Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Modelling of a generalized thermal conductivity for granular multiphase geomaterial design purposes

Soil thermal conductivity has an important role in geo-energy applications such as high voltage buried power cables, oil and gas pipelines, shallow geo-energy storage systems and heat transfer modelling. Hence, improvement of thermal conductivity of geomaterials is important in many engineering applications. In this thesis, an extensive experimental investigation was performed to enhance the thermal conductivity of geomaterials by modifying particle size distribution into fuller curve gradation, and by adding fine particles in an appropriate ratio as fillers. A significant improvement in the thermal conductivity was achieved with the newly developed geomaterials. An adaptive model based on artificial neural networks (ANNs) was developed to generalize the different conditions and soil types for estimating the thermal conductivity of geomaterials. After a corresponding training phase of the model based on the experimental data, the ANN model was able to predict the thermal conductivity of the independent experimental data very well. In perspective, the model can be supplemented with data of further soil types and conditions, so that a comprehensive representation of the saturation-dependent thermal conductivity of any materials can be prepared. The numerical 'black box' model developed in this way can generalize the relationships between different materials for later added amounts of data and soil types. In addition to the model development, a detailed validation was carried out using different geomaterials and boundary conditions to reinforce the applicability and superiority of the prediction models

Mineral Asset Valuation Under Economic Uncertainty: A Complex System for Operational Flexibility

The global mineral industry faces constant challenges that are incited and intensified by market uncertainty. Demand constrictions, resource scarcity, and market volatility all generate market risk that is compounded by the high capital and long payback periods inherent to mining projects. Quantitative risk assessments provide a methodology to leverage uncertain economic scenarios and accurately assess profitability; however, current mine valuation techniques and engineering economic approaches tend to scrutinize the uncertainty of technical factors, such as ore grade and metallurgical recovery, to a much greater degree than market factors, like price-demand restrictions. Nevertheless, the optimal operating conditions for mining, mineral processing and refining must reflect the true dynamics of uncertain commodity prices, and typical operational responses, such as modifications to mine production and material stockpiling.;This thesis presents a new mineral asset valuation methodology based on economic uncertainty in the commodity market and operational flexibility for mining operations. This novel valuation approach resulted in the generation of a complex system that consists of three primary components. First, a price forecasting component was used to generate future commodity price scenarios with two different stochastic differential equation models (Geometric Brownian Motion and Mean-Reverting-drift). Second, a dynamic methodology of discounted cash flow (DCF) was developed, allowing operational flexibility for mining, processing, stockpiling, and selling material. Third, two distinct optimization techniques (Interior-point method and genetic algorithms) were applied for identification of optimal operating parameters in a mining operation, with a particular focus on using buffer stockpiles to ameliorate the impacts of volatile price fluctuations. The dynamic model was applied in a case study assessing the valuation of a greenfield Ni-Co-Sc mine project. The hypothetical deposit was subjected to different levels of commodity price trends, price volatility, discount rates and maximum stockpiling capacity. Overall, the dynamic valuation model obtained NPV results ranging from 2% to 11% higher than standard static DCF techniques. Operational flexibility and ore inventory management proved to be crucial for profit increase on the project

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications