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Evaluating the impact of underwater skimming on slow sand filter performance and operation
Get PDFConventional cleaning of slow sand filters (SSFs) requires the beds to be drained before a layer of media and the Schmutzdecke are removed, called ‘dry skimming’ (DS), which can result in significant downtime. An alternative is proposed whereby the filter is skimmed whilst still submerged, called ‘underwater skimming’ (UWS). Previous attempts to avoid draining the bed have led to concerns about the risks of UWS in terms of headloss development, particle penetration, and microbial water quality. In this study, pilot scale SSFs, cleaned by either DS or UWS, were operated concurrently, to compare and assess the potential risks of UWS in terms of filtrate quality, microbial removal, and recovery following skimming. While all filters exhibited effective turbidity removal (0.04–0.9 NTU turbidity from outlet), UWS filters had improved performance immediately after cleaning compared to DS in terms of recovery of filtrate microbial water quality. Specifically, total coliforms in the UWS filter outlets, in the first seven days post-skimming, ranged from 1 to 109 most probable number (MPN)/mL, compared to 1 to 1414 MPN/mL for DS filters. Both methods yielded satisfactory headloss recovery, indicative of limited particle penetration at depth, and effective cleaning. Exploring different sweetening flow rates during UWS revealed no observable differences in headloss, turbidity reduction, or microbial quality between the flow rates tested. Schmutzdecke microbial community was similar irrespective of cleaning method and was governed instead by seasonal changes and the ripening process. The improved microbial removal afforded by UWS provides a means of significantly improving water productivity and enabling options for much better utilisation of SSFs.Engineering and Physical Sciences Research CouncilThe authors acknowledge the financial support of the Engineering and Physical Sciences Research Council (ESPRC) through the STREAM Industrial Doctorate Centre (EP/L015412/1), and financial support from Thames Water and Northumbrian Water Group.Water Researc
Traffic prediction with shared causal inference in ORAN computing continuum
Get PDFData Availability: Most of the raw data used is available in a previous data release on Dryad: https://doi.org/10.5061/dryad.35m1f4qData-driven proactive network optimisation is critical for 5G advanced and 6G, allowing operators to dynamically allocate cellular spectrum reuse in anticipating for demand surges. Current approaches to traffic prediction are largely temporal correlation based. We know causal inference of key factors can help to improve prediction accuracy for spike traffic events and identify pathways to improve services. Current causal inference identify stationary independent variables, but real environments have open challenges: (i) dynamic and heterogeneous causal maps, (ii) cascade partially observable variables, and/or (iii) have coupled / confounding relationships. Currently there is no research that dynamically configures the causal relationship according to emerging real-time data and shares inference outcomes across the data sharing and computing continuum of Open-RAN (ORAN) architecture. Here, we use both real cellular network traffic and social event triggers to perform nonlinear causal inference as an rApp: Predictability Improvement (PI), Conditional Mutual Information (CMI), and Convergent Cross Map (CCM). This causal knowledge is then shared across the ORAN to be embedded in traffic prediction xApps: hard causal embedding to Recurrent Neural Network (RNN) and soft causal feature embedding to a Gaussian Processes (GP). The results show a significant accuracy improvement (93-99%) over baseline non-causal correlated prediction (76-94%) and blind multi-variate approaches (87-95%). This work paves the way to causal proactive network optimisation.Engineering and Physical Sciences Research CouncilThis work has been supported by the EPSRC and DSIT: Communications Hub For Empowering Distributed Cloud Computing Applications And Research (EP/X040518/1, EP/Y037421/1).GLOBECOM 2024 - 2024 IEEE Global Communications Conferenc
The use of chemiluminescence nitrogen oxides analysis for the study of the decomposition of nitrocellulose
Get PDFUnderstanding the decomposition of nitrocellulose (NC) and other nitrate esters within storage and usage temperature ranges is essential for managing the service life and safety of (NC)‐containing formulations. High‐temperature decomposition studies often fail to reflect typical storage conditions due to temperature‐dependent mechanisms. This study uses chemiluminescence nitrogen oxides (NOx) analysers to examine NC decomposition, measuring NOx evolution across a wide temperature range. From 20°C to 135°C, decomposition modes include thermolysis, hydrolysis, and physical desorption. Results show NOx can desorb from NC at ambient temperatures, potentially misleading traditional stability tests. The quantity of NOx generated depends on material history and can be reduced by pre‐test procedures. While thermolysis dominates at higher temperatures with an activation energy of 140 kJ.mol−1, hydrolysis is predominant at lower temperatures with an activation energy of 46 kJ.mol−1. This low activation energy should be considered in any life assessment predictions. In this lower temperature regime, moisture significantly affects decomposition rates, especially below 50°C. Whilst the rate increases in the presence of moisture, the activation energy for the hydrolysis process is unaffected. Chemiluminescence NOx analysis has proven to be a powerful tool for studying the low‐temperature decomposition behaviours of NC and NC‐containing formulations. This innovative approach not only enhances the understanding of NC decomposition but also offers a more efficient and accurate method for assessing the stability of NC‐containing formulations.Propellants, Explosives, Pyrotechnic
Facilitating the predictions of batch and continuous anaerobic digestion processes performance with statistical tools
No full textAnaerobic digestion (AD) is a promising technology for waste management and
renewable energy production. Determining the biomethane potential (BMP) of a
material is crucial when considering it as feedstock for the digester. The practical
BMP of a material and its degradation kinetics can be derived from the batch
BMP test, typically taking at least 30 days, which can be onerous to the industrial
operator. Many studies have attempted to predict BMP test results by building
regressions between various feedstock physiochemical characteristics and BMP
test result. However, these methods primarily predict the ultimate biogas yield of
the BMP test and are unable to capture the reaction kinetics.
