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Modern Process Control

COM 2009 - Pyrometallurgy of Nickel Short Course

August 23rd, 2009

Phil Nelson, Xstrata Process Support

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Outline

Introduction

Best practices

• Traditional process control

• Opportunities in modern process control

Examples

Conclusions

References

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XPS : Process Support Groups

Process Control - Identify and deliver robust process control technology and engineering solutions to achieve ‘Operational Performance Excellence.’

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

Extractive Metallurgy – Provide specialized extractive metallurgy services (hydro-and pyro-metallurgical). Flowsheet/project development using modeling and piloting, new process development and plant optimization.

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

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XPS Process Control Group & its Business Niche

Complementing site resources,

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

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

Managed from XPS, this is done using:

enabling (and appropriate) technologies

through the involvement of engineering specialists.

The Goal is: ‘Operational Performance Excellence’

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Definition of Process Control

(McKee, AMIRA P9L)

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

‘Process control is considered as the technology required to obtain information in real time on process behaviour and then use that information to manipulateprocess variables with the objective of improving the metallurgical performance of the plant.’

Control for the purpose of process improvement.

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Economics of Process Control

Typical benefits sought are:

• Increased quality/decreased variation

• Increased throughput

• Reduced cost

• Reduced environmental impact

– Energy

– Water

– Emissions

• Regulatory compliance

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Importance of Control Performance

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General Process Control Hierarchy

Field / Panel / DCS / PLC

Instrumentation - Inputs / Outputs

Advanced

Regulatory

Manual

PlantOptimization

Optimize

Stabilize

FunctionObjective

Processes

Optimizing Control

Process

Plant

Optimization

Cash OptimizationEconomicsSite

Loop Control

Measure

Economic Return

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Importance of Fundamental Process Control

Requirements for your base:

• Appropriate process

– Process control will not correct inherent design or equipment problems – physical constraints

– highly varying feeds

– malfunctions

– Correct basic problems first

• Process and plant appropriate

– Not individual loops

– Variation is not eliminated by feedback, it is transferred

• Business goal appropriate

– What is being optimised?– Throughput, efficiency, quality (product, environmental)

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Basic requirements continued

• Appropriate measurements

– Technology/measuring principle

– Sufficiently accurate and precise

– Sized/ranged correctly

– Robust

– Well maintained

• Control System

– Up to date and stable

– PLC and HMI vs DCS– Both workable, different strengths and capital and maintenance costs

– PID’s in PLC’s often are too basic, need extra logic to complete them to a best practice standard

– Trending is critical to good operation

– Have found that event and alarm logging needs more attention in PLC and HMI

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Basic requirements continued

Control Configuration

• Plant is dynamic, control configuration must be dynamic as well

• Control configuration includes– Instrument ranges and calibration

– Tuning

– Structure

• Routine plant changes requiring configuration check– Significant production level increase or decrease

– Feed changes

– Equipment replacement

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Advanced Process Control

Where are technological frontiers?• Many involve computerisation• Asset monitoring – including control loops

– ABB, Emerson, E&H, others do asset management– Control loop monitoring by Matrikon, ExperTune, others– Buzz words are “condition based maintenance”– Optimize scarce maintenance resources and dollars– Can also provide hard numbers for benefits

• Networking/IT for instruments, interfacing, drives– Profibus and Foundation Fieldbus– Ethernet everywhere

• Discrete event simulation• Alarm management• Fault/failure detection• Wireless instruments (ISA 100)

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Advances in Measurement

• Some example technologies

– Coriolis meters for flow and density

– Radar level– More powerful with better signal processing

– Ultra-sonic flow– Viewed as inaccurate, we have had recent successes

– Cameras/image processing

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MPC/Expert System

MPC=model predictive control

• Use a (usually simplified) model to predict process trajectory and an optimiser to determine inputs to drive to targets

• Pioneered in the oil and gas industry in the 1980’s

• Still ‘new’ and advanced!

Expert System

• Rule based controller

Remember importance of base layer!

