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Assessing resource recovery potentials of industrial metal-bearing by-products using bioleaching.
Get PDFWagland, Stuart - Associate SupervisorThe global transition to a circular economy calls for research and development
on technologies facilitating sustainable resource recovery from wastes and by-
products as some possessing comparable or superior quality to natural ores.
Traditional methods e.g., pyrometallurgy and hydrometallurgy are considered as
inefficient for processing secondary resources as they often cause harmful
emissions and loss of metals, require high capital cost. Bioleaching, which
emerges as an eco-friendly and cost-effective alternative to conventional
methods, is well established for metal extraction from low-grade sulfidic ores,
tailings, and metallurgical side streams, yet the method is at the research stage
for other secondary resources such as metallurgical slag and dust, fly ash, e-
waste. The aim of this PhD study was assessing the resource recovery potentials
of industrial metal-bearing by-products using biomining. Firstly, this study
critically reviewed the microbial diversity and specific mechanisms of bioleaching
as well as the current operations and approaches of bioleaching at various scales
and summarised the influence of a broad range of operational parameters. Then,
suggested an optimisation route for bioleaching of metal-bearing materials for
laboratory scale bioleaching operations to further inform pilot/commercial scale
operations. Further to this, feasibility of extracting metals from two industrial
metal-bearing by-products which were basic oxygen steelmaking dust (BOS-D)
and goethite were investigated using Acidithiobacillus ferrooxidans. Taguchi
orthogonal array design was used to evaluate the effect of four parameters which
were pulp density, energy source concentration, inoculum concentration, and pH
at three different levels. Then methods were explored for enhancing and scaling
up the extraction of metals from BOS-D. Finally, techno-economic assessment
conducted for two potential industrial scale bioleaching technologies including an
aerated bioreactor and an aerated and stirred bioreactor, for different metal
recovery scenarios from the industrial metal-bearing by-products.PhD in Wate
RGANFormer: Relativistic Generative Adversarial Transformer for time-series signal forecasting on intelligent vehicles
Get PDFTime-series modelling (TSM) is a critical task for intelligent vehicles (IVs), covering areas like fault detection, health monitoring, and inference of road user intentions. In this study, we present a novel TSM approach for enhancing the accuracy of multi-variate signal forecasting in intelligent vehicles. Our method leverages advanced Transformer networks within a relativistic generative adversarial network (RGAN) training framework. The RGAN training framework efficiently improves the accuracy of vehicle states forecasting for IV, demonstrating effective learning of long-time dependencies for more accurate predictions over extended sequences. Additionally, we introduce a high-dimensional extension (HDE) built-in block for the time-series Transformer to explore the impact of higher-dimensional features on representing long-term sequences. The experimental data is collected from a real-world electric vehicle testing bed. We evaluate the proposed RGANFormer framework and the HDE block on two popular time-series models, namely, Autoformer and FiLM. The results demonstrate that the RGANFormer, along with the built-in HDE block, significantly enhances long-term sequential forecasting accuracy for both multivariate and univariate tasks.2024 IEEE Intelligent Vehicles Symposium (IV
Why IoT enablement of agrifood transportation disappoints its stakeholders: unravelling barriers for enhanced logistics
Get PDFThis article is part of Special Issue: Digital Transformation in Food Processing: From Industry 4.0 to Industry 5.0.The present research work investigates the barriers of weak IoT adoption in agrifood transportation, with special reference to India. It is built on a premise that few barriers upshots from the other more impactful ones. Thus, it is important to identify their linkages and classify them based on their strength of relationship. The data collected from 13 agricultural technology (AgriTech) firms of India were subjected to integrated techniques of M‐TISM and Fuzzy MICMAC. As a result, a unique position of autonomous, dependent, linkage, and independent barriers was obtained which revealed that inadequate Internet connectivity, interoperability, and unclear roadmaps are precarious to the use of IoT in agrifood transportation. They are responsible for creating issues like data processing, vehicle tracking, and data privacy. This study offers a contextual phenomenon of barriers that may assist AgriTech stakeholders in developing appropriate strategies to embrace IoT transformation. It extends the theoretical literature by providing critical connections that aspiring researchers can examine through hypothesis testing or building a hierarchical framework. A sensitivity analysis is suggested to optimise decision‐making and bring out a robust and reliable set of obstacles.Journal of Food Qualit
Nacelle optimisation through multi-fidelity neural networks
Get PDFPurpose
Aerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.
Design/methodology/approach
The nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.
Findings
Ratios of low- and high-fidelity training samples to degrees of freedom of nLF/nDOFs = 50 and nHF/nDOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.
Originality/value
The application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.International Journal of Numerical Methods for Heat & Fluid Flo
Dataset "Development and optimisation of rapid analysis of weathered slag using portable XRF - Supplementary information and code"
No full textpXRF is widely used for rapid measurement of heavy metals in soils, however, thorough evaluation of common pre-processing methods and their effectiveness is limited. This study addresses processing methods using samples collected at a high heterogenetic post-metallurgical site containing,; basic oxygen steelmaking (BOS) slag and soil; the former being an important source of potentially toxic and valuable elements. Impact of pre-treatment processes, including sieving, drying, grinding, sample vessel, and ignition on the accuracy of pXRF measurements of samples were compared against ICP-MS.