Part I of this study proposed a method to predict the BMP test result of a material,
not only the ultimate biogas yield but also the degradation kinetics, which was
achieved by discovering a model that describes the biogas production well and
then inferring parameters of this model from feedstock’s physiochemical
characteristics. The machine learning (ML) model, Decision Tree, was adopted
to predict the parameters of the time series model, first-order Autoregressive
Model, from the characteristics including total solids, volatile solids, total volatile
fatty acid, total ammonia nitrogen, chemical oxygen demand, alkalinity, elemental
composition, pH, heavy metals, etc. The DT trained and tested by leave-one-out
cross validation with 25 BMP test data had a mean absolute percentage error
(MAPE) of 45.63% on the BMP test results, showing unsatisfactory prediction
accuracy and unreliable feature importance analysis results.
To obtain sufficient data for ML model training and avoid the consistency issues
with BMP test data collected from diverse sources, a data augmentation method
employing response surface design was proposed. With sufficient data for model
training, eXtreme Gradient Boosting (XGBoost) models with three important
features determined via feature importance analysis could predict biogas
production model parameters with R²values above 0.99 on the test set.
Despite the strong regression capabilities of ML models compared to simple
statistical model, their explainability remains a challenge. The current popular
methods for ML model interpretation focus on feature importance analysis. In this
study, Meijei G-functions were used to interpret the predictions of the XGBoost
model mathematically to enhance the accessibility and transparency of the black-
box ML model to domain experts as a tool for material realistic BMP prediction.
The general predictability of the mathematical metamodel was tested using 13
BMP test data sourced from the literature, all within the applicability range of the
trained ML model, resulting in a mean absolute error of 38.074 mL CH ₄/g volatile
solid added and MAPE of 15.424%.
Besides the BMP of a material, operational parameters of the digester are critical
to the performance of a continuous AD process. The application of Anaerobic
Digestion Model No.1 (ADM1) for continuous AD simulation, which assists in
decision-making in the AD industry by predicting digester performance under
various operational schemes, is often hindered by its calibration difficulties,
especially with limited data.
Part II of this study presents a Bayesian inference-based framework to reliably
calibrate ADM1 using only initial-stage digester data. A sequence of sensitivity
analysis (SA) was applied to identify the most influential kinetic parameters and
initial values to be calibrated. SA results revealed that steady-state biogas
production was collaboratively influenced by the disintegration rate, hydrolysis
rates, and initial concentrations of acetate degraders, cations, and anions. In
contrast, Total Ammonia Nitrogen and pH of digestate were predominantly
influenced by initial values of cations and anions. These findings challenge the
common practice in ADM1 studies of only calibrating kinetic and stoichiometric
parameters. Then, using biogas production and digestate data from less than two
hydraulic retention times and informative prior distributions determined from
domain knowledge, seven the most influential uncertain inputs were calibrated.
