Search

Domains define the boundaries of mineralisation zones, within which the grade distribution of the target minerals can be quantified via an established mineral resource estimation procedure. Available domain modelling techniques include manual interpretation, implicit modelling and advanced geostatistical approaches. In mining applications, the most commonly used method is manual domaining, which is labour-intensive and prone to subjective judgement errors. In addition, the output is largely deterministic and ignores the significant uncertainty associated with the domain interpretation and boundary definitions. There is, therefore, a need for a more objective framework that can automatically define mineral domains and quantify the associated uncertainty. This paper describes such a framework, which consists of a hybrid approach based on simulated grade distributions and a machine learning (ML) classification technique, XGBoost, trained on lithological properties. Data from an Iron Oxide Copper Gold (IOCG) deposit are used as a case study to demonstrate the application of the proposed method. The study shows that the approach can handle complex multi-class problems with imbalanced features, and it can quantify the uncertainty of domain boundaries. A noise filtering method is used as a pre-processing step to improve the performance of the ML classification, especially in the case of problematic classes where domain boundaries are difficult to predict due to the similarity in geological characteristics.

Most rock masses contain natural fractures. In many engineering applications, a detailed understanding of the characteristics of fluid flow through a fractured rock mass is critically important for design, performance analysis, and uncertainty/risk assessment. In this context, rock fractures and fracture networks play a decisive role in conducting fluid through the rock mass as the permeability of fractures is in general orders of magnitudes greater than that of intact rock matrices, particularly in hard rock settings. This paper reviews the modelling methods developed over the past four decades for the generation of representative fracture networks in rock masses. It then reviews some of the authors’ recent developments in numerical modelling and experimental studies of linear and non-linear fluid flow through fractures and fracture networks, including challenging issues such as fracture wall roughness, aperture variations, flow tortuosity, fracture intersection geometry, fracture connectivity, and inertia effects at high Reynolds numbers. Finally, it provides a brief review of two applications of methods developed by the authors: the Habanero coupled hydro-thermal heat extraction model for fractured reservoirs and the Kapunda in-situ recovery of copper minerals from fractures, which is based on a coupled hydro-chemical model.

The use of sequestered carbon dioxide (CO2) as the heat exchange fluid in enhanced geothermal system (EGS) has significant potential to increase their productivity, contribute further to reducing carbon emissions and increase the economic viability of geothermal power generation. Coupled CO2 sequestration and geothermal energy production from hot dry rock (HDR) EGS were first proposed 15 years ago but have yet to be practically implemented. This paper reviews some of the issues in assessing these systems with particular focus on the power generation and CO2 sequestration capacity. The Habanero geothermal field in the Cooper Basin of South Australia is assessed for its potential CO2 storage capacity if supercritical CO2 is used as the working fluid for heat extraction. The analysis suggests that the major CO2 sequestration mechanisms are the storage in the fracture-stimulation damaged zone followed by diffusion into the pores within the rock matrix. The assessment indicates that 5% of working fluid loss commonly suggested as the storage capacity might be an over-estimate of the long-term CO2 sequestration capacity of EGS in which supercritical CO2 is used as the circulation fluid.

The data presented in this article relates to the crystallisation of hexadecane (C16H34) and octadecane (C18H38), being the predominant alkanes present in hydrotreated vegetable oil (HVO), from solvents representative of fuel (dodecane, toluene and kerosene). Data was collected for eleven C16H34/C18H38 compositions for each solvent used. Raw crystallisation and dissolution data is provided over a range of solution concentrations and cooling rates used under a poly-thermal crystallisation methodology. Equilibrium saturation temperature data is also presented for each composition, concentration and solvent system, indicating the trend in solubility for each solution.

Background Young people living with long term conditions are vulnerable to health service disengagement. This endangers their long term health. Studies report requests for digital forms of communication – email, text, social media – with their health care team. Digital clinical communication is troublesome for the UK NHS. Aim In this article we aim to present the research protocol for evaluating the impacts and outcomes of digital clinical communications for young people living with long term conditions and provide critical analysis of their use, monitoring and evaluation by NHS providers (LYNC study: Long term conditions, Young people, Networked Communications). Methods The research involves: (a) patient and public involvement activities with 16–24 year olds with and without long term health conditions; (b) six literature reviews; (c) case studies – the main empirical part of the study – and (d) synthesis and a consensus meeting. Case studies use a mixed methods design. Interviews and non-participant observation of practitioners and patients communicating in up to 20 specialist clinical settings will be combined with data, aggregated at the case level (non-identifiable patient data) on a range of clinical outcomes meaningful within the case and across cases. We will describe the use of digital clinical communication from the perspective of patients, clinical staff, support staff and managers, interviewing up to 15 young people and 15 staff per case study. Outcome data includes emergency admissions, A&E attendance and DNA (did not attend) rates. Case studies will be analysed to understand impacts of digital clinical communication on patient health outcomes, health care costs and consumption, ethics and patient safety.