scholarly journals Practical Identifiability Analysis and Optimal Experimental Design for the Parameter Estimation of the ASM2d-Based EBPR Anaerobic Submodel

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhenliang Li ◽  
Peili Lu ◽  
Daijun Zhang ◽  
Tian Zhang

Identifiability analysis is a precondition for reliable parameter estimation. Building on previous work on structural identifiability, this paper focuses on the practical identifiability and optimal experimental design (OED) of the EBPR anaerobic submodel. The nonnegative determinant of the Fisher information matrix (FIM) found in this study clearly demonstrates that the parametersYPO4,KA,qPHA, andXPAOin the submodel are practically identifiable usingSAandSPO4as the measured variables and fixingKPPas the default value. Furthermore, fixingKPPto study the practical identifiability of the other parameters and to estimate their values is shown to be valid. Subsequently, a modeling-based procedure for the OED for parameter estimation was proposed and applied successfully to anaerobic phosphorus release experiments. According to the FIMD-criterion, the optimal experimental condition was determined to be an initialSAconcentration of 300 mg/L. Under the optimal experimental condition, errors in the values ofYPO4,KA,qPHA, andXPAOare all below 20%, and the estimated values were 0.35 ± 0.02 mg P/mg COD, 3.88 ± 0.41 mg COD/L, 3.35 ± 0.27 mg P/(mgCOD⁎d-1), and 1500 ± 72 mg COD/L, respectively. Compared to the results from the nonoptimal experimental condition, the practical identifiability and the estimation precision of the four parameters were improved.

2001 ◽  
Vol 43 (7) ◽  
pp. 339-346 ◽  
Author(s):  
M. E. Hidalgo ◽  
E. Ayesa

This paper describes a mathematical tool for identifiability analysis, easily applicable to high order non-linear systems modelled in state-space and implementable in simulators with a time-discrete approach. This procedure also permits a rigorous analysis of the expected estimation errors (average and maximum) in calibration experiments. The methodology is based on the recursive numerical evaluation of the information matrix during the simulation of a calibration experiment and in the setting-up of a group of information parameters based on geometric interpretations of this matrix. As an example of the utility of the proposed test, the paper presents its application to an optimal experimental design of ASM Model No.1 calibration, in order to estimate the maximum specific growth rate μH and the concentration of heterotrophic biomass XBH.


2018 ◽  
Vol 144 (3) ◽  
pp. 1730-1730 ◽  
Author(s):  
Tracianne B. Neilsen ◽  
Mark K. Transtrum ◽  
David F. Van Komen ◽  
David P. Knobles

2001 ◽  
Vol 48 (2) ◽  
pp. 109-119 ◽  
Author(s):  
H.B. Nahor ◽  
N. Scheerlinck ◽  
R. Verniest ◽  
J. De Baerdemaeker ◽  
B.M. Nicolaı̈

2021 ◽  
Author(s):  
Johanna Fink ◽  
Ralf Seidler

<p>Drilling boreholes during exploration and development of geothermal reservoirs not only involves high cost, but also bears significant risks of failure. In geothermal reservoir engineering, techniques of optimal experimental design (OED) have the potential to improve the decision making process. Previous publications explained the formulation and implementation of this mathematical optimization problem and demonstrated its feasibility for finding borehole locations in two- and three-dimensional reservoir models that minimize the uncertainty of estimating hydraulic permeability of a model unit from temperature measurements. Subsequently, minimizing the uncertainty of the parameter estimation results in a more reliable parametrization of the reservoir simulation, improving the overall process in geothermal reservoir engineering.</p><p>Various OED techniques are implemented in the Environment for Combining Optimization and Simulation Software (EFCOSS). To address problems arising from geothermal modeling, this software framework links mathematical optimization software with SHEMAT-Suite, a geothermal simulation code for fluid flow and heat transport through porous media. This contribution shows how to determine experimental conditions such that the uncertainty when estimating different parameters of model units from temperature measurements in the borehole is minimized. Numerical simulations of synthetic geothermal reservoir scenarios are presented to demonstrate the OED workflow and its applicability to geothermal reservoir modeling</p>


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