Browsing by Author "Schellart, Alma"

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    Accounting for variation in rainfall intensity and surface slope in wash-off model calibration and prediction within the Bayesian framework
    (Elsevier, 2018-06-12) Muthusamy, Manoranjan; Wani, Omar; Schellart, Alma; Tait, Simon
    Exponential wash-off models are the most widely used method to predict sediment wash-off from urban surfaces. In spite of many studies, there is still a lack of knowledge on the effect of external drivers such as rainfall intensity and surface slope on the wash-off prediction. In this study, a more physically realistic “structure” is added to the original exponential wash-off model (OEM) by replacing the invariant parameters with functions of rainfall intensity and catchment surface slope, so that the model can better represent catchment and rainfall conditions without the need of lookup table and interpolation/extrapolation. In the proposed new exponential model (NEM), two such functions are introduced. One function describes the maximum fraction of the initial load that can be washed off by a rainfall event for a given slope and the other function describes the wash-off rate during a rainfall event for a given slope. The parameters of these functions are estimated using data collected from a series of laboratory experiments carried out using an artificial rainfall generator, a 1 m2 bituminous road surface and a continuous wash-off measuring system. These experimental data contain high temporal resolution measurements of wash-off fractions for combinations of five rainfall intensities ranging from 33-155 mm/hr and three catchment slopes ranging from 2-8 %. Bayesian inference, which allows the incorporation of prior knowledge, is implemented to estimate parameter values. Explicitly accounting for model bias and measurement errors, a likelihood function representative of the wash-off process is formulated, and the uncertainty in the prediction of the NEM is quantified. The results of this study show: 1) even when OEM is calibrated for every experimental condition, NEM’s performance, with parameter values defined by functions, is comparable to OEM. 2) Verification indices for estimates of uncertainty associated with NEM suggest that the error model used in this study is able to capture the uncertainty well.
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    Heat recovery and thermal energy storage potential using buried infrastructure in the UK
    (Institution of Civil Engineers (ICE), 2022-04-14) Loveridge, Fleur; Schellart, Alma; Rees, Simon; Stirling, Ross; Taborda, David; Tait, Simon; Alibardi, Luca; Biscontin, Giovanna; Shepley, Paul; Shafagh, Ida; Shepherd, Will; Yildiz, Anil; Jefferson, Bruce
    Dispersed space heating alone accounts for 40% of UK energy use and 20% of CO2 emissions. Tackling heating and building cooling demands is therefore critical to achieve net zero ambitions in the UK. The most energy efficient way to decarbonise heating and cooling is through the use of ground source heat pumps and district heating technology. However, capital costs are often high, sometimes prohibitively so. To reduce investment costs, it is proposed to use buried infrastructure as sources and stores of thermal energy. Barriers to this innovative approach include lack of knowledge about the actual net amount of recoverable energy, and impacts on the primary function of any buried infrastructure, as well as the need for new investment and governance strategies integrated across the energy and infrastructure sectors. Additional opportunities from thermal utilisation in buried infrastructure include the potential mitigation of damaging biological and/or chemical processes that may occur. This paper presents a first assessment of the scale of the opportunity for thermal energy recovery and storage linked to new and existing buried infrastructure, along with strategic measures to help reduce barriers and start the UK on the journey to achieving of its infrastructure energy potential.
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    Modelling the potential for multi-location in-sewer heat recovery at a city scale under different seasonal scenarios
    (Elsevier, 2018-09-01) Abdel-Aal, Mohamad; Schellart, Alma; Kroll, Stefan; Mohamed, Mostafa; Tait, Simon
    A computational network heat transfer model was utilised to model the potential of heat energy recovery at multiple locations from a city scale combined sewer network. The uniqueness of this network model lies in its whole system validation and implementation for seasonal scenarios in a large sewer network. The network model was developed, on the basis of a previous single pipe heat transfer model, to make it suitable for application in large sewer networks and its performance was validated in this study by predicting the wastewater temperature variation across the network. Since heat energy recovery in sewers may impact negatively on wastewater treatment processes, the viability of large scale heat recovery was assessed by examining the distribution of the wastewater temperatures throughout a 3000 pipe network, serving a population equivalent of 79500, and at the wastewater treatment plant inlet. Three scenarios; winter, spring and summer were modelled to reflect seasonal variations. The model was run on an hourly basis during dry weather. The modelling results indicated that potential heat energy recovery of around 116, 160 & 207 MWh/day may be obtained in January, March and May respectively, without causing wastewater temperature either in the network or at the inlet of the wastewater treatment plant to reach a level that was unacceptable to the water utility.
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    Potential influence of sewer heat recovery on in-sewer processes
    (IWA Publishing, 2020-02-13) Abdel-Aal, Mohamad; Villa, Raffaella; Jawiarczyk, Natalia; Alibardi, Luca; Jensen, Henriette; Schellart, Alma; Jefferson, Bruce; Shepley, Paul; Tait, Simon
    Heat recovery from combined sewers has a significant potential for practical renewable energy provision as sources of heat demand and sewer pipes are spread across urban areas. Sewers are continuously recharged with relatively hot wastewater, as well as interacting with heat sources from surrounding air and soil. However, the potential effects of modifying sewage temperature on in-sewer processes have received little attention. The deposition of fats, oils and greases (FOGs) and hydrogen sulphide formation are biochemical processes and are thus influenced by temperature. This paper utilises a case study approach to simulate anticipated temperature reductions in a sewer network due to heat recovery. A laboratory investigation into the formation of FOG deposits at temperatures varying between 5 °C and 20 °C provided mixed results, with only a weak temperature influence, highlighting the need for more research to fully understand the influence of the wastewater composition as well as temperature on FOG deposit formation. A separate modelling investigation into the formation of hydrogen sulphide when inflow temperature is varied between 5 °C and 20 °C showed considerable reductions in hydrogen sulphide formation. Hence, heat extraction from sewers could be a promising method for managing some in-sewer processes, combined with traditional methods such as chemical dosing
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    Recent insights on uncertainties present in integrated catchment water quality modelling
    (Elsevier, 2018-12-05) Tscheikner-Gratl, Franz; Bellos, Vasilis; Schellart, Alma; Moreno-Rodenas, Antonio; Muthusamy, Manoranjan; Langeveld, Jeroen; Clemens, Francois; Benedetti, Lorenzo; Rico-Ramirez, Miguel Angel; Fernandes de Carvalho, Rita; Breuer, Lutz; Shucksmith, James; Heuvelink, Gerard B. M.; Tait, Simon
    This paper aims to stimulate discussion based on the experiences derived from the QUICS project (Quantifying Uncertainty in Integrated Catchment Studies). First it briefly discusses the current state of knowledge on uncertainties in sub-models of integrated catchment models and the existing frameworks for analysing uncertainty. Furthermore, it compares the relative approaches of both building and calibrating fully integrated models or linking separate sub-models. It also discusses the implications of model linkage on overall uncertainty and how to define an acceptable level of model complexity. This discussion includes, whether we should shift our attention from uncertainties due to linkage, when using linked models, to uncertainties in model structure by necessary simplification or by using more parameters. This discussion attempts to address the question as to whether there is an increase in uncertainty by linking these models or if a compensation effect could take place and that overall uncertainty in key water quality parameters actually decreases. Finally, challenges in the application of uncertainty analysis in integrated catchment water quality modelling, as encountered in this project, are discussed and recommendations for future research areas are highlighted.