IMPACTS OF MINING AND METALLURGICAL FACTORS UPON ORE
Resources to Reserve Inputs Seminar, September 1997, AusIMM Melbourne, pp31-32.
Author: Pavel Vassiliev
Address: Getos Ltd, Shershneva Street, 28-60
Belgorod 308007, RUSSIA
Phone: + 7 0722 267841, Fax: + 7 0722 267841
Before estimating economically mineable ore reserves of a deposit, the ore body
configuration must first be defined. After sampling the concentration of components
at a set of sample points, extrapolation and geostatistical interpolation to the nodes of
a three-dimensional model of the ore body is performed. This can be conveniently
visualized as iso-surfaces and iso-solids. But there are a number of problems that
occur while determining values for certain variables and these can be of great
importance, especially for multicomponent ores. Along with mining selectivity factors
such as dilution and losses, which impact on excavating reserves, there are a lot of
geotechnical and metallurgical ones that also have great influence on the dilatation or
compression of ore body economic boundaries at the time of mining. The significant
factors are the recovery oscillations and ease of crushing and grinding at every sample
point. This information is used to determine integrated economic values for each
block. The difficulties associated with ore body characterization and reserve
estimation are discussed and certain methods to overcome these problems are
The preliminary modeling and estimation of a mineral deposit is based on geological
research data, and is intended to reveal all potential mineral resources. In the project
stage during exploitation of the resource, the mining engineers can consider ore
bodies with the knowledge of the complex mining and metallurgical technologies
available at their site. It is obvious that, after taking into consideration unreliable
reserves, inadequately chosen equipment and technology can lead to disastrous
financial problems. The improvements through a range of mining sequences and
beneficiation circuits can be achieved by using a more reliable economic definition of
ore (Lane, 1988) and inculcating optimization principles of operations (Lynch, 1981).
The essential differences in the methods for estimation of various categories of
Resources and Reserves are marked for deposits with low-grade multi-component
ores, having indistinct boundaries of bodies, and significant variability of
components. In many cases, the allocation of multiple geological ore types to
describe an ore body brings no advantages, because of the lack of correlation with
mining and metallurgical factors. The preferred transition from element/mineral
composition to a discrete scale of artificial ore types relies on explicit technological
criterion of classification, taking into consideration irregular metallurgical recoveries
without fail. Consequently, the Reserve estimate can be searched for by analysing the
maximum deposit data points through a spectrum of mining and processing
technologies, designed or already performed in practice. So this paper deals mainly
with a technique involving ore dressing simulations, that has been designed for the
mineral reserves estimation purpose, on a deposit with iron and phosphorus value
components. Since the laboratory methodology of sample crushing, grinding,
separation and refinery process is very time-consuming, expensive and hard to repeat,
its results are more suitable as calibration data in technological deposit modelling.
When a set of ordinary sample scatter points is determined, it should be possible to
predict important technical parameters by using an advanced computer technique.
STEPS TO ORE EVALUATION
The main objective of mining and ore dressing operations is usually to provide
concentrates suitable to feed downstream processes. The simplified schematic
sequence of technological steps from excavation volumes to final metal is given on
In a general way, having a geological drill-hole database of sample assays for a
deposit, it is reasonable to compute expected parameters of the processes, even if only
for a stationary case.
Expected technological ore dressing parameters, such as yield and grade of a
concentrate, recovery of a valuable component in a concentrate, can be calculated
1. On the regression equations using statistical approach
2. On the analog approach using inquiry to database of ores from different deposits
to identify similar ores
3. On the bases of physical simulation or modelling a mineral liberation phenomenon,
grinding and extraction processes.
In general case the dependency between ore dressing parameters and ore
characteristics can be expressed as
f = F( g1, g2, …, gn) + d (1)
where: f - an important ore dressing parameter (yield, recovery, etc.); F - functional
dependence from geological factors; d - randomized or casual variability of the
For more authentic prediction of ore dressing parameters it is expedient to use a
method of geometrical modelling for mineral liberation and comminution simulation.
So the steps in estimating recoverable reserves for each block of a deposit model are
• Simulate the ore texture and breakage pattern of samples to predict the joint
distribution of size and composition of particles. Calculate the integral concentrate
grade, yield and recovery for a target component using generalised comminution
and separation functions.
• Construct a geostatistical block model of the deposit using the predicted
parameters. Outline conditioned ore bodies with cut-off grades by concentrate that
satisfy metallurgical requirements.
• Estimate the tonnage of blocks within the range of required concentrate grades and
calculate recoverable reserves of target component(s) in conditioned blocks.
Supposedly, there is a high probability that material in blocks with concentrate grades
beneath the cut-off will be processed by the current industrial ore dressing technology
ineffectively, for instance, due to fine ore texture of target minerals or because it is
impossible to separate the target minerals from the matrix because of their physical
properties. Accordingly, no such blocks are included in the recoverable reserves
Finally, to prepare for the open pit optimization procedure, each model block is
assigned with a positive or negative value using an appropriate evaluating technique
(Whittle and Wharton, 1995; Korobov, 1993).
