Roadmap to Optimize Process Control

Michel Ruel, PresidentMichel Ruel, President

Roadmap to OptimizeRoadmap to OptimizeRoadmap to Optimize Roadmap to Optimize Process Control PerformanceProcess Control Performance

1PresentedTo Matrikon Summit 2007

AgendaAgenda

1 I t d ti• 1-Introduction• 2-Evolution• 3-Data→Knowledge →Diagnostics• 4-Performance4 Performance• 5-Optimizing Process Control Performance

6 E pected Res lts• 6-Expected Results• 7-Conclusions


A New EraA New Era…• New tools available for management, g

production, operation, engineering…– Data collection– Data visualizationData visualization– Data analysis– Etc.

I t l• In process control– Alarm management– Condition monitoring

Improve•Reliability•Performanceg

– Performance supervision– Taiji for MPC and PID

•Quality•Agility•ProfitabilityP d ti it


•Productivity •Gross margin

Looking atLooking at…Performance Real-Time Financial M t A ti R tiPlant Resource Production Enterprise Management Management Management O

p

Measurement Accounting Reporting

Mea

Operation

ProductionSupervisor

S perintendent VP

CEO

COO

peration

asurement

Process Control

Maintenance

Superintendent

Managers

VPsCFO

t

Real time Hour Shift Day Week Month Quarter Year

Quality



Control SystemsControl Systems

• 75% of assets are manufacturing assets75% of assets are manufacturing assets– Majority under process control– Millions of dollars

• > 97% of control loops are PID• > 3 000 000 PID controllers in NA 5 Billions $ for

• > 3000 MPC in NA • 10 to 30% of loops are in manual mode

Process Control

10 to 30% of loops are in manual mode• 20% of loops have a direct impact on final product


Control LoopPerformance

S it S d

Control Loop

FIC-101

Security SpeedEconomy Handling

FT-101

Typically 10 to 30K$Typically 10 to 30K$

Sensor Control Valve Share of DCSEngineering Installation Commissionning


Engineering Installation Commissionning

The Reality yNumbers from audits, articles and our field experience

• 20% of control loops have improper design• 20% of control loops have improper design

• 30% of control valves have related problems

15% f i t i t i t ll d l• 15% of equipment is not installed properly

• 30% of controllers have nonsensical tuning parameters

• 85% of controllers have improper tuning parameters

• Only 25% of control loops give acceptableOnly 25% of control loops give acceptable performance in automatic control!


Typical NumbersTypical Numbers

• Small plant • Large plantSmall plant• 200 loops

P

Large plant• 2000 loops

P• Process control ~3MM$

• Process control ~30MM$

• Assets: ??MM$ • Assets: ???MM$

• Assets/Engineer ~ 100 to 1000• Assets/Operator ~ 100 to 1000


AgendaAgenda

1 Introduction• 1-Introduction

• 2-Evolution• 3-Data→Knowledge →Diagnostics• 4 Performance• 4-Performance• 5-Optimizing Process Control Performance• 6-Expected Results• 7-Conclusions


EvolutionEvolution

C tProfits

Optimize the Efficiency

Costs

Run the plant

Optimize the plant business

Business efficiency

Operator efficiency

Engineering efficiency

Profitsagility

1970’ 1980’ 1990’ 2000’ 2010’

DCSERP


1970’s 1980’s 1990’s 2000’s 2010’s

Evolution Example: TuningEvolution, Example: TuningTaiji

Automated tests

Optimizing tools

Automated testsAutomated

identification

MPC, multi PIDTuning and analysis

softwareTuning tools

softwareBump tests

MPC, multi PID

T i b

softwareBump testsmodelsformulas

Tuning by trial and error

1970’ 1980’ 1990’ 2000’ 2010’


1970’s 1980’s 1990’s 2000’s 2010’s

AgendaAgenda

• 1 Introduction• 1-Introduction• 2-Evolution

3 D t K l d Di ti• 3-Data→Knowledge →Diagnostics• 4-Performance• 5-Optimizing Process Control Performance• 6-Expected Resultsp• 7-Conclusions


Tools for Operational ExcellenceTools for Operational ExcellenceValue added solutions:

• Alarm management• Alarm management• Control performance monitoring• Equipment condition monitoring• Process performance monitoring

• Data connectivity• Data management• Data and process visualization• Engineering analysisg ee g a a ys s


Converting Data in ResultsConverting Data in ResultsIdentify bad actors

Di ti $Hit th j k t Diagnostics

Causes

Optimization$Hit the jackpot

• Warehouses of data

p

Etc.



production, operation, engineering…– Data collection– Data visualisationData visualisation– Data analysis– Etc.







