dl_cim pt process control lecture 2013-01-24 rev.0 [40 mins] - cmp2013 2nd plenary

7212019 DL_CIM PT Process Control Lecture 2013-01-24 Rev0 [40 Mins] - CMP2013 2nd Plenary

httpslidepdfcomreaderfulldlcim-pt-process-control-lecture-2013-01-24-rev0-40-mins-cmp2013-2nd 131

11020

CIM Distinguished Lecturer Series

Manual Control Process Automation ndash orOperational Performance Excellence

What is the Difference

by Phil Thwaites 2012-2013

(XPS ndash Xstrata wwwmyxpsca)pthwaitesxstratapsca 705-699-3400 x 3463

Introductory RemarkKonigsman (Noranda) - VP Engineering amp Projects

ldquoProcess control is now an essential part of any concentrator operationIt provides a proven vehicle for improving operation economics by

increasing revenues and reducing costsrdquo

ldquoA substantial number of Canadarsquos mineral processing plants have incorporatedthis technology and most have realized impressive returns on investmentrdquo

ldquoThe question one must ask is whether we are tapping the fullpotential of this technology It is the opinion of the MITEC Mineral

Processing Technical Advisory Committee (TAC) that the answer is lsquoNorsquo rdquo

(They saw 2 problem areas 1) small plant amp 2) those plants who use some control but not achieving maxim benefit)

I agree and say we can do MUCH MORE as I will present

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

correspondencendash ldquoControl is not a high priority here (Australia) even though the effectof inadequate control on the loss of mineral tailings is obviousrdquo

Konigsman 1992 ForewardText Book lsquoA Practical Guide to Process Control in theMinerals Industryrsquo by Flintoff and Mularrsquo



11020

Manual Control Process Automation ndash or OperationalPerformance Excellence What is the difference

Discussion topics include ndash Control Engineering as applied to Process Control

bull Process Variability Control amp Process Optimization

bull Control Solutions from Manual control amp operation

bull Control Loop Applications amp Tuning Impacts

bull The Technology Important Aspects System Selection

bull Automation and Advanced Control

ndash Elements Necessary for Success

ndash Instrumentation Solutions hellip lsquoTo Be Aware ofrsquo

ndash Operational Performance Excellencebull Examples selected are Flotation Level Control Reagent Control

ndash Asset and Performance Monitoring

ndash Opportunities hellip for You and for Your Plants to Consider

Process Automation

Process Automation is understood in a wider sense to include

bull measurement and instrumentation

bull process modelling and simulation

bull process monitoring and data reconciliation

bull data mining and multivariate statistics

bull fault diagnosis and fault tolerant control

bull process control monitoring of product quality amp control performance

bull off-line and on-line process optimisation

bull AI (Artificial Intelligence) methods expert systems neural networks

fuzzy control

bull sequencing

In other words a very large and fascinating field

Ideal for a Control Engineer



11020

Definition of Process Control(McKee AMIRA P9L) 1997 Review

lsquoProcess control is a broad term which often

means different things to different peoplersquo

Process control is

lsquothe technology required to obtain information in real time

on process behaviour and then use that information tomanipulate process variables with the objective of improving

the (metallurgical) performance of the plantrsquo

Control for the purpose of operations and processimprovement

Process Control Will Not Correct InherentDesign Flowsheet Problems (McKee AMIRA P9L)

lsquoThere is a need to determine and if necessary correct the condition of the plantas a pre-requisite to control development A good example is the importance of

classifier operation and its effect on comminution circuit performancersquo

lsquoTechniques exist (plant sampling modelling and simulation) to audit the actualplant operationrsquo

lsquoCorrecting plant limitations should be seen as a first step in the approachrsquo