A review of the problems encountered in developing prediction models for the
composition distribution for particles obtained by the breakage of multicomponent
ores has been made, for example, by Barbery and Leroux, 1988. The simulation
model developed to predict size/grade distribution in this paper includes a linear one-
dimensional representation of ore textures and breakage patterns (Figure 2) similar to
that used by other authors (King, 1979; Klimpel and Austin, 1983; Yingling, 1991,
As against purely analytical decisions, the original algorithmic approach was
implemented. The main steps are follows:
1. Define input parameters: mean sizes of mineral phases, expected generalized
particle size distribution and particle separation probability function for
comminution and separation circuits;
2. Simulate ore texture with breakage pattern along the X direction as interconnected
line segments using a Monte Carlo procedure. If stereological size distributions of
polished samples are not measured, the average sizes of mineral phases and
sequences that satisfy an empirical or lognormal particle distributions are
3. Extract of distribution by grade line fragments, as if they were locked and free
particles, according to separation probability matrix. Calculate the expected
size/grade distribution of concentrate;
4. Summarize all fractions of concentrate fragments to compute the expected grade
and yield of products and the recovery of target component(s) (Vassiliev and
In this case the common ore dressing parameters are expressed as:
yield of concentrate:
=1 1 (2)
grade of concentrate:
and recovery of target component:
- size/grade fraction of fragments (particles) for i-size class and j-grade class
- probability extracion function for i-size class and j-grade class of particles
- grade class values for j = 1,2,…,m scale classes (for m=12 it could be, for
instance: 0, 5, 15, 25, 35, 45, 55, 65, 75, 85, 95, 100)
When a data set was obtained along sampling traces, horizontal trenches or vertical
bore-holes, and there were explicit borders between geological rock types, the same
algorithm was applied to predict mining dilution and losses. In this case, the sequence
of ore intervals along drill-holes was generated before interpolation procedures using
interconnected segments of the mining selectivity size and grade restrictions for
The consideration of the main mining and metallurgical factors that powerfully
impact reserve calculations closely relates to the natural properties of materials to be
mined and processed. To calculate economically mineable reserves the steps are:
firstly, predict expected grades and yields of products and recoveries of target
components in every mining block; secondly, assign values in blocks to design an
open pit or underground mine with optimization; and, thirdly, estimate reliable
reserves inside the outlined shell of conditioned ores.
Technological factors impact on mineable reserves, as a result of changing processing
parameters. Subsequently, the improvements in mining, ore dressing and
metallurgical technologies can essentially expand regions of economically suitable
reserves inside low-grade iron ore bodies. Using simulation algorithms to represent
both ore properties and processing parameters, the approach proposed in this paper
was implemented as a computational unit in the general mining program. The
proposed technique results in improved accuracy of recoverable reserve estimations,
especially in the case when low-grade orebodies have unclear contacts with waste
rocks and multiple ore types are involved.
The author acknowledges and sincerely thanks Mr. David Whittle of Whittle
Programming Pty Ltd for his support and co-ordination.
Lane, K.F., 1988, The Economic Definition of Ore, Mining Journal Books Limited,
Lynch, A.J., 1977, Mineral Crushing and Grinding Circuits. Elsevier Scientific
Publishing Company, New York.
Barbery, G. and Leroux, D., 1988, Prediction of Particle Composition Distribution
after Fragmentation of Heterogeneous Materials., International Journal of Mineral
Processing, vol.22, pp.9-24.
King, R.P., 1979, A Model for the Quantitative Estimation of Mineral Liberation by
Grinding, International Journal of Mineral Processing, vol. 6, pp. 207-220.
Klimpel, R.R., and Austin, L.B., A Preliminary Model of Liberation from a Binary
System, Powder Technology, v. 34, pp. 121-130.
Yingling, J.C., 1991, Liberation model for multi-component ores, Minerals and
Metallurgical Processing. May, pp.65-72.
Korobov, S.D. , 1993, Parametric Analysis of Open Pit Mines, Proceedings of the
XXIV APCOM Symposium. Montreal, Quebec, Canada, pp. 57-66.
Whittle, J. and Wharton, C.L., 1995, Optimizing Cut-Offs Over Time, APCOM XXY
Conference, Brisbane, 9-14 July, Australia, pp. 261-265.
Vassiliev, P.V. and Tikhonov, O.N., 1988, Evaluation of Size-Grade Particles
Distributions in Ores Using Data from Image Analysis of Monolithic Textures,
Izvestiya vuzov, Tsvetnaya Metallurgiya (Non-ferrous Metallurgy in Russia), No.6,
Figure 1 - Schematic sequence
Figure 2 - One dimensional representation of ore texture and breakage pattern
Figure 3 - A plan view of FE contents in blocks
Figure 4 -A plan view of FE concentrate grades in blocks
Ore block Waste block
Particle size reduction
Figure 1. Schematic sequence.
Figure 2. One dimensional representation of ore texture breakage pattern
C C A B A D A