With Extracts from P. Thwaites’ CIM Distinguished Lecture on Process Control

Developments in Process Control –Grinding Controls

by Phil Thwaites, P.Eng., ARSM, MAusIMM;

XPS Consulting & Testwork Services (Glencore); www.xps.ca ; phil.thwaites@xps.ca ; processcontrol@xps.ca

705-699-3400 x 3463

CMIC – Canadian Mining Innovation Council –Sept. 2015 ‘Energy in Comminution’ Workshop’

Energy in Comminution Workshop

CEEC and CMIC teamed together to bring the CEEC Energy curve to Toronto to examine its features and benefits. Topics are: – Advanced Control in comminution

• Developments in Process Control, with particular reference to measurements, comminution, cyclone classification:

– New sensor technologies, control strategies, optimization, asset performance monitoring etc.

– Energy recovery

– Advanced technologies for comminution

Working Together to Address Energy Challenges in Comminution

The grinding circuit is a big power consumer representing approx. 25-35% * of total concentrator operating costs.

Insufficient grinding produces, coarse, unliberated & locked particles, while too much grinding gives unnecessary ultrafines – difficult to float & losses to tailings.

Consequently we have to improve efficiency for the CORRECT product size (range) – with maximum efficiency.

Grinding cost breakdown * : • Power = 40-60%• Grinding Media = 40%• Other = 10-20%

Problem Statement

* Referencing:Robert McIvor – The Role of the Grinding Circuit in Mineral Plants & A Management System for Plant Grinding Processing Performance

Developments in Process Control

Discussion topics included today are:

– Instrumentation, Measurements ….. ‘To Be Aware of’– Control Engineering, as applied to Process Control:– Grinding Control Examples and ‘Our’ Experience

• Eland Mill• Mt. Isa Mill• Raglan Mill• Strathcona Mill• Collahuasi Mill• Antapaccay Mill (our newest)

– Conclusions & Opportunities …. for You; and for Your Plants

Endress and Hauser (E+H), Siemens –Field Instruments in Mining

Mine On-Line Sampling and Analysis by IMA Eng. www.ima.fi

Introducing GEOSCAN

� Real-Time On-Belt Analysis

� Monitors the full stream to avoid sampling errors and expenses

� 3 frame sizes available to suit belt width and material depth

� Small footprint and very light

� Easy to install, no contact

with the belt

� High performance multi-detector system with digital electronics

� + 60 units in Minerals - in iron ore, copper, lead-zinc, manganese & phosphate.

SCANTECH (Henry Kurth) Cross Belt Analysis (PGNAA)

www.scantech.com.au

Elements Measuredby PGNAA GEOSCAN:(Prompt Gamma Neutron Activation Analysis)

Flow Measurement – Cidra SONAR

SONAR – A New Class of Meters with Distinct Application Advantages

SONAR

VOLUMETRIC FLOW METERS

Ultrasonic

Coriolis

Vortex

Multivariable DP

Magmeter

Ref: Christian O’Keefecokeefe@cidra.com

Hydrocylone feed line –as used by Collahuasi, ChileAntamina, Peru,Kidd, Canada.

Xstrata Cu - Standard Concentrator (5): Antapaccay, Las Bambas, (Peru) selected SONARtracfor the principal slurry lines

Flow Measurement - In Circuit

Platinum 2012: The Impact of Entrained Air and Enhanced Flow Measurements at the Eland Concentrator

SONAR

VOLUMETRIC FLOW METER

Ref: Christian O’Keefe, cokeefe@cidra.comFelix, Peacock, Huysamen, Thwaites

“Your accurate flow measurements are helping us with flotation control:

• We have automated mass pull control across the cleaner section.• Our next phase is utilising the Level controllers, Flow Optimiser,

Analyser & Cidra meters to implement our advanced flotation control.”Buang Moloto (Eland – Xstrata Alloys)

