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Developments in Process Control
With particular reference to comminution and flotation
(McGill Professional Development Seminar Mineral Processing Systems)
by P. Thwaites, May 15th, 2009
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Xstrata OrganizationalStructure
Xstrata Executive Committee
X Ni X Cu X Coal XTS X Alloys X Zn
XPS XT
XTS : Xstrata Technology Services Thras Moraitis - LondonXPS : Xstrata Process Support Frank McGlynn - SudburyXT : Xstrata Technology Joe Pease - Brisbane
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Objectives of McGill Short Course
The objectives of the Mineral ProcessingSystems, Professional Development Seminarare:
To cover new and important developments in
mineral processing in the areas of comminution,flotation, process control, environment andoptimization.
Developments in Process Control, with particular
reference to comminution and flotation: New sensor technologies, control strategies, optimization
etc.
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Opportunities to consider
Operational Performance Excellence requires a solid performance of theregulatory layer AND process optimisation.
For Plant Operators: Is your feed stable? Are your instruments calibrated and performing? Are you aware of wireless instruments (including vibration)? Is your control system up to date and stable? Are you in manual or auto control? Are your Operators acting on alarms or are they nuisance? Do you understand and accept your process variability? Are you operating within the design targets and process constraints
(pumps, cyclones, supplies, roasters, furnaces etc.)?
Are you using your surge capacity, . or running tight level control? Are you at optimum and are the controls robust? Are you benefiting from asset management systems? Are failure / fault detection systems implemented? Can you make the same product for less energy consumption?
(P. Thwaites, AUTOMINING2008, Chile)
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Downtime Reporter(Matrikon)
Automatic downtimerecognition withdirect data fromProcess Control
System
PlantEquipment
PLC/DCS
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(Matrikon) Downtime Reporter:How do we improve OEE*?
Improve Availability
Downtime Paretoreport clearly identifiesthe big-ticket items
causing the mostdowntime in the plant.
Strategies can then be formulated that eliminatethese problems and thus improve availability.
* OEE Overall Equipment Effectiveness = Availability x Performance x Qualityi.e. 100% = 100% x 100% x 100%OEE defines the expected performance of a machine, measures it and providesa loss structure for analysis, which leads to improvement.
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Definition of Process Control
(McKee, AMIRA P9L)
Process control is a broad term which often meansdifferent things to different people.
Process control is considered as the technology
required to obtain information in real time on processbehaviour and then use that information to manipulate
process variables with the objective of improving themetallurgical performance of the plant.
Control for the purpose of process improvement.
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Importance of Control Performance(Emerson)
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Audits
Audits are the first step to ensure control system
investments meet expectations(Feb. 1999 CONTROL ENGINEERING Journal, by Dave Harrold)
Shown are the defects per loop e.g.
Filter Time Constant
Integral Period Derivative Period
Sampling Period
Proportional Band
Current Operating Mode
0%
16%
7%
4%
0%
34%
39%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
0 1 2 3 4 5 6
Number of Defects per Loop
Frequency
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XPS PC GrpCapabilities &Services
XPS PC Group www.myxps.ca
1. Process Design & Commissioning
2. Controls Auditing
3. Control Loop Optimisation
4. Advanced Controls
5. Slow Process Response
6. Off-Gas System Controls
7. Grinding Controls
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Process Control Will Not CorrectInherent Design / Flowsheet Problems
(McKee, AMIRA P9L)
There is a need to determine, and if necessary correct, thecondition of the plant as a pre-requisite to controldevelopment. A good example is the importance of classifieroperation and its effect on comminution circuit performance.
Techniques exist (plant sampling, modelling and simulation)to audit the actual plant operation. Correcting plantlimitations should be seen as a first step in the control
approach.
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XPS : Process Support Groups
Process Control - Identify and deliver robustprocess
control technology and engineering solutions to achieveOperational Performance Excellence.
Process Mineralogy - Design, implement and optimizemineral processing flowsheets by matching the flowsheetto the mineralogy.
Extractive Metallurgy Provide specialized extractivemetallurgy services (hydro-and pyro-metallurgical).
Flowsheet/project development using modeling andpiloting, new process development and plant optimization.
Materials Technology - Improve the reliability ofcritical equipment through appropriate implementation
of well proven materials engineering practices atessential stages of design, procurement and operation.
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XPS Process Mineralogy
PROCESS
MINERALOGY
Sampling and Statistics
MineralP
roce
ssing Mi
neralScience
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XPS Process MineralogyCrushing and Blending Plant
Staged crushing and screening prevents overproduction of fines
Drill core or ROM 150mm rock at 150 kg/hr
Blending technology produces RSD of < 5% in test charges
Any product size down to 1.7 mm split into any unit mass
Semi-continuous operation
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XPS Process MineralogyMineral Science
Developing Flowsheets that Work the first time
Modern Quantitative Mineralogy
Strategic
Virtual Flowsheeting/Flowsheet Implications
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XPS Process MineralogyMineral Processing
Developing Flowsheets that Work the first time
High-Confidence Flotation Testing*
Established 1995 95% confidence level Tried and tested Reproducible results Reliable scale-up
* Lotter, N.O., 1995, A Quality Control Model for the Development of High-Confidence Flotation Test Data,M.Sc. Thesis, University of Cape Town, June 1995
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XPS Process MineralogyMineral Processing
- Commissioned 2005
- 11 campaigns to date
- Reproducible results- Proven ability to mimic
operations
Mini-Pilot Plant
Demonstrating theOptimised Flowsheet
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XPS Process Mineralogy Montcalm Project
Flowsheeting fromDrill-Core to PilotPlant
Finds optimumflowsheet, or findsbest performanceattainable withknown flowsheet
Montcalm Project- Type 1 Startup*
Comparison of Montcalm Start-up Curve with McNulty Curves
0
20
40
60
80
100
120
0 2 4 6 8 10 12 14
Quarter after start-up
NioutputinNia
ndCuconc,%o
fdesign
Type 4
Type 3
Type 2
Type 1Montcalm start-up
Montcalm start-up - October 2004
*Type 1 reaches design capacity ~4 quarters
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Good Process ControlImplementation
(McKee, AMIRA P9L)
A good implementation requires a well definedoperating strategy and an associated control strategy.
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Effective Process Control Cycle
(for New Processes / Plants)
Process Opt.(Production)
Falconbridge Limited - Process Control
poor controlbettercontrol
higher production
lower costs
setpoint
constraint
best control
time
units
Overall Process Control Objective
Control OptimiseMeasure
As-builts
(Commissioning)
P&IDs
(Basic Eng)
Control Config.
(Construction-EPCM)
Logic Diagrams
(Detailed Eng-EPCM)
Process Flowsheets &
Control Philosophy(Development)
Deliverable
(Stage)
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Overall Process Control Objective
units
Control Optimize
poor controlbettercontrol
higher production
lower costs
setpoint
Process constraint
best control
time
Measure
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Poor to Optimized Control
Best Practise:
Necessary for:OperationalPerformanceExcellence
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5 Essential Control Loop Elements
& Consider the Whole Loop (ABB)
ABBABB Corporate ResearchK. Forsman, 1998, No. 10
Consider the whole loop
PC-program in
Advant OCS
D/A
I/P
Positioner
FT
A/D
Deadband
Filter
Ramp rate Filter
Actuator
Filter
3-15 psi
0-6 bar
4-20 mA
4-20 mA
mV
Five Essential*
Control Loop Elements:
1. Sensing element2. Transmitter3. Controller
4. Final Control Element (e.g.Actuator or VSD)5. Process
Only when all five elementsare performing their best will
the control system meetexpectations!
* February 1999 CONTROL ENGINEERINGby Dave Harrold.
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Regulatory Control improvements(AG Mill Bearing Pressure measurement)
Careful:
1) Appropriate filtering;
2) PI Data compaction.
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Mill Feed -
Manual Setpoint Control
Opportunity in optimising feed tonnage has been estimated at $8.8m/yr!
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General Process Control Hierarchy
Field / Panel / DCS / PLC
Instrumentation - Inputs / Outputs
Advanced
Regulatory
Manual
PlantOptimization
Optimize
Stabilize
FunctionObjective
Processes
Optimizing Control
Process
Plant
Optimization
Cash Optimization
EconomicsSite
Loop Control
Measure
EconomicReturn
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Era ofEra of ObjectivesObjectives ControlControlTechnologyTechnology
Quantity Tonnage ofConcentrate
PLC or
DCS
QualityTonnage + Quality
Client Satisfaction
Tonnage + Quality+
Peak EconomicPerformance
PeakPerformance
OptimizingControl Systems
OCS
Metso minerals - Jan. 2004 Technology Presentation
The evolution of
control can besummarized in thisslide.
