Evaluation of the information content of long-term wastewater characteristics data in relation to activated sludge model parameters

2017 ◽  
Vol 75 (6) ◽  
pp. 1370-1389 ◽  
Author(s):  
Jamal Alikhani ◽  
Imre Takacs ◽  
Ahmed Al-Omari ◽  
Sudhir Murthy ◽  
Arash Massoudieh
Keyword(s):  
Activated Sludge ◽  
Hybrid Genetic Algorithm ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Main Role ◽  
Activated Sludge Model ◽  
Parameter Estimations ◽  
Reliable Parameter ◽  
Denitrification Bioreactor ◽  
Sensitive Parameters

A parameter estimation framework was used to evaluate the ability of observed data from a full-scale nitrification–denitrification bioreactor to reduce the uncertainty associated with the bio-kinetic and stoichiometric parameters of an activated sludge model (ASM). Samples collected over a period of 150 days from the effluent as well as from the reactor tanks were used. A hybrid genetic algorithm and Bayesian inference were used to perform deterministic and parameter estimations, respectively. The main goal was to assess the ability of the data to obtain reliable parameter estimates for a modified version of the ASM. The modified ASM model includes methylotrophic processes which play the main role in methanol-fed denitrification. Sensitivity analysis was also used to explain the ability of the data to provide information about each of the parameters. The results showed that the uncertainty in the estimates of the most sensitive parameters (including growth rate, decay rate, and yield coefficients) decreased with respect to the prior information.

Model Calibration for the High-Purity Oxygen Activated Sludge Process – Algorithm Development and Evaluation

1993 ◽  
Vol 28 (11-12) ◽  
pp. 163-171 ◽  
Author(s):  
Weibo (Weber) Yuan ◽  
David Okrent ◽  
Michael K. Stenstrom
Keyword(s):  
Activated Sludge ◽  
High Purity ◽  
Model Calibration ◽  
Process Model ◽  
Model Parameters ◽  
Complex Method ◽  
Activated Sludge Process ◽  
Activated Sludge Model ◽  
Kinetic Coefficients ◽  
Calibration Algorithm

A model calibration algorithm is developed for the high-purity oxygen activated sludge process (HPO-ASP). The algorithm is evaluated under different conditions to determine the effect of the following factors on the performance of the algorithm: data quality, number of observations, and number of parameters to be estimated. The process model used in this investigation is the first HPO-ASP model based upon the IAWQ (formerly IAWPRC) Activated Sludge Model No. 1. The objective function is formulated as a relative least-squares function and the non-linear, constrained minimization problem is solved by the Complex method. The stoichiometric and kinetic coefficients of the IAWQ activated sludge model are the parameters focused on in this investigation. Observations used are generated numerically but are made close to the observations from a full-scale high-purity oxygen treatment plant. The calibration algorithm is capable of correctly estimating model parameters even if the observations are severely noise-corrupted. The accuracy of estimation deteriorates gradually with the increase of observation errors. The accuracy of calibration improves when the number of observations (n) increases, but the improvement becomes insignificant when n>96. It is also found that there exists an optimal number of parameters that can be rigorously estimated from a given set of information/data. A sensitivity analysis is conducted to determine what parameters to estimate and to evaluate the potential benefits resulted from collecting additional measurements.

scholarly journals A practical protocol for calibration of nutrient removal wastewater treatment models

2011 ◽  
Vol 13 (4) ◽  
pp. 575-595 ◽  
Author(s):  
Giorgio Mannina ◽  
Alida Cosenza ◽  
Peter A. Vanrolleghem ◽  
Gaspare Viviani
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Nutrient Removal ◽  
Model Calibration ◽  
Wastewater Treatment Plants ◽  
Optimal Parameter ◽  
Model Parameters ◽  
Activated Sludge Model ◽  
Treatment Models ◽  
Glue Method

Activated sludge models can be very useful for designing and managing wastewater treatment plants (WWTPs). However, as with every model, they need to be calibrated for correct and reliable application. Activated sludge model calibration is still a crucial point that needs appropriate guidance. Indeed, although calibration protocols have been developed, the model calibration still represents the main bottleneck to modelling. This paper presents a procedure for the calibration of an activated sludge model based on a comprehensive sensitivity analysis and a novel step-wise Monte Carlo-based calibration of the subset of influential parameters. In the proposed procedure the complex calibration issue is tackled both by making a prior screening of the most influential model parameters and by simplifying the problem of finding the optimal parameter set by splitting the estimation task into steps. The key point of the proposed step-wise procedure is that calibration is undertaken for sub-groups of variables instead of solving a complex multi-objective function. Moreover, even with this step-wise approach parameter identifiability issues may occur, but this is dealt with by using the general likelihood uncertainty estimation (GLUE) method, that so far has rarely been used in the field of wastewater modelling. An example from a real case study illustrates the effectiveness of the proposed methodology. Particularly, a model was built for the simulation of the nutrient removal in a Bardenpho scheme plant. The model was successfully and efficiently calibrated to a large WWTP in Sicily.

scholarly journals Fitting drift-diffusion decision models to trial-by-trial data

2020 ◽  
Author(s):  
Q. Feltgen ◽  
J. Daunizeau
Keyword(s):  
Empirical Distribution ◽  
Reaction Times ◽  
Mechanistic Explanation ◽  
Relevant Information ◽  
Added Value ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Drift Diffusion ◽  
Evidence Strength ◽  
Reliable Parameter