CiDRA’s Flowmeter Application at Glencore Zinc – Kidd Mill

Kidd Mill Canada, Canada - 2 meters: B Division Primary & Sec. Cyclone Feed Lines:Measurement of primary and secondary cyclone feed flow: approx. measurement of 2361 gallon/min flow.As part of the ongoing Met Tech work on grinding circuit performance, new cyclone feed flow measurements have been installed (Dec. 2014). Process Control assisted with options for the new instruments, and a decision was made to use CiDRA SONARTrac meters for this application. The main objective is to improve measurement of the circulating load in the Prim. Ball Mill circuit. This is currently calculated using the Rod Mill feed rate and density measurements in the Primary circuit. Recently it was shown that, due to variations in the process and the fixed assumptions used in the model, the resulting value can be as much as 40% in error . Directly measuring cyclone feed flow greatly simplifies the calculation and eliminates the inaccurate assumptions.

Alan Hyde

SONARtrac flow meters on Neutral leaching line

(one in the middle is presently not working) Kidd Mill

2361 gallon/min flow

2200

2400

2000

US GPM

Online Mineral Particle Size Measurement – OPUS Andrew Smith, Sympatec GmbH, AUTOMINING 2010

Full particle size distribution:• from 1 (to 3,000) micron on flows < 1.5 m/s

Ultrasonic Extinction for real time Measurement ……

Definition of Process Control(McKee, AMIRA P9L); 1997 Review

‘Process control is a broad term which often means different things to different people .’

Process control is:‘the technology required to obtain information in real

time on process behaviour and then use that information to manipulate process variables

with the objective of improving the (metallurgical) performance of the plant.’

Control for the purpose of operations and process improvement!

Process Control Will Not Correct Inherent Design / Flowsheet Problems (McKee, AMIRA P9L)

‘There is a need to determine, and if necessary correct, the condition of the plant as a pre-requisite to control development. A good example is the importance of

classifier operation and its effect on comminution circuit performance.’

‘Techniques exist (plant sampling, modelling and simulation) to audit the actual plant operation.’

‘Correcting plant limitations should be seen as a first step in the approach.’

e.g. Co Purification: 4.4 tpd saving @ $1.8m/yr value & 88->95% online time!

www.myxps.ca

XPS Equipment & Services – Process Mineralogy Grp :

Correct Plant limitations

units

Process Control – XPS

Control Optimize

poor controlbettercontrol

higher production

lower costs

setpoint

Process constraint

best control

time

Measure

As we have practiced for over 3 decades

Best Practice:

Necessary for: ‘Operational Performance Excellence’

1.Measure 2.Control

3.Optimize

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

-30% -25% -20% -15% -10% -5% 0% 5%

Bins - 106B Level Set-Point Deviations (LIC-3506)

Freq

uenc

y

BEFOREAFTER

STD DEV: 2.7%

STD DEV: 6.0%

56% Reduction of STD deviation

100% Reduction of average deviation

Bias elimination

Reduction: 56% & Bias Elimination

Poor to Optimized Control

Effect of 115sump

on grinding (COFD & RMF)

limit

Control Technology Options

• Manual Control … usually acting on valve position (i.e. % opening, or motor drive speed)

• PID Control … process variable (PV) & setpoints (SP) in eng. units –i.e. automatic regulatory control based upon the PID control algorithm:

• Feedback only (SISO …. Single input, single output)• Feedforward and Feedback (MIMO, Multiple input, multiple output)• Ratio Control and Cascade Control• Split Range Control• Smith Predictor …. dealing with significant time delay

• MPC (Multivariable Predictive Control) or Model Based Control … based on true process dynamics, and using recent history & a model based trajectory

• Rule or Fuzzy Control … based on knowledge / experience• Automation Control of:

• specific known actions or sequences; batch processes; etc.

• Advanced (Optimizing) Process Control

Simplest of Controllers

… we all know!

Note: No Setpoint or Measurement Shown!

Dual Zone Controller:AUTO (or Man)SP’s, Frost Warning, & Outside Temp

Note: No Measurement Shown!

