Process Control in Metallurgical Plants: Towards Operational

Performance ExcellencePerformance Excellence

Plenary lecture (& presentation)

by

Phil Thwaites P EngPhil Thwaites P. Eng.(Xstrata Process Support)(Xstrata Process Support)

Objectives of Automing2008Objectives of Automing2008 AUTOMINING 2008 is organized to offer an international forum AUTOMINING 2008 is organized to offer an international forum

where professionals can meet and interact about innovations and recent developments related to automation applied to mining, mineral processing, pyrometallurgy, hydrometallurgy, p g, py gy, y gy,electrometallurgy, and metal manufacturing.

The objectives of the Congress are: j g

To promote the interaction of knowledge and experiences about automation applied on the mining, metallurgical processes, and metal

f t imanufacturing.

To discuss the emerging developments, identify the technologies and successful automation practices in the mining industryand successful automation practices in the mining industry.

To promote an international network for collaboration and technical exchange among professionals dedicated to develop, to g g p p,operate and to maintain automation systems for the mining industry

Presentation & Discussion Topics Objectives of AUTOMINING2008 Opportunities To consider;Opportunities . To consider; Xstrata Profile & Organizational Structure; XPS ( Xstrata Process Support);XPS ( Xstrata Process Support); Definition of Process Control & Control Performance; Importance of Correct Process; Control Objective Importance of Correct Process; Control Objective XPS Process Control Group;

Elements Necessary for Successful Process Control; Elements Necessary for Successful Process Control; Good Process Control Implementation;

(Some) Enabling Technologies; (Some) Enabling Technologies; (Sandoz) Vision;

Concluding Remarks Concluding Remarks.

Opportunities to considerppOperational Performance Excellence requires a solid performance of the regulatory layer AND process optimisation.

For Plant Operators: Is your feed stable?

Are o r instr ments calibrated and performing? 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 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 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?y p Are you benefiting from asset management systems? Are failure / fault detection systems implemented? Can you make the same product for less energy consumption?

Xstrata ProfileXstrata Profile

Xstrata Plc is a major global diversified mining group.

Headquartered in Zug, Switzerland, Xstrata maintains a position in the following major international commodity markets:

copper, coal, ferrochrome, nickel, vanadium and zinc, and

additional exposures to platinum group metals, gold, cobalt, lead, silver,

recycling facilities,

suite of global technologies, (some are industry leaders).

The Group's operations and projects span 19 countries:

Argentina, Australia, Brazil, Canada, Chile, Colombia, Dominican Republic, Germany, New Caledonia, Norway, Papua New Guinea, Peru, the Philippines, South Africa, Spain, Tanzania, the USA, the UK and the Republic of Ireland.

Xstrata employs more than 50,000 people (including contractors).

Xstrata OrganizationalStructure

Xstrata Executive Committee

X Ni X Cu X Coal XTS X Alloys X Zn

XPS XTXPS XT

XTS X t t T h l S i Th M i ti L d UKXTS : Xstrata Technology Services Thras Moriatis London, UK

XPS : Xstrata Process Support Frank McGlynn Sudbury, Canada

XT : Xstrata Technology Joe Pease Brisbane AustraliaXT : Xstrata Technology Joe Pease Brisbane, Australia

XPS : Process Support

Process Mineralogy - Design implement and optimizeProcess Mineralogy Design, implement and optimize mineral processing flowsheets by matching the flowsheet to the mineralogy.

Process Control - Identify and deliver robust process control technology and engineering solutions.

Extractive Metallurgy Provide high-end extractive metallurgy services. Flowsheet/project development

i d li d il iusing modeling and piloting.

Materials Technology - Improve the reliability of gy p ycritical equipment through appropriate implementation of well proven materials engineering practices at essential stages of design, procurement and operation.

Definition of Process Control(McKee, AMIRA P9L)Process control is a broad term which often

means different things to different people.g p pProcess control is considered as the technology

required to obtain information in real time onrequired to obtain information in real time on process behaviour and then use that information to manipulate process variablesinformation to manipulate process variableswith the objective of improving the metallurgical performance of the plant.performance of the plant.