eg Co Purification 44 tpd saving $18myr value amp 88-gt95 online time

wwwmyxpsca

XPS Equipment amp Services ndash Process Mineralogy Grp

Correct Plant limitations



11020

u n i t s

Process Control ndash XPS

Control Optimize

poor control better control

higher production

lower costs

setpoint

Process constraint

best control

time

Measure

As we have practiced for over 3 decades

Poor to Optimized Control

Best Practice

Necessary forlsquoOperationalPerformanceExcellencersquo

1Measure 2Control

3Optimize



11020

Effect of 115sumpEffect of 115sumpEffect of 115sumpEffect of 115sump

on grinding (COFD amp RMF)on grinding (COFD amp RMF)on grinding (COFD amp RMF)on grinding (COFD amp RMF)

l i m i t

Process Control Objectives Summary

Management of TOOLS amp RESOURCES to

bull Minimize Process Variation

bull Optimize Process Performance

bull Minimize (Variable) Costs

bull Control Product Quality

bull Maximize Safety

For operational performance excellence ndash ldquowhatever it takesrdquo

Tied to theBusiness

Objectives





11020

Simplest of Controllers

hellip we all know

Note No Setpoint or Measurement Shown

Dual Zone ControllerAUTO (or Man)

SPrsquos Frost Warningamp Outside Temp

Note No MeasurementShown

How is Control Applied(in our industry)

bull Mines

bull Ventilation air flow (and pressure) control compressed air and dewatering controls

bull Milling

bull Feed and Tonnage control water ratio control

bull Pumpbox and surge tank controls

bull Cyclone pressure amp density controls

bull Flotation pH air flow amp level controlshellip for Grade Recovery Control

bull Reagent (chemical controls) amp ratio controls hellip for Grade Recovery

bull Thickener bed level flocculant density for clear overflows and final moisture controls

bull Roasting Smelting

bull Feed controls hellip of all feed materials (concentrates fluxes etc)

bull Air flow pressure and temperature controls

bull Power controls hellip ideally as a ratio to feed rate

bull Duct pressure fans blower and acid strength reagent amp quality controls

bull Inventory controls

bull Refining

bull Purification electrolysis chemical and electroplating stripping packaging controls etc etc

hellip predominantly simple regulatory control



11020

Layering of Process Control

Field Panel DCS PLC

Instrumentation - Inputs Outputs

Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

Function Objective

Processes

Optimizing Control

Process

Plant

Optimization

Cash OptimizationEconomics

Site

Loop Control

Measure

EconomicReturn

General Process Control Hierarchy

16

Leveraging Control TechnologyBBA Inc (MRuel)

or Control

ImpactsBusinessProfitability($$)



11020

Advanced Process Control (APC)

ldquoEconomic Benefits of Supervisory Process Control at Kidd CreekrdquoDec 1993 Noranda Technology Conference by P Thwaites J Carriere

bull Hydro Management amp Site Peak Control Objectivesbull Improve knowledge amp awareness of power costs

bull Improve amp stabilize SITE power control (at approx 133 MW of power consumption)

bull Control the lsquodailyrsquo Peak load and shift power consumption to lsquoOff Peakrsquo while maintaining production

bull Using 3 zinc plating rectifiers at approx 22 MW maximum power each

bull Results

1 Lowest overall (ckWh) Ontario Hydro Mining customer using TOU (time ofuse) Hydro rates

2 Cumulative (5 year) total savings from 1989 to 1993 incl = $176000001 1989 = $20M saving on 1089851 MWh

2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh

bull Conclusions1 There are MANY more OPPORTUNITIES throughout

the Metallurgical processes for supervisory control

ldquo APC can have considerable returns

hellip to help the Businessrdquo

Hydro Management ampSite Peak Power Control

Importance of Control Performance

EMERSON Global Users Exchange (2007)



11020

Control System Loop Diagnostics (eg ABB)

Diagnostic Conditions Monitored are

bull Final Control Element (FCE) Stiction

Size amp Leakage

bull Excessive FCE Action

bull Tuning Problem

bull Loop Oscillations

bull External Disturbances

bull Loop Nonlinearity

bull Data Reliability

bull Insufficient Travel

bull No Response to Signal Change

bull Noisy or Unstable Outputbull Sluggish Response

bull Valve BodySeat Wear

Diagnostics hellip are you using them

5 Essential Control Loop Elements (D Harrold)

amp lsquoConsider the Whole Looprsquo (Forsman ABB)

ABBABB Corporate ResearchK Forsman 1998 No 10

Consider the whole loop

PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV

Five EssentialControl Loop Elements

1 Sensing element2 Transmitter3 Controller4 Final Control Element

(eg Actuator or VSD)

5 Process

Only when all five elements are performing at their bestwill the control system meetexpectations

February 1999 ndash CONTROL ENGINEERINGby Dave Harrold

1

2

3

4

5 Process



11020

Importance of Regulatory Loop Tuning(Flotation air flow loop)

Retuned Old to New hellipPoor to Improved prior to Optimized

Process Control Improvements - PrimaryGrinding Circuit

With proper filtering amp

tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0

Bins 983085 Feed Rate Set983085Point Deviation

F r e q u e n c y ( )

BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation

Primary Feed Tonnage

Reduction 72

hellip regulatory control

Note we normally ratio Mill water to feedrate



11020


Benefits rougher feed density

Standard Dev

0017rarr0007 kgl

Mill Discharge Water Addition

Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5

Bins 983085 Primary Rougher Feed Density Set983085Point Deviation

F r e q u e n c y

BEFORE

AFTER58 reduction of

standard deviation

STD=0007

STD=0017


Eland Mill (2010) Overall Recovery Improvement fromPrimary Grind amp Primary Rougher Recovery Improvements

1) Prim Grind Improvement

Before

42 -75microns

After

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

254 gt PGE

bull Sec roughertails dropped

024 gt

bull Overall plantrec increased

3

bull Finalconcentrate

grade increasedby 10 gt



11020

Control Loop Improvement(Flotation level controller)

Several loops were updated

Typical results

bull Reduction of variability

bull Elimination of bias

ndash lsquovirtual absence of Integralactionrsquo

bull Smoother control action

bull KPIrsquos (key performance

indicators) at target00

50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5

Bins 983085 106B Level Set983085Point Deviations (LIC9830853506)

F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60

56 Reduction of STD deviation

100 Reduction of average deviation

Bias elimination

Flotation Cell Level Control

Reduction 56 ampBias Elimination

hellip regulatory control bias removal

Column Flotation - Feed Control

After STD=314 m3 h

Before STD=754 m3 h

Objective1 More flexibility2 Increased feed stability

Feed VariabilityReduction 58

Feed variability reduction (of 58)then correlated to concentrategrade variability reductionbull 79 (std dev) reduction

for the column Ni (impurity)

in Cu concentrate grade




11020

Control Tuning Summary

ldquoProper controller tuning is the largest quickest and leastexpensive improvement one can make in the basic

control system to decrease process variability rdquo

ndash TL Blevins GK McMillan WK Wojsznis MW Brown Advanced Control Unleashed ISAPress Research Triangle Park NC 2003

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

Donrsquot underestimate the impact









ndash Instrumentation Solutions hellip lsquoTo Be Aware ofrsquo ndash Operational Performance Excellence

bull Examples selected are Flotation Level Control Reagent Control





11020

Automation is hellip

1 the technique method or system of operating or

controlling a process by highly automatic means

reducing human intervention to a minimum

2 a mechanical device operated electronically

that functions automatically without continuous inputfrom an operator

3 act or process of automating

4 the state of being automated

Ref Dictionarycom

Automation ndash How Do We Apply It

bull Can be done on various platforms (PLC or DCS) on specific process unitsor areas of the Plant

bull While I am aware of fully automated unmanned (Hydro) generation stations (turbines) ndash egSandy Falls (Timmins) where the process is simple we do not know of a fully automatedMill But recently PYROGENESIS (GH) reported on their lsquoPlasma Arc Waste DestructionSystemrsquo for the US Navy Carrier Marine vessels amp mobile units ndash a fully automated process