Of the twelve elements detected, four showed qualitative (Cr and Fe r² ≥0.60, RSD ≤ 30%) or quantitative (Mn and Ca r² ≥0.70, RSD ≤ 20%) measurements for raw samples. This improved to six elements after pre-processing (Sr qualitative, and Pb, Cr, Mn, Ca, Fe quantitative). Sieving and grinding improved precision (average RSD fell by 7.17% and 8.37% respectively), while drying and grinding enhanced accuracy (average r2 increased by 0.03 and 0.10 respectively). This study provides the first evidence that organic matter does not significantly impact pXRF accuracy. The two distinct matrices (BOS slag and soil) on- site resulted in a bimodal concentration distribution and a negative correlation for Ti. Importantly, this research proposes that not all common pre-processing steps are necessary to generate high-quality data, thereby increasing the speed and reducing the cost of data collection. Further analysis is required to develop a methodology to generate high-quality data across all elements of interest with BOS slag, or other relevant high heterogeneity samples.
The supplementary information for this study includes :
the full unprocessed dataset.
all code used to for statistical analysis and graph generationEuropean Commissio
Data for: Anisotropy visualisation from X-ray diffraction of biological apatite in mixed phase samples
No full textGRANT TITLE: Breast Cancer: Early diagnosis using materials immortalisation. MRC Grant Ref: MR/T000406/1.Medical Research Council (MRC
Blast wave ingress into a room through an opening – review of past research and US DoD UFC 3-340-02
Get PDFBlast wave ingress into a room through a facade opening results in complex pressure-time loadings on interior surfaces due to shock diffraction and interior reflections. The U.S. DoD UFC 3-340-02 Structures to Resist the Effects of Accidental Explosions includes a method to predict internal loading for such cases. Parameters such as the opening size, room dimensions and the external pressure wave characteristics influence the interior loading. Recent work suggests that the UFC methodology might overpredict the interior loading by some 600%. Such conservatism can result in over-engineered or prohibitively expensive protective solutions. In this paper we critically review the methodology of the UFC, that of Kaplan’s preceding work (on which the UFC relies), and the experimental data that informed both these works. Through a series of 45 case-studies we compare the UFC and Kaplan’s predictions with those of a computational fluid dynamics (CFD) model. The UFC consistently overpredicts the CFD area-averaged peak pressure of the back wall by up to 290% and the side-wall by up to 425%. Similarly, the UFC overpredicted the CFD side wall positive phase impulse by up to 565%. By contrast, the UFC predicted back wall positive phase impulse was similar to the CFD results. As our CFD results for side and back walls are area averaged, and not solely for the wall centre-point, as in other recent work, our paper gives support for the use of CFD prediction over the UFC for cost-effective design of structures to resist blast ingress.International Journal of Protective Structure
Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding
Get PDFClimate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures. To date, most research on SWF has focused on modelling and prediction, but these models have been limited to 2 m resolution for England to avoid excessive computational burdens. The lead time for predicting convective rainfall responsible for SWF can be as little as 30 minutes for a 1 km x 1 km part of the storm. Therefore, it is useful to identify the locations most vulnerable to SWF based on past rainfall data and microtopography to provide better risk management measures for properties. In this study, we present a framework that uses long-term gridded rainfall data to quantify SWF hazard at the 1 km x 1 km pixel level, thereby identifying localized areas vulnerable to SWF. We also use high-resolution photographic (10 cm) and LiDAR (25 cm) DEMs, as well as a property flood resistance and resilience (PFR) database, to quantify SWF exposure at property level. By adopting this methodology, locations and properties vulnerable to SWF can be identified, and appropriate SWF management strategies can be developed, such as installing PFR features for the properties at highest risk from SWF.We acknowledge EPSRC funding EP/ N010329/1 for Building Resilience into Risk Management (BRIM).PLoS ON
Systematic selection of the next generation Martian rotorcraft configurations
Get PDFThe successful deployment of NASA's Ingenuity Helicopter has paved the way for the development of advanced rotorcraft for Martian exploration. Despite Ingenuity's achievements, its limitations in endurance, payload, and range highlight the need for optimised designs for future Martian aerobot missions. This paper addresses these challenges by proposing and systematically analysing various rotorcraft configurations, including single rotors, coaxial designs, tandem rotors, quadcopters, and hexacopters, under Martian conditions. Through a detailed parametric analysis based on simplified momentum theory, power requirements are calculated for each configuration during hover, vertical climb, and forward flight. Special attention is given to coaxial multirotor designs, which offer improved performance in terms of power efficiency and payload capacity. The application of the Battery Mass Fraction (BMF) methodology further demonstrates the viability of electric-powered rotorcraft for extended Martian operations. The study identifies coaxial hexacopters as the most efficient configurations, providing a strong basis for the next generation of Martian aerial vehicles.75th International Astronautical Congress IAC-202
BioMoon: a concept for a mission to advance space life sciences and astrobiology on the Moon
Get PDFAs humans advance their presence in space and seek to improve the quality of life on Earth, a variety of science questions in support of these two objectives can be answered using the Moon. In this paper, we present a concept for an integrated mission focused on answering fundamental and applied biological questions on the Moon: BioMoon. The mission was designed to investigate the effects of the lunar radiation, gravity, and regolith on biological systems ranging from biomolecules to systems with complex trophic interactions, spanning a range of model organisms. Using common analytical systems and data processing, BioMoon represents a systems-level integrated life sciences mission. It would provide fundamental insights into biological responses to the lunar environment, as well as applied knowledge for In-Situ Resource Utilisation (ISRU), closed-loop life support system development, planetary protection and human health care. The mission was conceived to test biotechnology and sensor technology for lunar and terrestrial application and provide education and outreach opportunities. Although BioMoon was considered in the context of the European Space Agency’s Argonaut (European Large Logistics Lander) concept, the mission design provides a template for any integrated life sciences experimental suite on the Moon and other celestial bodies, implemented either robotically or by human explorers.CSC acknowledges support from the Science and Technology Facilities Council (Grants ST/V000586/1 and ST/Y001788/1).Discover Spac