C t l f th f i tControl for the purpose of process improvement.

Importance of Control Performance (Emerson)

Process Control Will Not Correct Inherent Design / Flowsheet Problems

(McKee, AMIRA P9L)There is a need to determine and if necessaryThere 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 thecontrol development. A good example is the importance of classifier operation and its effect on comminution circuit performancecomminution circuit performance. Techniques exist (plant sampling, modelling and i l ti ) t dit th t l l t tisimulation) to audit the actual plant operation.

Correcting plant limitations should be seen as a first t i th t l h step in the control approach.

XPS Process Mineralogygy95% Confidence

Sampling and Statistics3. Optimise

1. Sample

2. Measure

PROCESSMINERALOGYMINERALOGY

XT : Technologies ISASMELT high intensity smelting process with low capital cost, energy efficient, flexible scale.

ISA PROCESS and Kidd Process producing most of h ld f d d kh

p , gy ,

the worlds refined and EW copper. Experts in tankhouse design.

IsaMill step change in grinding technology MuchIsaMill step change in grinding technology. Much higher energy efficiency. Metallurgy improvements from inert grinding media.

JAMESON CELL - high intensity flotation. Ideal in combination with conventional flotation for low cost circuit expansions and higher concentrate grades. p g g

ALBION PROCESS simple atmospheric leaching. To treat low grade or refractory copper streamstreat low grade or refractory copper streams.

Overall Process Control Obj iObjective

Control OptimizeMeasure

hi h d ti

Process constraint

nits

higher productionsetpoint

un

b tt

lower costs

poor controlbettercontrol best control

titime

Poor to Optimized Control

Best Practise:

Necessary for: yOperational Performance E ll Excellence

General Process Control HierarchyGeneral Process Control HierarchyF tiObj ti FunctionObjective

Cash OptimizationEconomicsSite

PlantOptimization

Plant

OptimizationAdvanced

Optimize O ti i i C t lProcess

Optimize

Stabilize

Optimizing Control

L C t l

Field / Panel / DCS / PLC

I t t ti I t / O t t

Regulatory

Manual

Stabilize Loop Control

MeasureInstrumentation - Inputs / Outputs Manual

ProcessesEconomic

Return

(ABB) Background for Optimisation I t d ti i l t t i

Production cost

Impact on production via loop structuringProduction cost

Lower KnowledgeBasedExpert Control

Degree ofOptimisation

Multi VariableT h i

p

Cross-Coupled

Techniques

Feedforward &Cascade

Cross Coupled

FeedbackHigher Production or Quality

Lower Higheror Quality

XPS Process Control Group p& its Business Niche Complementing site resources,

Business Niche is:ability to identify and deliver robust process control technology and engineering solutions to Xstrata operations and strategic projects.

Solutions implemented are based upon: solid control engineering practice and operating experience.g g p p g p

Managed from XPS, this is done using:enabling (and appropriate) technologiesenabling (and appropriate) technologies through the involvement of engineering specialists.

The Goal is: Operational Performance ExcellenceThe Goal is: Operational Performance Excellence

Xstrata Operations Impacted by the XPS Process Control Groupthe XPS Process Control Group

Strathcona Mill

RaglanFNA

B th t

Raglan Mill

Ni Smelter

TimminsSudbury Montreal

Bathurst Nikkelverk

Falcondo

Falcondo Kidd

Brunswick Smelter

Lomas Bayas

Alt tKoniambo

Collahuasi CCR Refinery

Horne Smelter

Alt t S lt

Mt. Isa

Altonorte Altonorte Smelter

Collahuasi Mill

Pilot Plant Activities Pilot Plant Activities

Mt. Isa Cu Smelter

Elements Necessary for SuccessfulProcess Control in Mineral Plants

Tools: People:

ProcessC t l

Instruments; Systems etc.

Control / Process Knowledge

Control ->OperationalPerformancePerformance

Excellence

Successes:Actions: Successes:Results & Examples

Support Management;

Technology Transfer pgy

Good Process Control Implementation

(McKee, AMIRA P9L)

A good implementation requires a well defined operating strategy and an associated control strategy.

