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Rougher Flotation Operation Aided
by Industrial Simulator
Luis G. Bergh and Juan B. YianatosAutomation and Supervision Centre for Mineral Industry
Santa Mara University
Valparaso, Chile
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Content
Introduction: motivation, plant control
Develop control strategy based on Distributed models
Experimental data
Simulator design
Some results (sensitivity to level profile)
Application: Level profile selection criteria, case studies
Conclusions and further work
Air
LICLIC
LIC
Tailings
Concentrate
LIC
Feed
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Typical copper flotation plant
From grinding
Rougher Circuit
Scavenger Circuit
Cleaning
Circuit
Regrinding
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Rougher flotation circuit (El Teniente)
Objectives : Max recoverySubject to concentrate mass and grade
Feed characteristics:
Flow, density, solid percentage, pH,particle size distribution, grades, .
Air
LICLICLIC
Tailings
Concentrate
LIC
Feed
Manipulated variables:Level profile
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A distributed model for each cell
Each cell is an independent unit with two different zones:collection and froth
Detailed phenomenological description
Considering the variation of collection flotation rates andresidence time, as well as froth transport characteristics andfroth recovery downstream the bank of cells
Air
Tailings
Concentrate
LIC
Feed
RF
RC
Feed
Concentrate
Tailings
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Pulp and froth models RF
RC
Feed
Concentrate
Tailings
Adjusted mass balanceper size class
For feed, concentrate andtailings
Operating conditions: Jg, h,
Solids input: Tph, %, density
Cu and Mo grade andsolid mass (%) per sizeclass: fine, intermediateand coarse
Kinetics responses per size class
Bubble load, top of froth
Residence time distribution: E
Cell volume
Froth recovery, Collectionrecovery, kinetics constant persize class
Calibrated model
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Simulation results
Rougher objectif: Max Cu Recovery subject to Cu concentrategrade is at least 5%
Find the appropriate froth depth profile (eg. 8-5-8-9 cm)
Air
LICLICLIC
Tailings
Concentrate
LIC
Feed
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88
89
90
91
92
93
94
95
0 5 10 15
Recovery(%)
Change in froth depth (cm)
Bank 1 Bank 2 Bank 3
For a typical feed (800 tph, 0.98% copper grade, 40% solids
and 18% +100#) and a froth depth profile of 8-5-8-9 cm, a
94.6% recovery and 5% Cu concentrate grade were obtained.
Increasing frothdepth of one bank ata time from 8-5-8-9profile
Remember bank 1 isone cell, other banksare two cells
Air
L
IC
LI
C
L
IC
Tailings
Concentrate
LI
C
Feed
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4
4,5
5
5,5
6
6,5
7
7,5
8
0 5 10 15
Concentrate
grade(%)
Change in froth depth (cm)
Bank 1 Bank 2 Bank 3
Also, the minimumfroth depth in firstbank is sometimesconstrained (absorbfeed flowratedisturbances, avoid
pulp overflow)
Higher impact of frothdepth changes onrecovery and grade arein first banks
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Considering the effects and constraints, some simple logic rules
can be written:
1. Keep the froth depth in first bank as low as rejection of feedflow disturbances permit it, avoiding pulp overflow (e.g. 8 cm)
2. Keep the froth depth in the second bank as low as possible
(e.g. 5 cm)
3. Regulate the concentrate grade by increasing the froth depth
of the last bank while froth overflows
4. If the concentrate grade is below the target, then increase the
froth depth in the penultimate bank as before
5. Continue increasing the froth depth upstream until the target is
met.
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Some comments:
The controller performance in the first bank and the collapse of froth in
later banks can both be detected by using cameras and image analysis.
An alternative form of setting the froth depth set points is to set the froth
discharge velocities from high to low along the banks of the circuit
(discharge velocity is a distributed property!!).
Next take a look at some simulations of a Rougher Circuit, to illustrate how
to aid operating decisions to adequately respond to some common
disturbances.
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Since usually a coarser feed is produced along with
a larger tonnage, a simultaneous change in feed
PSD (from 18 to 25% +100#) and tonnage (from
700 to 800 tph) were simulated.R = 95,34 L = 5,03
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Sin control: R = 93,7 L=6,4
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Con controlR = 94,95
L = 4,99
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The effect of decreasing the copper feed grade from 0.98
to 0.7% was evaluated. In this case, the following
variables were kept constant: feed tonnage 700 tph, solid
percentage 40%, 18% +100# in mineral feed size. R = 95,34 L = 5,03
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Sin control: R = 94,8 L=3,8
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Con control
R = 92,89L = 5,02
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Conclusions
To advance in the holistic control of grinding flotation plants, a capability for adjusting
flotation plant operation to feed characteristics is needed.
To control the whole flotation plant, it is necessary that each circuit can be under
control, and specially the rougher circuit, since plant recovery depends heavily on this
operation.
Furthermore, the operation of cleaning and scavenger circuits will depend on thecharacteristics of the rougher concentrate.
In this sense, a simulator for rougher circuits has been developed and calibrated for a
specific flotation plant, by estimating parameters to fit collection and froth recovery
models, from a set of designed experimental data.
The simulator has been used to sensitize the effects of changing the froth depth profile
on targets.
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Conclusions and further work
A set of logic rules has been proposed and successfully tested for maindisturbances coming into the plant.
Installed cameras can play an important role in on-line estimating when control
performance or froth quality may become constraints.
This information can be considered in decision systems to avoid operatingproblems and to enhance the evolution of the operation to the desired targets.
In a near future it is expected to validate these results in an industrial application,
as well as to develop similar models for regrinding, cleaning and scavenger circuits.
After then, a control strategy for the whole flotation plant can be developed.
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Automation and Supervision Centre for Mineral Industry
Santa Mara University
Valparaso, Chile
Rougher Flotation Operation Aided byIndustrial Simulator