Elisabetta Giusti

A modified Activated Sludge Model No. 3 (ASM3) with two-step nitrification-denitrification

A common limitation of the Activated Sludge Models (ASM) [Henze, M., Gujer, W., Mino, T., van Loosdrecht, M.C.M., 2000. Activated Sludge Models ASM1, ASM2, ASM2d, and ASM3. IWA Scientific and Technical Report No. 9. IWA Publishing, London, UK] is the representation of the nitrification dynamics as a single-step process and the consequent denitrification on nitrate alone. This generally acknowledged simplification may represent a serious limitation in specific applications where nitrites become important, either as a target final product or an unwanted intermediate. This paper proposes an enhancement to the basic ASM3 model, introducing a two-step model for the process nitrification and, consequently, considering denitrification on both nitrite and nitrate. After introducing the relevant process kinetics and adapting the stoichiometric matrix accordingly, the model implementation in the Matlab/Simulink(TM) platform is described with reference to the benchmark setting. To obtain a fast implementation, the process units (reaction tanks and secondary settler) have been implemented as DLLs linked to the Simulink blocks, whereas the model parameters and stoichiometric matrix remain accessible to the user. The new model is compared with the standard ASM3 and checked for consistency and mass conservation. It is also shown that with the default kinetic parameters nitrite may represent a considerable fraction of the nitrified effluent, thus revealing a design limitation in the benchmark sizing. In the last part, an optimization of the benchmark plant volumes has been attempted in order to minimize such violations, resulting in a moderate increase of the overall reaction volume. The pertinent software is freely available for research purposes.

Water quality modelling for small river basins

Water quality modelling in small rivers is often considered unworthy from a practical and economic viewpoint. This paper shows instead that a simple model structure can be set up to describe the stationary water quality in small river basins in terms of carbon and nitrogen compounds, when the use of complex models is unfeasible. In short rivers point and nonpoint sources play a key role in shaping the model response, being as important as the self-purification dynamics. Further, the varying river characteristics, in terms of morphology, hydraulics and vegetation, require the introduction of variable parameters, thus complicating the originally simple model structure. To determine the identifiability of the resulting model an identifiability assessment was carried out, based on sensitivity analysis and optimal experiment design criteria. The identifiable subset was determined by ranking the parameters in terms of sensitivity and computing the associated Fisher Information Matrices. It was found that the inclusion of the nonpoint sources as piecewise constant parameters affected the identifiability to a considerable extent. However, the combined parameter-sources calibration was made possible by the use of a robust estimation algorithm, which also provided estimation confidence limits. The calibrated model responses are in good agreement with the data and can be used as scenario generators in a general strategy to conserve or improve the water quality.

Fuzzy modelling of the composting process

Composting is a solid waste treatment process consisting of the biochemical degradation of organic materials. A controlled microbial aerobic decomposition produces stabilized organic materials to be used as soil conditioners or organic fertilizers. The efficiency of this process is strongly temperature-dependent and the key to successful composting lies in the tracking of an appropriate temperature batch curve based on experience and related to a complex succession of differing microbial activities. Such a complexity is modelled in this paper with a fuzzy structure composed of clustered antecedents, describing the process regimes, and consequent linear models driven by the aeration cycle and in-cycle temperature evolution. This fuzzy model was adapted to the data by cluster training and minimization of a model/data error criterion. The calibrated model was able to describe the temperature profile during the most significant part of the composting batch.

A new instream flow assessment method based on fuzzy habitat suitability and large scale river modelling

This paper proposes an integrated tool for the assessment of fish habitat suitability based on synthetic hydraulic and water quality parameters. There are three innovative features in this study: (a) The proposed approach seeks to improve the capabilities of IFIM (Instream Flow Incremental Methodology) by extending the assessment to a larger spatial scale, which can be helpful for river managers in decision making. (b) The method is based on the suitability response of target species to hydraulic and water quality parameters through a fuzzy model, which is a novel application of a Takagi-Sugeno fuzzy logic. (c) The introduction of simulated river conditions enables the generation of a wide range of scenarios and the detection of potentially critical situations. After introducing the main algorithm, a sensitivity analysis is provided for the assessment of critical river segments and for ranking the influence of each parameter on the habitat. Then, a second algorithm is developed to produce an instream flow assessment method by determining the range of admissible flows that preserve the habitat suitability to a prescribed degree. The combined method is demonstrated with the application to two Italian rivers in the Tuscany region. In the case of the Arno River, the method highlights the habitat diversity for the two target species along its course, and the critical conditions that may develop during the summer low flow. In the case of the Serchio River, the analysis helps to assess habitat alterations likely to be caused by a planned diversion to feed a nearby lake. In both instances, with a minimum requirement of field data, this method shows its flexibility and seems better able to detect critical situations than the conventional IFIM approach.

PEAS: A toolbox to assess the accuracy of estimated parameters in environmental models

This paper presents a Matlab(TM) toolbox to assess the accuracy of the estimated parameters of environmental models, based on their approximate confidence regions. Before describing the application, the underlying theory is briefly recalled to familiarize the reader with the numerical methods involved. The software, named PEAS as an acronym for Parameter Estimation Accuracy Software, performs both the estimation and the accuracy analysis, using a user-friendly graphical interface to minimize the required programming. The user is required to specify the model structure according to the Matlab/Simulink(TM) syntax, supply the experimental data, provide an initial parameter guess and select an estimation method. PEAS provides several model assessment tools, in addition to parameter estimation, such as error function plotting, trajectory sensitivity, Monte Carlo analysis, all useful to assess the adequacy of the experimental data to the estimation problem. After the parameters have been estimated, the reliability assessment is performed: approximate and exact confidence regions are computed and a confidence test is produced. The Monte Carlo analysis is available for approximate accuracy assessment whenever the model structure prevents the application of the confidence regions method. The software, which is freely available for research purposes, is demonstrated here with two examples: a dynamical and an algebraic model. In both cases, software usage and outputs are presented and commented. The examples show how the user is guided through the application of the methods and how warning messages are returned if the estimation does not satisfy the accuracy criteria.