Identifiability analysis and improvement of a tree water flow and storage model.

A recently published tree water flow and storage model (RCGro) for simulating water transport dynamics in trees and related stem diameter variations was improved in order to better describe a data set gathered under mild drought stress conditions. Model improvements were carried out based on the results of a mathematical identifiability analysis. This analysis provided important information with respect to the balance between model complexity and data availability. Using the identifiability analysis results, we were able to (1) highlight weaknesses of the model; (2) obtain information on how the model could be reduced in some places, to improve its identifiability properties, and extended in others, to enhance model performance; (3) identify which measurements are necessary to optimally calibrate the model. The resulting improved model was less complex (contained less unidentifiable parameters), had better dynamic properties and was able to better describe the stress data set.

Parameter subset selection for the dynamic calibration of activated sludge models (ASMs): experience versus systems analysis.

In this work we address the issue of parameter subset selection within the scope of activated sludge model calibration. To this end, we evaluate two approaches: (i) systems analysis and (ii) experience-based approach. The evaluation has been carried out using a dynamic model (ASM2d) calibrated to describe nitrogen and phosphorus removal in the Haaren WWTP (The Netherlands). The parameter significance ranking shows that the temperature correction coefficients are among the most influential parameters on the model output. This outcome confronts the previous identifiability studies and the experience based approaches which excluded them from their analysis. Systems analysis reveals that parameter significance ranking and size of the identifiable parameter subset depend on the information content of data available for calibration. However, it suffers from heavy computational demand. In contrast, although the experience-based approach is computationally affordable, it is unable to take into account the information content issue and therefore can be either too optimistic (giving poorly identifiable sets) or pessimistic (small size of sets while much more can be estimated from the data). An appropriate combinations of both approaches is proposed which offers a realistic (doable) and sound approach for parameter subset selection in activated sludge modelling.

Designing and performing experiments for model calibration using an automated iterative procedure.

Optimal experimental design for parameter estimation involves complex mathematical and practical steps in order to obtain a model with sufficiently accurate parameters. This paper proposes a methodology where user interaction is only required at the beginning of the experimental design procedure. All subsequent steps are carried out automatically, including: (1) finding the optimal experiment, (2) performing the experiments in practice, and (3) recalibrating the model. A software extension to an existing modelling and simulation package which performs this automatic procedure is also presented. Finally an illustration for the calibration of a one-step nitrification model using respirometric data is given. In this case, the application of the proposed procedure resulted, after three iterations, in considerably better confidence intervals on the parameter estimates, within the desired boundaries.

An integrated system to remote monitor and control anaerobic wastewater treatment plants through the internet.

The TELEMAC project brings new methodologies from the Information and Science Technologies field to the world of water treatment. TELEMAC offers an advanced remote management system which adapts to most of the anaerobic wastewater treatment plants that do not benefit from a local expert in wastewater treatment. The TELEMAC system takes advantage of new sensors to better monitor the process dynamics and to run automatic controllers that stabilise the treatment plant, meet the depollution requirements and provide a biogas quality suitable for cogeneration. If the automatic system detects a failure which cannot be solved automatically or locally by a technician, then an expert from the TELEMAC Control Centre is contacted via the internet and manages the problem.