bull Some Automation examples we see

bull Motor starts and stops eg crushing plants and sump pump controls

bull Remote starts start-up and shutdown sequences which can be quitecomplex

bull Mill lube systems Conveyor belt material handling systems

bull Mine Drilling equipment and (Mine) Autonomous haulagebull ldquoVale puts Railveyor to testrdquo ndash Sudbury Mining Solutions Journal Sept 1st 2012

bull ldquoA key pillar of Riorsquos mine automation initiative is autonomous (vehicle) haulagerdquo -Technology Case Study Rio Tinto Mining People amp the Environment Jan 2012

bull Pressure filtration amp Batch processes

bull Automated metal (plate) stripping as we use for Ni Cu amp Zn stripping

helliphellip etc etc

Ensures steps are not missedprotects assets amp improves safety



11020


bull ldquoIn terms of automation functionality regulatory control and APC serve much differentpurposesrdquo

bull ldquoThe primary role of regulatory control is to ensure stable safe and reliableoperations while maintaining process units at a desired or specified condition Itdoes not attempt to continuously improve operations in an economically optimalmannerrdquo

bull ldquoAPC on the other hand is a supervisory control application that coordinates alarge number of parameters to maintain control closer to operating constraints andmore favorable economic operating conditionsrdquo

Eg

bull Roaster Control ndash Layered MPC (Model Based Control with optimization logic) ontop of well tuned PID mass feed controllers

bull Results

1 77 reduction in bed temperature Stnd Dev allowing temperature andthroughput optimization closer to Roaster constraints

2 Overall throughput increase over extended periods of 4

Implemented Multivariable Predictive Control

Approx 230 known MPC applications hellip all vendorsof these installations 70 Honeywell B Jonas

Note In mining (mineral processing) excludes cement steel

Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves

FiltrationEvaporation

Heat Interchange

Precipitation

Calciner

Kiln

Other

MPC in Mining All Vendors (Over 230 Units Controlled)

T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020

Process Instrumentation

IndustrialCommunication

Process Analytics Factory Sensors

Continuous Gas Analyzer In Si tu Gas Analyzer

Natural Gas Analyser (CV)Kiln Sampling Probe

ProximitySwitches

Fail SafeSensors

Code ReadingVision Sensors

RFIDSystems

CommunicationProcessors PLC PC

SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing

Flow Valve positioner

Pressure

Siemens Sensors amp CommunicationComprehensive Product Portfolio for us

Processprotection

Temperature

S Shephard - Solutions for the Mining Industry

E+H Instrumentation for the Mining amp MetalsIndustry

pH lsquobestpracticesrsquoExample

E+HCanada

Slide16

Digital pH sensors with Memosens

Memosens with inductive datatransmission eliminates problemsbased on

Corrosion

Contacting problems

Salt bridges

Moisture

Leakages

High impedance connection

Ground loops

Safe connectorsystem

Dirty connectionsare irrelevent

17 units = 70 savings at MineraLos Pelambres - Chile CPF81D + CM42

E+H C anada

Slide 1

MemosensLiquiline

Digital pH Systemsndash Maximize process up-time

2011 Xstrata forbull Antapaccay Mill (Peru)

bull Las Bambas Mill (Peru)



11020

EampH Typical positioning of a thickenerscritical parameter measurement devices

S Rigling ndash E+H Australia

E+H Combined Technologies ndash Instrumentation Solutions for our Industry

Radar amp Capacitance Level



11020

Flow Measurement ndash Cidra SONAR

SONARSONAR ndashndash A New Class of Meters with Distinct Application Advantages A New Class of Meters with Distinct Application Advantages

SONAR

VOLUMETRIC FLOW METERS

Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter

Ref Christian OrsquoKeefecokeefecidracom

Hydrocylone feed line ndash as used by Collahuasi

Xstrata Cu - StandardConcentrator (5)Antapaccay Las Bambasselected SONARtrac for theprincipal slurry lines