Effective Process Control Cycle(f N P / Pl t )(for New Processes / Plants)

Deliverable

Process Opt.(Production)

Process Flowsheets & Control Philosophy

(Development)

Deliverable (Stage)

(Production)

Overall Process Control Objective

(Development)

higher productionsetpoint

constraint

Control OptimiseMeasure

As-builts(C i i i )

P&IDs(Basic Eng)

Falconbridge Limited - Process Control

poor controlbetter control

lower costs

p

best control

time

units(Commissioning) (Basic Eng)

Control Config. Logic Diagrams(D il d E EPCM)(Construction-EPCM) (Detailed Eng-EPCM)

Enabling TechnologiesEnabling Technologies

1. Multivariable Controller in a PLC Function Block:SAG feed control, is generally done using an Expert system., g y g p y

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 auxiliary Expert System, and training / support of this system.

(McKee, 1999). In grinding, control of AG/SAG mill circuits is the ( ) g gdominant area. While some systems have emerged which provide a reasonable level of control, there is still much not understood about the dynamic behaviour of these mills, and there is considerable scope for further development scope for further development.

Entire Grinding Controls Focus:(Lib ti ffi i d th h t)(Liberation efficiency and throughput)

21-CV-04Wipfrag(Size analysis) ASRi

Sandvik Cone Crusher Hydrocone H4800

(250 kW) FlotationA B

ASRi

150 mtSurge Bin

SAG Mill24 diameter

11-CV-03

21-CV-01

21-CV-0521-CV-06

Cyclone Feed Density Control

DI

Cyc O/F Density byP/Box water addition

Cyc O/F Density byP/Box water addition

24 diameter(2240 kW)Underground

Storage BinsMICFIDI

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 2005Cyc Feed Density by

trimming O/F density SPCyc Feed Density by

trimming O/F density SP

CONSTRAIN circulatingload by trimming Cyc

Feed Density SP

Metso Double DeckVibrating Screen (8 x 16)

1- 8 x 40 mm2 5 4 3 8

6 x 15 Krebs GMAX Cyclones

Vort: 4.5, Apx: 3

21-CV-02FFE Impact Meter

2- 5 x 4 mm, 3 x 8 mmBall Mill

14 dia. x 21(2240 kW)

SAG Charge Multivariable Fuzzy Controller (Bartsch)

Inputs (Measurements)

Outputs(Set-points)( )

FuzzyficationPower

Charge

Feed Rate

Density

Fuzzy Rules

Charge

Impactmeter

Density (water addn)

Crusher Gap

De-fuzzification

Granulometry (prediction !)

Crusher Gap

Mill SpeedDe fuzzification

AssaysProgrammed in

i i l Cexisting plant PLCs

ASRi (Automatic Setpoint Regulation) Sandvik Technology Crushing Plants

Crusher Gap Control:

Sandvik Technology - Crushing Plants

Kidd Mill -> Strathcona Mill -> Raglan Mill

Field Device

DCS Display

(via OPC)

Enabling Technologies

Excellent practise is an OPPORTUNITY in our Canadian Mills!2. On-Demand Sampling Automation:

Enabling Technologiesg g3. Camera Imaging:

- Feed size analysis;

- Froth camera- Froth camera imaging;

O ti l S t fFroth Camera Imaging Technology

- Optical System for cathode quality:

CSQA:Cathode Surface Quality Analyzery y(Aplik)

39

Rougher Level Disturbance

Level

Setpoint

Level

1. Velocity (mass pull) changes by over

Froth Velocity

changes by over 1000%!

