Linearization of the activated sludge model ASM1 for fast and reliable predictions.

In this paper a strategy is proposed to reduce the complexity of the activated sludge model no. 1 (ASM1) which describes the biotransformation processes in a common activated sludge process with N-removal. The key feature of the obtained reduced model is that it combines high predictive value (all state variables keep their biological interpretation) with very low computation time. Therefore, this model is a valuable tool in a risk assessment environment (designed for the evaluation of wastewater treatment plants facing stricter effluent norms) as well as in on-line (MPC) control strategies. The complexity reduction procedure consists of four steps. In the first step representative input/output data sets are generated by simulating the full ASM1 model. In the second step the ASM1 model is rewritten in state space format with linear approximations of the nonlinear (kinetic) terms. In the third step the unknown parameters in the linear terms are identified based on the generated input/output data. To reduce the amount of parameter sets that have to be identified (to cover the full operation range of the plant), a Multi-Model interpolation procedure is introduced as a last step.

[1]  W. Gujer,et al.  Activated sludge model No. 3 , 1995 .

[2]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[3]  G. Van Dongen,et al.  Multivariate time series analysis for design and operation of a biological wastewater treatment plant , 1998 .

[4]  A. Cheruy,et al.  MULTIMODEL SIMULATION AND ADAPTIVE STOCHASTIC CONTROL OF AN ACTIVATED SLUDGE PROCESS , 1983 .

[5]  S. Isaacs,et al.  An analysis of nitrogen removal and control strategies in an alternating activated sludge process , 1995 .

[6]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[7]  Aris P. Georgakakos,et al.  Accounting for Different Time Scales in Activated Sludge Process Control , 1992 .

[8]  R. B. Newell,et al.  A systematic approach for reducing complex biological wastewater treatment models , 1997 .

[9]  E. Ayesa,et al.  Observability of reduced order models - application to a model for control of alpha process , 1995 .

[10]  Jes la Cour Jansen,et al.  Batch test procedures as tools for calibration of the activated sludge model - a pilot scale demonstration , 1998 .

[11]  P. Lessard,et al.  A Reduced Order Model for Control of a Single Reactor Activated Sludge Process , 1999 .

[12]  G. Olsson,et al.  Reduced Order Models for On-Line Parameter Identification of the Activated Sludge Process , 1993 .

[13]  Karel J. Keesman,et al.  Analysis of endogenous process behaviour , 1998 .

[14]  P A Vanrolleghem,et al.  Development of a risk assessment based technique for design/retrofitting of WWTPs. , 2001, Water science and technology : a journal of the International Association on Water Pollution Research.

[15]  M. Stenstrom,et al.  Model Calibration for the High-Purity Oxygen Activated Sludge Process – Algorithm Development and Evaluation , 1993 .

[16]  Andrea G. Capodaglio,et al.  Time Series Analysis Models of Activated Sludge Plants , 1991 .

[17]  Aris P. Georgakakos,et al.  Optimal control of the activated sludge process. , 1990 .

[18]  Roderick Murray-Smith,et al.  Multiple Model Approaches to Modelling and Control , 1997 .

[19]  P. Vanrolleghem,et al.  Estimating (combinations of) Activated Sludge Model No. 1 parameters and components by respirometry , 1999 .