Flow Measurement - In Circuit

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

SONAR

VOLUMETRIC FLOW METER

Ref Christian OrsquoKeefe cokeefecidracomFelix Peacock Huysamen Thwaites

ldquoYour accurate flow measurements are helping us with flotation control

bull We have automated mass pull control across the cleaner sectionbull Our next phase is utilising the Level controllers Flow Optimiser

Analyser amp Cidra meters to implement our advanced flotation controlrdquo Buang Moloto (Eland ndashXstrata Alloys)



11020

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

Full particle size distributionbull from 1 micron on flows lt 15 ms

Ultrasonic Extinction for real timeMeasurement helliphellip

bull Moisture content is determined bymeasuring the effect on a microwavesignal being passed through

bull The emitted signal passes through(Ore or Concentrate) and is detectedby a receiving antenna

bull The microwave signal interacts withany moisture in the process materialDegree of interaction is measured andused to derive a moisture value

bull An ultrasonic level sensor alsomeasures the amount of material onthe belt and this is factored in

bull Accuracy 05 ndash 1

bull Range 1 ndash 90 moisture

PC Group Review Meeting 13th Dec 2012

TransmittingAntenna

ReceivingAntenna

Online Moisture Analysis - MicrowaveMoistScan reg - Real Time Instruments Aus

Strathcona Mill (amp Xstrata Cu Mt Isa Mine Australia)Supported by Bondfield Group North Bay





11020

ldquoIn an ideal situation collectors should absorb selectively on thevaluable minerals and depressants should absorb selectivelyon the unwanted gangue mineralsrdquo

ldquoUnfortunately this is almost never the case and the rightcombination of collectors amp depressants

bull requires the development of dedicated chemicals

bull a lot of fine-tuning in the flotation processrdquo

So what are the components forOperational Performance Excellence in flotation

Flotationhellip ref Akzo Nobel - Cellulosic Specialties (Depramin gangue depressants)

Components forOperational Performance Excellence

Flotation Objective

1 Always Prevent Flooding hellip or empty banks amp

2 Maintain an Accurate responsive Pulp Level on a second basis thereby allowing control of the setpoint

Note pulp level is the variable known to the Operator

and is correlated to mass pull and recovery

One is Pulp Level hellip



11020

- implement quick access to floats

- Cleaning water to rod

Level measurement Access to floats yes important

No access to clean floats- Build up on floats- Inaccurate reading

XPSFloattradeFlotation level measurement

Flotation Level Control -Best Practice Example

1 Basic Level Device2 Level Control

Tuning (PID

Feedback)

5 Level Control Tuning(+ Feedforward)

4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range

Actuation(2 Dart Valves)

8 Best Practices ndash

Control to Metallurgy ($$) Flotation

Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020

Flotation Control Points on ControlStructure and importance of layering (Gilles)

Flow Control

MetallurgicalControl

Ratio Control

Set Pointccmin

MeasurementTonnage orTonnageHead Grade

Set PointGramTon

Measurement(Tail or ConcAssay)

Set PointOperator Recovery orGrade Target

All of the Met Loops are PIPID controllers for supportability

If problems occur operators may turn off one level of control ata time Independently for each reagent

Use standardratio blocksto ensure youcan layer ontop anotherlevel of control

Flotation Reagent Control -Best Practice Example

1 Basic MeasurementMag meter or Coriolisor pulsed device

3 Flow Controltuning (engUnits)

Robust Reagent Control(Feedforward only)

