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Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received June 29, 2011
Accepted July 12, 2011

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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A systematic model calibration methodology based on multiple errors minimization method for the optimal parameter estimation of ASM1

JungJin Lim MinHan Kim MinJung Kim TaeSuk Oh OnYu Kang Booki Min Ambati Seshagiri Rao¹ ChangKyooYoo^†

Department of Environmental Science and Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea ¹Department of Chemical Engineering, National Institute of Technology (NIT), Tiruchirappalli 620 015, India

Korean Journal of Chemical Engineering, March 2012, 29(3), 291-303(13), 10.1007/s11814-011-0178-2

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Abstract

A one-step model calibration methodology of the activated sludge model no. 1 (ASM1) of a full-scale wastewater treatment plant (WWTP) is proposed. First, the key parameters among all parameters of the ASM1 model are selected by sensitivity analysis based on the effluent quality index. Second, multiple response surface methodology (MRSM) is conducted to find the optimal parameter values of the ASM1 model. Lastly, an MRSM analysis is conducted in order to determine the optimal parameter values. This study was conducted in order to develop a new systematic model calibration methodology that can greatly help the modeler to find the optimal solution by selecting the key parameters and optimizing the parameters. In two case studies of simple activated sludge process and a full-scale plant, the experimental results indicated that the calibrated models can improve the prediction quality of the ASM model and the efficiency of the modeling.

Keywords

Activated Sludge Model Multiple Error Minimization Sensitivity Analysis Response Surface Methodology,Multiple Response Surface Methodology

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