2 Baseline pull is2. Baseline pull is only 0.5 1 cm/s! : Level SP too low?Level SP too low?

Escondida Froth Velocity is Cascaded t C t l Fl t ti C ll ( t i l )to Control Flotation Cell (metso minerals)

SP Aire SP Velocidad

PV Velocidad

Escondida: Instalacin en la Flotacin Primaria Lneas 1,2,3,4,5,6

(54 Cameras)

Camara

(54 Cameras)

SP Nivel

Tapon

Celda WEMCO

Enabling TechnologiesEnabling Technologies4. Rotating Equipment Machinery Health:

Enabling Technologiesg g5. Wireless Instrumentation:

HighServiceINDUSTRIAL SUPPORT COMPANY

Business Case 3: Smart Wear Sensor

2.- Proposed Solution

Electronic Wear Sensor (EWS) embedded in liners andlifters fastening bolts

Main components of the EWS: Transducer

8-12 levels

GITE / DES

8-12 levels

CPU (coder & signal processing)statistical filtering

RF Transmitter, FM FSK, 916 MHz, 1mW,anti-collision algorithmSensor Life: 18 month

GITE-322-05-PRE-15 GITE / DESRev 0 Fecha 100807 Pgina 19 de 29

Enabling Technologies Tankhouses (Outotec)

Used as a flow indicator Kennecott Utah Cu; Boliden Harjavalta Pori Refinery;

Norddeutsche Affinerie; Akita Zinc (21 Zn cells); Used as a flow indicator Boliden Harjavalta Pori Refinery;

Boliden Kokkola; Akita Zinc (21 Zn cells); OMG Kokkola Chemicals.

Tankhouse Automation & Management System(XT and MIPAC)(XT and MIPAC)

e.g. XTs Kazzinc Project (startup late 2009)

Enabling Technologies(Overall Process Unit Control)

6. MPC (Model Based)6. MPC (Model Based)

Control:Production cost

Lower KnowledgeBasedE t C t lSeveral tools are available:

Mintek (FloatStar)Multi Variable

Expert Control

Emerson (DeltaV MPC)

ABB (Linkman, Expert Optimizer)Cross-Coupled

Techniques

Invensys (Connoisseur)

Honeywell (Profit Suite)

G (G2)

Feedforward &Cascade

Gensym (G2)

Prediktor

Metso (Adaptive Predictive Model)Lower

FeedbackHigher

Higher Metso (Adaptive Predictive Model) g

Connoisseur (Invensys) Overview( y )

Neural Net

LP O ti i

Connoisseur EnvironmentNeural Net

Adaptive Cont Non-linear

Optimal Setpoint& MV T

LP OptimizerC P

M

Fuzzy Logic

Director Calc

Inferential

Model Predictive Controller

& MV TargetM ec o C c

1454 dxSj ++

Constraint Management(LR or QP methods)

CVs, MVs & DVs

10*349304 LD

CVs, MVs & DVs

Regulatory Control System

Processes to which MPC has been applied i th Mi l P i I d tin the Mineral Processing Industry

(Dr. D. Sandoz presentation Laurentian Conference, June 2006)

Xstrata: Roasters (Connoisseur e g Kidd Zn implemented in 1993) Roasters (Connoisseur e.g. Kidd Zn - implemented in 1993) Flotation Process (G2/Generalised Predictive Control

GPC) originally implemented by Noranda in 1999 Flotation Level Control (Mintek - e.g. Collahuasi Mill, 2004) 12 Nickel Shaft Furnaces (Connoisseur) at Falcondo (2002)

Kidd C S lt (DIY DCS) M tt G d C t l (2002) Kidd Cu Smelter (DIY on DCS) Matte Grade Control (2002) Ni Smelter Electric Arc Furnace (Connoisseur) Fe:SiO2

control (2005)( )

Ni Smelter Electric FurnaceElectric Furnace

Calcine + Flux + Revert + Coke + Custom FeedAuto

Unmeasured Disturbance: Coke Efficiency

AFFECTS:

1. Fe:SiO2 Control Auto

2. Matte % Fe Metalization Control Manual

3. Matte Grade Control Manual

Model Identification : Expert

Fe

pKnowledge + METSIM + HSC to determine Dynamics for an Inferential Model Based Predictive Controller

Enabling TechnologiesEnabling Technologies

7. Dry Feed and Injection to +/- 1% overall addition accuracy minute to minute:addition accuracy minute to minute:

Clyde Technologies Objective to put theClyde Technologies Objective to put the Operator in control:

On-line mixing of materials;

Injection;Injection;

Bag house collection & removal of drag conveyors.