The first shift wasfrom quantity onlyto quantity and
quality, bothapplied in a DCSor PLC.
However, to shiftfrom maintainingquality to peakperformancerequiressomething morethan a DCS orPLC: an optimizingcontrol system.
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Enabling Technologies - ABB
(Overall Process Unit Control)
MPC (Model Based)
Control:
Several tools are available:
Mintek (FloatStar) Emerson (DeltaV MPC)
ABB (Linkman, Expert Optimizer)
Invensys (Connoisseur)
Honeywell (Profit Suite)
Gensym (G2)
Prediktor
Metso (Adaptive Predictive Model)
Production cost
Lower
FeedbackHigher
Lower
Higher
Feedforward &Cascade
Cross-Coupled
Multi VariableTechniques
Knowledge
BasedExpert Control
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Model Predictive Controller
Constraint Management(LR or QP methods)
CVs, MVs & DVs
Optimal Setpoint& MV Target
LP Optimizer
Connoisseur Environment
Connoisseur (Invensys) Overview
Regulatory Control System
Neural Net
Adaptive Cont
C P
M
Fuzzy Logic
Director Calc
10*349304
1454
LD
dxSj
++
Non-linear
Inferential
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Elements Necessary for Successful
Process Control in Mineral Plants(P. Thwaites, IFAC MMM07, Plenary Address, Quebec City)
ProcessControl ->
OperationalPerformance
Excellence
Tools:Instruments;
Systems etc.
People:Control / Process
Knowledge
Successes:Results &
Examples
Actions:
Support
Management;
Technology Transfer
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Enabling Technologies
(McKee, 1999). In grinding, control of AG/SAG mill circuits is the dominantarea. While some systems have emerged which provide a reasonable level ofcontrol, there is still much not understood about the dynamic behaviour ofthese mills, and there is considerable scope for further development.
1. Multivariable Controller in a PLC Function Block (Bartsch):
SAG feed control, is generally done using an Expert system.
These systems often deliver improved control and 4 to 5%increased throughput (e.g. Collahuasi and Raglan Mills);
XPS has recently implemented a complex controller in a
(Concept) control block, negating the costs of an auxiliaryExpert System, and training / support of this system.
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Entire Grinding Controls Focus:
(Liberation efficiency and throughput)
150 mt
Surge BinSAG Mill
24 diameter
(2240 kW)
Metso Double Deck
Vibrating Screen (8 x 16)
1- 8 x 40 mm
2- 5 x 4 mm, 3 x 8 mm
Sandvik Cone Crusher
Hydrocone H4800
(250 kW)
6 x 15 Krebs GMAX
Cyclones
Vort: 4.5, Apx: 3
Ball Mill
14 dia. x 21
(2240 kW)
Flotation
Underground
Storage Bins
A B
11-CV-03
21-CV-01
21-CV-04
21-CV-0521-CV-06
21-CV-02
Cyclone Feed Density Control
MICFI
DI
DI
LI
PI
DIC
DIC
RSP
PIC
LICSelectLogic
SY
OUT
OUT
OUT
OUT
ADJUST FEED SPOR
CYC FEEDDENSITYSP
H/LLim
H/LLim
Ref: FTC Report Raglan: CycloneDensity Control,E.Bartsch, 11 November 2005
Cyc O/F Density byP/Box water addition
Cyc O/F Density byP/Box water addition
Cyc Feed Density by
trimming O/F density SPCyc Feed Density by
trimming O/F density SP
CONSTRAIN circulatingload by trimming Cyc
Feed Density SP
FFE Impact Meter
Wipfrag
(Size analysis) ASRi
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SAG Feed Rate Control (PID)
SAG Control - December 27, 2005
100
110
120
130
140
150
160
170
180
04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 09:36:00
Time
Tonnag
e-mtph
4300
4350
4400
4450
4500
SAG
KPA
Tonnage
kPa
Setpoint = 4450 kPa
SAG Feed SAG Load
mpth kPa
Average 131.3 4 454
Std Dev. 9.7 24Minimum 100.5 4 392
Maximum 152.7 4 503
In Cascade control (constant kPa),
The SAG tonnage varies tremendouslyto maintain the requested setpoint.
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Why Online Feed Size Distribution?
