Practical identifiability and uncertainty analysis of the one-dimensional hindered-compression continuous settling model

Hindered and compression solid settling functions - Sensor data collection, practical model identification and validation

Secondary settling tanks (SSTs) are the most hydraulically sensitive unit operations in activated sludge water resource recovery facilities (WRRF). Mathematical models for predicting activated sludge solids settling velocity include parameters that show irreducible epistemic uncertainty. Therefore, reliable and periodic calibration of the settling velocity model is key for predicting activated sludge process capacity, thus averting possible failures under wet-weather flow- and filamentous bulking conditions. The two main knowledge gaps addressed here are: (1) Do constitutive functions for hindered and compression settling exist, for which all velocity parameters can be uniquely estimated? (2) What is the optimum sensor data requirement of developing reliable settling velocity functions? Innovative settling column sensor and full-scale data were used to identify and validate amended Vesilind function for hindered settling and a new exponential function for compression settling velocity using one-dimensional and computational fluid dynamics simulations. Results indicate practical model identifiability under well-settling and filamentous bulking conditions.

A systematic model identification method for chemical transformation pathways - the case of heroin biomarkers in wastewater

This study presents a novel statistical approach for identifying sequenced chemical transformation pathways in combination with reaction kinetics models. The proposed method relies on sound uncertainty propagation by considering parameter ranges and associated probability distribution obtained at any given transformation pathway levels as priors for parameter estimation at any subsequent transformation levels. The method was applied to calibrate a model predicting the transformation in untreated wastewater of six biomarkers, excreted following human metabolism of heroin and codeine. The method developed was compared to parameter estimation methods commonly encountered in literature (i.e., estimation of all parameters at the same time and parameter estimation with fix values for upstream parameters) by assessing the model prediction accuracy, parameter identifiability and uncertainty analysis. Results obtained suggest that the method developed has the potential to outperform conventional approaches in terms of prediction accuracy, transformation pathway identification and parameter identifiability. This method can be used in conjunction with optimal experimental designs to effectively identify model structures and parameters. This method can also offer a platform to promote a closer interaction between analytical chemists and modellers to identify models for biochemical transformation pathways, being a prominent example for the emerging field of wastewater-based epidemiology.

Batch settling curve registration via image data modeling

To this day, obtaining reliable characterization of sludge settling properties remains a challenging and time-consuming task. Without such assessments however, optimal design and operation of secondary settling tanks is challenging and conservative approaches will remain necessary. With this study, we show that automated sludge blanket height registration and zone settling velocity estimation is possible thanks to analysis of images taken during batch settling experiments. The experimental setup is particularly interesting for practical applications as it consists of off-the-shelf components only, no moving parts are required, and the software is released publicly. Furthermore, the proposed multivariate shape constrained spline model for image analysis appears to be a promising method for reliable sludge blanket height profile registration.