Classic & HART I/O

H1AS-i

DeviceNet/Profibus

PIConsisting of:• Flexible I/O structure• 10/100 MBaud Ethernet• PC technology• Integrated Historians• Plant LAN connectivity using

OPC• Easy to use Software

Integrating it all Together

Plant LAN

Modern Data Acquisition & Control System(Emerson DeltaV DCS Distributed Control System):

Importance of Control Performance

EMERSON Global Users Exchange (2007)

Control System Loop Diagnostics (e.g. ABB, and DeltaV InSight)

Diagnostic Conditions Monitored are:• Final Control Element (FCE) Stiction,

Size & Leakage• Excessive FCE Action• Tuning Problem• Loop Oscillations• External Disturbances• Loop Nonlinearity• Data Reliability• Insufficient Travel• No Response to Signal Change• Noisy or Unstable Output• Sluggish Response• Valve Body/Seat Wear

Diagnostics …. are you using them?

Importance of Regulatory Loop Tuning (Flotation air flow loop)

Retuned, Old to New …Poor to Improved, prior to Optimized

Process Control Improvements - Primary Feed Tonnage Control

With proper filtering & tuning

Standard Dev: 50 →14 t/h

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

-105 -9

0

-75

-60

-45

-30

-15 0 15 30 45 60 75 90

Bins - Feed Rate Set-Point Deviation

Freq

uenc

y (%

)BEFOREAFTER

STD=14 t/h

STD=50 t/h

72 % reduction of standard variation

Primary Feed Tonnage

Reduction: 72%

… regulatory control

Note, we normally ratio Mill water to feedrate!

Process Control Improvements - Primary Grinding Circuit

Benefits rougher feed density

Standard Dev: 0.017→0.007 kg/l

Mill Discharge Water Addition

Reduction: 58%0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

-0.0

5

-0.0

4

-0.0

3

-0.0

2

-0.0

1 0

0.01

0.02

0.03

0.04

0.05

Bins - Primary Rougher Feed Density Set-Point Deviation

Freq

uenc

y

BEFOREAFTER

58% reduction of standard deviation

STD=0.007

STD=0.017

… regulatory control

Eland Mill (2010) Overall Recovery Improvement from:Primary Grind & Primary Rougher Recovery Improvements

1) Prim. Grind Improvement:Before:

42% -75micronsAfter:

52% -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher Recovery Improvement:Before:

60%After:

73%

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of 2.54 g/t PGE:

• Sec. rougher tails dropped

0.24 g/t;

• Overall plant rec. increased

3%;

• Final concentrate

grade increased by 10 g/t.

Control Tuning Summary

“Proper controller tuning is the largest, quickest, and least expensive improvement one can make in the basic

control system to decrease process variability.”– T.L. Blevins, G.K. McMillan, W.K. Wojsznis, M.W. Brown, Advanced Control Unleashed , ISA

Press, Research Triangle Park, NC, 2003.

------------------------------

Don’t underestimate the impact!

N.B. Book was inspired by DeltaVAdvanced Control Products . Available from ISA or may be ordered at EasyDeltaV.com/Bookstore

Hello Philip

I enjoyed our discussion and I believe that your talk at Townsville was very well received. I am sure that you inspired some engineers and I hope control is taken seriously here after years of neglect.

A book of photos from the old MIM concentrator where so much modelling and control work was done has come to life, I arranged for them to be taken 30 years ago and then they disappeared. I was astonished when the librarian asked me to identify a book of large photos and they turned out to be the missing photos.

I attach a copy of one photo. It shows a plaque we made to put on the

box containing the instruments which comprised our first control system and was made about 1968. The logic is shown, we used an equation to calculate cyclone overflow size , the

term soft sensor was not known to me then. The controller gave a large increase in throughput but this was due as much to the VS drive on the pump which stabilised the cyclone feed as to the control system.

I look forward to further discussions with you.RegardsAlban

Alban Lynch 1968 Grind Control

Pioneer:

Oscillating on/off Cyclone Switching

=> Oscillations in Cyclone AND Process Feed rates

Cyclones operating below their design pressure ( 40-60 vs 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);

Corrective Actions:

1) Cyclone pressure should be controlled using the cyclone feed-rate.

