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Extracting metal ions from basic oxygen steelmaking dust by using bio-hydrometallurgy
Get PDFThis study aimed to optimise metal extraction from secondary hazardous sources, such as basic oxygen steelmaking dust (BOS-D). Initially, three batch systems approaches, including bioleaching using Acidithiobacillus ferrooxidans, chemical leaching using choline chloride-ethylene glycol (ChCl-EG) and a combined approach were compared. Then, scaling up was evaluated through a semi-continuous bioleaching column system with varied leachate recirculation over 21 days, focusing on Y, Ce, Nd, Li, Co, Cu, Zn, Mn, and Al. Bioleaching outperformed the control experiments within 3 days in the batch, demonstrating the key role of A. ferrooxidans. Chemical leaching conducted with a solid concentration of 12.5 % (w/v) successfully dissolved over 50 % of all metals within 2 h. For rare earth elements (REE), both bioleaching and hybrid leaching outperformed chemical leaching. However, considering factors such as process duration, overall efficiency, and ease of extraction, chemical leaching was the most effective method. Leachate recirculation reached a plateau after 11 days, resulting in extraction efficiency of 39 % when semi-continuous column set-up was used. Interestingly, variations in recirculation rates did not influence the extraction efficiency. Overall, this study emphasizes the considerable potential of bioleaching for metal recovery, but also highlights the need for further studies for enhancing permeability for percolation methods and optimisation, particularly in parameters such as aeration rate, when transitioning to larger scale systems.This research was funded by the European Regional Development Fund as part of the Interreg Northwest Europe project “Regeneration of past metallurgical sites and deposits through innovative circularity for raw materials” (REGENERATIS) (NWE918).Heliyo
Data supporting "Set Based Design Techniques for Evolvability Exploration During Conceptual Aircraft Design”
No full textResults of the use case (Section 4) employed to demonstrate the methods developed in "Set-Based Design Techniques for Evolvability Exploration During Conceptual Aircraft Design". The dataset contains the required Multi Attribute Trade Space exploration values (cost and utility metrics for each aircraft under each different scenario) plus evolvability values for each pair of aircraft.Innovate U
The Lake Paravani archive – a contribution to the late Quaternary landscape evolution of the Lesser Caucasus (Georgia)
Get PDFLake Paravani, located on the volcanic Javakheti Plateau in the central part of the Lesser Caucasus at 2073 m a.s.l., forms a unique geo-bio-archive for palaeoenvironmental reconstructions in this remote region. Based on sediment cores from the southwestern part of the lake we expand the existing palynological and sedimentological records beyond the Last Glacial Maximum (LGM). For the first time, it is possible to reconstruct the palaeoenvironment in this part of the Lesser Caucasus back to c. 28 cal. ka BP. Our study shows that until 16 cal. ka BP glacial conditions dominated (Phase I) in the region; there is, however, proof that the lake already existed during the LGM. In the following transitional Phase II from 16 until 6 cal. ka BP, cold and arid conditions with sparse steppe vegetation and a lowered lake level prevailed. Around 10 cal. ka BP, tree pollen started to expand while herbaceous pollen, especially Chenopodiaceae, declined. In Phase III, since 6 cal. ka BP, mixed forest probably represented the Holocene climatic optimum. Fluctuating lake levels indicate shifting climatic conditions. The minor changes of arboreal pollen hin the uppermost part of Phase II may be an indication of human activity. The more humid, vegetation-rich environment and mild climate around 4.5–2 cal. ka BP correlate with the expansion of the Late Bronze Age settlements in this area (from ~3.5 cal. ka BP/~1.5 ka BC). The proliferation of sites on the plateau, along with even higher-altitude sites possibly dating to the same period, may indicate that this climate amelioration played an important role in enabling more sustained human occupation. The results extend the record on Lake Paravani by several millennia beyond the LGM and complement the palaeo-lake reconstructions of the wider region, e.g. at Lake Van (Türkiye) or Lake Sevan (Armenia).Georgian National Science Foundation (SRNSF). Grant Number: FR-18-22377International Science and Technology Foundation. Grant Number: G-2153Borea
Data for Charge measurements for optimised NOM characterisation and removal by coagulation
No full textThis study explored the measurement of charge load and zeta potential, in parallel with common water quality parameters, throughout an extensive yearlong sampling campaign
A whole systems view to driving decentralised renewable energy investments in Sub-Saharan Africa.
Get PDFDrew, Gill - Associate SupervisorThe scaling-up of decentralised renewable energy (DRE), such as solar mini-
grids, is vital to achieving climate goals and universal electricity access in sub-
Saharan African (SSA) countries. However, high investor risk perception
continues to impede DRE investment in SSA, highlighting the importance of
understanding investors' risk perception and developing appropriate risk
mitigation actions. Yet, the risk management (RM) literature offers a fragmented
and singular approach, where the multidimensional nature of risk factors and their
interactions are overlooked. In addition, current studies do not consider DRE site-
specificity alongside investor heterogeneity in quantifying the implications of
mitigation actions on the evolution of investment decisions. In this context, and to
address these research gaps, this thesis aims to develop, validate, and
implement a unified RM framework incorporating an investment decision model
to assess the impacts of actions on investment and electricity access spatially,
thus offering a more holistic outcome for decision-makers. This thesis focuses on
solar mini-grids in Nigeria, which has one of the highest electricity deficits in SSA.
The framework is implemented in two phases. In phase one, investment risks and
potential mitigations were evaluated as perceived by four investor groups and
various stakeholders through questionnaires, semi-structured interviews, focus
groups, and an analytic hierarchy process methodology. In phase two, a novel
DRE decision-support model was deployed to enhance existing methods by
using a system dynamics-agent-based modelling (SD-ABM) approach. This
approach incorporates complex interactions and feedback between
heterogeneous investor and location attributes to establish investment outcomes
for various case study mitigation scenarios. This thesis the following
contributions. Phase one provides new empirical data comprising: identifying 13
additional risk factors compared to the literature, establishing importance of risk
factors as perceived by diverse investor groups in Nigeria, and proposing
mitigation strategies, some of which were tested in phase two as scenarios. The
results indicated variations in risk importance among investors, with the most
critical risk factors being revenue risks, limited access to low-cost capital,
currency risks, insecurity, and inadequate policy implementation. Phase two
contributes to the knowledge of how complex system modelling can be applied
to evaluate the impact of mitigation actions on the spatial evolution of DRE
investment in a liberalised market. The case study results revealed that the most
impactful mitigation scenarios were increased funding availability and the
implementation of renewable energy mandates for domestic finance institutions.
Whilst our findings confirm the criticality of concessional investors as identified in
the literature, we find that meeting electrification targets necessitates
incentivising risk-averse non-concessional-type investors. The developed model
can additionally enable policymakers to explore the potential implications of
further policy actions and investors to identify potential projects that suit their
investment profiles during the feasibility phase.PhD in Energy and Powe
Supersaturation control in membrane distillation crystallisation
Get PDFCampo Moreno, Pablo - Associate SupervisorMembrane distillation crystallisation (MDC) has emerged as a potential
alternative to conventional industrial crystallisers. MDC provides controlled
hydrodynamics and uniform supersaturation conditions for crystallisation, thereby
enhancing scalability, which is desperately lacking in conventional crystallisation
systems. Unfortunately, crystallisation near the membrane surface is also
associated with inorganic fouling (scaling), which can ultimately lead to process
failure. As such, the viability of the technology is dependent on scale-free bulk
crystallisation. To date however, scaling mitigation strategies have been based
on empirical observations with contradictory postulations regarding the governing
crystallisation mechanism(s). In this work, the distinct mechanisms of scaling and
bulk crystallisation have been elucidated for the first time. The application of novel
inline and online experimental techniques facilitated the development of a
mechanistic framework which is able to predict the likelihood of scaling in addition
to mediating bulk nucleation kinetics. As such, scale-free operation was achieved
at temperatures and hydrodynamic conditions which were previously associated
with scaling. This study has therefore broadened the perceived range of kinetic
trajectories achievable with MDC and evidenced its applicability to multi-
component systems, polymorph selection, and a variety of product specifications.
Furthermore, the use of hydrodynamics to decouple nucleation and growth
kinetics revealed the potential of MDC to minimise the usual trade-off between
product quality and yield in crystallisation systems. While existing scaling
mitigation strategies are largely hydrodynamic and thermodynamic in nature, this
study has shown that the contribution of crystallisation kinetics (supersaturation
rate) to scaling propensity cannot underestimated. Hence, application of the
kinetic framework developed could provide more targeted strategies for scaling
prevention in various applications such as heat exchangers and reverse
osmosis/nanofiltration (RO/NF), where polarisation phenomena are prevalentPhD in Water, including Desig
Touching the void: The loss of containment and the space between operational and entrepreneurial leadership in the K2 disaster
Get PDFIn this paper, we seek to understand how members of a collective facing a novel,
unprecedented challenge can lose an integrated and realistic connection to the people, events,
opportunities, and threats around them. Using extensive data, including interviews with
survivors and unique video footage we analyze how eleven experienced climbers lost their lives
in 2008 attempting to summit K2, the world’s second-highest mountain. Existing theories of
leadership and information-processing views of human cognition do not fully explain
observations from our qualitative study. However, containment and social defense constructs
suggest how and why people failed to respond to the impending disaster. We offer four key
findings. First, destabilizing conditions can erode operational leadership resulting in a
breakdown of the traditional sanctuaries of procedure, role clarity, hierarchy, and positional
authority. Second, despite clear, escalating threats and the potential for impending disaster,
individual and collective responsiveness, proactivity, and adaption can fail to materialize.
