Wolfgang Rauch

The role of inorganic carbon limitation in biological nitrogen removal of extremely ammonia concentrated wastewater

It is clear from the fundamental biochemical processes that nitrification of extremely concentrated ammonia loads requires-among others-(1) sufficient alkalinity to buffer acidification and (2) bicarbonate as the substrate for the autotrophic biomass. However, at low pH values the aeration process causes CO(2) stripping and consequently a decrease of the available inorganic carbon. In order to analyse such complex interactions, we suggest in this paper an enhanced version of the widely acknowledged IWA (formerly IAWQ) activated sludge models. These model enlargements comprise an ion-balance for the calculation of the pH value and of dissociation species, a balance of inorganic carbon and a more detailed description of the relevant N-elimination processes and their inhibitions. The model was successfully employed to optimise a treatment strategy for rejection-water and landfill leachate (500-2000 mg ammonia-Nl(-1), COD/N ratio of 0.25-4). Detailed data from two full-scale rejection-water treatment plants were used for systems identification, model calibration and validation. The results suggest that inhibition and limitation by nitrous acid (HNO(2)) and unionised ammonia (NH(3)) have often been overestimated. In this investigation the bicarbonate concentration proved to be crucial for the process. The optimisation of the bicarbonate concentration in the reactor could improve the nitrozation rate up to 100mg NH(4)(+)-Nl(-1)h(-1).

Genetic algorithms in real time control applied to minimize transient pollution from urban wastewater systems

Real time control aims at optimization of the urban wastewater system performance under dynamic loading from rain. This paper presents a novel approach to control the whole system: sewer system, treatment plant and receiving water with the aim to achieve minimum effects of pollution. The application of nonlinear model predictive control by means of a genetic algorithm reveals excellent results with hypothetical problem sets. The methodology makes it possible to optimize the system performance directly with respect to water quality parameters and to avoid the traditional indirect and artificial performance criteria, such as permissible annual overflow volume. The relevance of this novel approach is illustrated by the fact that no stringent correlation has been found in the investigation between the reduction of overflow volume and the increase of oxygen concentration in the receiving water.

Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the models structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.

River water quality modelling: I. state of the art

River water quality models are used extensively in research as well as in the design and assessment of water quality management measures. The application of mathematical models for that purpose dates back to the initial studies of oxygen depletion due to organic waste pollution. Since then, models have been constantly refined and updated to meet new and emerging problems of surface water pollution, such as eutrophication, acute and chronic toxicity, etc. In order to handle the complex interactions caused by the increased influence of human activities in rivers it is today mandatory to couple river water quality models with models describing emissions from the drainage and sewerage system (such as the IAWQ Activated Sludge model No.1). In this paper—which is the first of a three-part series by the IAWQ Task Group on River Water Quality Modelling— the state of the art is summarized with the above aim in mind. Special attention is given here to the modelling of conversion processes but also the methods and tools to work with the models, i.e. parameter estimation, measurement campaign design, and simulation software, are discussed.

Automated Creation of District Metered Area Boundaries in Water Distribution Systems

AbstractAccounting for water in a distribution system can be improved by dividing systems into smaller, metered zones. This paper proposes an approach that could create boundaries for district metered areas (DMA) automatically on the basis of the community structure of water distribution systems. Community structure—the gathering of vertices into communities such that there is a higher density of edges within communities than between them—is a common property of many complex systems. For verification, the method was tested on a real-world distribution system, and the result was compared with a manually designed DMA layout. Although further improvements are necessary, because the achieved community structure is in excellent agreement with the zoning plan in reality, this approach is a new addition to the number of automated methods aimed at complementing and eventually substituting the empirical trial-and-error approach.

