Peter Vanrolleghem

Uncertainty in the environmental modelling process - A framework and guidance

A terminology and typology of uncertainty is presented together with a framework for the modelling process, its interaction with the broader water management process and the role of uncertainty at different stages in the modelling processes. Brief reviews have been made of 14 different (partly complementary) methods commonly used in uncertainty assessment and characterisation: data uncertainty engine (DUE), error propagation equations, expert elicitation, extended peer review, inverse modelling (parameter estimation), inverse modelling (predictive uncertainty), Monte Carlo analysis, multiple model simulation, NUSAP, quality assurance, scenario analysis, sensitivity analysis, stakeholder involvement and uncertainty matrix. The applicability of these methods has been mapped according to purpose of application, stage of the modelling process and source and type of uncertainty addressed. It is concluded that uncertainty assessment is not just something to be added after the completion of the modelling work. Instead uncertainty should be seen as a red thread throughout the modelling study starting from the very beginning, where the identification and characterisation of all uncertainty sources should be performed jointly by the modeller, the water manager and the stakeholders.

Dynamical modelling and estimation in wastewater treatment processes.

Dynamical Modelling Dynamical Mass Balance Model Building and Analysis Structure Characterisation (SC) Structural Identifiability Practical Identifiability and Optimal Experiment Design for Parameter Estimation (OED/PE) Estimation of Model Parameters Recursive State and Parameter Estimation Glossary Nomenclature

Real-time control of urban wastewater systems - Where do we stand today ?

This paper presents a review of the current state of the art of real time control (RTC) of urban wastewater systems. Control options not only of the sewer system, but also of the wastewater treatment plant and of receiving water bodies are considered. One section of the paper provides concise definitions of terms frequently used in the literature. Recent developments in the field of RTC include the consideration of the urban wastewater system in its entirety. This allows information from all parts of the wastewater system to be used for control decisions and can lead to a significant improvement of the performance of the wastewater system. Some fundamental concepts of this approach are outlined. Particular emphasis in this paper is laid on methodologies of how to derive a control procedure for a given system. As an example of an RTC operational in practice, the Québec Urban Community global predictive RTC system is presented. The paper concludes with an outlook into current and future developments in the area of real time control.

Nitrification monitoring in activated sludge by oxygen uptake rate (OUR) measurements

Abstract A simple measuring system was developed that yields information about the presence of NH + 4 and NO − 2 nitrogen in mixed liquor samples. In addition, it allows monitoring of the rate of both NH + 4 and NO − 2 oxidation simultaneously with the carbon substrate oxidation using OUR measurements. The method is based on the subsequent addition of NaClO 3 and allylthiourea, selective inhibitors of Nitrobacter and Nitrosomonas respectively, to the mixed liquor sample in a closed batch respirometer. The presented method is valuable for detailed monitoring of the nitrification process in a reactor: it is simple, inexpensive, robust and generally applicable for nitrifying reactor systems. The measurement of NH + 4 and NO − 2 oxidation rates enables the operator to detect the presence of NH + 4 and NO − 2 species. This allows action to be taken to improve the performance of the system. The method is validated on a SBR. It is indicated that optimal SBR phase scheduling can be based on such OUR measurements.

Modelling and real-time control of the integrated urban wastewater system

Abstract In the European Union, the Water Framework Directive (WFD) enforces a good ecological and chemical status of all surface waters. In-stream (immission) concentrations and populations need to comply with certain standards. In order to deal with this new legislation, integrated urban water management is an important issue. Real-time control (RTC) is one approach that may be used to improve the performance of the system. Immission-based RTC has been suggested as a proper instrument to help fulfilling the WFD requirements. In order to design and tune an immission-based RTC scheme and to judge the overall effect on the receiving water, an integrated mathematical model of the urban wastewater system is necessary. Several problems are encountered when creating such a model and solutions are discussed in this paper. With this integrated model, an immission-based control strategy is developed for a particular case study and is shown to be able to improve the water quality compared to the uncontrolled case. In the final part, the robustness of this control strategy is tested, as an important additional measure of performance. It can be concluded that there are tools available to help dealing with the operational consequences of the WFD.

