A parameter estimation framework was used to evaluate the ability of observed data from a full-scale nitrification–denitrification bioreactor to reduce the uncertainty associated with the bio-kinetic and stoichiometric parameters of an activated sludge model (ASM). Samples collected over a period of 150 days from the effluent as well as from the reactor tanks were used. A hybrid genetic algorithm and Bayesian inference were used to perform deterministic and parameter estimations, respectively. The main goal was to assess the ability of the data to obtain reliable parameter estimates for a modified version of the ASM. The modified ASM model includes methylotrophic processes which play the main role in methanol-fed denitrification. Sensitivity analysis was also used to explain the ability of the data to provide information about each of the parameters. The results showed that the uncertainty in the estimates of the most sensitive parameters (including growth rate, decay rate, and yield coefficients) decreased with respect to the prior information.
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Research Article|
January 02 2017
Evaluation of the information content of long-term wastewater characteristics data in relation to activated sludge model parameters Available to Purchase
Jamal Alikhani;
Jamal Alikhani
1Department of Civil Engineering, The Catholic University of America, 630 Michigan Ave NE, Washington, DC 20064, USA
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Imre Takacs;
Imre Takacs
2Dynamita, 7 Eoupe, Nyons 26110, France
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Ahmed Al-Omari;
Ahmed Al-Omari
3DC Water and Sewer Authority, 5000 Overlook Avenue SW, Washington, DC 20032, USA
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Sudhir Murthy;
Sudhir Murthy
3DC Water and Sewer Authority, 5000 Overlook Avenue SW, Washington, DC 20032, USA
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Arash Massoudieh
1Department of Civil Engineering, The Catholic University of America, 630 Michigan Ave NE, Washington, DC 20064, USA
E-mail: [email protected]
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Water Sci Technol (2017) 75 (6): 1370–1389.
Article history
Received:
August 21 2016
Accepted:
December 14 2016
Citation
Jamal Alikhani, Imre Takacs, Ahmed Al-Omari, Sudhir Murthy, Arash Massoudieh; Evaluation of the information content of long-term wastewater characteristics data in relation to activated sludge model parameters. Water Sci Technol 23 March 2017; 75 (6): 1370–1389. doi: https://doi.org/10.2166/wst.2017.004
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