No abstract

Modelling is considered to be an inherent part of the design and operation of a wastewater treatment system. The models used in practice range from conceptual models and physical design models (laboratory-scale or pilot-scale reactors) to empirical or mechanistic mathematical models. These mathematical models can be used during the design, operation and optimisation of a wastewater treatment system. To do so, a good software tool is indispensable. WEST is a general modelling and simulation environment and can, together with a model base, be used for this task. The model base presented here is specific for biological wastewater treatment and is written in MSL-USER. In this high-level object-oriented language, the dynamics of systems can be represented along with symbolic information. In WEST's graphical modelling environment, the physical layout of the plant can be rebuilt, and each building block can be linked to a specific model from the model base. The graphical information is then combined with the information in the model base to produce MSL-EXEC code, which can be compiled with a C++ compiler. In the experimentation environment, the user can design different experiments, such as simulations and optimisations of, for instance, designs, controllers and model fits to data (calibration).

The urban wastewater system components (sewer, treatment plant, and river) are often modelled using complex mechanistic models. Mechanistic surrogate models are introduced here as simplified models that still contain some physical knowledge. Surrogate models are faster, but are less but still sufficiently accurate, and require more data to be calibrated. The possibilities of replacing actual field data by virtual data generated with a complex mechanistic model for calibration of the surrogate model are examined. As an example, a series of tanks with variable volume is shown to approximate sufficiently well the flow propagation in the river Zwalm (Belgium) as predicted by the "de Saint-Venant" equations. The three surrogate models can be implemented in the WEST simulator, which makes a simultaneous simulation of the system possible. In this work a connection is made between the ASM1 and the new IWA River Model No. 1 (RWOM1) by using a translator between the models in such a way that both mass and elemental balances remain closed for the overall system. This approach is illustrated with a case study on the river Lambro (Italy). The dispersion process in this river with steady flow could be modelled by using a tanks in series model, while the water quality in the river was predicted to improve substantially with an increase in hydraulic capacity of the treatment plant. The simulation results with the upgraded plant still need to be checked by field data.

Up to now, within the design/retrofit of wastewater treatment plants (WWTPs), deterministic models were used to evaluate different scenarios on their merits in terms of effluent compliance. This paper describes an approach in which a Monte Carlo engine is coupled to a deterministic treatment plant model, followed by risk interpretation in the form of concentration-duration-frequency (cdf) curves of norm exceedance. The combination of probabilistic modelling techniques with the currently available deterministic models allows to determine the probability of exceeding the effluent limits of a WWTP. This percentage of exceedance is accompanied by confidence intervals resulting from the inherent uncertainty of influent characteristics and model parameters. The approach is illustrated for a hypothetical case study, consisting of a denitrifying plant model inspired by the benchmark model described by Spanjers et al.

Uncertainty is a central concept in the decision-making process, especially when dealing with biological systems subject to large natural variations. In the design of activated sludge systems, a conventional approach in dealing with uncertainty is implicitly translating it into above-normal safety factors, which in some cases may even increase the capital investments by an order of magnitude. To obviate this problem, an alternative design approach explicitly incorporating uncertainty is herein proposed. A probabilistic Monte Carlo engine is coupled to deterministic wastewater treatment plant (WWTP) models. The paper provides a description of the approach and a demonstration of the general adequacy of the method. The procedure is examined in an upgrade of a conventional WWTP towards stricter effluent standards on nutrients. The results suggest that the procedure can support the decision-making process under uncertainty conditions and that it can enhance the likelihood of meeting effluent standards without entailing above-normal capital investments. The analysis led to reducing the capital investment by 43%, producing savings of more than 1.2 million euro.