Plot-0
TONNAGE ALIM.BROYEUR AUTOG
PUISSANCE BROYEUR AUTOGENE
PRESS. D'HUILE PALIER DECH
Alimentation Moulin AG D75
9/29/2002 9:30:00 AM 9/29/2002 9:30:00 PM12.00 Hour(s)
PRI-21WIC0204.SP
Mtph
PRI-21ML01.AV
kW
PRI-21PIT0457C.AV
kPa
PRI-21VT28.D75.AVG
m
20
40
60
80
100
120
140
160
180
0
200
1600
2500
0
4500
0.035
0.085
130.00000
2163.43750
4363.49463
0.04566
PRI-21WIC0204.SP
Mtph
PRI-21ML01.AV
kW
PRI-21PIT0457C.AV
kPa
PRI-21VT28.D75.AVG
m
Bearing Pressure
Mill PowerMill Feed Set-point
D75 Size Fraction
Large size
fraction (D75)INCREASING
Mill Load &Power
INCREASING
Large sizefraction (D75)
DECREASING
Mill Load &
PowerDECREASING
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SAG Charge Multivariable FuzzyController (Bartsch, CMP 2007)
Inputs
(Measurements)
Outputs
(Set-points)
Fuzzyfication
Fuzzy Rules
De-fuzzification
Power
Charge
Impactmeter
Granulometry(prediction !)
Assays
Feed Rate
Density
(water addn)Crusher Gap
Mill Speed
Programmed inexisting plant PLCs
ASRi (Automatic Setpoint Regulation)
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Crusher Gap Control:
Kidd Mill -> Strathcona Mill -> Raglan Mill
Field Device
DCSDisplay
(via OPC)
ASRi (Automatic Setpoint Regulation)
Sandvik Technology - Crushing Plants
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Cyclone Feed Density Control
Mill Objectives *:
1. Increase the fineness of the grind to P80 = 752. Reduce Cyclone Feed Density to improve
classification efficiency.
* Ref: Report Raglan Optimization Project : Raglan Site Visit L Urbanowski, Jan 14 2006
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Cyclone Feed Density Control
Typical Control Strategy:
LI LIC
SY
DIC
DI
Water addition monitored /
controlled to the pumpbox?:
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Cyclone Feed Density Control..or Cyclone Overflow Density Control?
Deficiencies in existing Strategy:Controlling cyclone feed density directlyby water addition not
considered best practice.
1. The feed density is slow to respond which means the loopcannot be tuned for load disturbance
2. The influence of adding water changes depending on your millcapacity:A. spare grinding capacity it will trim the feed density as
expected.B. at or over capacity -; it will increase circulating load and
density, resulting in more water addition .
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Cyclone Feed Density Control
Requirements of a Density Control Strategy
1. Control water addition by using the moreresponsiveprocess variable (overflowdensity).
2. Consider the circulation load to avoidoverloading the ball mill.
3. Maximize circulating load in order to maximisemill efficiency.
4. Filter measured variables appropriately (i.e.match the process response times)
Ref: FTC Report Raglan: Cyclone Density Control, E. Bartsch, 11 November 2005
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Cyclone Feed Density Control(Best Practice)
MICFI
DI
DI
LI
PI
DIC
DIC
RSP
PIC
LICSelectLogic
SY
OUT
OUT
OUT
OUT
ADJUST FEED SPOR
CYC FEED DENSITY SP
H/LLim
H/LLim
Ref: FTC Report Raglan: Cyclone Density Control, E. Bartsch, 11 November 2005
1. Control water byusing the moreresponsive process
variable (CycloneOverflow Density).
2. Use the output ofthe Cyclone FeedDensitycontrolleras the remote(ext.) set-point forCyclone OverflowDensitycontroller.(This cascade is moresuited to the slowdynamics of the loop.)
3. Use a CirculatingLoadcontroller toeither trim FeedRate or CycloneFeed Densityset-point. N.B. feed willneed adjusting ifcirculating load doesnot reach steady state.
4. Filter appropriately
1
2
3
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Raglan Cyclone Feed Density ControlRef: FTC (XPS) Report Raglan: Cyclone Density Control, E. Bartsch, 11 November 2005
0
100
200
300
400
500
600
700
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77
February - March
40.0
50.0
60.0
70.0
80.0
90.0
100.0
110.0
120.0
130.0
Circ Load
Circ Load SP
D80
Reduced* p80 by 10 microns = 0.6% Ni rec increase worth approx. $2,000,000 pa.
