Generation of (synthetic) influent data for performing wastewater treatment modelling studies

The success of many modelling studies strongly depends on the availability of sufficiently long influent time series - the main disturbance of a typical wastewater treatment plant (WWTP) - representing the inherent natural variability at the plant inlet as accurately as possible. This is an important point since most modelling projects suffer from a lack of realistic data representing the influent wastewater dynamics. The objective of this paper is to show the advantages of creating synthetic data when performing modelling studies for WWTPs. This study reviews the different principles that influent generators can be based on, in order to create realistic influent time series. In addition, the paper summarizes the variables that those models can describe: influent flow rate, temperature and traditional/emerging pollution compounds, weather conditions (dry/wet) as well as their temporal resolution (from minutes to years). The importance of calibration/validation is addressed and the authors critically analyse the pros and cons of manual versus automatic and frequentistic vs Bayesian methods. The presentation will focus on potential engineering applications of influent generators, illustrating the different model concepts with case studies. The authors have significant experience using these types of tools and have worked on interesting case studies that they will share with the audience. Discussion with experts at the WWTmod seminar shall facilitate identifying critical knowledge gaps in current WWTP influent disturbance models. Finally, the outcome of these discussions will be used to define specific tasks that should be tackled in the near future to achieve more general acceptance and use of WWTP influent generators

Evaluating the impact of plant-wide WWTP control strategies on energy consumption and greenhouse gas emissions

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Microbially catalyzed production of gaseous fuels in bioelectrochemical systems

This research focuses on the production of hydrogen and methane by using microorganisms as the electrocatalytic agents for the reduction of H+ or CO2 respectively, at the cathode of bioelectrochemical systems. As for H2 generation, our previous results (Villano et al., 2011) showed the ability of hydrogenophilic dechlorinating bacteria to catalyze the reaction with graphite electrodes serving as electron donors. Based on these findings, we are currently exploring the potential for application of other hydrogenase-possessing microorganisms, such as sulphate-reducing bacteria (e.g., Desulfovibrio sp.). To accomplish this objective, a combination of electrochemical techniques (i.e., cyclic voltammetry, impedance spectroscopy, etc...) and potentiostatic H2-production experiments have been performed. With regard to CH4 production, a fully biological bioelectrochemical reactor coupling acetate oxidation to CO2 reduction has been developed. The reactor consists of two compartments filled with graphite granules serving as both electrodic material and support for microbial biofilm formation. The performance of the reactor, in terms of methane generation, organic substrates removal, and coulombic and energy efficiency, has been evaluated under different operating conditions (such as: potentiostatic control of the biocathode or bioanode potential; type and amount of microorganisms in each compartment, temperature, etc…). Main attention has been paid at identifying the rate-limiting steps in order to optimize the process performance

Comparison of different modeling approaches to better evaluate greenhouse gas emissions from whole wastewater treatment plants

New tools are being developed to estimate greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). There is a trend to move from empirical factors to simple comprehensive and more complex process-based models. Thus, the main objective of this study is to demonstrate the importance of using process-based dynamic models to better evaluate GHG emissions. This is tackled by defining a virtual case study based on the whole plant Benchmark Simulation Model Platform No. 2 (BSM2) and estimating GHG emissions using two approaches: (1) a combination of simple comprehensive models based on empirical assumptions and (2) a more sophisticated approach, which describes the mechanistic production of nitrous oxide (N2O) in the biological reactor (ASMN) and the generation of carbon dioxide (CO2) and methane (CH4) from the Anaerobic Digestion Model 1 (ADM1). Models already presented in literature are used, but modifications compared to the previously published ASMN model have been made. Also model interfaces between the ASMN and the ADM1 models have been developed. The results show that the use of the different approaches leads to significant differences in the N2O emissions (a factor of 3) but not in the CH4 emissions (about 4%). Estimations of GHG emissions are also compared for steady-state and dynamic simulations. Averaged values for GHG emissions obtained with steady-state and dynamic simulations are rather similar. However, when looking at the dynamics of N2O emissions, large variability (36?ton?CO2e?day-1) is observed due to changes in the influent wastewater C/N ratio and temperature which would not be captured by a steady-state analysis (4.4?ton?CO2e?day-1). Finally, this study also shows the effect of changing the anaerobic digestion volume on the total GHG emissions. Decreasing the anaerobic digester volume resulted in a slight reduction in CH4 emissions (about 5%), but significantly decreased N2O emissions in the water line (by 14%). Biotechnol. Bioeng. 2012; 109: 28542863. (c) 2012 Wiley Periodicals, Inc

Challenges encountered when expanding activated sludge models: a case study based on N2O production.

It is common practice in wastewater engineering to extend standard activated sludge models (ASMs) with extra process equations derived from batch experiments. However, such experiments have often been performed under conditions different from the ones normally found in wastewater treatment plants (WWTPs). As a consequence, these experiments might not be representative for full-scale performance, and unexpected behaviour may be observed when simulating WWTP models using the derived process equations. In this paper we want to highlight problems encountered using a simplified case study: a modified version of the Activated Sludge Model No. 1 (ASM1) is upgraded with nitrous oxide (N2O) formation by ammonia-oxidizing bacteria. Four different model structures have been implemented in the Benchmark Simulation Model No. 1 (BSM1). The results of the investigations revealed two typical difficulties: problems related to the overall mathematical model structure and problems related to the published set of parameter values. The paper describes the model implementation incompatibilities, the variability in parameter values and the difficulties of reaching similar conditions when simulating a full-scale activated sludge plant. Finally, the simulation results show large differences in oxygen uptake rates, nitritation rates and consequently the quantity of N2O emission (GN2O) using the different models

Zastrupitve s paracetamolom: kako učinkovita je zakonodaja v Veliki Britaniji in kakšno je stanje v Sloveniji?