2) Implement Surge Tank Control (using 150 m3 of tank) NOT Tight Level Control.

PPL – A & B IsaMill Control Review

Cyclone Feed Density Control…..or Cyclone Overflow Density Control ?

Deficiencies in existing Strategy: Controlling cyclone feed density directly by

water addition not considered best practice.

1. The feed density is slow to respond which means the loop cannot be tuned for load disturbance

2. The influence of adding water changes depending on your mill capacity:

A. ‘spare’ grinding capacity – it will ‘trim’ the feed density as expected.

B. at or over capacity -: it will increase circulating load & density, resulting in more water addition ….

LI LIC

SY

DIC

DI

Typically:

Cyclone Overflow Density Control

Requirements of a Density Control Strategy

1. Control water addition by using the more responsiveprocess variable ( overflow density).

2. Consider the circulation load to avoid overloading the ball mill.

3. Maximize circulating load in order to maximise mill 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

Cyclone Overflow Density Control

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 by using the more responsiveprocess variable (Cyclone OverflowDensity).

2. Use the output of the Cyclone Feed Densitycontroller as the remote (ext.) set-point for Cyclone Overflow Density controller. (This cascade is more suited to the slow dynamics of the loop.)

3. Use a Circulating Load controller to either trim Feed Rate or Cyclone Feed Density set-point. N.B. feed will need adjusting if circulating load does not reach steady state.

4. Filter appropriately

1

2

3

(Bartsch ‘Best Practice’)

Cyclone 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 80 microns

P80 68 microns

P80 90 microns

% Circ

Load

Controller placed in service

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

Ton

nage

- m

tph

4300

4350

4400

4450

4500

SA

G K

PA

Tonnage

kPa

Setpoint = 4450 k Pa

SAG Feed SAG Loadmpth kPa

Average 131.3 4 454Std Dev. 9.7 24Minimum 100.5 4 392Maximum 152.7 4 503

In Cascade control (constant kPa), The SAG tonnage varies tremendously to maintain the requested setpoint.

Plot-0

TONNAGE ALIM.BROYEUR AUTOGPUISSANCE BROYEUR AUTOGENEPRESS. D'HUILE PALIER DECHAlimentation 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

0.085130.00000

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 Power

Mill Feed Set-point

D75 Size FractionLarge size

fraction (D75) INCREASING

Mill Load & Power

INCREASING

Large size fraction (D75) DECREASING

Mill Load & Power

DECREASING

SAG Mill Under Manual Control

Feed rate, fixed at 130 tph

Bartsch, CMP 2008

Multivariable issues: But note, rich feedforward D75 size information:

SAG Charge Multivariable FuzzyController (Bartsch, CMP 2008)

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 in existing plant PLCs

Strathcona Mill Grinding Circuit

McIvor and Finch, 1991: “Besides achieving the desired mineral d80 size, it is clearly desirable to produce as narrow a size distribution as possible to squeeze

the maximum amount of the mineral value into the highest recovery region.”

“Inadequate mineral liberation in itself leads to higher energy consumptions, as finer grinding has to be performed for liberation.”

24521017514010570350

0.16

0.12

0.08

0.04

0.00

RMF (Tons/hr)

De

ns

ity

173.3 33.95 719

188.2 2.866 721

Mean StDev N

1Before

2A fter

Status

6057545148454239

0.8

0.6

0.4

0.2

0.0

COFD (%Solids)

De

ns

ity

49.81 4.844 704

47.14 0.4740 721

Mean StDev N

1Before

2A fter

Status

Histogram of RMF (Tons/hr)Normal

Histogram of COFD (%Solids)Normal

Strathcona Grinding Circuit(CMP 2009 Presentation from Eduardo Nunez)

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

P um p-box

level (P B L)

R od m ill

feed

(R M Fsp)

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

C yclone

O F density

(C O FD )

P um p-box

w ater

(P B W sp)

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)

CO

F P.

Size

(%-1

50 m

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 (quantitative obj.) while maintaining

• the cyclone OF density at target (qualitative obj.)

