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By Z. Levinson, R. Dimitrakopoulos

A mining complex is an engineering system where raw materials are extracted from several mines, processed, transported, and sold to the market. The uncertainty and local variability of material grades and their locations within the related mineral deposits are critical for determining the optimal life-of-mine extraction sequence, destination policy, operating modes, and capital investment decisions, which define the production schedule of a mining complex. Infill drilling is used to further characterise a mineral deposit and reveals information that changes the representation of the resource model and potentially the resulting optimal long-term production schedule. A self-learning reinforcement learning approach is proposed to determine the optimal drilling locations that maximise the net-present value of the long-term production schedule and the associated financial forecasts by adapting the schedule to additional information. A case study at a copper mining complex demonstrates a $70 M increase in project value by collecting drilling information with a $1 M budget.

Sep 1, 2021

By S. T. L. Harrison, A. Kotsiopoulos, J. R. Amaral Filho, J. L. Broadhurst

The land disposal of voluminous fine-grained tailings from the processing of mined ore bodies poses a long-term risk to the surrounding environment and local communities, while also representing a loss of valuable natural resources; including water, land and mined minerals. To date, the development and practical implementation of alternative approaches and technologies for removing risk and reducing the burden associated with these wastes, particularly in South Africa, remains limited, and value recovery is largely limited to the extraction of precious metals. To this end, researchers at the University of Cape Town have developed a programmatic multi-phased approach to explore and develop opportunities for the valorisation of large-volume mine wastes, based on the innovation value chain. This paper outlines this conceptual approach and presents key outcomes from its application for the specific case of sulfide-bearing coal processing wastes from South African collieries. Through the application of a combination of qualitative and quantitative tools and methodologies, a number of potentially viable opportunities and processing routes for value recovery and bulk re-purposing of these wastes have been identified and tested on a laboratory scale. Studies have, however, also shown that there are several technical and non-technical factors currently constraining commercial application of these opportunities. Overcoming these constraints will require a commitment to innovative waste management approaches and effective multi-sectoral collaboration. Keywords: Coal processing wastes, physical separation, waste re-purposing and utilisation, technology transfer

Jan 1, 2020

By R. B. Khumalo, F. K. Mulenga

Sampling in a continuous flotation plant is always a challenge as these plants are meant for production and not research work. The continuous flotation operation used in this work produced three sets of data: the first was based on overall plant performance, done by sampling the feed, concentrate, and tails; the second was based on the roughers row cell-by-cell; the third set of results came from the cleaners/recleaners row cell-by-cell. The highest recovery results in all sets of data obtained lie in the particle size ranges of 40 μm and 100 μm, which is in agreement with results by other researchers in the same field. King’s theoretical (model) recovery proved to be able to fit the experimental data but there is still room for improvement as the sum of squared error ranged between 400– 1200. Data obtained in continuous flotation operation could be used with batch flotation test-work to characterize scale-up of flotation cells.

Jan 1, 2023

By J. Yianatos, P. Vallejos

In the last decades, the exponential increase in the cells’ volume resulted in significant advantages such as investment costs, footprint and energy savings. However, in larger size cells, despite the increased diameter/height ratio, new constraints arose related to the need for decreasing the larger froth transport distances to enhance the froth recovery. Thus, the use of froth crowders and internal launders became compulsory to maintain the froth transport distances but decreasing the ratio between gas and collected mineral flowrates. Consequently, the bubble load will increase until reaching a critical bubble surface coverage. In this paper, the concentrate carrying capacity (tph/m2) is described in terms of the bubble surface coverage (bubble loading) at the pulp-froth interface and the froth recovery (froth transport) and is evaluated as a function of the cells’ volume and operating variables. A sensitivity analysis, based on industrial operating and design conditions, such as superficial gas rate, particles size, froth crosssectional area and cells’ volume, was developed for evaluating the metallurgical performance of cells from 100 to 630 m3. Results show that limiting carrying capacity can arise for the whole range of cells’ volume, depending on critical operating conditions, particularly in first rougher cells and cleaning stages of industrial flotation circuits. The increase in cells’ volume consistently increases the bubble loading (bubble surface coverage) under all conditions, approaching the limiting conditions more rapidly in the case of cells without internal launders. Otherwise, the limiting condition was achieved when the particle size DPS decreased from 50 to 20 (μm) and for superficial gas rates significantly less or larger than 1 (cm/s). The use of internal launders allows for increasing the range of the operating conditions needed to reach a certain carrying capacity. Keywords: Bubble carrying capacity, flotation cells, bubble surface coverage, cells’ volume

Jan 1, 2020

By D. Wines

"Slope stability analyses have traditionally been undertaken in two dimensions assuming plane strain conditions. Although three-dimensional analysis techniques are widely available and used routinely for open pits, two-dimensional analyses are still more common due to the relative ease of model construction and the relatively rapid simulation times.Two-dimensional analyses will often produce different results to three-dimensional analyses for the same slope. It is generally thought that two-dimensional analyses will produce more conservative results. The main reason for the differences is the ability of three-dimensional analyses to account for the three-dimensional nature of the various model inputs, including the slope geometry, the distribution of soil and rock mass domains, the orientation of geological structures with respect to the excavation face, the orientation of the in situ stresses, and the distribution of pore pressure. In some cases, for a long, straight slope in basic geological conditions, two-dimensional analyses can provide a reasonable representation of the problem. However, in many cases, the inability of a two-dimensional analysis to represent the true three-dimensional nature of the problem will lead to unrealistic analysis results.This paper discusses the reasons, as detailed above, for the differences in two-dimensional and three-dimensional analysis results. Work by others is summarized, and additional numerical analyses are performed to provide an improved understanding of the effects of slope geometry, structural orientations, and in situ stresses on predicted stability. Case studies are presented for both stable and unstable slopes, and the behaviour of these slopes is related to the three-dimensional nature of the slope geometry and geology.The previous work, the new analyses performed here, and the case studies show that it is often important to provide a realistic representation of the slope in three dimensions in order to obtain reasonable stability analysis results. This is particularly true for hard rock environments where structurally controlled failure mechanisms are most likely. The paper also highlights the fact that back-analysed properties obtained from one analysis technique are not necessarily applicable to forward analyses using another technique."

Jan 1, 2016