902,0.0004463769,0.0006439488,0.000890968,0.0006337877,0.001150217,0.002164982],"category_scores_gemma":[0.0007507535,0.0004833908,0.0006083852,0.0003795468,0.0006277232,0.0005776029,0.0004421139,0.0007736064,0.0001151427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001272161,"about_ca_system_score_gemma":0.001011871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01089862,"about_ca_topic_score_gemma":0.009437533,"domain_scores_codex":[0.9998491,0.0000443122,0.000008307187,0.00003505057,0.0000353521,0.00002777539],"domain_scores_gemma":[0.9995942,0.0002580386,0.00003496705,0.00002100336,0.00006921304,0.00002272144],"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":[0.0002769928,0.0001517158,0.0005232753,0.00009198258,0.00003630684,0.00004502835,0.0000148609,0.9881495,0.006414775,0.0003328965,0.0000797044,0.003882936],"study_design_scores_gemma":[0.00002687311,0.0001475931,0.0003499846,0.000001934238,0.00001157204,0.000002789916,0.000009055972,0.9952005,0.004109809,0.00007173178,0.00006268179,0.000005423547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688522,0.0003063876,0.0253,0.0002066322,0.00006559608,0.00007880061,0.000184099,0.0001287888,0.004877533],"genre_scores_gemma":[0.9958901,0.00004093474,0.003256371,0.00001050153,0.000003162279,0.00004877755,0.00004218472,0.00000731813,0.0007007284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01089862,"threshold_uncertainty_score":0.02167034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124857971561438,"score_gpt":0.2351907613584558,"score_spread":0.222704964202312,"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."}}