(* ref feasibility study benchmarking 2004)
P80 80microns
P80 68microns
P80 90microns
%Circ
Load
Controllerplaced in
service
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Oscillating on/off Cyclone Switching
=> Oscillations in Cyclone AND Process Feed rates
- Cyclones (often) operating below their design pressure (40-60 instead of 100 kPa):
Increased short-circuiting of water & undersized particles;
IsaMill feed density decreases below design value;
Low IsaMill feed density may create pumping issues (& trip on low flow/pressure);
- Cyclone pressure should be controlled using the cyclone feed-rate.- Implement Surge Tank Control (using 150 m3 of tank) NOT Tight Level Control.
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Strathcona Mill Regrind Cyclone Overflow DensityResults - 3 days operation (including startup)
Plot-0
BREG CYCPRESSURE BREG CYCO/FDENSITY
1/18/02 7:16:18 PM 1/21/02 7:16:18 PM3.00Day(s)
CGCYCRBmv~C_PIC_2057:B_PID2057.MEAS
kPa
CGCYCRBmv~C_DIC_2060:B_AIN2060_1.PNT
% solids
50
100
150
200
250
300
0
350
20
50
192.74
29.0277
CGCYCRBmv~C_PIC_2057:B_PID2057.MEAS
kPa
CGCYCRBmv~C_DIC_2060:B_AIN2060_1.PNT
% solids
Plot-0
B REGCYC PRESSURE B REGCYC O/F DENSITY
3/21/02 7:16:18 PM 3/24/02 7:16:18 PM3.00 Day(s)
CGCYCRBmv~C_PIC_2057:B_PID2057.MEAS
kPa
CGCYCRBmv~C_DIC_2060:B_AIN2060_1.PNT
% solids
50
100
150
200
250
300
0
350
20
50
148.07
38.3157
CGCYCRBmv~C_PIC_2057:B_PID2057.MEAS
kPa
CGCYCRBmv~C_DIC_2060:B_AIN2060_1.PNT
% solids
Improved startup
CycloneFeedPressure
Cyclone
Overflowdensity
Overflow Density:
9% density
improvement
65% reduction instandard deviation
Enabling Technologies
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Enabling Technologies
Excellent practise is an OPPORTUNITY in our Canadian Mills!
2. On-Demand Sampling Automation:
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Enabling Technologies
3. Camera Imaging, flotationlevel & reagent controls
- Feed size analysis;
- Froth camera imaging;
- Optical System forcathode quality:
39
Froth Camera Imaging Technology
CSQA:Cathode SurfaceQuality Analyzer(Aplik)
Fl t t th T E i
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Float to the Top Energise your
Flotation PerformanceAuthors: A Okely, A Rinne, A Peltola (Outotec)
The relevant cost factors for a flotation plant are investment, energy, reagentconsumption, and maintenance.
The chart below shows the breakdown of these factors, based on typical ownershipcosts of a large mechanical flotation machine (100-200 m3) over a 25 yearlifespan.
If we look at the energy
efficiency we find that threeaspects are critical:
1. Air dispersion2. Rotational speed of the
mechanism3. Component wear
Optimal air dispersion is one of the basic requirements for goodmetallurgical performance. Plants operating with forced air cells have
often noticed that the best results are achieved using individual andvarying air feed rate in each cell.
Canty Vision Froth Camera
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Canty Vision Froth Camera
(Strathcona Mill)
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Flotation Level Controls
Level Control:
Absolute basics for Flotation.
The movement of the pulp level setpoint provides opportunities forflotation optimization if there is tight control in each cell. (Ref., PT, 1983)
R h L l Di b
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Rougher Level Disturbance
Setpoint
Level
FrothVelocity
1. Velocity (mass pull)changes by over1000%!
2. Baseline pull isonly 0.5 1 cm/s! :Level SP too low?
Escondida Froth Velocity is Cascaded to
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yControl Flotation Cell
(metso minerals & Ramon A. Brito Minera Escondida)
SP AireSP Velocidad
SP Nivel
PV Velocidad
Tapon
Celda WEMCO
Camara
Escondida: Instalacin en la FlotacinPrimaria Lneas 1,2,3,4,5,6
(54 Cameras)
Rougher flotation
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Rougher flotationTest Control strategy step1
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Froth velocity control
Feed
grade
Froth
veloc.
