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Practical identifiability and uncertainty analysis of the one-dimensional hindered-compression continuous settling
1Dept. of Civil and Environmental Engineering, University of California Los Angeles, Los Angeles, CA 90095, USA.
Water Research
|January 7, 2016
Summary
Model calibration for hindered-compression settling is challenging due to limited data. This study identifies key parameters and experimental setups for accurate model application, improving prediction uncertainties.
Area of Science:
- Chemical Engineering
- Particle Science and Technology
Background:
- Hindered-compression settling models are crucial for industrial processes but face calibration challenges.
- Limited experimental data often restricts the practical application and accuracy of these models.
Purpose of the Study:
- To evaluate the identifiability of parameter subsets for one-dimensional hindered-compression settling models.
- To determine optimal experimental layouts and data collection strategies for reliable model calibration.
Main Methods:
- Global sensitivity analysis to identify influential parameters.
- Identifiability analysis using local sensitivity functions and collinearity measures.
- Evaluation across various experimental layouts, including batch settling curves and concentration profiles.
Main Results:
- Batch settling curves effectively calibrate hindered parameters; concentration profiles are needed for compression parameters.
- At least three parameters are identifiable, potentially increasing to five with combined data.
- Identifiable parameter subsets are sensitive to initial and fixed parameter values.
Conclusions:
- Optimal experimental designs, incorporating both settling curves and concentration data, enhance parameter identifiability.
- Accurate estimation of identifiable parameter subsets significantly reduces model prediction uncertainties.
- Measuring hindered settling velocities and static sediment top concentration improves initial value determination and reduces sensitivity.
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