CITY DRAIN © - An open source approach for simulation of integrated urban drainage systems

In the last years design procedures of urban drainage systems have shifted from end of pipe design criteria to ambient water quality approaches requiring integrated models of the system for evaluation of measures. Emphasis is put on the improvement of the receiving water quality and the overall management of river basins, which is a core element of the Water Framework Directive (WFD) as well. Typically, it is not necessary to model the whole variety of effects on the receiving water but to focus on the few dominating ones. Only pollutants and processes that have a direct and significant influence on the selected impacts need to be described quantitatively, whereas all other processes can be neglected. Hence, pragmatism is required to avoid unnecessary complexity of integrated models. This is as well true for software being used in daily engineering work, requiring simplicity in handling and a certain flexibility to be adjusted for different scenarios. CITY DRAIN (C) was developed to serve these needs. Therefore it was developed in the Matlab/Simulink (C) environment, enabling a block wise modelling of the different parts of the urban drainage system (catchment, sewer system, storage devises, receiving water, etc.). Each block represents a system element (subsystem) with different underlying modelling approaches for hydraulics and mass transport. The different subsystems can be freely arranged and connected to each other in order to describe an integrated urban drainage system. The open structure of the software allows to add own blocks and/or modify blocks (and underlying models) according to the specific needs. The application of CITY DRAIN is shown within the integrated modelling case study Vils/Reutte. Further additional applications for CITY DRAIN, including batch simulations, real time control (RTC) and model based predictive control (MBPC) are presented and discussed.

River water quality modelling: II.: Problems of the art

The U.S. EPA QUAL2E model is currently the standard for river water quality modelling. While QUAL2E is adequate for the regulatory situation for which it was developed (the U.S. wasteload allocation process), there is a need for a more comprehensive framework for research and teaching. Moreover, QUAL2E and similar models do not address a number of practical problems such as stormwater flow events, nonpoint source pollution, and transient streamflow. Limitations in model formulation affect the ability to close mass balances, to represent sessile bacteria and other benthic processes, and to achieve robust model calibration. Mass balance problems arise from failure to account for mass in the sediment as well as in the water column and due to the fundamental imprecision of BOD as a state variable.

Performance and sensitivity analysis of stormwater models using a Bayesian approach and long-term high resolution data

Stormwater models are important tools in the design and management of urban drainage systems. Understanding the sources of uncertainty in these models and their consequences on the model outputs is essential so that subsequent decisions are based on reliable information. Model calibration and sensitivity analysis of such models are critical to evaluate model performance. The aim of this paper is to present the performance and parameter sensitivity of stormwater models with different levels of complexities, using the formal Bayesian approach. The rather complex MUSIC and simple KAREN models were compared in terms of predicting catchment runoff, while an empirical regression model was compared to a process-based build-up/wash-off model for stormwater pollutant prediction. A large dataset was collected at five catchments of different land-uses in Melbourne, Australia. In general, results suggested that, once calibrated, the rainfall/runoff models performed similarly and were both able to reproduce the measured data. It was found that the effective impervious fraction is the most important parameter in both models while both were insensitive to dry weather related parameters. The tested water quality models poorly represented the observed data, and both resulted in high levels of parameter uncertainty.

A simplified mixed-culture biofilm model

A simple dynamic model is presented for fast simulation of the removal of multiple substrates by different bacterial species growing in a biofilm reactor. The model is an extension to the well-known half-order reaction concept that combines a zero-order kinetic dependency on substrate concentration with diffusion limitation. The basic idea behind this model implementation is to decouple the calculations of the two major processes in the biofilm: substrate diffusion and biochemical conversion. The separate assessment of substrate diffusion allows to relate the penetration depth of substrates to a fraction of biomass that is active in conversion. The conversion is then calculated considering only the active fraction of the biomass. The model is compared to experimental data from the literature and is found to be able to closely replicate the overall dynamics in a biofilm system. The major advantage of the proposed model is the simple structure which leads to a reduction of the computational effort as compared to state-of-the-art mixed-culture biofilm models.

Toward the Sustainable Management of Urban Storm-Water

Despite the development of urban drainage systems over the past 5000 years, there are still many challenges to their effective use. There are growing demands with respect to runoff quantity and quality, visual amenity (landscape aesthetics), protection of ecology and beneficial water uses and interaction with the operation of existing municipal wastewater systems. Current solutions that rely mainly on pipe networks may not be sustainable, especially in developing countries. By considering the driving forces in action during the first years of the 21st century, different scenarios for the future use and development of urban drainage systems can be proposed; all of them rather pessimistic. The implementation of the sustainable management of urban water will require an integrated approach to all the related problems, including the better delivery of urban drainage services.