Level
SP
Air SP
Tails
%Ni
Flotation Control: Points on Control
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Structure and importance of layering(Gilles)
Flow Control
MetallurgicalControl
Ratio Control
Set Point:
cc/min
Measurement:Tonnage orTonnage*Head Grade
Set Point:Gram/Ton
Measurement:(Tail or Conc
Assay)
Set Point:Operator Recovery orGrade Target
All of the Met. Loops are PI/PID controllers for supportability.
If problems occur, operators may turn off one level of control at a time, independently for
each reagent.
Addendum:
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Addendum:Thickener Controls
For effective Thickener control 3 measurements are important:
1. Inventory (Inferred from pressure measurement in the Cone) controls underflow pump speed.2. Bed level
controls flocculent addition rate.3. Overflow clarity
indication/warning of poor control.
None of the above measurementsare trivial and require attention toequipment selection and installation.
Typical Bed Mass Level Measurement on aThi k
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Thickener
Shown is anE&H Deltabar S
smart (2 wire)differentialpressuretransmitter.
It is ideally used to
measure level,pressure ordifferential pressure.
Uses a flushmounted ceramicdiaphragm sincethey out last themetallic diaphragmsby at least 10 years.
(Ref. M. Gribbons)
Enabling Technologies
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Enabling Technologies
4. Flow Measurement:
SONARSONAR A New Class of Meters with Distinct Application AdvantagesA New Class of Meters with Distinct Application Advantages
SONAR
Gas Void Fraction GVF-100
ENTRAINED AIR Meters
Gas Holdup MeterGH-100
Volumetric Flow Meter
Ultrasonic
Coriolis
Vortex
MultivariableDP
Magmeter
Ref: Christian [email protected]
SONARtrac Enables Accurate MassSONARtrac Enables Accurate Mass
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SONARtrac Enables Accurate MassSONARtrac Enables Accurate Mass
Balance in the Presence of AirBalance in the Presence of Air
Application:Hydrocyclone Feed Line
Challenge: Variable entrained air levels causing errors in density reading and hydrocyclone split This affects both flotation performance as well as ball mill circulating load
0
2 0 0 0
4 0 0 0
6 0 0 0
8 0 0 0
1 0 0 0 0
1 2 0 0 0
1 4 0 0 0
1 6 0 0 0
1 8 0 0 0
1 /2 5 /2 0 0 6 4:4 8 :0 0 1 /2 5 /2 0 0 6 6:0 0 :0 0 1 /2 5 /20 0 6 7 :1 2 :0 0 1 /25 /20 0 6 8 :2 4 :0 0 1 /25 /2 00 6 9 :3 6 :0 0
T im e
Volumetr
icFlow(gpm)
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
3.5%
4.0%
4.5%
5.0%
GasVolu
mneFraction(%)
V F ( g p m )
G as Volum e Frac t ion
Flow Rate
Entrained Air %
Enabling Technologies
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Enabling Technologies
5. Rotating Equipment Machinery Health:
Enabling Technologies
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Enabling Technologies
6. Wireless Instrumentation:
GITE-322-05-PRE-15
HighServiceINDUSTRIAL SUPPORT COMPANY
2.- Proposed Solution
Electronic Wear Sensor (EWS) embedded in liners andlifters fastening bolts
GITE / DES
Main components of the EWS: Transducer
8-12 levels
CPU (coder & signal processing)statistical filtering
RF Transmitter,FM FSK, 916 MHz, 1mW,
anti-collision algorithmSensor Life: 18 month
Business Case 3:Smart Wear Sensor
Rev0 Fecha 100807 Pgina 19 de 29
C St d E l
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Case Study Example
CBU: Xstrata Nickel (Sudbury operations)Site: Strathcona Mill
Project: Primary Grinding ControlStatus: Completed in 2008Comments:
Maintain Consistent Grind to Flotation Adjust, & optimise Rod Mill Feed
Automatically to Capacity of GrindingCircuit Eliminate Process Upsets due to Ore
Variability (i.e. Mill Overloads) Reduced energy consumption by 7.1% &
7.5% in the rod & ball mills respectively
Implement on 2 grinding lines Presented at the Jan. 2009 meeting of the
Canadian Mineral Processors.8.48.17.87.57.26.96.6
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
BM kW/t
Density
7.773 0.2732
7.189 0.2862
Mean StDev
Old Ctrol
New Ctrol
Status
Histogram of BM kW/tNormal
3.363.243.123.002.882.762.64
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
RM kW/t
Density
3.123 0.1275
2.900 0.1168
Mean StDev
Old Ctrol
New Ctrol
Status
Histogram of RM kW/tNormal
Rod Mill (Power/tonne):
Ball Mill (Power/tonne):
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Strathcona Mill Grinding Circuit
McIvor and Finch, 1991: Besides achieving the desired mineral d80size, it is clearly desirable to produce as narrow a size distribution as
possible to squeeze the maximum amount of the mineral value intothe highest recovery region.