The calibrated model predicted the steady state performance satisfactorily. The
95% credible intervals of the calibrated model encompassed 66.047% of the 10-
day moving average trendline of the daily biogas flow data and all of the steady-
state digestate pH and total chemical oxygen demand data.PhD in Energy and Powe
Chlorate occurrence in drinking water
No full textJarvis, Peter - Associate SupervisorThe use of chlorine for disinfection of potable water has been the major public
health advancement in the last century. Sodium hypochlorite is currently used
worldwide for potable water disinfection. Arising from sodium hypochlorite
solutions, chlorate forms as the sodium hypochlorite ages. Chlorate has been
recently regulated in the EU directive and is catalogued as a compound of
concern for the Drinking Water Inspectorate. The WHO recommended in 2015 a
guideline level of 0.7mg/L, and chlorate is currently set at level of 0.25 mg/L in
potable water supplies. As chlorate was previously a guidance, and not
extensively monitored, this presents a regulatory challenge for most water
companies to adopt.
From a large historical data set, from 2014 to 2020, it was extrapolated that
chlorate monthly running average values were expectedly higher during the
warmer season, likely explained by the increased chlorine demand during
warmer months, but far from the current regulation limit for well-resourced sites.
A questionnaire was completed in cooperation with operators and process
scientist on site. The interviews were completed across various WTW in Scotland.
The aim was to demonstrate the varying disinfection practices and extract
conclusions on the hypothetical chlorate levels arising during the dosing and
storage of sodium hypochlorite. The selection of sites provided a good overview
on the particularities of the disinfection stage, from small WTW, where the sodium
hypochlorite gets diluted on site, to large WTW with bulk storage of 15% sodium
hypochlorite. Sites with a varied risk of chlorate occurrence were also included
such as on-site electro-chlorination and chlorine gas disinfection.
It was concluded that there are correct measures in place during the operation
and maintenance of the disinfection stage, but chlorate levels during storage are
not centrally reported. The questionnaire has shown some sites where the
solutions of sodium hypochlorite were potentially exposed to warm temperatures
and extended periods of storage. It is likely that high room temperature is the
underpinning cause leading to sudden chlorate increase in combination with high
chlorine demands during the warmer months. This emphasised the need for
longitudinal studies on the degradation of hypochlorite solutions during storage.
It has been identified that a robust supply chain providing fresh hypochlorite
deliveries could be a major implementation aiming to tackle high chlorate levels,
particularly for remote and isolated potable water treatment works. The need for
an accurate determination of chlorine demand on site remains of crucial
relevance aiming to adjust disinfectant capability across varying treatment
processes. The importance of regular procurement of hypochlorite solutions and
the need for contingency was emphasised by the operators in order to avoid high
seasonal chlorate levels.
As a part of the experimental plan, the aim was to analyse the long-term stability
of sodium hypochlorite during storage with a focus on the impact of disinfectant
concentration on chlorate formation. The decay rates for sodium hypochlorite
solutions and chlorate formation have been determined at varying initial
concentrations using incubation experiments in the laboratory. It was determined
the application of consecutive refilling during sodium hypochlorite storage with
remaining old solutions of hypochlorite. Via bench scale kinetic experiments, it
was determined whether the use of a 10% free chlorine concentration of sodium
hypochlorite is less prone to promote further chlorate formation compared to the
currently used 15% hypochlorite solutions. It has been found the relative chlorate
to free chlorine content remains high even after the adoption of lower
concentration hypochlorite solutions. This has implications for sites currently
using dilution of hypochlorite and high chlorine demands. Lower initial
concentrations of sodium hypochlorite also presented more stability and
remaining disinfectant capacity during the bench scale studies. It is concluded
that extensive monitoring and control will be required in order to achieve tighter
chlorate standards.
The relative chlorate to free chlorine ratio (mg Chlorate/ mg of free chlorine) has
been highlighted as a concern resulting from high values in diluted solutions of
hypochlorite and on-site electro chlorination systems. Further mitigation
strategies have been summarised discussing the risk factors for future chlorate
occurrences, implementations aiming to tackle chlorate occurrence pre-emptively, and limit exceedances of the EU directive, now adopted in Scotland.