This paper demonstrates how occurrence, transport and fate of pharmaceuticals at trace levels can be assessed when modelling wastewater treatment systems using two case studies. Firstly, two approaches based on: 1) phenomenology; and, 2) Markov Chains, are developed to describe the dynamics of pharmaceuticals with or without clear administration patterns. Additional simulations also show that sewer conditions might have an important effect on the behaviour of the generated compounds and their metabolites. The results demonstrate that different operating conditions in wastewater treatment plants can have opposite effects on the studied pharmaceuticals, especially when they present co-metabolic or inhibitory behaviour in the presence of biodegradable substrate. Finally, the paper ends with: i) a critical discussion of the presented results; ii) a thorough analysis of the limitations of the proposed approach; and, iii) future pathways to improve the overall modelling of micropollutants. (C) 2014 Elsevier Ltd. All rights reserved

Introducing Power-to-H3: Combining renewable electricity with heat, water and hydrogen production and storage in a neighbourhood

In the transition from fossil to renewable energy, the energy system should become clean, while remaining reliable and affordable. Because of the intermittent nature of both renewable energy production and energy demand, an integrated system approach is required that includes energy conversion and storage. We propose a concept for a neighbourhood where locally produced renewable energy is partly converted and stored in the form of heat and hydrogen, accompanied by rainwater collection, storage, purification and use (Power-to-H3). A model is developed to create an energy balance and perform a techno-economic analysis, including an analysis of the avoided costs within the concept. The results show that a solar park of 8.7 MWp combined with rainwater collection and solar panels on roofs, can supply 900 houses over the year with heat (20 TJ) via an underground heat storage system as well as with almost half of their water demand (36,000 m3) and 540 hydrogen electric vehicles can be supplied with hydrogen (90 tonnes). The production costs for both hydrogen (8.7 €/kg) and heat (26 €/GJ) are below the current end user selling price in the Netherlands (10 €/kg and 34 €/GJ), making the system affordable. When taking avoided costs into account, the prices could decrease with 20–26%, while at the same time avoiding 3600 tonnes of CO2 a year. These results make clear that it is possible to provide a neighbourhood with all these different utilities, completely based on solar power and rainwater in a reliable, affordable and clean way.Sanitary EngineeringEconomics of Technology and InnovationEnergy Technolog

Sulfate reducing bacteria applied to domestic wastewater

The application of sulfate reducing bacteria (SRB) to treat municipal wastewater is seldom considered. For instance, due to low sludge yield it can reduce the amount of excess sludge produced significantly. Several studies, mainly at laboratory-scale, revealed that SRB can proliferate in artificial wastewater systems at temperatures of 20°C and lower. So far, the application of SRB in a domestic wastewater treatment plant has been limited. Therefore, this study evaluates the proliferation of SRB at pilot-scale in a moderate climate. This study revealed that SRB were present and active in the pilot fed with domestic wastewater at 13°C, and outcompete methanogens. Stable, smooth and well-settled granule formation occurred, which is beneficial for full-scale application. In the Netherlands the sulfate concentration is usually low (,500 mg/L), therefore the application of SRB seems challenging as sulfate is limiting. Additional measurements indicated the presence of other sulfur sources, therefore higher sulfur levels were available, which makes it possible to remove more than 75% of the chemical oxygen demand (excluding sulfide) based on SRB activity. The beneficial application of SRB to domestic wastewater treatment might therefore be valid for more locations than initially expected.</p

Generation of synthetic influent data to perform (micro)pollutant wastewater treatment modelling studies

The use of process models to simulate the fate of micropollutants in wastewater treatment plants is constantly growing. However, due to the high workload and cost of measuring campaigns, many simulation studies lack sufficiently long time series representing realistic wastewater influent dynamics. In this paper, the feasibility of the Benchmark Simulation Model No. 2 (BSM2) influent generator is tested to create realistic dynamic influent (micro)pollutant disturbance scenarios. The presented set of models is adjusted to describe the occurrence of three pharmaceutical compounds and one of each of its metabolites with samples taken every 2–4 h: the anti-inflammatory drug ibuprofen (IBU), the antibiotic sulfamethoxazole (SMX) and the psychoactive carbamazepine (CMZ). Information about type of excretion and total consumption rates forms the basis for creating the data-defined profiles used to generate the dynamic time series. In addition, the traditional influent characteristics such as flow rate, ammonium, particulate chemical oxygen demand and temperature are also modelled using the same framework with high frequency data. The calibration is performed semi-automatically with two different methods depending on data availability. The ‘traditional’ variables are calibrated with the Bootstrap method while the pharmaceutical loads are estimated with a least squares approach. The simulation results demonstrate that the BSM2 influent generator can describe the dynamics of both traditional variables and pharmaceuticals. Lastly, the study is complemented with: 1) the generation of longer time series for IBU following the same catchment principles; 2) the study of the impact of in-sewer SMX biotransformation when estimating the average daily load; and, 3) a critical discussion of the results, and the future opportunities of the presented approach balancing model structure/calibration procedure complexity versus predictive capabilities