Inadequate mineral liberation in itself leads to higher energyconsumptions, as finer grinding has to be performed for liberation.
24521017514010570350
0.16
0.12
0.08
0.04
0.00
RMF (Tons/hr)
Density
173.3 33.95 719
188.2 2.866 721
MeanS tDev N
1Before
2After
Status
6057545148454239
0.8
0.6
0.4
0.2
0.0
COFD (%Solids)
D
ensity
49.81 4.844 704
47.14 0.4740 721
Mean StDev N
1Before
2After
Status
Histogram of RMF (Tons/hr)Normal
Histogram of COFD (%Solids)Normal
Strathcona Grinding Circuit
Process ModelingProcess ModelingProcess ModelingProcess Modeling
0.5
0.6
0.7
0.8Step Response
PBLs730 10
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Strathcona Grinding Circuit
Process Modeling:
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 1000040
50
60
70
80
y1
Input and output signals
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000140
150
160
170
180
Time
u1
PumpPumpPumpPump----boxboxboxbox
level (PBL)level (PBL)level (PBL)level (PBL)
Rod millRod millRod millRod mill
feedfeedfeedfeed
((((RMFspRMFspRMFspRMFsp))))
0 500 1000 1500 2000 2500 3000 350043
44
45
46
47
48
49
y1
Input and output signals
0 500 1000 1500 2000 2500 3000 3500115
120
125
130
135
140
145
Time
u1
CycloneCycloneCycloneCyclone
OF densityOF densityOF densityOF density
(COFD)(COFD)(COFD)(COFD)
PumpPumpPumpPump----boxboxboxbox
waterwaterwaterwater
((((PBWspPBWspPBWspPBWsp))))
0 5000 10000 15000-15
-10
-5
0
5
10
15
Time
Measured and simulated model output
0 500 1000 1500 2000 2500 3000 3500 4000-3
-2
-1
0
1
2
3
Time
Measured and simulated model output
COF Particle Size and Pulp Density
Correlation between COF Particle Size and Pulp Density
72.00
74.00
76.00
78.00
80.00
82.00
84.00
86.00
35.00 40.00 45.00 50.00 55.00
COF Density (% solids)
COFP.S
ize(%-150m
esh)
Data from Strathcona Mill November 2007 Grinding surveys
Two SISO, simple PI controllers:1. Cyclone Over Flow Density (COFD) by
manipulating the Pump-Box Water(PBWsp)
2. Rod Mill Feed (RMFsp) based on the
Pump-Box Level (PBLsp)
Grinding Control Objective:
Maximize the throughput (quantitativeobj.) while maintaining
the cyclone OF density at target(qualitative obj.)