5 Met Control(Ratio) GMUgMetal Unit

4 Ratio ControlgTonne

7 Met Controlwith feedback Optimization (Mass PullAnalyzerrecovery orfroth quality)

8 Best PracticesFlotation reagent Control

=gt Optimum Metallurgy ($$)+ 25 lower reagent costs

2 Specialty(pure undiluted)

collectors

6 Met Controlwith strategies for start-up shutdownamp short stoppagesor losses in XRFdata

Metallurgical (Met) Control is the goal



11020


Discussion topics will include ndash Control Engineering as applied to Process Control




bull The Technology Some Important Aspects System Selection






ndash Opportunities hellip for You and for your Plants to Consider

Process (amp Plant) MonitoringOSIsoft PI System 2010

Used extensively by Xstrata



11020

Measure Control OptimizeFlotation Control Best Practices

S t a g e

1

S t a g e 2

S t a b i l i z a t i on

S u s t a i n e d V a r i a b i l i t y R e d u c t i o n

S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure

Sustaining the gains

Control Performance Monitor (CPM)

Control Performance Monitor (CPM)MatrikonHoneywell ishellip

Plant Control

a control performance improvement product thatcan diagnose causes of poor control at all levels ofthe plant control hierarchy

Advanced Process Control

Analyzers amp OnlineInferentials

Regulatory Control

Instrumentation Layer

CPM can enable a sustainable workflow that enables tracking of maintenance efforts as well asarchiving maintenance history on each loop

bull MONITORS advanced control assets for poorperformance amp provides detailed diagnoses

bull PROVIDES TOOLS detecting instrumentfreezes data spikes rate of change alarms andhigh-low limit violations

bull MONITORS regulatory control assets for poor

performance amp provides detailed diagnoses



11020

1st (of 150) CPM Controller ndash Strathcona Mill

Motivation Continuous monitoring prioritization of process variability for Sustained Variability Reduction

Continuous monitoring of controller service factors ( ie Manual or Auto how the controllerperforms etc )

Indication of the root cause of deteriorating controller performance

View all area controls

Report results for

Managers

Supervisors

Technical staff

also utilized at Raglan ampEland Platinum Mills

How we should use CPM

983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159

bull CPM providesassessment notification

bull User needs to take actionfix the problem

bull Move loops with knownissue to anotherdisposition category

bull Donrsquot be nagged on thesame problem again

983107983120983117

983107983144983137983149983152983145983151983150

Sustained Variability Reduction CPM trial at StrathconaMill

Xing Jin (XPS Control Engineer)



11020













Perceptive Engineeringrsquos Focus Vision(Dr D Sandoz DJ Lovett)

Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent

amp ldquoSoftrdquo Sensors

Conventional Sensors amp Instrumentation

Operating Constraints

Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit

Early Warning Process

Condition Monitors

Classification forQuality Control

0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors

Integrated Condition Monitoring and Advanced Process Control

Control System

Integration



11020

CMP 2010 PAPER CMP 2010 PAPER 2727 byby

Kairos Mining (Chile) ndash A ComprehensiveApproach to Sustained Benefits in Automation

TESTING LABORATORY

TRAINING CENTER

KAIROS REMOTE SUPPORT CENTER

REMOTE MONITORING amp SUPPORT CENTER

DATACENTER

Giselle Schifferli

Opportunities helliphellip for You and OperatingPlants to consider hellip

For Plant Operators ndash Is your feed stable ndash Are your instruments calibrated and performing ndash Are you aware of wireless instruments (including vibration) ndash Is your control system up to date and stable ndash Are you in manual or auto control ndash Are your Operators acting on alarms or are they nuisance ndash Do you understand and accept your process variability ndash Are you operating within the design targets and process constraints

bull (pumps cyclones supplies thickeners roasters furnaces etc etc)

ndash Are you using your surge capacity hellip or running tight level control ndash Are you at optimum and are the controls robust ndash Are you benefiting from asset management systems ndash Are failure fault detection systems implemented ndash Can you make the same product for less energy or material consumption ndash Do you Operate with or without a Control Engineer

ndash Does your Plant Process or Processes run as well as your car

We can do Much More and be Much More Efficient

lsquoOperational Performance Excellencersquorequires a solid performance of theregulatory layer AND process optimization

(P Thwaites AUTOMINING2008 Plenary lecture ChileCMP 2013 Plenary lecture CIM DL Series 2012-13)





11020













Process Automation

Process Automation is understood in a wider sense to include

bull measurement and instrumentation

bull process modelling and simulation

bull process monitoring and data reconciliation

bull data mining and multivariate statistics

bull fault diagnosis and fault tolerant control

bull process control monitoring of product quality amp control performance

bull off-line and on-line process optimisation

bull AI (Artificial Intelligence) methods expert systems neural networks

fuzzy control

bull sequencing

In other words a very large and fascinating field

Ideal for a Control Engineer



11020




Process control is











wwwmyxpsca





11020

u n i t s


Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure



Best Practice


1Measure 2Control

3Optimize



11020



l i m i t









Tied to theBusiness

Objectives





11020


hellip we all know






bull Mines


bull Milling













bull Refining





11020


Field Panel DCS PLC


Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

Function Objective

Processes

Optimizing Control

Process

Plant

Optimization


Site

Loop Control

Measure

EconomicReturn


16


or Control




11020







bull Results



2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh










11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020




Process control is











wwwmyxpsca





11020

u n i t s


Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure



Best Practice


1Measure 2Control

3Optimize



11020



l i m i t









Tied to theBusiness

Objectives





11020


hellip we all know






bull Mines


bull Milling













bull Refining





11020


Field Panel DCS PLC


Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

Function Objective

Processes

Optimizing Control

Process

Plant

Optimization


Site

Loop Control

Measure

EconomicReturn


16


or Control




11020







bull Results



2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh










11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020

u n i t s


Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure



Best Practice


1Measure 2Control

3Optimize



11020



l i m i t









Tied to theBusiness

Objectives





11020


hellip we all know






bull Mines


bull Milling













bull Refining





11020


Field Panel DCS PLC


Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

Function Objective

Processes

Optimizing Control

Process

Plant

Optimization


Site

Loop Control

Measure

EconomicReturn


16


or Control




11020







bull Results



2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh










11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020



l i m i t









Tied to theBusiness

Objectives





11020


hellip we all know






bull Mines


bull Milling













bull Refining





11020


Field Panel DCS PLC


Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

Function Objective

Processes

Optimizing Control

Process

Plant

Optimization


Site

Loop Control

Measure

EconomicReturn


16


or Control




11020







bull Results



2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh










11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli















11020


hellip we all know






bull Mines


bull Milling













bull Refining





11020


Field Panel DCS PLC


Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

Function Objective

Processes

Optimizing Control

Process

Plant

Optimization


Site

Loop Control

Measure

EconomicReturn


16


or Control




11020







bull Results



2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh










11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020


Field Panel DCS PLC


Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

Function Objective

Processes

Optimizing Control

Process

Plant

Optimization


Site

Loop Control

Measure

EconomicReturn


16


or Control




11020







bull Results



2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh










11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020







bull Results



2 1990 = $24M

3 1991 = $35M

4 1992 = $56M

5 1993 = $41M saving on 1076755 MWh










11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020




Size amp Leakage


bull Tuning Problem














PC-program in

Advant OCS

DA

IP

Positioner

FT

AD

Deadband

Filter

Ramp rate Filter

Actuator

Filter

3-15 psi

0-6 bar

4-20 mA

4-20 mA

mV




5 Process



1

2

3

4

5 Process



11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020





tuning

Standard Dev

50rarr14 th

0

2

4

6

8

10

12

14

16

18

20

983085 1 0 5 983085 9

0 983085 7 5 983085 6

0 983085 4

5 983085 3

0 983085 1

5 0 1 5 3 0 4 5 6 0 7 5 9 0



