{"id":"W4200522034","doi":"10.1002/cjce.24349","title":"Modelling and optimization of the ferrous to ferric sulphate conversion with hydrogen peroxide using <scp>polynomial‐PSO</scp> and <scp>PSO‐ANNs</scp> models","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Particle swarm optimization; Ferric; Ferrous; Artificial neural network; Response surface methodology; Polynomial; Polynomial and rational function modeling; Hydrogen peroxide; Factorial experiment; Biological system; Process (computing); Chlorine; Computer science; Process engineering; Chemistry; Mathematical optimization; Algorithm; Mathematics; Engineering; Artificial intelligence; Machine learning; Organic chemistry","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":[0.0004469764,0.0005421493,0.0004133811,0.0002621833,0.0002032721,0.000613888,0.0004109114,0.0007055512,0.0007150437],"category_scores_gemma":[0.000559397,0.0002704134,0.0007170666,0.000244814,0.0002456904,0.0002951549,0.0002061892,0.0004875589,0.0001543997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005990749,"about_ca_system_score_gemma":0.0006766791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01183543,"about_ca_topic_score_gemma":0.009046099,"domain_scores_codex":[0.9998953,0.00003196485,0.0000065672,0.00002356032,0.00002708003,0.00001548838],"domain_scores_gemma":[0.9998081,0.0001135954,0.00002332149,0.00001096537,0.00003693096,0.000006994186],"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":[0.00002487606,0.0000311559,0.0002915238,0.00002821627,0.00001049893,0.00001465147,0.000009060141,0.9930698,0.002558564,0.0001512791,0.00003791928,0.003772371],"study_design_scores_gemma":[0.000001968043,0.00002068493,0.0001229677,0.000001023233,0.000002494915,0.00000129078,0.000002144079,0.9988095,0.000964602,0.00003155681,0.00004045785,0.000001335073],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7188554,0.000352968,0.2739386,0.000155494,0.00004240733,0.00009989964,0.0001311792,0.0003021127,0.006121933],"genre_scores_gemma":[0.9839942,0.0000944101,0.01443249,0.000007291709,0.000002498072,0.00005905555,0.00005204993,0.00001068286,0.001347207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01183543,"threshold_uncertainty_score":0.02353305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378285903343722,"score_gpt":0.1905314877659005,"score_spread":0.1767486287324633,"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."}}