11
0 100 200 300 400 5 00 600 7 00 8 00 900 1 00 0-0.2
-0.18
-0.16
-0.14
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
Time
Step Response
0 500 1000 1500 2000 2500 30000
0.1
0.2
0.3
0.4
Time
s
e
RMFsp
PBLs
4851
73.0 10
+
=
s
e
PBWsp
COFD s
1251
18.0 15
+
=
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Old Control Strategy
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New Control Strategy
B f Old C t l
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Before - Old Control
OLD CONTROL
PBL manipulating PBWPBL manipulating PBWPBL manipulating PBWPBL manipulating PBW
Some control, but not process control
Fixed tonnage and oscillating cyclone overflow density
Mill feed shut down intermittently to handle mill overloads Upsetting all downstream flotation processes
Increase of milling rate can take hours
After New Control
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After New Control
11
NEW CONTROLNEW CONTROLNEW CONTROLNEW CONTROL1. COFD manipulating PBW and1. COFD manipulating PBW and1. COFD manipulating PBW and1. COFD manipulating PBW and
2.RMF based on PBL2.RMF based on PBL2.RMF based on PBL2.RMF based on PBL
Process control
Controlled cyclone overflow density (particle size) and maximisedrod mill feed rate
No more mill feed shutdowns controller prevents mill overloads
No more grindouts
Controller reacts quickly to changes in ore hardness
Easier to operate, consistently for each shift
Key Results
200
180
160
55
45
35S
PT(t/h)
RMWi
RMWi
RMF SPT (t/h)
V ariable
Time Series Plot of RMWi, RMF SPT (t/h)
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Key Results(Over a Longer Period)
Period: (Jan.07-May.08 vs Jun.08-Sept.08)
Increase in circuitthroughput of7.7%:
(168 to 181 tph); fully realised upon treatment
of Ni Rim South ore;
Grinding circuit feed toflotation (COFD) maintainedat target density: reduction in variability from
2.0 to 0.8;
An increase in energy efficiency(kW/t) of:
7.1% for the rod mill and
7.5% for the PBM;
No Mill overloads;
No degradation in Ni or Cu
Recoveries.
09/24/0806/13/0804/18/0802/25/0812/09/0710/02/0707/29/0706/08/0704/14/0703/01/0701/10/07
140
120
25
15
Date
RMFS R
198192186180174168162156
0.060
0.045
0.030
0.015
0.000
RMF SPT (t/h)
Density 168.2 5.756 517
181.2 7.098 77
Mean StDev N
1Before
2After
Status
Histogram of RMF SPT (t/h)Normal
09/24/0806/13/0804/18/0802/25/0812/09/0710/02/0707/29/0706/08/0704/14/0703/01/0701/10/07
60
50
40
30
Date
COFD(%)
525048464442
0.48
0.36
0.24
0.12
0.00
COFD (%)
Density 46.61 2.008 517
46.22 0.7656 77
Mea n S tDev N
1Before
2After
Status
Time Series Plot of COFD (%)
Histogram of COFD (%)Normal
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Feedback CommentsOperators:
This has made the operations of the mills that much
easier and efficient!DD
It is working great and it is helping flotation as well. RA
This is better and the right way to do it. SH
To do better than the controller, I have to take samplesevery 15 minutes. BR
I thought this will not work, but it is working very well.JM
Phil Thwaites (Manager Process Control):
Xstrata operates many grinding circuits. I believe thatbenefits such as these (as also demonstrated at RaglanMill & Kidd Mill) can be duplicated at several other plantswith similar grinding circuits.We often see very poor cyclone and grinding circuitcontrols that have not been tuned or optimised.In this project we have found the best way to control
these two circuits the sweet spots (after all these yearsof operation).
Perceptive Engineerings Focus:
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p g g
(Vision - Sandoz)
Optimisation
& Scheduling
Advanced Process
Control
Conventional
Regulatory Controls
Management
Information Systems
Intelligent& Soft Sensors
Conventional Sensors
& Instrumentation
Operating Constraints
Valve position
Setpoints
Performance Reports
Operating Plans
0 20 40 60 80 100 120 140 160 1800
5
10
15
20
25
30
35
40
Time
SPE
SPEConfidence Limit
Early Warning Process
Condition Monitors
Classification for
Quality Control0 20 40 60 80 100 120
10
15
20
25
30
35
40
Sample No.
FeO
Soft Sensors
Integrated Condition Monitoring
and Advanced Process Control
Control System
Integration
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Concluding Remarks
Plant automation is often seen as the project deliverable,when what is really required is plant control.
There are many approaches, instrumentation, and multiplecontrol systems, together with numerous advanced controlpackages to select from.
Process control is more than just tools. Successful plant implementation is reliant on these together
with: process knowledge, a solid control engineering background /experience, and
the operations team willing to act / implement / support theimplementations.
Together, robust solutions can be realised, minimising
process variation and optimising process performance. This will result in an easier, efficient and safer process to operate.
(P. Thwaites, AUTOMINING2008, Chile)
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At previous (high) metal prices the results from good
control, and Operation Performance Excellence are
substantial!
At current metal prices good control and Operational
Performance Excellence is essential!
Operational Performance Excellence requires a solid
performance of the regulatory layer AND process optimisation.
Organizational structure and human resources are important
in achieving Operational Performance Excellence.
Thank you.