BEFORE

AFTER

STD=14 th

STD=50 th

72 reduction of

standard variation


Reduction 72





11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020



Standard Dev

0017rarr0007 kgl


Reduction 5800

20

40

60

80

100

120

140

160

180

200

983085 0 0 5

983085 0 0 4

983085 0 0 3

983085 0 0 2

983085 0 0 1 0 0 0 1

0 0 2 0 0 3

0 0 4 0 0 5


F r e q u e n c y

BEFORE


standard deviation

STD=0007

STD=0017




Before

42 -75microns

After

52 -75microns30

35

40

45

50

55

Before After

Before

After

2) Primary Rougher


Before

60

After

73

30

35

40

45

50

55

60

65

70

75

Before After

Before

After

On a feed grade of

254 gt PGE


024 gt


3





11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020



Typical results







50

100

150

200

250

98308530 98308525 98308520 98308515 98308510 9830855 0 5


F r e q u e n c y

BEFORE

AFTER

STD DEV 27

STD DEV 60



Bias elimination





After STD=314 m3 h

Before STD=754 m3 h









11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020





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
















11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020









Ref Dictionarycom
















11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020





Eg


bull Results






Crushing

SAGAG Mills

BallRod Mills

Flotation

CCD Thickeners

Roaster

Induration

EAF

Converter

Acid Plant

SX

Alum Autoclaves


Heat Interchange

Precipitation

Calciner

Kiln

Other


T

T

T

TT

T

T=Train

Alumina

Cu Ni ZiUr Au

T





11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli















11020






ProximitySwitches

Fail SafeSensors


RFIDSystems


SecurityProducts

IndustrialWLAN

Industrial

NetworkSwitches

Level Weighing


Pressure


Processprotection

Temperature




E+HCanada

Slide16



Corrosion

Contacting problems

Salt bridges

Moisture

Leakages


Ground loops




E+H C anada

Slide 1

MemosensLiquiline






11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020







11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020



SONAR


Ultrasonic

Coriolis

Vortex

MultivariableDP

Magmeter






SONAR








11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020











TransmittingAntenna

ReceivingAntenna







11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli















11020








Flotation Objective








11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020








Tuning (PID

Feedback)


4 Smart

Positioner(Valve

opening

Feedback)

3 Split Range




Level Control

6 Surge Tank

ControlTuning

7 Level Setpoint

Optimization

(eg FrothVelocity

Controller)



11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020


Flow Control


Ratio Control

Set Pointccmin


Set PointGramTon
















collectors





11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020

















11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020


S t a g e

1

S t a g e 2



S t a

g e

3

O p t i m i z a

t i o n

Flotation Control

Stabilization

to

Optimization7

u n i t s

Process Control

Control Optimize


higher production

lower costs

setpoint

Process constraint

best control

time

Measure




Plant Control




Regulatory Control









11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020






Report results for

Managers

Supervisors

Technical staff



983120983126

983124983145983149983141

983120983126

983124983145983149983141

983117983137983150983157983137983148

983108983137983145983148983161983127983141983141983147983148983161

983108983137983145983148983161

983122983141983153983157983145983154983141983155

983105983156983156983141983150983156983145983151983150983105983155983155983141983155983155

983120983154983151983138983148983141983149

983127983119

983120983141983150983140983145983150983143

983122983141983155983151983148983158983141983140 983108983145983155983152983151983155983145983156983145983151983150

983127983151983154983147983142983148983151983159





983107983120983117

983107983144983137983149983152983145983151983150





11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020














Optimisation

amp Scheduling

Advanced Process

Control

Conventional

Regulatory Controls

Management

Information Systems

Intelligent




Valve position

Setpoints

Performance Reports

Operating Plans

0 20 40 60 80 100 1 20 140 160 1800

5

10

15

20

25

30

35

40

Time

S P E

SPE

ConfidenceLimit


Condition Monitors


0 20 40 60 80 100 12010

15

20

25

30

35

40

Sample No

F e O

Soft Sensors


Control System

Integration



11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli













11020



TESTING LABORATORY

TRAINING CENTER



DATACENTER

Giselle Schifferli











dl_cim pt process control lecture 2013-01-24 rev.0 [40 mins] - cmp2013 2nd plenary

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