AbstractDrift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches do not provide reliable parameter estimates when, e.g., evidence strength is varied over trials. In this note, we propose a fast and efficient approach that is based on fitting a self-consistency equation that the DDM fulfills. Using numerical simulations, we show that this approach enables one to extract relevant information from trial-by-trial variations of RT data that would typically be buried in the empirical distribution. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.

scholarly journals Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

2017 ◽  
Author(s):  
Jochen Kursawe ◽  
Ruth E. Baker ◽  
Alexander G. Fletcher
Keyword(s):  
Computational Models ◽  
Imaging Techniques ◽  
Quantitative Model ◽  
Repeated Measurements ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Summary Statistics ◽  
Imaging Data ◽  
Reliable Parameter ◽  
Approximate Bayesian

AbstractThe growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.

Calibration of an Activated Sludge Model Based on Human Expertise and on a Mathematical Optimization Technique – a Comparison

1992 ◽  
Vol 25 (6) ◽  
pp. 141-148 ◽  
Author(s):  
Oskar Wanner ◽  
Jürg Kappeier ◽  
Willi Gujer
Keyword(s):  
Activated Sludge ◽  
Optimization Technique ◽  
Mathematical Optimization ◽  
The Other ◽  
Sequential Procedure ◽  
Alternative Methods ◽  
Model Parameters ◽  
Activated Sludge Model ◽  
Parameter Values ◽  
Mathematical Optimization Technique

Two alternative methods, which both can be used to estimate some of the kinetic parameters of the IAWPRC Activated Sludge Model Nr. 1, are compared. By one method, which is based on professional experience and expertise, the unknown parameter values are determined one after the other by a sequential procedure. By the other method, the parameter values are determined simultaneously by use of a mathematical optimization technique. Both methods allow a good fit of a set of 25 experimental oxygen respiration rate time-series and yield accurate estimates of the model parameters. The sequential procedure can readily be employed for the evaluation of single experiments. The optimization technique is more suitable for the evaluation of larger data sets and allows for additional analysis of the data.

Respirometry as a tool for rapid characterization of wastewater and activated sludge

1995 ◽  
Vol 31 (2) ◽  
pp. 105-114 ◽  
Author(s):  
Henri Spanjers ◽  
Peter Vanrolleghem
Keyword(s):  
Activated Sludge ◽  
Nitrification Inhibitor ◽  
Wastewater Sludge ◽  
Process Models ◽  
Parameter Estimates ◽  
Reliable Parameter ◽  
Autotrophic Biomass ◽  
Rapid Characterization ◽  
Biokinetic Parameters

A procedure is presented to estimate biokinetic parameters for heterotrophic and autotrophic process models and to estimate wastewater characteristics in the context of the Activated Sludge Models No. 1 and No. 2. The procedure is based on respirometric measurements at low substrate to biomass ratio (S/X). The addition of nitrification inhibitor is avoided by applying a calibrated nitrification model to the respiration rate data resulting from both heterotrophic and autotrophic degradation. Furthermore, a new procedure is developed for simultaneous assessment of decay coefficients for heterotrophic and autotrophic biomass. The results show that, for a given wastewater/sludge combination S/X can be crucial in obtaining reliable parameter estimates: at a very low ratio not all parameters could be identified. A higher ratio caused problems because of nitrification inhibition.

MODELING OXIDATION DITCHES USING THE IA WPRC ACTIVATED SLUDGE MODEL WITH HYDRODYNAMIC EFFECTS

1994 ◽  
Vol 30 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Anastasios I. Stamou
Keyword(s):  
Steady State ◽  
Activated Sludge ◽  
Flow Velocity ◽  
Dispersion Coefficient ◽  
Aerobic Oxidation ◽  
Model Parameters ◽  
Activated Sludge Model ◽  
Average Flow Velocity ◽  
Oxygen Requirements ◽  
Hydrodynamic Effects

A mathematical model is presented to predict the concentrations of the active heterotrophic biomass, the readily biodegradable substrate (soluble COD) and the dissolved oxygen (DO) in a completely aerobic oxidation ditch. The model involves the one-dimensional convection-dispersion equations for biomass, COD and DO. Hydrodynamic effects are represented in the model by the values of the average flow velocity and the dispersion coefficient. Biological processes are described in the model according to the IA WPRC activated sludge model, using typical values for the model parameters at 10°C. The equations are solved with the finite volume method. The application of the model leads to the following conclusions: (i) Steady state biomass concentrations are almost constant throughout the ditch. (ii) Steady state COD concentrations in the ditch are very low, and COD removal efficiency is practically independent of the values of the flow velocity and the dispersion coefficient. The distribution of the COD concentration in the ditch is less uniform, when small values of the dispersion coefficient are used. (iii) The distribution of the DO concentration in the ditch is very sensitive to the values of the flow velocity, the dispersion coefficient and to the capacity of the rotors. DO concentrations increase when the dispersion coefficient decreases or the flow velocity increases. (v) Daily sludge production, oxygen requirements and sludge age are calculated equal to 0.44 g (g COD removed)‒1, 0.56 g (g incoming COD)‒1 and 6.3 days, respectively.

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