Harnessing Natural Resources for Development: New Legal Regimes for Localized Benefit Sharing in the Mining Sector

Thesis (Ph.D.)--University of Washington, 2016-06

How cost efficient are Australia's mining industries?

This paper uses a stochastic translog cost frontier model and a panel data of five key mining industries in Australia over 1968-1969 to 1994-1995 to investigate the sources of output growth and the effects of cost inefficiency on total factor productivity (TFP) growth. The results indicate that mining output growth was largely input-driven rather than productivity-driven. Although there were some gains from technological progress and economics of scale in production, cost inefficiency which barely exceeded 1.1% since the mid-1970s in the mining industries was the main factor causing low TFP growth. (C) 2002 Elsevier Science B.V. All rights reserved.

Fuzzy logic in web data mining for website assessment

This paper proposes a novel application of fuzzy logic to web data mining for two basic problems of a website: popularity and satisfaction. Popularity means that people will visit the website while satisfaction refers to the usefulness of the site. We will illustrate that the popularity of a website is a fuzzy logic problem. It is an important characteristic of a website in order to survive in Internet commerce. The satisfaction of a website is also a fuzzy logic problem that represents the degree of success in the application of information technology to the business. We propose a framework of fuzzy logic for the representation of these two problems based on web data mining techniques to fuzzify the attributes of a website.

On the Dynamics of Mining Operations in Open Pit Mines

In this paper, mining dynamics is defined as the relationship between the mining rate and movement of mining operations conducted on the benches of a surface mine. This relationship describes the intensity of the pit development in space, in order to meet ore demand at the mill over time. Meeting the mill ore demand is a key factor in optimizing production scheduling in surface mines. Displacement velocity of mining operations within cutbacks, or independent pit units, is introduced in the context of long-term mine planning. Displacement velocity allows the place and time of transition of the mining operations from one independent pit unit to another to be determined as the condition for meeting the mill ore demand. An application using data from Mt Keith Nickel Operations in Western Australia is used to elaborate on the methods presented.

The productivity-inflation nexus: The case of the Australian mining sector

This paper examines the causal links between productivity growth and two price series given by domestic inflation and the price of mineral products in Australia's mining sector for the period 1968/1969 to 1997/1998. The study also uses a stochastic translog cost frontier to generate improved estimates of total factor productivity (TFP) growth. The results indicate negative unidirectional causality running from both price series to mining productivity growth. Regression analysis further shows that domestic inflation has a small but adverse effect on mining productivity growth, thus providing some empirical support for Australia's 'inflation first' monetary policy, at least with respect to the mining sector. Inflation in mineral price, on the other hand, has a greater negative effect on mining productivity growth via mineral export growth.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.

Data mining and simulation: a grey relationship demonstration

Fuzzy data has grown to be an important factor in data mining. Whenever uncertainty exists, simulation can be used as a model. Simulation is very flexible, although it can involve significant levels of computation. This article discusses fuzzy decision-making using the grey related analysis method. Fuzzy models are expected to better reflect decision-making uncertainty, at some cost in accuracy relative to crisp models. Monte Carlo simulation is used to incorporate experimental levels of uncertainty into the data and to measure the impact of fuzzy decision tree models using categorical data. Results are compared with decision tree models based on crisp continuous data.

Tyre-force estimation by Kalman inverse filtering: applications to off-highway mining trucks

Kalman inverse filtering is used to develop a methodology for real-time estimation of forces acting at the interface between tyre and road on large off-highway mining trucks. The system model formulated is capable of estimating the three components of tyre-force at each wheel of the truck using a practical set of measurements and inputs. Good tracking is obtained by the estimated tyre-forces when compared with those simulated by an ADAMS virtual-truck model. A sensitivity analysis determines the susceptibility of the tyre-force estimates to uncertainties in the truck's parameters.

IJDWM Special Issue: Advances in Data Mining Applications

This special issue is a collection of the selected papers published on the proceedings of the First International Conference on Advanced Data Mining and Applications (ADMA) held in Wuhan, China in 2005. The articles focus on the innovative applications of data mining approaches to the problems that involve large data sets, incomplete and noise data, or demand optimal solutions.