Greenhouse Gas Emissions from Membrane Bioreactors

Nowadays, it is widely accepted that wastewater treatment plants (WWTPs) are significant sources of greenhouse gas (GHG) emission, contributing to the anthropogenic sources. Among the GHG emitted from WWTPs, nitrous oxide (N2O) has been identified of having the major interest/concern, since its high global warming potential (GWP), is 298 times higher than that of CO2 and also to its capability to react with stratospheric ozone causing the layer depletion. Up to now, most of the experimental investigations have been carried out on conventional activated sludge (CAS) processes. The knowledge of N2O emission from advanced technologies such membrane bioreactors (MBRs) is still very limited. The present paper is aimed at providing a picture of the GHG emissions from MBR systems. In particular, data of N2O acquired from pilot plant systems monitoring are here presented. The key aim of the study was to highlight the effect of wastewater features and operational conditions on N2O production/emission from MBRs

Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution

Mathematical models are of common use in urban drainage, and they are increasingly being applied to support decisions about design and alternative management strategies. In this context, uncertainty analysis is of undoubted necessity in urban drainage modelling. However, despite the crucial role played by uncertainty quantification, several methodological aspects need to be clarified and deserve further investigation, especially in water quality modelling. One of them is related to the “a priori” hypotheses involved in the uncertainty analysis. Such hypotheses are usually condensed in “a priori” distributions assessing the most likely values for model parameters. This paper explores Bayesian uncertainty estimation methods investigating the influence of the choice of these prior distributions. The research aims at gaining insights in the selection of the prior distribution and the effect the user-defined choice has on the reliability of the uncertainty analysis results. To accomplish this, an urban stormwater quality model developed in previous studies has been employed. The model has been applied to the Fossolo catchment (Italy), for which both quantity and quality data were available. The results show that a uniform distribution should be applied whenever no information is available for specific parameters describing the case study. The use of weak information (mostly coming from literature or other model applications) should be avoided because it can lead to wrong estimations of uncertainty in modelling results. Model parameter related hypotheses would be better dropped in these cases

Quantifying sensitivity and uncertainty analysis of a new mathematical model for the evaluation of greenhouse gas emissions from membrane bioreactors

A new mathematical model able to quantify greenhouse gas (GHG) emissions in terms of carbon dioxide (CO2) and nitrous oxide (N2O) for a Membrane Bioreactor (MBR) is presented. The proposed mathematical model is of the Activated Sludge Model (ASM) family and takes into account simultaneously both biological and physical processes (e.g., membrane fouling). An analysis of the key factors and sources of uncertainty influencing GHG emissions is also presented. Specifically, the standardized regression coefficient, the Extended-FAST and a Monte Carlo based method are employed for assessing model factors which influence three performance indicators: effluent quality index, operational costs and GHGs. Model factors are classified as important, non-influential and interacting. The model is applied to a University Cape Town-MBR pilot plant which was object of an extensive field gathering campaign. The results reveal that model factors related to nitrogen transformation and membrane separation processes play a central role in the uncertainty of GHG estimation. Model factors that are associated with physical processes exhibit large first-order and total-order effects, which emphasises the importance of a holistic approach that jointly considers biological and physical processes. Furthermore, the membrane has a key role in GHG emissions as a result of the cake layer thickness which in turns influences the mass of substrate retained by the membrane and, thus, the biological process in the MBR. The results show that a modeller should not exclude the role of phosphorus in the contribution of accumulating organisms during the prediction of GHGs due to the high interaction of N2O. The results reveal that the uncertainty in the emission factors for CO2 is higher than the uncertainty in the emission factors for N2O (namely, 2.2 and 0.17%, respectively)

The sludge dewaterability in membrane bioreactors

The influence of the sludge origin on the dewaterability features has been investigated by comparing the experimental results of six membrane bioreactor pilot plants with different configurations. The capillary suction time (CST) and the specific resistance to filtration (SRF), identified as representative of sludge dewaterability features, were measured. The results were related to operational parameters, such as extracellular polymeric substances (EPS) and soluble microbial product (SMP), influent salinity and hydrocarbon, in order to elucidate the influence exerted on the dewaterability. Furthermore, the effect of biofilm and suspended biomass was also investigated. The results showed that during the experimentation carried out with salt and hydrocarbon the sludge dewaterability features significantly worsened (CST above 120 s and SRF above 20 * 1012 m kg-1). Furthermore, the sludge derived from the anoxic reactor resulted as the most affected by EPS and SMP concentration

The fouling phenomenon in membrane bioreactors: Assessment of different strategies for energy saving

Membrane fouling represents one of the major issues for a membrane bioreactor (MBR). Membrane fouling and high aeration requirements (for inducing shear stress to limit fouling) make MBR operation economically demanding due to high energy costs. Although several studies on MBR fouling have been performed, comprehensive knowledge on how to reduce membrane fouling and consequently save energy is still lacking. An integrated mathematical model for MBR is applied to a University of Cape Town membrane bioreactor with the final aim to reduce the energy costs. In particular, the influence of the aeration intensity, the duration of filtration/backwashing cycles, and the number of membrane cleanings are investigated. Five scenarios are analyzed and compared, each implementing different operating conditions. The features of the analyzed scenarios are quantified by employing Monte Carlo simulations and performance indices partially drawn from literature. The results provide insights about the role played by the main physical/chemical/biological processes in view of a system optimization. As expected, MBR operation at low air flow rate (qa) leads to a substantial reduction of the operational costs (specifically, 20% with respect to the suggested manufacturers ones in terms of qa). Despite such a reduction of qa, a good effluent quality is also obtained as an effect of a high biological cake thickness. Results also show that the values of filtration time (Tf) higher than those suggested by manufacturers (e.g., Tf=9 min) can be used to increase effluent quality. This study demonstrates that both energy savings and effluent quality can be improved by varying the operational variables with respect to those of the suggested manufacture. One of the main insights gained from this study is that the values of the operating variables (i.e., qa, Tb and Tf) suggested by the manufactures can be changed to obtain a system that still respects high effluent quality and is characterized by lower economical cost. The proposed modeling approach can be an useful tool for the optimization of the operating conditions in order to reduce the operational costs for MBR systems

Intermittent aeration in a hybrid moving bed biofilm reactor for carbon and nutrient biological removal

The paper presents an experimental study on a lab scale hybrid moving bed biofilm reactor with intermittent aeration. Specifically, a comparison between two different operating conditions was analyzed: continuous and intermittent aeration. Both continuous and intermittent aeration were monitored and compared in order to get the best operational conditions. The intermittent aeration campaign was sub-divided in three phases with different duration of alternation of aerobic and anoxic times and organic and nitrogen loading rates. The effciency of N-removal improved by 70% during the intermittent aeration. The best condition was observed with 40 min of aeration and 20 min of no-aeration, an organic loading rate of 2.2 kgCODm-3day-1 and a nitrogen loading rate of 0.25 kgNm-3day-1: under these operational conditions the removal effciencies for carbon and nitrogen were 93% and 90%, respectively. The derived results provide the basis for WWTP upgrade in order to meet stricter euent limits at low energy requirements

Micropollutants throughout an integrated urban drainage model: Sensitivity and uncertainty analysis

The paper presents the sensitivity and uncertainty analysis of an integrated urban drainage model which includes micropollutants. Specifically, a bespoke integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole \ue2\u80\u93 SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Overall, five scenarios, each characterized by different uncertainty combinations of sub-systems (i.e., SS, WWTP and RWB), have been considered applying, for the sensitivity analysis, the Extended-FAST method in order to select the key factors affecting the RWB quality and to design a reliable/useful experimental campaign. Results have demonstrated that sensitivity analysis is a powerful tool for increasing operator confidence in the modelling results. The approach adopted here can be used for blocking some non-identifiable factors, thus wisely modifying the structure of the model and reducing the related uncertainty. The model factors related to the SS have been found to be the most relevant factors affecting the SMX modeling in the RWB when all model factors (scenario 1) or model factors of SS (scenarios 2 and 3) are varied. If the only factors related to the WWTP are changed (scenarios 4 and 5), the SMX concentration in the RWB is mainly influenced (till to 95% influence of the total variance for SSMX,max) by the aerobic sorption coefficient. A progressive uncertainty reduction from the upstream to downstream was found for the soluble fraction of SMX in the RWB

Simplified model to evaluate the fate of micropollutants in an integrated urban drainage system: sensitivity analysis

The paper presents the sensitivity analysis of an integrated urban water quality system by means of the global sensitivity analysis (GSA). Specifically, an home-made integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole - SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and the receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Five scenarios each characterized by different combinations of sub-systems (i.e., SS, WWTP and RWB) have been considered applying the Extended-FAST method in order to select the key factors affecting the RWB quality and to design a reliable/useful experimental campaign. Results demonstrated that GSA is a powerful tool for increasing operator confidence in the modelling results; the approach can be used for blocking some non-identifiable parameters thus wisely modifying the structure of the model and reducing the related uncertainty. The model factors related to the SS have been found to be the most relevant factors affecting the SMX modelin

BioMAc 2016 Bioreattori a membrane (MBR) e trattamenti avanzati per la depurazione delle acque

Questo volume raccoglie i contributi presentati nella manifestazione BioMAc 2016: Bioreattori a Membrane (MBR) e trattamenti avanzati per la depurazione delle Acque, che ha avuto luogo presso il Dipartimento di Ingegneria Civile, Ambientale, Aerospaziale, dei Materiali dell'Universit\ue0 degli Studi di Palermo, Aula G. Capit\uf2, nei giorni 27 e 28 ottobre 2016

Greenhouse gases from membrane bioreactors: Mathematical modelling, sensitivity and uncertainty analysis

In this study a new mathematical model to quantify greenhouse gas emissions (namely, carbon dioxide and nitrous oxide) from membrane bioreactors (MBRs) is presented. The model has been adopted to predict the key processes of a pilot plant with pre-denitrification MBR scheme, filled with domestic and saline wastewater. The model was calibrated by adopting an advanced protocol based on an extensive dataset. In terms of nitrous oxide, the results show that an important role is played by the half saturation coefficients related to nitrogen removal processes and the model factors affecting the oxygen transfer rate in the aerobic and MBR tanks. Uncertainty analysis showed that for the gaseous model outputs 88\ue2\u80\u9393% of the measured data lays inside the confidence bands showing an accurate model prediction