{"id":"W2745452866","doi":"10.1515/ijcre-2017-0033","title":"Bed Material Agglomeration Behavior in a Bubbling Fluidized Bed (BFB) at High Temperatures using KCl and K <sub>2</sub> SO <sub>4</sub> as Simulated Molten Ash","year":2017,"lang":"en","type":"article","venue":"International Journal of Chemical Reactor Engineering","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Western University","funders":"University of Toronto","keywords":"Economies of agglomeration; Fluidized bed; Agglomerate; Melting point; Alkali metal; Materials science; Eutectic system; Combustion; Chemical engineering; Fluidization; Fluidized bed combustion; Mineralogy; Chemistry; Metallurgy; Composite material; Microstructure; Organic chemistry","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":[0.0002124793,0.0004417161,0.0003364487,0.0002925455,0.0002815086,0.0003073777,0.0002599886,0.0003321271,0.0007355249],"category_scores_gemma":[0.0002443211,0.0002211143,0.000303761,0.0001918289,0.0002790837,0.0003110514,0.000152652,0.0003423641,0.0002710226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003942799,"about_ca_system_score_gemma":0.0001764158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002351391,"about_ca_topic_score_gemma":0.002722993,"domain_scores_codex":[0.9998276,0.00002548632,0.00001219754,0.000050452,0.00005916939,0.00002513215],"domain_scores_gemma":[0.9998643,0.00003943789,0.00003009636,0.000007348763,0.00003813257,0.00002060811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001348813,0.00001233877,0.0003788919,0.00002039234,0.00000449989,0.00002510151,0.00003174069,0.0001395534,0.9987335,0.00001328658,0.00001133447,0.0004945367],"study_design_scores_gemma":[0.00000672118,0.000212472,0.004595924,0.000002101723,0.000008291037,0.00002193009,0.00003157627,0.001926926,0.992963,0.000008030494,0.0002151895,0.000007822116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983907,0.0001544208,0.0009714545,0.00002512879,0.000009962518,0.00001073826,0.00009717738,0.00007526708,0.0002652106],"genre_scores_gemma":[0.9978051,0.000137325,0.001179714,0.00001354122,0.000005134953,0.00001391837,0.0001559079,0.00001637897,0.0006729873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002351391,"threshold_uncertainty_score":0.004675448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007738041478110958,"score_gpt":0.2338631283159359,"score_spread":0.2261250868378249,"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."}}