Mine evaluation optimisation
- Authors: Grigoryev, Igor
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: The definition of a mineral resource during exploration is a fundamental part of lease evaluation, which establishes the fair market value of the entire asset being explored in the open market. Since exact prediction of grades between sampled points is not currently possible by conventional methods, an exact agreement between predicted and actual grades will nearly always contain some error. These errors affect the evaluation of resources so impacting on characterisation of risks, financial projections and decisions about whether it is necessary to carry on with the further phases or not. The knowledge about minerals below the surface, even when it is based upon extensive geophysical analysis and drilling, is often too fragmentary to indicate with assurance where to drill, how deep to drill and what can be expected. Thus, the exploration team knows only the density of the rock and the grade along the core. The purpose of this study is to improve the process of resource evaluation in the exploration stage by increasing prediction accuracy and making an alternative assessment about the spatial characteristics of gold mineralisation. There is significant industrial interest in finding alternatives which may speed up the drilling phase, identify anomalies, worthwhile targets and help in establishing fair market value. Recent developments in nonconvex optimisation and high-dimensional statistics have led to the idea that some engineering problems such as predicting gold variability at the exploration stage can be solved with the application of clusterwise linear and penalised maximum likelihood regression techniques. This thesis attempts to solve the distribution of the mineralisation in the underlying geology using clusterwise linear regression and convex Least Absolute Shrinkage and Selection Operator (LASSO) techniques. The two presented optimisation techniques compute predictive solutions within a domain using physical data provided directly from drillholes. The decision-support techniques attempt a useful compromise between the traditional and recently introduced methods in optimisation and regression analysis that are developed to improve exploration targeting and to predict the gold occurrences at previously unsampled locations.
- Description: Doctor of Philosophy
- Authors: Grigoryev, Igor
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: The definition of a mineral resource during exploration is a fundamental part of lease evaluation, which establishes the fair market value of the entire asset being explored in the open market. Since exact prediction of grades between sampled points is not currently possible by conventional methods, an exact agreement between predicted and actual grades will nearly always contain some error. These errors affect the evaluation of resources so impacting on characterisation of risks, financial projections and decisions about whether it is necessary to carry on with the further phases or not. The knowledge about minerals below the surface, even when it is based upon extensive geophysical analysis and drilling, is often too fragmentary to indicate with assurance where to drill, how deep to drill and what can be expected. Thus, the exploration team knows only the density of the rock and the grade along the core. The purpose of this study is to improve the process of resource evaluation in the exploration stage by increasing prediction accuracy and making an alternative assessment about the spatial characteristics of gold mineralisation. There is significant industrial interest in finding alternatives which may speed up the drilling phase, identify anomalies, worthwhile targets and help in establishing fair market value. Recent developments in nonconvex optimisation and high-dimensional statistics have led to the idea that some engineering problems such as predicting gold variability at the exploration stage can be solved with the application of clusterwise linear and penalised maximum likelihood regression techniques. This thesis attempts to solve the distribution of the mineralisation in the underlying geology using clusterwise linear regression and convex Least Absolute Shrinkage and Selection Operator (LASSO) techniques. The two presented optimisation techniques compute predictive solutions within a domain using physical data provided directly from drillholes. The decision-support techniques attempt a useful compromise between the traditional and recently introduced methods in optimisation and regression analysis that are developed to improve exploration targeting and to predict the gold occurrences at previously unsampled locations.
- Description: Doctor of Philosophy
A framework for sustainability performance assessment for manufacturing processes
- Authors: Singh, Karmjit
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sustainable manufacturing methods make it possible to develop products in ways which minimize negative environmental impacts, conserve energy and save natural resources whilst being economically sound. The concepts of sustainability in manufacturing being are still fairly broad, in scope, and need to be more focused and firmly established at the process, machine or factory levels. This project proposes a structure for manufacturing with a main objective to develop a sustainability framework which encompasses various production processes. Structured information models for the seamless flow of information across the design and manufacturing domains, for selected manufacturing processes, are defined. The thesis work identifies key performance indicators (KPIs) for the assessment of manufacturing sustainability and performs analysis of selected unit manufacturing processes and their sub-processes with the aim of proposing a methodology for determining science-based measurements of the manufacturing processes affecting these KPIs. The theoretical foundations established are then used to develop a model that could evaluate sustainability of selected manufacturing processes and their respective process plans providing a basis for inter-process comparison and selection of the most sustainable process plan. The proposed framework is presented in form of a manufacturing planning computer-based package which is designed to to consider different influencing factors such as product information, part geometry, material related physical and processing properties and the manufacturing equipment operating data. The thesis presents a number of case studies which have been published in international journals. The case studies present estimates of the manufacturing sustainability KPIs for a number of production methods. These estimates have been verified with available shop floor data. The work in the thesis makes it possible to establish manufacturing industry equipped to deal the challenges of the future when sustainability will be the major factor up on which the quality of success will be determined.
- Description: Doctor of Philosophy
- Authors: Singh, Karmjit
- Date: 2019
- Type: Text , Thesis , PhD
- Full Text:
- Description: Sustainable manufacturing methods make it possible to develop products in ways which minimize negative environmental impacts, conserve energy and save natural resources whilst being economically sound. The concepts of sustainability in manufacturing being are still fairly broad, in scope, and need to be more focused and firmly established at the process, machine or factory levels. This project proposes a structure for manufacturing with a main objective to develop a sustainability framework which encompasses various production processes. Structured information models for the seamless flow of information across the design and manufacturing domains, for selected manufacturing processes, are defined. The thesis work identifies key performance indicators (KPIs) for the assessment of manufacturing sustainability and performs analysis of selected unit manufacturing processes and their sub-processes with the aim of proposing a methodology for determining science-based measurements of the manufacturing processes affecting these KPIs. The theoretical foundations established are then used to develop a model that could evaluate sustainability of selected manufacturing processes and their respective process plans providing a basis for inter-process comparison and selection of the most sustainable process plan. The proposed framework is presented in form of a manufacturing planning computer-based package which is designed to to consider different influencing factors such as product information, part geometry, material related physical and processing properties and the manufacturing equipment operating data. The thesis presents a number of case studies which have been published in international journals. The case studies present estimates of the manufacturing sustainability KPIs for a number of production methods. These estimates have been verified with available shop floor data. The work in the thesis makes it possible to establish manufacturing industry equipped to deal the challenges of the future when sustainability will be the major factor up on which the quality of success will be determined.
- Description: Doctor of Philosophy
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