{"id":"W3085207866","doi":"10.1007/s40313-020-00640-6","title":"A Novel Exergy-Based Optimization Approach in Model Predictive Control for Energy-Saving Assessment","year":2020,"lang":"en","type":"article","venue":"Journal of Control Automation and Electrical Systems","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Model predictive control; Control (management); Exergy; Computer science; Energy (signal processing); Environmental science; Process engineering; Reliability engineering; Engineering; Mathematics; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004266117,0.0001964335,0.0006191341,0.0002322402,0.00005278971,0.00009330458,0.0001055491,0.0001444869,6.608912e-7],"category_scores_gemma":[0.0001299937,0.000178263,0.0001011217,0.0003079039,0.00001063749,0.0004031218,0.000003325098,0.0001810265,8.045191e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000296054,"about_ca_system_score_gemma":0.0001029028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003685941,"about_ca_topic_score_gemma":4.926616e-7,"domain_scores_codex":[0.9982108,0.00009232783,0.000960401,0.0001641461,0.0003241977,0.0002480824],"domain_scores_gemma":[0.9988007,0.0002343727,0.0004359074,0.00006966102,0.0002931169,0.0001662205],"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":[0.0002282726,0.00004879379,0.0001128067,0.0001033044,0.00008183224,8.621113e-7,0.00005639207,0.9933791,0.003834812,0.001259274,0.00004399196,0.0008506223],"study_design_scores_gemma":[0.009666118,0.0002645077,0.0001843168,0.00006879492,0.0000734816,0.00001397429,0.00002558765,0.9894733,0.0000122623,0.00002594532,0.0000304317,0.0001612893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002761765,0.0007988582,0.9976051,0.0002098605,0.0001181562,0.0007200715,0.00001919554,0.0001135863,0.0001389578],"genre_scores_gemma":[0.9608262,0.00002070969,0.03857386,0.0001646694,0.0002046453,0.0001617489,0.000009926112,0.00003492869,0.000003312592],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.96055,"threshold_uncertainty_score":0.7269352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008724996924415045,"score_gpt":0.2152580262500091,"score_spread":0.206533029325594,"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."}}