Kidd: Direct Limestone Injection+/- 1% overall addition accuracy min. to min.

Superior ConceptClyde RotoFeed Graph illustrates improved draft

lInlet Spheri

valve

DispenseVessel

LoadCell

Inlet Spherivalve

control Better draft control has also lead to

hi h bl i tRotaryFeeder

Conveying Line to Lances

LockVessel

Isolating SpheriValve

LoadCell

Converting Furnace Draft Profile Comparison4

Batch Limestone Injection

higher blowing rates

31

Valve

1

2

3

H2O

)

Continuous Limestone InjectionLower LimitUpper Limit

-1

0

nace

Dra

ft (m

m

Batch Injection Continuous InjectionConverting Furnace Draft (mm H2O)

Draft Control Improvement

-4

-3

-2

Furn Batch Injection Continuous InjectionMaximum 2.76 -0.85

Minimum -4.01 -3.99Average -1.71 -2.21Standard Deviation 1.21 0.58

-5

Time Series

Enabling Technologiesg g1. Design Good Draft Control

8. Off Gas Controls:CPS Off-Gas Ductwork

Very Long Ducts ~50 m

Process control must play an increasing role to better control our off-gas processes;

Design is critical for achieving responsive control

SULFURIC ACID STORAGE TANKS

SPRAY PONDS

#2 SULPHURIC ACID PLANT

Fundicin AltonorteOctober 2007

MajorEngineering

14

Ni Smelter Blower Suction ControlID Fan

I.D. FansNEW #3 SULPHURIC

ACID PLANTSTACK

ESP

DesignChallenge

CONCENTRATE RECEIVING &

REVERB FURNACE

P-S CONVERTERS

CASTING WHEEL

BLOWERS AND COMPRESSORS

ROTARY DRYERSLAG COOLING BEDS

BLOWERS AND COMPRESSORS BINS

BINS

2nd CASTING WHEELROTARY DRYER

Falconbridge Limited - Process Control

RECEIVING & STORAGE SHED ANODE

FURNACES

SLAG FLOTATION

PLANT

NEW #1 Cv

CONTINUOUS REACTOR

NEW#2 Cv

NEW#3 Cv

NEW ANODE

FURNACE

Enabling TechnologiesEnabling Technologies9. Asset Monitoring:g

provides real-time asset monitoring, notification, and p g, ,maintenance workflow optimization of automation equipment, plant infrastructure, plant equipment, field q p , p , p q p ,devices, IT assets, and production processes. (ABB)

D t t h lth d f diti Detect health and performance conditions

Assist in diagnosis of the problemg p

Offer correction recommendations

Enabling TechnologiesEnabling TechnologiesAsset Condition Monitoring (ABB, Emerson, E&H)Plant Assets

ABB System 800xA asset monitors are designed with the flexibility and open standards in mind to support assets of all levels, from field instrumentation, to major plant equipment and processes, to IT assets.IT

With a correctly configured and installed system, plant information can be collected, aggregated, analyzed and compared to historical data to provide advanced warning of degrading device, equipment, or process performance and their impending failure.

Predictive maintenance then becomes a reality.Control

- Plant Equipment:

Process

q p Motors, drives, pumps, mills etc.;

- Control Network Asset Monitoring;Control Loop Asset Monitor;

Field

- Control Loop Asset Monitor;- Production Performance;- PC, Network and software monitoring;- Field Devices (HART, Fieldbus, PROFIBUS)Field

Enabling TechnologiesEnabling Technologies

10. Control Room Design:

There is a recognised standard mostlyThere is a recognised standard mostly unknown to Engineering EPCM Contractors -for control room designfor control room design.

ISO11064:ISO11064: 1. Principles for design of control centres; 2. Principles for arrangement of Control suites; 3. Control room layout; 4. Workstation layout and dimensions; 5. Displays and controls . etc.