Third, people’s responses to novel, unprecedented circumstances are deeply connected to and
reliant on the ways collectives develop to contain anxiety. Finally, loss of containment can
result in a void, disabling people from confronting and adapting to challenging situations
realistically and competently
Explainable data-driven Q-learning control for a class of discrete-time linear autonomous systems
Get PDFExplaining what a reinforcement learning (RL) control agent learns play a crucial role in the safety critical control domain. Most of the approaches in the state-of-the-art focused on imitation learning methods that uncover the hidden reward function of a given control policy. However, these approaches do not uncover what the RL agent learns effectively from the agent-environment interaction. The policy learned by the RL agent depends in how good the state transition mapping is inferred from the data. When the state transition mapping is wrongly inferred implies that the RL agent is not learning properly. This can compromise the safety of the surrounding environment and the agent itself. In this paper, we aim to uncover the elements learned by data-driven RL control agents in a special class of discrete-time linear autonomous systems. Here, the approach aims to add a new explainable dimension to data-driven control approaches to increase their trust and safe deployment. We focus on the classical data-driven Q-learning algorithm and propose an explainable Q-learning (XQL) algorithm that can be further expanded to other data-driven RL control agents. Simulation experiments are conducted to observe the effectiveness of the proposed approach under different scenarios using several discrete-time models of autonomous platforms.Information Science
Enhancing object detection and localization through multi-sensor fusion for smart city infrastructure
Get PDFThe rapid advancement in autonomous systems and smart city infrastructure demands sophisticated object detection and localization capabilities to ensure safety, efficiency, and reliability. Traditional single sensor approaches often fall short, especially under complex environmental conditions. This paper introduces the CLR-Localiser, a novel multi-sensor fusion framework that synergistically integrates data from cameras, LiDAR and radar sensors mounted on roadside infrastructure to enhance object detection and 3D localization. Leveraging the complementary strengths of each sensor type, the CLR-Localiser employs an early fusion approach and deep learning techniques, including convolutional neural networks for object detection and regression networks for precise localization. We rigorously validated the performance of the CLR-Localiser against the benchmark Kitti dataset, and a custom dataset specifically designed for this research, demonstrating significant improvements in detection accuracy, localization precision, and object-tracking capabilities under diverse conditions. Our findings highlight the CLR-Localiser's potential to overcome the limitations of conventional monocular and single-sensor methods, offering a robust solution for autonomous driving, robotics, surveillance, and industrial automation applications. The development and validation of the CLR-Localiser not only prove the technical feasibility of early sensor data fusion but also pave the way for future advancements in multi-sensor fusion technology for enhanced environmental perception in autonomous systems.2024 IEEE International Workshop on Metrology for Automotive (MetroAutomotive
The effect of the interactions of water activity, and temperature on OTA, OTB, and OTα produced by Penicillium verrucosum in a mini silo of natural and inoculated wheat using CO2 production as fungal activity sentinel
Get PDFOchratoxin A (OTA) is a nephrotoxin that contaminates grains in storage. Moisture and temperature sensors give delayed responses due to their slow kinetic movement within the silo. This study examines if CO2 production could predict OTA contamination and identify storage conditions exceeding the maximum limit (5 μg/kg). The impact of water activity levels (0.70–0.90 aw), temperatures (15 and 20 °C), and storage duration on (a)Penicillium verrucosum population, (b)CO2 respiration rates (RR), and (c)ochratoxins concentrations in stored wheat was investigated. 96 samples were analysed for ochratoxins with LCMS-MS. RR was >7 times higher at wetter conditions than at drier aw levels. A positive correlation between CO2, OTA, OTB, and OTα was observed at the wettest conditions. OTA exceeded the limit at >0.80 aw (16% moisture content) with RR > 0.01 mg CO2 kg−1 h−1. The knowledge of the RR of stored grain would alert grain farmers/managers to improve grain storage management.UKRI: Biotechnology and Biological Sciences Research Council (BBSRC).
FoodBioSystem Doctoral Training Programme (FBSDTP); grant reference: BB/T008776/1Food Chemistr