Global parameter optimization for biokinetic modeling of simple batch experiments

2016 ◽  
Vol 85 ◽  
pp. 356-373 ◽  
Author(s):  
Alma Mašić ◽  
Kai M. Udert ◽  
Kris Villez
Keyword(s):  
Parameter Optimization ◽  
Batch Experiments ◽  
Global Parameter ◽  
Biokinetic Modeling

Practical approach to parameter estimation for ASM3+bio-P module applied to five-stage step-feed EBPR process

2006 ◽  
Vol 53 (1) ◽  
pp. 139-148 ◽  
Author(s):  
S.H. Lee ◽  
J.H. Ko ◽  
K.M. Poo ◽  
T.H. Lee ◽  
H.J. Woo ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Kinetic Parameters ◽  
Parameter Optimization ◽  
Prediction Performance ◽  
Data Sources ◽  
Batch Experiments ◽  
Function Calculation ◽  
Characteristic Features ◽  
Parameter Values

Various parameter optimization approaches to a five-stage step-feed EBPR process modeled using the ASM3+bio-P module were examined. Five stoichiometric (YSTO,NO, YH,O2, YH,NO, YPAO,O2, YPO4) and seven kinetic parameters (kSTO, ηNO, bH, μmax,PAO, qPHA, qPP, μmax,A) were estimated. The optimization approaches could be classified based on the data sources (batch experiments or CSTR operation data) and the number of target variables used in calculating the objective function. Optimized parameter values obtained by each approach were validated with CSTR operation data that were not used for parameter optimization. The results showed that the parameter optimization only with batch experimental results could not be directly applied to CSTR operation data. ASM3+bio-P module parameters could be finely optimized only with CSTR operation data when sufficient target variables for objective function calculation were applied. When the number of target variables was increased, prediction performance was significantly improved. Once optimized, the model was able to predict the characteristic features of the five-stage step-feed process; namely, a high PAO yield, fast PAO growth, fast XPP storage, slow XSTO and XPHA storage.

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