Data Handling and Parameter Estimation

At full-scale wastewater treatment plants (WWTPs), mechanistic modelling using the ASM framework and concept (e.g. Henze et al., 2000) has become an important part of the engineering toolbox for process engineers. It supports plant design, operation, optimization and control applications. Models have also been increasingly used to help take decisions on complex problems including the process/technology selection for retrofitting, as well as validation of control and optimization strategies (Gernaey et al., 2014; MauricioIglesias et al., 2014; Vangsgaard et al., 2014; Bozkurt et al., 2015).

[1]  Gürkan Sin,et al.  Uncertainty analysis in WWTP model applications: a critical discussion using an example from design. , 2009, Water research.

[2]  Gürkan Sin,et al.  Global sensitivity analysis in wastewater treatment plant model applications: prioritizing sources of uncertainty. , 2011, Water research.

[3]  P. Reichert,et al.  A comparison of techniques for the estimation of model prediction uncertainty , 1999 .

[4]  Krist V. Gernaey,et al.  Assessing reliability of cellulose hydrolysis models to support biofuel process design - Identifiability and uncertainty analysis , 2010, Comput. Chem. Eng..

[5]  Gürkan Sin,et al.  An efficient approach to automate the manual trial and error calibration of activated sludge models , 2008, Biotechnology and bioengineering.

[6]  J. Heijnen,et al.  Linear constraint relations in biochemical reaction systems: II. Diagnosis and estimation of gross errors , 1994, Biotechnology and bioengineering.

[7]  P. Vanrolleghem,et al.  Extensions to modeling aerobic carbon degradation using combined respirometric-titrimetric measurements in view of activated sludge model calibration. , 2007, Water research.

[8]  J. A. Roels,et al.  THE APPLICATION OF MACROSCOPIC PRINCIPLES TO MICROBIAL METABOLISM , 1981 .

[9]  Krist V. Gernaey,et al.  Development of novel control strategies for single-stage autotrophic nitrogen removal: A process oriented approach , 2014, Comput. Chem. Eng..

[10]  J. Heijnen,et al.  Bioenergetics of Microbial Growth , 2010 .

[11]  Krist V. Gernaey,et al.  A novel control strategy for single-stage autotrophic nitrogen removal in SBR , 2015 .

[12]  Juan A. Baeza,et al.  Respirometric estimation of the oxygen affinity constants for biological ammonium and nitrite oxidation , 2005 .

[13]  M C M van Loosdrecht,et al.  Error diagnostics and data reconciliation for activated sludge modelling using mass balances. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[14]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[15]  J. Roels.  Application of macroscopic principles to microbial metabolism. , 1980, Biotechnology and bioengineering.

[16]  Krist V. Gernaey,et al.  A mathematical programming framework for early stage design of wastewater treatment plants , 2015, Environ. Model. Softw..

[17]  H. Künsch,et al.  Practical identifiability analysis of large environmental simulation models , 2001 .

[18]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[19]  Denis Dochain,et al.  Dynamical modelling and estimation in wastewater treatment processes. , 2015 .

[20]  Ulf Jeppsson,et al.  Benchmarking of Control Strategies for Wastewater Treatment Plants , 2014 .

[21]  Stefano Tarantola,et al.  Sensitivity Analysis as an Ingredient of Modeling , 2000 .

[22]  Peter Reichert,et al.  Practical identifiability of ASM2d parameters--systematic selection and tuning of parameter subsets. , 2002, Water research.

[23]  Jens Nielsen,et al.  Elemental and Redox Balances , 2011 .

[24]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .