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A kinetic model of a recirculated upflow anaerobic sludge blanket treating phenolic wastewater
Author(s) -
Wen TenChin,
Cheng ShengShung,
Lay JiunnJyi
Publication year - 1994
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/wer.66.6.5
Subject(s) - blanket , wastewater , phenol , chemistry , biogas , anaerobic exercise , saturation (graph theory) , pulp and paper industry , methanogenesis , sewage treatment , chemical oxygen demand , anaerobic digestion , environmental science , biogas production , bioreactor , methane , chromatography , environmental engineering , waste management , mathematics , materials science , biology , organic chemistry , physiology , combinatorics , engineering , composite material
Sludge taken from a recirculated upflow anaerobic sludge blanket (RUASB) treating phenolic wastewater at organic loadings of 6, 12, 16, and 20 kg COD/m 3 day was tested by the biochemical methane potential (BMP) method at intervals over a three‐year period for an accurate determination of the specific gas production rate. A modified version of the Haldane equation was used in the selection of a statistically significant model describing this process. Statistical diagnosis was carried out with student t ‐test, Durbin‐Watson test for two‐tail distribution and Kalmogorov‐Smirmov statistical approach. The estimated ranges of the maximum specific reaction rate, K ; saturation constant, K s inhibitor constant, K i ; and the order of inhibition, n were as follows: 280–764 mL biogas/gVSS‐day, 202–398 mg phenol/L, 516–880 mg phenol/L, and 1.47–2.51, respectively. The physical meaning of these parameters is explained. BMP tests with sludge taken from the recirculating upflow anaerobic sludge blanket (RUASB) bioreactor running at a loading of 14 kg COD/m 3 day validated the effectiveness of the selected model through comparisons of experimental data and predictions. The results indicate that model predictions accurately reflect experimental data within one standard deviation.