Control Room DesignISO11064

Production Stage gateUpdate Aug 25th 2004New Central Control Room at Nikkelverk

EPC Update for Stage Gate Approval Committee

ISO11064 Ergonomic Design

1. Principles for design of control centres; 2. Principles for arrangement of Control suites; 3. Control room layout; 4. Workstation layout and dimensions; 5. Displays and controls . etc.

Nikkelverk, New Central Control Room Location

Production Stage gateUpdate Aug 25th 2004Project Progress Documentation

Production Stage gateUpdate Aug 25th 2004

Average numbers of alarms per hour, updated statistics Sept 02 Mar 04 Aug 04 Target

Alarm handling - Status and Target

KL / ER Operator DeskKL / ER Operator Desk El/El/GuardGuard desk desk

ER ER : 20 9 13 10KL KL : 32 17 17 16RS RS : 36 21 17 18

T a r g e t A la r m R e d u c t i o n s 5 0 % F N A

1 0 0

S u m k t r l r o m ( u t e n s t o p p )

KL/ER operator deskKL/ER operator desk

R/ R/ OperOper. desk. desk

6 0

7 0

8 0

9 0

tall

alar

mer

pr.

time

S u m k t r l . r o m ( u t e n s t o p p )T a r g e t k t r l . r o mE k s k l . O v n - 5L in e r ( S u m k t r l . r o m ( u t e n s t o p p ) )

New New CentralCentral Control Control RoomRoom, , JulyJuly 15th 2004 15th 20042 0

3 0

4 0

5 0

Sep-

02Oc

t-02

Nov-

02De

c-02

Jan-

03Fe

b-03

Mar-0

3Ap

r-03

May-

03Ju

n-03

Jul-0

3Au

g-03

Sep-

03Oc

t-03

Nov-

03De

c-03

Jan-

04Fe

b-04

Mar-0

4Ap

r-04

May-

04Ju

n-04

Jul-0

4Pr

ogn.

6/8Se

p-04

Oct-0

4No

v-04

Dec-

04

Ant

Process Control Enabling Technologies Summary

Enabling Technologies:1. Multivariable controller in a block for SAG Mill control;

2. On Demand Sampling Automation

3. Camera Imaging;

4. Rotating Equipment Machinery Health;

5 Wireless Instrumentation;5. Wireless Instrumentation;

6. Model Based Control;

7. Dry Feed and Injection; y j

8. Off-gas Handling Controls;

9. Asset Monitoring;

10. Control Room Design;

Vision (Sandoz);

Concluding Remarks;

Perceptive Engineerings Focus:(Vision Dr D Sandoz)(Vision Dr. D. Sandoz)

IntelligentClassification for

20

25

30

35

40

FeO

Soft Sensors

Optimisation & Scheduling Management

Information Systems

Intelligent & Soft Sensors

Operating Constraints

Operating PlansQuality Control

0 20 40 60 80 100 12010

15

Sample No.

Advanced Process Control

Operating Constraints

Setpoints

Performance Reports

Conventional Regulatory Controls

Conventional Sensors

Valve position

30

35

40

SPEConfidence Limit

Early Warning Process Condition Monitors

Control SystemIntegration

& Instrumentation

5

10

15

20

25

30

SP

E

0 20 40 60 80 100 120 140 160 1800

Time

Integrated Condition Monitoringand Advanced Process Controland Advanced Process Control

Concluding RemarksConcluding Remarks Plant automation is often seen as the project deliverable, when

what is really required is plant control. There are many approaches, instrumentation, and multiple There are many approaches, instrumentation, and multiple

control systems, together with numerous advanced control packages to select from.

Process control is more than just tools.Process control is more than just tools. Successful plant implementation is reliant on these together with:

process knowledge, a solid control engineering background / experience andexperience, and

the operations team willing to act / implement / support the implementations.

Together robust solutions can be realised minimising process 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.

At present metal prices the results from good controlAt present metal prices the results from good control, and Operation Performance Excellence are substantial!

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 achieving Operational Performance Excellence.

Thank you.