Key Results(Over a Longer Period)

Period: (Jan.07-May.08 vs Jun.08-Sept.08)

• Increase in circuit throughput of 7.7%:

– (168 to 181 tph);– fully realised upon treatment of

Ni Rim South ore;• Grinding circuit feed to flotation

(COFD) maintained at 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

200

180

160

140

120

55

45

35

25

15

Date

RM

F S

PT

(t/

h)

RM

Wi

RMWi

RMF SPT (t/h)

V ariable

198192186180174168162156

0.060

0.045

0.030

0.015

0.000

RMF SPT (t/h)

De

nsit

y

168.2 5.756 517

181.2 7.098 77

Mean StDev N

1Before

2A fter

Status

Time Series Plot of RMWi, RMF SPT (t/h)

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

CO

FD

(%

)

525048464442

0.48

0.36

0.24

0.12

0.00

COFD (%)

De

nsit

y

46.61 2.008 517

46.22 0.7656 77

Mean StDev N

1Before

2A fter

S tatus

Time Series Plot of COFD (%)

Histogram of COFD (%)Normal

Strathcona Mill - Summary

CBU: Xstrata Nickel (Sudbury operations)Project: Primary Grinding ControlStatus: Completed in 2008

Outcome: – Maintain Consistent Grind to Flotation– Adjust, & optimise Rod Mill Feed

Automatically to Capacity of Grinding Circuit

– 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

De

nsit

y

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

De

nsit

y

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):

Advanced Process Control (APC)

“In terms of automation functionality, regulatory control & APC serve much different purposes.”

• “The primary role of regulatory control is to ensure stable, safe, and reliable operations while maintaining process units at a desired or specified condition.”

“It does not attempt to continuously improve operati ons in an economically optimal manner.”

• “ APC, on the other hand, is a supervisory control application that coordinates a large number of parameters to maintain control closer to operating constraints and more favorable economic operating conditions.”

E.g.

• Roaster Control – Layered, MPC (Model Based Control, with optimization logic), on top of well tuned PID mass feed controllers.

• Results: 1. 77% reduction in bed temperature Stnd. Dev allowing temperature an d

throughput optimization, closer to Roaster constraints. 2. Overall throughput increase over extended periods of 4%.

DeltaV (Emerson) – Systematic Approach

DeltaV APC blocks are “Drag-n-Drop” &Embedded in controllers, not in a separate system

Advanced Control:● Fuzzy Logic● Neural● MPC (Model Predictive Control)

Predict / PredictPro

Other Control System Suppliers MUST follow Emerson’s lead making Advanced Control more easily available / configurable in the Control Systems used by Mineral

Processing Plants.

ABB Launches SmartMill

ABB in Mining is delighted to inform you about its new SmartMill solution , an optimization package for your mill including advanced control embedded within a variable-speed drive. Using real-time data for continuous control of grinding mills, it simplifies your operator tasks and provides support to deal with multiple variables at once, helping you achieve maximum grinding efficiency.

SmartMill can be applied to ring-geared or gearless mills. As an attractive alternative to revamps of existing fixed speed mills, SmartMill offers a wide range of benefits, including not only the mill application features such as automatic positioning, creeping speed and the frozen charge protection, but now also the possibility to actively control the speed in a conscious manner in order to achieve optimized grinding results.

Among other benefits, SmartMill will help you to achieve higher efficiency, energy savings, as well as longer mill lifetime, increasing asset availability and operator productivity.

To learn more about the innovative SmartMill solution, please visit the product webpage here: http://new.abb.com/mining/grinding/smartmill

For more information, please contact marcelo.perrucci@ch.abb.com

an optimization package for your mill

The Collahuasi Solution: MPC ProfitSAG replaces Expert Systems (Rules)

Honeywell’s ProfitSAG is an MPC solution for SAG Mills– Objective function designed to accomplish the goals (maximize

fresh feed rate)– Fault tolerant policies (anti-windup integration with regulatory

control level)– Fully integrated with measured disturbances

SAG 1011

• Fresh Feed• Mill Speed• Solids

• Mill’s Weight• Mill’s Power• Mill’s Noise• Mill’s Torque• Produced Pebbles

• Returned pebble• Particle size

Process Value Predictions

MVs (Manipulated)

