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IMPACTS OF MINING AND METALLURGICAL FACTORS UPON ORE

RESERVE CALCULATIONS

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

Email: getos@getos.belgorod.su

ABSTRACT

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

proposed.

INTRODUCTION

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

figure 1.

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

technological parameter.

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

as follows:

• 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

calculations.

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).

COMPUTATIONAL DETAILS

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,

etc.).

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

undertaken;

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

Tikhonov, 1987).

In this case the common ore dressing parameters are expressed as:

yield of concentrate:

γ

ε

ij

n

i

m

jij

Yk⋅

∑

=∑

=

=1 1 (2)

grade of concentrate:

gjij

n

i

m

jij

Yk

Gk⋅⋅

∑

=∑

=

⋅

−

=

γ

ε

1 1

1 (3)

and recovery of target component:

⋅

⋅

=G

ore

Gk

Yk

Rk

(4)

where

γ

ij

- size/grade fraction of fragments (particles) for i-size class and j-grade class

ε

ij

- probability extracion function for i-size class and j-grade class of particles

gj

- 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

excavating ores.

CONCLUSIONS

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.

REFERENCES

Lane, K.F., 1988, The Economic Definition of Ore, Mining Journal Books Limited,

London.

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,

pp. 2-9.

FIGURE CAPTION

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

ILLUSTRATIONS

Ore block Waste block

Losses

Mining selectivity

Dilution

Dump

Particle size reduction

Grinding

Liberation

Separation process

Tailingspiles

Tailings

Concentrate

Metallurgical process

Metal

Slag

Feed

Recovery

Extraction

Losses

1.

2.

3.

4.

Figure 1. Schematic sequence.

Figure 2. One dimensional representation of ore texture breakage pattern

Mineral Species

Breakage Pattern

C C A B A D A