Overall, the thesis provided a better understanding on the drivers prompting
chlorate levels derived from sodium hypochlorite disinfection, a list of
comprehensive evidence-based interventions at water treatment facilities and
highlighted best management practices.PhD in Water, including Desig
Enhanced data-driven economic assessment of fuel cell electric buses utilizing an improved Markov chain Monte Carlo approach
Get PDFAccurate economic assessment of proton exchange membrane fuel cell (PEMFC) vehicles is essential for optimizing control strategies in the PEMFC industry, which is largely driven by the need to reduce costs. Traditional data-driven approaches have focused on reconstructing typical driving cycles from real-world speed data, often overlooking the intensity and acceleration of these cycles. These factors are crucial for water and heat management in PEMFCs and can lead to inaccurate estimates of hydrogen consumption. This paper introduces a novel algorithm for typical driving cycles reconstruction based on real-world data, named the improved two-dimensional Markov Chain Monte Carlo (2D MCMC) approach using Metropolis-Hastings (M − H) sampling. The approach innovatively encodes the integration of real-time vehicle speed and acceleration sequences into a hierarchical 2D state transition probability matrix. To optimise both accuracy and computation time, the M − H based sampler is newly introduced to generate typical driving cycle without the computational burden of multiplying large matrices. Moreover, by integrating the agglomerative nesting (AGNES) alongside a comprehensive evaluation system that incorporates simulation and bench testing, the proposed approach effectively weights real-world route conditions in the economic assessment. Case studies involving 10 PEMFC hybrid buses in Shanghai, China, validate the effectiveness and robustness of the proposed method. Comparative analyses show that the relative errors in hydrogen consumption per 100 km between the reconstructed and real-world driving cycles are within 1.20–3.01% for all ten buses in Shanghai, with computation times reduced by up to 12.60% compared to the existing methods.International Journal of Hydrogen Energ
Past, present, and future of battlefield forensics. What is battlefield forensics in the contemporary world?: Developing a collaborative framework for military and civilian forensics capabilities on the battlefield - Presentation
Get PDF10th Anniversary Special Joint Academic Sessio
Numerical modelling of hydrogen leakages in confined spaces for domestic applications
Get PDFThis paper is published as:
Thawani B, Hazael R, Critchley R. (2024) Numerical modelling of hydrogen leakages in confined spaces for domestic applications. International Journal of Hydrogen Energy, Volume 56, February 2024, pp.797-806
https://doi.org/10.1016/j.ijhydene.2023.12.279. Also available at: https://dspace.lib.cranfield.ac.uk/handle/1826/20667The research has been funded by Cranfield University and DNV Energy Systems, UK.Defence and Security Doctoral Symposia 2024 (DSDS24
Advancing two-phase wet gas flow measurement with Coriolis meters: optimal sensor orientation and a new model development
Get PDFIn this study, a novel wet gas model was developed based on the internal parameter of a Coriolis prototype to measure two-phase wet gas flow. Additionally, an optimal orientation of the Coriolis sensor was proposed to address challenges in the horizontal wet gas flow. Two Coriolis prototypes– one with a long-bent flow tubes (Type A), and another with short-bent flow tubes (Type B) – were employed to conduct tests in both vertical upward and horizontal pipelines. Different sensor axial angles (0, 40, 90, and 180°) were selected for testing in the horizontal section. Among different orientations (angles), it was found that the 40-degree angle outperforms the other installations in terms of the response proximity and over-reading of gas flow (OR). To understanding the impact of flow pattern on the response, a detailed analysis of different flow patterns in the wet gas was considered. A new correlation was developed between the damping factor of the Coriolis and both XLM and the total mass flow rate, leading to the proposal of a new wet gas model to predict gas and liquid flow rates with acceptable accuracy. In scenarios involving higher Froude numbers and annular flow patterns, which are more likely to occur, the Mean Absolute Percentage Error (MAPE) for the entire range of wetness (0 < XLM < 0.3) is 3.9 % for gas flow rate and 4.3 % for liquid flow rate with an uncertainty of 2.7 %.The authors would like to express their sincere appreciation to Endress+Hauser Flow, for their invaluable financial and technical support.Flow Measurement and Instrumentatio
Editorial: Scientific advances in river restoration
No full textSpecial Issue: River RestorationIn September 2023, the River Restoration Centre (RRC) hosted the inaugural Scientific Advances in River Restoration (SARR) conference in collaboration with the University of Liverpool, UK. As we confront the twin crises of climate change and biodiversity loss, this event underscored the importance of global collaboration among river restoration scientists to help inform evidence‐led solutions. Fluvial systems are particularly vulnerable to global climatic pressures, with droughts and floods exacerbating the impacts of human‐induced river modifications. River restoration is a crucial tool in addressing these pervasive challenges, capable of benefiting both people (e.g., flood mitigation, community engagement) and nature (e.g., ecological recovery, ecosystem functionality). The SARR conference aimed to unite scientists from various disciplines and countries, foster collaborations, and highlight new advancements to enhance global progress in river restoration science. This river restoration special issue features a diverse selection of papers presented at the SARR conference, showcasing the multidisciplinary nature of contemporary river restoration.River Research and Application