DVs (Disturbance)

CVs (Controlled)

(CMP 2010 Presentation from Iván Yutronic (CMDIC), Rodrigo Toro (Honeywell); see also automining2012

Antapaccay – FLSmidth MPC Approach (PxP)

From Oct. 2013; Presented at Automining2014 (King Becerra)

The primary process control technique used in the structure of the application is model predictive control (MPC), complemented with fuzzy logic rules.

Online Measurements:• SAG Wt.• Standard Impacts• Critical Impacts• SAG Power• Rejects Total

Actuators:• SAG Mill Fd• SAG Mill Speed• SAG Mill Solid %

Antapaccay (Peru) –Glencore

The (PxP) control strategies were developed with the following main goals:

• SAG mill weight stability and preventing of over filling of the mill • Reduction of undesirable critical impacts, through the integration with the

impactmeter• SAG mill throughput optimization and improve energy efficiency • Improve process stability & maintain constant flow to the flotation plant

Results:• + 8.75% more fresh feed;• - 8.75% SAG Mill kWh/t;• - 46.9% SAG Mill Critical Impacts

1st of StndardConcentrators (Xstrata) expansion

Developments in Process Control – Grinding Circuit Controls – Workshop on Energy Use ….

Improved measurements & control can do much to impro ve energy consumption, product p80 size & throughput in comminution– There are key new measurement technologies that present new

opportunities:• Ore analyses, OPUS/Outotec particle size, CiDRA (Vol.) flows, cyclone

performance …

– There are fundamental measurements that are often missing: • Ore size, densities, water flows, product particle size, Mill load …

– There are key performance parameters that are abused:• Cyclone feed pressure, circulating load, cyclone OF size, density …

– There are several successful control technologies, approaches:• Regulatory PID, Fuzzy logic, MPC & combinations of each

– There are Big Opportunities …. for You; and for your Plants to Consider, with Control Engineers.

• Control is complex and should be done using ‘best practices’ i.e. tried & true approaches, with experienced Engineers!

Conclusion …

‘Post Remarks’ – Process ControlKonigsman (Noranda) - VP Engineering & Projects

“Process control is now an essential part of any concentrator operation. It provides a proven vehicle for improving operation economics by

increasing revenues and reducing costs.”

“The question one must ask is whether we are tapping the full potential of this technology? The answer: ...... is ‘No!’ ”

[MITEC Mineral Processing Technical Advisory Committee (TAC), whom acknowledged 2 problem areas: 1) small plant & 2) those plants who use some control but not achieving maximum benefit.]

I agree and say we can do MUCH MORE and be More Efficient..... as I have presented in my CIM Distinguished Lectures, Keynotes & Plenaries!

Alban Lynch, (founder of the Julius Kruttschnitt Mineral Research Centre in Brisbane, Australia), Oct. 2012:

“Control is not a high priority here (Australia) even though the effect of inadequate control on the loss of mineral tailings is obvious.”

Konigsman, 1992; Foreward: Text Book ‘A Practical Guide to Process Control in the Minerals Industry’ {by Flintoff and Mular}’

Dominic Fragomeni, DirectorDominic.Fragomeni@xps.ca(+1) 705-699-3400 x3492

For More on XPS www.xps.cacontact us directly:

Thank you. Come visit us in Sudbury, Ontario, Canada

Process Mineralogy/Plant SupportMika Muinonen, Gen. ManagerMika.Muinonen@xps.ca(+1) 705-699-3400 x3490

Process ControlPhil Thwaites, ManagerPhil.Thwaites@xps.ca(+1) 705-699-3400 x3463

Extractive Metallurgy Mika Muinonen, Gen. ManagerMika.Muinonen@xps.ca(+1) 705-699-3400 x3490

Materials Technology Wilson Pascheto, ManagerWilson.Pascheto@xps.ca(+1) 705-699-3400 x3402