{"id":"W4410631340","doi":"10.1016/j.apenergy.2025.126124","title":"Energy management for modular proton exchange membrane water electrolyzers under fluctuating solar inputs: a constrained nonlinear optimization approach","year":2025,"lang":"en","type":"article","venue":"Applied Energy","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modular design; Nonlinear system; Energy exchange; Proton exchange membrane fuel cell; Solar energy; Energy (signal processing); Computer science; Membrane; Control theory (sociology); Control engineering; Mathematical optimization; Process engineering; Engineering; Chemistry; Physics; Mathematics; Electrical engineering; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004849609,0.000677507,0.0007418871,0.0005929605,0.0003911297,0.0001506653,0.0005614719,0.0003694214,0.00009798171],"category_scores_gemma":[0.00001111429,0.0005942268,0.0002310777,0.0006673523,0.000122554,0.000159216,0.0002245815,0.000148921,0.000005116229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003485967,"about_ca_system_score_gemma":0.00009709808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001618497,"about_ca_topic_score_gemma":0.0002092058,"domain_scores_codex":[0.9961213,0.0001428899,0.0008932466,0.00116755,0.0004794007,0.001195569],"domain_scores_gemma":[0.9985402,0.00007898453,0.0002033757,0.0008594595,0.0001495124,0.0001684316],"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":[0.0001739485,0.0001871416,6.93678e-7,0.0004950502,0.0006057691,0.000005401259,0.0001018817,0.8102525,0.03389317,0.143306,0.0001736562,0.01080486],"study_design_scores_gemma":[0.002660855,0.00005660518,3.069765e-7,0.00005330005,0.0001384806,0.000006942699,0.0001370628,0.5687603,0.325808,0.001524292,0.1002632,0.0005906193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001250237,0.000169282,0.9019032,0.0001828153,0.0003282999,0.0009644256,0.00001132155,0.00049654,0.09469386],"genre_scores_gemma":[0.8538519,0.0003024036,0.1113816,0.002614205,0.0007852352,0.01172006,0.002875544,0.0003348152,0.0161343],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8526016,"threshold_uncertainty_score":0.9996509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008035328423706977,"score_gpt":0.2094387829162304,"score_spread":0.2014034544925234,"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."}}