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On‐line estimation of the maximum specific growth rate of nitrifiers in activated sludge systems
Author(s) -
Yuan Zhiguo,
Bogaert Herwig,
Devisscher Martijn,
Vanrolleghem Peter,
Verstraete Willy
Publication year - 1999
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/(sici)1097-0290(19991105)65:3<265::aid-bit3>3.0.co;2-f
Subject(s) - nitrification , activated sludge , activated sludge model , biomass (ecology) , bioreactor , biological system , autotroph , nitrate , volumetric flow rate , sensitivity (control systems) , environmental science , simultaneous nitrification denitrification , nitrogen , process engineering , environmental engineering , mathematics , chemistry , ecology , sewage treatment , engineering , mechanics , biology , genetics , physics , organic chemistry , electronic engineering , bacteria
The on‐line estimation of the maximum specific growth rate of autotrophic biomass is addressed in this article. A general nitrification process model, which is valid for any realistic flow pattern, is used to develop the estimation algorithm. Depending on the measurements available, two estimation equations are derived. While both require measuring the nitrification activity of the activated sludge, one requires the additional measurement of the nitrifiable nitrogen concentrations at the two ends of the bioreactor, and the other requires the nitrate nitrogen concentrations at the same locations. The algorithm also requires some stoichiometric and kinetic parameters. However, sensitivity analysis shows that the estimate is insensitive to the parameters other than the autotrophic decay rate. Compared to the existing algorithms, the algorithm developed in this article does not rely on the assumption of ideal flow pattern in the plant and does not require an error‐prone estimate of the autotrophic biomass concentration. Experimental and simulation studies show that the algorithm performs well and is robust to influent variations and accidental sludge losses. © 1999 John Wiley & Sons, Inc. Biotechnol Bioeng 65: 265–273, 1999.