{"id":"W4229025714","doi":"10.14447/jnmes.v25i1.a10","title":"Improve Microbial Fuel Cell Efficiency Using Receding Horizon Predictive Control","year":2022,"lang":"en","type":"article","venue":"Journal of New Materials for Electrochemical Systems","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Government of Jiangsu Province","keywords":"Microbial fuel cell; Model predictive control; MATLAB; Control theory (sociology); Stability (learning theory); Renewable energy; Lyapunov function; Computer science; Population; Control (management); Horizon; Control engineering; Engineering; Mathematics; Electricity generation; Artificial intelligence; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000324417,0.0004882086,0.0003796099,0.0002578899,0.0002273248,0.0006320568,0.0006013119,0.000410325,0.001017365],"category_scores_gemma":[0.0006567089,0.000167608,0.0002595207,0.0002280592,0.0001857694,0.0005054857,0.0003131907,0.000461272,0.000227149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000332565,"about_ca_system_score_gemma":0.0003707733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003539145,"about_ca_topic_score_gemma":0.004249925,"domain_scores_codex":[0.9998393,0.0000229395,0.000007871509,0.00003485955,0.00007363708,0.00002137104],"domain_scores_gemma":[0.9998375,0.00006053815,0.00002477059,0.00001595575,0.00005383507,0.000007266749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000207459,0.0003019916,0.0007806548,0.0002610261,0.00005017823,0.0001416535,0.00008394405,0.7142289,0.07679046,0.00306364,0.0009631382,0.203127],"study_design_scores_gemma":[0.00001227341,0.0001004383,0.0001604511,0.000006478506,0.0000114192,0.00001211508,0.000006005071,0.9870761,0.0115852,0.0004115378,0.0006118575,0.000006161839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1441089,0.001498319,0.8409518,0.0002986236,0.0001436482,0.00008928199,0.00006162223,0.001261636,0.01158613],"genre_scores_gemma":[0.9692192,0.0003229929,0.02884561,0.000041338,0.00001438143,0.00003182579,0.00003336574,0.00001654403,0.00147476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003539145,"threshold_uncertainty_score":0.007037103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007238674694247021,"score_gpt":0.2062699514154524,"score_spread":0.1990312767212054,"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."}}