• At one estimate, 50% of benefits of “advanced control” are improvements in base layer control that must be maintained

Experience of vendor and follow up/maintenance are key

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More commercial software available

RMPCT (Honeywell)

OCS (Metso)DeltaV PredictPro (Emerson)

MinnovEX Expert Technology (SGS Minerals Services)

Connoisseur (Invensys)

Expert Optimizer (ABB)Expert Optimizer (ABB)

Expert SystemMPC

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Six Sigma in a nutshell (L. Vandamme)

Six Sigma started as a process-improvement methodology

• When a process yields “defects” how do we fix it?

• The word “defects” grew from its specific meaning at the beginning, and has come to include a much more general scope…

It is a collection of well-known tools and techniques

• There is nothing “new” in six-sigma

• The power of the method is in linking those tools in a certain order

• Used together, those tools & techniques have a synergetic effect

The 6σσσσ methodology now comes under two flavours:

• DMAIC, or “6σ classic” (Define, Measure, Analyze, Improve, Control)

• DFSS (Design for Six Sigma)

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Customer Surveys, Measurement Validation,Sampling, Process Capability, Quality Function Deployment, Cause & Effect Matrix,Failure Mode and Effect Analysis, Affinity DiagramCause & Effect Charts, Hypothesis Testing, Regression, Benchmarking, Brainstorming, Defect AnalysisFishbone, Failure Mode and Effect Analysis, Action PlansDesign Of Experiments, Simulation, Pilot

Mistake proof, Standard Procedures, Hand-Off Plans,Control Charts, SPC, Closure Report, Follow-up Plans

Strategic Business GoalsCritical To Satisfaction Criteria

DEFINE: Process Baseline Analysis, Outputs/Key Drivers, Key Customers

Tools

Audit, Review, Translate

6 Sigma ProcessBreakthrough Cookbook1. Select CTQ Characteristic2. Define Performance Standards3. Validate Measurement System4. Establish Product Capability5. Define Performance Objectives6. Identify Variation Sources7. Screen Potential Causes8. Discover Variable Relationship9. Establish Operating Tolerances10. Validate Measurement System11. Determine Process Capability12. Implement Process Controls

Measure

Analyze

Improve

Control

Management

Site ChampionsMaster Black Belts

Bla

ck B

elts

/pro

ject

tea

m

Six Sigma 12 Steps

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SlurryDry feed

Water

Air

Roaster

Spray Cooler ESP

T

TrimWater

• Fast control on density• Better temperature control

M

MFM

Mass Flow& Density

F

• Work to make air flow reliable and repeatable• Implement ratio control of Air to Feed

P

P

• Freeboard pressure control:protect plant

Basic Automation of A Fluidized Bed Roaster

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Roaster Automation Example

- Follow on benefits of roaster automation

- Mass flow measurement reduces variation in power and coke to feed ratios in the electric furnace downstream

- Measurements are feedforward variables in furnace slag composition MPC

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Direct Limestone Injection into a Mitsubishi Furnace

Converting Furnace Draft Profile Comparison

-5

-4

-3

-2

-1

0

1

2

3

4

Time Series

Fu

rnac

e D

raft

(m

m H

2O)

Batch Limestone Injection

Continuous Limestone Injection

Lower Limit

Upper Limit

Graph illustrates improved draft control

Better draft control has also lead to higher blowing rates

Batch Injection Continuous Injection

Maximum 2.76 -0.85

Minimum -4.01 -3.99

Average -1.71 -2.21

Standard Deviation 1.21 0.58

Converting Furnace Draft (mm H2O)

Draft Control Improvement

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Inlet Spherivalve

RotaryFeeder

DispenseVessel

Conveying Line to Lances

LoadCell

Inlet Spherivalve

LockVessel

Isolating SpheriValve

LoadCell

BatchInjection

ContinuousInjection

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Example Audit

Audits are the first step to ensure control system investments meet expectations(Feb. 1999 CONTROL ENGINEERING Journal, by Dave Harrold)

Results of a Recent Audit by XPS Process Control

Shown are the defects per loop e.g.

• Filter Time Constant

• Integral Period

• Derivative Period

• Sampling Period

• Proportional Band

• Current Operating Mode

0%

16%

7%

4%

0%

34%

39%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0 1 2 3 4 5 6

Number of Defects per Loop

Frequency

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MPC Control of Shaft Furnaces

Plant: 12 shaft furnaces feed calcine to 2 electric furnaces at Xstrata Nickel’s Falconbridge Dominicana operation

Shaft furnace regulatory controls

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MPC Control of Shaft Furnaces

Layered approach to control to stabilize

• Product quality

• Product throughput

• Product temperature

Results

• Reduced furnace energy cost due to hotter product calcine

• Increased production from shaft furnaces of 7% – shaft furnaces no longer the bottleneck

• Overall 3% increase in production

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Discrete Event Simulation

Discrete event simulation allows batch operations to be simulated and controlled

Examples• Xstrata Copper Horne Smelter converter aisle optimization

• ABB application on Norddeutsche Affinerie (Harjunkoski reference)

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Conclusions

Use control appropriately

• Get the process and the control right

Remember the pyramid

• Your structure is only as good as its base

Question the status quo

• Are your measurements the most appropriate?

• Are your systems maintained?

• Do you understand and accept your variability? Where are your opportunities?

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References

Basic Control• Boudreau, M, McMillan, G, New directions in bioprocess modeling and control: maximizing process analytical technology

benefits, ISA, 2007.• Ruel, M., Fundamentals of Process Control, Top Control, 1999.• AMIRA report Project P9L • Harrold, D., Turn Problem Loops Into Performing Loops, Feb. 1999, Control Engineering Journal.

http://www.controleng.com/article/271967-Turn_Problem_Loops_Into_Performing_Loops.php• Harrold, D., So Many Loops, So Little Time, Jan. 2004, Control Engineering Journal. http://www.controleng.com/article/268081-So_Many_Loops_So_Little_Time.php?rssid=20307&q=control+audit+harrold• Fanas, J., Tiburcio, P., Restituyo, W., Lovett, D., McEwan, M., Sandoz, D., and Ryan, L.A., Automatic Control

Development for the Falcondo Ferronickel Electric Arc Smelting Furnaces, 12th IFAC Symposium on Automation in Mining, Mineral and Metal Processing – IFAC MMM’07, Quebec City, August 2007, Symposium Preprints, pp 65-70.

Advanced Control• Ryan, L.A., Frias, J., Rodriguez, P., Morrow, A., Boland, M., and Sandoz, D., Falconbridge Dominicana Reduction Shaft

Furnace Advanced Control Development, TMS 2004, Charlotte, NC, March 2004.• Coursol, P, Mackey, P.J., Morisette, S. , Simard, J.-M., Optimization of the Xstrata Copper-Horne smelter operation

using discrete event simulation. CIM Magazine, March/April 2009, Volume 4 No. 2. • Coursol, P, Mackey, P.J., Bailey, M., Optimisation of the Xstrata Nickel-Sudbury Smelter Converter Aisle using Discrete

Event Simulation, COM 2009, Sudbury, On, August 2009.• Harjunkoski, I., Gallestey, E., Advanced Process Scheduling and Control Technology for Real Time Economic Process

Optimization of a Copper Plant, Mineral Process Modelling, Simulation and Control, Sudbury, Ontario, Canada, June 6-7, 2006.

Other• Nelson, P., Hyde, A., McEwan, M., and Sandoz, D., Integrity Monitoring of Xstrata Copper’s Kidd Metallurgical Division

Mitsubishi 3-Line Furnaces Using Multivariate Methods, 6th Copper Conference – Cu2007, Toronto, August 2007, Volume VII Process Control, Optimization, and Six Sigma, pp 229-239.

• XPS Process Control publications and XPS bulletins – http://www.xstrata.com/corporate/commodities/technology/publications

• Bascur, O.A. and Kennedy, J. P., Improving metallurgical performance in pyrometallurgical processes, JOM Journal of the Minerals, Metals and Materials Society, December, 2004 , Volume 56, No. 12.

• US-CERT Control Systems Security Program– http://www.us-cert.gov/control_systems/index.html