Adding Value to Your DataAdding Value to Your Data• Performance metrics

E t i l t t l– Enterprise, plant, sector, loop• Capturing models

– For tuning, process analysis, APC• Detecting equipment problems before they cause a shutdownDetecting equipment problems before they cause a shutdown

– Valve problems and terminal element– Transmitter problems

• Control problems– Tuning, oscillation– Interaction

• Process problemsPumping problems fouling entrapped air– Pumping problems, fouling, entrapped air

• Operation problems– Loops forgotten in manual mode– Loops on which operation makes frequent set point or mode changes


p p q p g

Data→Knowledge → Diagnosticsg g

R lt$Diagnostics Result$

Analysis Performance

Process Data


systems

AgendaAgenda

1 Introduction• 1-Introduction• 2-Evolution• 3-Data→Knowledge →Diagnostics

• 4-Performance4-Performance• 5-Optimizing Process Control Performance• 6-Expected Results• 7-Conclusions


Process Optimization: C ti P

O ti dPerformance decay

a Continuous Process1

100.00%

• Operation procedures• Raw material quality

E i i

Performance decay

90%

100%

0.8

0.9

1

70 00%

80.00%

90.00%

Th h ld• Equipment wearing out• Maintenance

50%

60%

70%

80%

%

Half life is generally 6 months0.5

0.6

0.7

Series150.00%

60.00%

70.00% ThresholdPerformance monitoring

• Configuration changes• Process changes

20%

30%

40%

50%%

0.3

0.4

20.00%

30.00%

40.00% No optimization

• Etc.0%

10%

0 20 40 60 80 100 120

Time6 months0

0.1

0.2

0 5 10 15 20 25 30

0.00%

10.00%

0 5 10 15 20 25 306 months to 1 year


Time0 5 10 15 20 25 30

Problems: What Could Go WrongProblems: What Could Go Wrong

Operations: SP Mode Tuning is only

FIC-101

Variability

TuningNormal mode?

Operations: SP, Mode

In service?FIC-101PV CO

SP

M d

g yone of the

problems…

C t l P f M it i

FT-101

Noise

Control design

OscillationsIn control?ModeControl Performance Monitoring

•Monitor

Non-linearities

P d i

Disturbance Hysteresis

Valve at limit

Interactions with other loops

Process modelFouling

•Detect

•DiagnoseProcess design Stiction

Valve at limit

•Tools to fix it


GainsGainsVariability reduction w ith optimization

25

30

Saving$

Saving$

SAVINGS $$$

20

25

(%) SP

PV

SAVINGS $$$

10

15

Moi

stur

e PV

CO

Limit(Client)Before optimization After optimization

0

5Saving$

Ti

SAVINGS $$$


400 600 800 1000 1200 1400 1600 Time

AgendaAgenda

1 I t d ti• 1-Introduction• 2-Evolution• 3-Data→Knowledge →Diagnostics• 4 Performance• 4-Performance• 5-Optimizing Process Control Performance• 6-Expected Results• 7-Conclusions


7 Conclusions

ImproveImproveShort Term Long Term

Oscillation

Service Stability, b t

Quality Customersatisfaction

Process Performance Profit Performance

Response

Saturation Variability

robustness

Energy cost

Uptime Throughput Gross margin

Responsetime

Valve stiction

Efficiency,productivity

Reliability

gy

Materialcosts

Unit costTotal cost

$Profits

Tuning

Process model Relative

performanceMaintenance

costs

Operatingcosts

Unit cost


issues

…

costs

Small Gestures That Count!Small Gestures That Count!• Actions 100% Interest Succes$

– Daily– Weekly

20%30%40%50%60%70%80%90%

100%

%

Interest

60%70%80%90%

100% SupportR l it

Succes$Result$

Ne $– Monthly– Yearly

• Workflow procedures

0%10%

0 20 40 60 80 100 120Time

0%10%20%30%40%50%

0 20 40 60 80 100 120Time

%

20%30%40%50%60%70%80%90%

100%

%

Regularity New$

• Workflow, procedures• What can be automated?• How can the process be

0%10%20%

0 20 40 60 80 100 120Time

How can the process be sustained?

• How can optimization be


poptimized?

Sustaining Gains Human Factor!Sustaining Gains, Human Factor!Watch 5 minutes 1 hour ½ day 1 dayEffort:

Production

Progression meeting

R li bilit

Alarm t

Condition monitoring

Production meeting

M i t

Reliability meeting

Performance monitoring

management

Alerts, reports

Maintenance planning


My Web page



Data→DiagnosticsData→Diagnostics

• Too much data?Too much data?– 1 000 loops x 10 assessments = 10 000 infos– Priorities, economic rank → 10 infosPriorities, economic rank 10 infos

• Too many things to do?– Realistic, e.g.:Realistic, e.g.:

• 25 loops/month• 5 minute daily reports• 15 minute weekly reports• 1 hour monthly meetings• 1 day yearly meetings


• 1 day yearly meetings

MethodologyMethodology• Benchmark control ⇔ plant profitability

• Determine priorities, economic value• Analyzey

– E.g.: valve stiction, tuning, oscillations…• Improve

– E.g.: repair valves, tune loops, review strategies• Sustain

Monitor control performance daily– Monitor control performance daily– Flag abnormal conditions and events– Procedures in place for continuous improvement


p p

Performance Over TimePerformance Over Time

P fPerformance

PerformanceSustain

I

SustainCondition based maintenance

Maintenance

-30%Installationbenchmark

Identify and fix bad actors

Improve

efforts, costs, workforce


Time (weeks)

MaintenanceMaintenance

• Failure based (reactive) firefightingFailure based (reactive), firefighting• Scheduled maintenance (preventive)

C diti b d i t ( ti )• Condition based maintenance (proactive)–Data–Knowledge–Results, diagnostics–Repairs, corrections


WorkflowWorkflow

• Condition based maintenance (proactive)Condition based maintenance (proactive)

Collect, analyze data

Diagnose, prioritize Confirm diagnostic

T k lt f Fi itTrack results, performance Fix it

Quantify results Advertise, inform


y

SuccessesSuccesses

• Don’t be shy or humbleDon t be shy or humble– Spread the good news

Claim your success– Claim your success– Write short stories, articles

Q tif d lif lt• Quantify and qualify results– Numbers, $– Prepare presentations


AgendaAgenda


3 Data Knowledge Diagnostics• 3-Data→Knowledge →Diagnostics• 4-Performance

5 O ti i i P C t l• 5-Optimizing Process Control Performance6 E t d R lt• 6-Expected Results

• 7-Conclusions


Expected ResultsExpected Results• Variability: ÷ 2• Service factor

• Cycling: removed• Valve travel: ÷ 5 (valve wear ÷ 2 )

• Robustness: X 2

• Tuning• Repair• Review control strategies • Robustness: X 2

• Performance: X 2

Quality: 30%

• Review control strategies• Optimize

Efficiency:Throughput :

1 to 5%1 to 5%

Energy:Maintenance costs:

MPC d l t t ti

1 to 10%30%70%


MPC deployment, re-testing: 70%

ControlControlComplexity

F ll ff tQualifications

RTO

Follow-up effort

1%

Control strategies

Multivariable

Tuning modeling

Models

1%

1%

PIDConfiguration, tuning

Tuning, modeling

1%

Valves, transmitters, sensors

Process equipments, pumps, reservoirs…Maintenance

Calibration, maintenance


Maintenance

Expected Benefits of OptimizationOptimization

• Increase process performanceIncrease process performance• Use resources (human & material) wisely

• Reduced energy costsR d d t

• Increased up-time I d d t lit• Reduced waste

• Reduced variability• Reduced valve maintenance

• Improved product quality• Improved efficiency• Better operation

• Reduced pollution• Cycling removed

• Improved safety• Smoother start-up


AgendaAgenda


3 Data Knowledge Diagnostics• 3-Data→Knowledge →Diagnostics• 4-Performance

5 O ti i i P C t l• 5-Optimizing Process Control Performance6 Expected Results• 6-Expected Results

• 7-Conclusions


ConclusionsConclusions

• Resources are used where they are• Resources are used where they are really needed

• Process control systems are used to their full potential

• Operation and production are optimal• Maintenance and engineering are• Maintenance and engineering are

efficiently used.


ConclusionsConclusions• Performance remains at its best• Process benchmarks• Warnings for problemsg p• Poor performers immediately identified• Automated troubleshooting and diagnostics• Automated modeling• Automated interaction analysis• Proactive maintenance, interventions sorted by priorities• Reports available via the web to all categories of users



production, operation, engineering…– Data collection– Data visualisationData visualisation– Data analysis– Etc.





$



Thank YouThank YouMichel RuelMichel Ruel

PresidentTop Control IncTop Control Inc

[email protected]

(877)867 6473(877)867-6473 www.topcontrol.com


Control Assets→Plant PerformanceControl Assets→Plant PerformancePoor control poor plant performance poor financial performance

Quality, throughput, raw

Decreased revenues,increased costs

Pl t f

Financial performance

Yields, conversions, separations etc

Qua ty, t oug put, amaterials & utilities usagePlant performance

Process unit performance

POOP equipmentPERFORMANCE

separations, etc.

Physical equipmentReactors, compressors, heat

exchangers, etc.

POOR CONTROL

75% of your physical assets are under process control:

Process control assetsValves, sensors, regulatory control, advanced control,

analyzers, inferentials


75% of your physical assets are under process control:

Performance SupervisionPerformance Supervision

KPIPerformanceAvailabilityAvailabilityEquipmentOperation

Performance

UsersManagementEngineering

AssetsPl t

Health

Effi i

Performance

$$$EngineeringMaintenance

Operation

PlantUnitsLoops

Equipment

Efficiency


Documents

Roadmap to Optimize Process Control