Modelling the production and degradation of soluble microbial products (SMP) in membrane bioreactors (MBR)

MBR biochemical conditions have an effect on membrane fouling and SMP have been attributed to be the main MBR foulant. Thus, predicting the SMP concentration is essential for understanding and controlling MBR fouling. However, existing SMP models are mostly too complex and over-parameterized, resulting in inadequate or absent parameter estimation and validation. This study extends the existing activated sludge model No. 2d (ASM2d) to ASM2dSMP with introduction of only 4 additional SMP-related parameters. Dynamic batch experimental results were used for SMP parameter estimation leading to reasonable parameter confidence intervals. Finally, the ASM2dSMP model was used to predict the impact of operational parameters on SMP concentration. It would found that solid retention time (SRT) is the key parameter controlling the SMP concentration. A lower SRT increased the utilization associated products (UAP) concentration, but decreased the biomass associated products (BAP) concentration and vice versa. A SRT resulting in minimum total SMP concentration can be predicted, and is found to be a relatively low value in the MBR. If MBRs operate under dynamic conditions and biological nutrient removal is required, a moderate SRT condition should be applied.

Validation of a Metabolic Network for Saccharomyces cerevisiae Using Mixed Substrate Studies

Setting up a metabolic network model for respiratory growth of Saccharomyces cerevisiae requires the estimation of only two (energetic) stoichiometric parameters: (1) the operational PO ratio and (2) a growth‐related maintenance factor k. It is shown, both theoretically and practically, how chemostat cultivations with different mixtures of two substrates allow unique values to be given to these unknowns of the proposed metabolic model. For the yeast and model considered, an effective PO ratio of 1.09 mol of ATP/mol of O (95% confidence interval 1.07−1.11) and a k factor of 0.415 mol of ATP/C‐mol of biomass (0.385−0.445) were obtained from biomass substrate yield data on glucose/ethanol mixtures. Symbolic manipulation software proved very valuable in this study as it supported the proof of theoretical identifiability and significantly reduced the necessary computations for parameter estimation. In the transition from 100% glucose to 100% ethanol in the feed, four metabolic regimes occur. Switching between these regimes is determined by cessation of an irreversible reaction and initiation of an alternative reaction. Metabolic network predictions of these metabolic switches compared well with activity measurements of key enzymes. As a second validation of the network, the biomass yield of S. cerevisiae on acetate was also compared to the network prediction. An excellent agreement was found for a network in which acetate transport was modeled with a proton symport, while passive diffusion of acetate gave significantly higher yield predictions.

The role of blocking and cake filtration in MBR fouling

Abstract Membrane fouling in a side stream biomass separation MBR pilot plant was investigated. Constant flux filtration (18–721/M2h) was employed. Air was continuously supplied to the MBR system with the feed (sludge) to flush the membrane surface, and backwashing was applied every 5–10 min for 8 s to control membrane fouling. Although the duration of pore blocking was generally short (completed in 8 s at a flux of 52 llm2h), blocking resistance (mainly irreversible blocking resistance) was the main cause of membrane fouling. However, the resistance of the filter cake also played an important role, particularly when the backwashing interval was extended to 10 min. In terms of fouling reversibility, blocking resistance was not completely reversible by backwashing, especially at higher fluxes (e.g. 69 I/m2h), and frequent chemical cleaning (once every week at 401/m2h) was required. However, cake filtration was easily reversible via a combination of backwashing and sludge/air flushing of the membrane surface. Finally, a simple method to identify both irreversible and reversible blocking resistance and filter cake resistance was proposed.

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.

Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies.

The main objective of this paper is to demonstrate how greenhouse gas (GHG) emissions can be quantified during the evaluation of control strategies in wastewater treatment plants (WWTP). A modified version of the IWA Benchmark Simulation Model No 2 (BSM2G) is hereby used as a simulation case study. Thus, the traditional effluent quality index (EQI), operational cost index (OCI) and time in violation (TIV) used to evaluate control strategies in WWTP are complemented with a new dimension dealing with GHG emissions. The proposed approach is based on a set of comprehensive models that estimate all potential on-site and off-site sources of GHG emissions. The case study investigates the overall performance of several control strategies and demonstrates that substantial reductions in effluent pollution, operating costs and GHG emissions can be achieved when automatic control is implemented. Furthermore, the study is complemented with a scenario analysis that examines the role of i) the dissolved oxygen (DO) set-point, ii) the sludge retention time (SRT) and iii) the organic carbon/nitrogen ratio (COD/N) as promoters of GHG emissions. The results of this study show the potential mechanisms that promote the formation of CO2, CH4 and N2O when different operational strategies are implemented, the existing synergies and trade-offs amongst the EQI, the OCI and TIV criteria and finally the need to reach a compromise solution to achieve an optimal plant performance.