1. APC Best Practice
Tom Fiske
Senior Analyst
ARC Advisory Group
tfiske@arcweb.com
tfiske@arcweb com

2. Agenda
APC Overview
Market Perspective
APC Best Practice
Summary
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3. APC and Local Optimization
Definitions
APC
• Online real-time control
• Popular method: linear multivariable control
Optimization
• Online real-time to determine optimum setpoints
• Rigorous first-principle models
• Empirical models
• Offline or open loop for advisory
O e o ope oop o ad so y
• Rigorous or empirical models
• Ad-hoc analytic tools
• Statistical methods
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4. Types and Value of APC
Real-time Optimization
lue
asing Val
Nonlinear Multivariable
Control
ncreasin Effort & Increa
Linear Multivariable Control
Other Supervisory Control
Methods
Inferential Sensors
ng
Advanced Regulatory
Control
In
Regulatory Control
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5. Advanced Predictive Control Reduces Variance
Variance Reduction allows Processes to Operate
Closer to Limits or Constraints
Specification Limit
pecification Target
Sp
Before APC After APC Move closer to limit
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6. Benefits of Control and Optimization
100
Process Control Contribution
90
80
Capital Cos %
70
st
Real-time
60
Optimized
>25%
50
Model
40
Predictive
>20%
C
30
Regulatory
20
Basic Adv
>45% >10%
10
0
0 10 20 30 40 50 60 70 80 90 100
Potential Performance Improvement
APC & Optimization Benefit: 3.5 – 7% of Total Annual Gross Profits
O ti i ti B fit 3 5 fT t lA lG P fit
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7. Creating and Sustaining Value
Continuous
Improvement
p o
Target
Benefits
Revenue
Time
st
Cos
Sustained
S t i d
Performance
Improved
Time to Revenue
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8. Scope
The purpose of the research was to develop an
understanding of how process manufacturers around
the world are using APC to create a sustainable
competitive advantage. The survey was designed to
capture information about the current status of APC
usage.
ARC received about 50 responses from the survey on
APC usage
usage.
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9. Characteristics of Research Respondents
Respondent come from a variety of industries
Food & Bev, 2.0%
Others, 4.1% Pharmaceutical,
4.1%
Power, 6.1%
Chemicals, 24.5% Mining &
Metals,
8.2%
Oil & Gas, 10.2%
Refining, 22.4%
Petrochemical,
18.4%
The majority come from heavy process – big APC users
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10. Ranking of Research Respondents
ARC best practice reports group responses into the
categories of Leaders, Competitors, and Followers. Over
ten separate performance criteria were used to rank the
responses.
Ranking
Leader Top 20%
Competitor Next 50%
Follower Last 30%
For each survey response, each of the performance criteria
was given a quantitative measurement and the total used
as a ranking demarcation.
ki d i
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11. Organizational Aspects – APC Implementation
Number of APC Applications
Leaders
Competitors
Followers
0% 20% 40% 60% 80% 100%
0-5
05 6-10
6 10 11-25
11 25 25+
25
User’s Perspective
• About 22 % of users believe APC provides a competitive advantage
• