The Fouling phenomena in membrane bioreactors: a comparison of different mathematical modelling approaches

Eight different modelling approaches for membrane fouling modelling have been compared and the uncertainty has been assessed. Overall the eight approaches provided satisfactory results in terms of model fitting with the measured data. Different results have been obtained in terms of uncertainty bounds showing a different reliability of the model approaches. The study allowed to gain insights about the mechanisms which control fouling in membrane bioreactors

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

Polyhydroxyalkanoate production from fermentation of domestic sewage sludge monitoring greenhouse gas emissions: A pilot plant case study at the WRRF of Palermo University (Italy)

This paper presents a comprehensive study on polyhydroxyalkanoate (PHA) production from sewage sludge. Greenhouse gas (GHG) emissions were monitored for the first time to assess the impact of climate change and environmental sustainability. The pilot plant was composed of a fermenter with a membrane and two biological reactors (namely, selection and accumulation). Results showed that despite a low organic loading rate (namely, 0.06 kg BOD kg SS−1 day−1), a good PHA yield was obtained (namely, 0.37 g PHA/g volatile fatty acids), confirming that sewage sludge can be a suitable feedstock. GHG emissions were 3.85E-04 g CO2eq/g and 32.40 g CO2eq/g, direct and indirect, respectively. Results provided valuable insights in view of finding a trade-off between PHA production and GHG emissions to prove the PHA production process as an effective solution for biosolids disposal at a low carbon footprint

Evaluation of biomass activity in membrane bioreactors by means of respirometric techniques

The paper reports the main results of a respirometric experimental survey carried out on several membrane bioreactor pilot plants, characterized by different pilot plant layouts as well as operational conditions. The main aim was to assess the influence of specific conditions on biokinetic/stoichiometric parameters. In particular, the respirometric tests were specifically aimed at investigating the activity of both heterotrophic and autotrophic bacterial species. The achieved results showed that the plant configuration and the features of the feeding wastewater and operational conditions determine significant variation of the kinetic coefficients. The respirometric analysis was confirmed to be a simple and effective tool for the evaluation of the actual biomass kinetic parameters, to be used in mathematical models for the design phase as well as for monitoring the biomass viability during plant operations

A mathematical model for a sequential batch membrane bioreactor pilot plant

A mathematical model to quantify the nitrogen removal for a membrane bioreactor (MBR) has been presented in this study. The model has been applied to a pilot plant having a pre-denitrification MBR scheme. The pilot plant was cyclically filled with real saline wastewater according to the fill-draw-batch operation. The model was calibrated by adopting a specific protocol based on extensive field dataset. The Standardized Regression Coefficient (SRC) method was adopted to select the most influential model factors to be calibrated. Results related to the SRC method have shown that model factors of the efficiency of backwashing and the biological factors affecting the soluble microbial products (utilization-associated products) (namely, fUAP and KH,UAP) strongly affects the membrane resistance. In terms of model calibration excellent results in terms of model efficiency were found for the total membrane resistance model output (efficiency equal to 0.79). Regarding the biological model outputs acceptable were found in the case an high number of measured data was available. In terms of uncertainty, it was found that for the great part of the analyzed model outputs the measured data lay inside the uncertainty bands

Sensitivity and uncertainty analysis of an integrated membrane bioreactor model

Sensitivity and uncertainty analysis, although can be of primarily importance in mathematical modelling approaches, are scarcely applied in the field of membrane bioreactor (MBR). An integrated mathematical model for MBR is applied with the final aim to pin down sources of uncertainty in MBR modelling. The uncertainty analysis has been performed combining global sensitivity analysis (GSA) with the generalized likelihood uncertainty estimation (GLUE). The model and methodology were applied to a University Cape Town pilot plant. Results show that the complexity of the modelled processes and the propagation effect from the influent to the effluent increase the uncertainty of the model prediction. It was found that the uncertainty of nitrogen and phosphorus model outputs increases from the first reactor-section plant to the last. Results show also that the GSA-GLUE methodology is a valid tool for uncertainty assessment for MBR modelling. Furthermore, the GSA-GLUE allows to identify the most critical processes/plant sections and the key sources of uncertainty where attention should be paid in view of model predictions improvement

Global sensitivity analysis for micropollutant modeling by means of an urban integrated approach

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 is able to estimate 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. 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 modeling

Urban Storm-Water Quality Management: Centralized versus Source Control

The continuous growth of urban areas and the increasing public awareness of the environmental impacts of storm water have raised interest on the quality of the receiving water bodies. In the past two decades, many efforts have been directed at improving urban drainage systems by introducing mitigation measures to limit the negative environmental impacts of storm water. These mitigation measures are generally called best management practices (BMPs), sustainable urban drainage systems, or low impact developments, and they include practices such as infiltration and storage tanks that reduce the peak flow and retain some of the polluting materials. Choosing the best mitigation measure is still a controversial topic. To gain insight on the best technique, this study compares different distributed and centralized urban storm-water management techniques, including infiltration and storage facilities. The main objective of this study is to use modeling to assess the effects of the different urban drainage techniques. To this end, a homemade model that was developed in previous studies is applied. This model enables us to simulate both combined sewer systems and ancillary structures such as storm tanks or infiltration trenches to determine water quantity and quality characteristics. A long-term simulation is employed to account for the effects of sediments in BMPs, which generally reduce the hydraulic capacity. The results allow us to draw some conclusions on the peculiarities of BMP techniques, on the possibility of integrating different techniques for improving efficiency, and on BMP maintenance planning

Greenhouse gas emissions from integrated solid waste management: a new mathematical model

Municipal solid waste management significantly contributes to the emission in the atmosphere of greenhouse gases (e.g. CO2, CH4, N2O) and therefore the management process from collection to treatment and disposal has to be optimized in order to reduce these emissions. Many literature models developed for the evaluation of greenhouses gases emissions from the waste management system are based on the analysis of the life cycle. These models are not optimized for evaluation of emissions. The aim of this study is to overcome these limitations by proposing a mathematical model to estimate greenhouse gas emissions resulting from the integrated waste management. The model is aimed to be a verification tool for assessing the optimum system management in terms of greenhouse gas emissions. The model quantify the emissions associated with: heat treatment, landfill disposal, anaerobic digestion plants, recycling, composting. Different combinations of collection scenarios and disposal options have been considered in the Municipal Solid Waste management of the Province of Palermo. The obtained results applying the model show that limits to solid waste management must be clearly defined. In fact changing the limits, the emissions vary. The lower emissions are due to the use of different energy sources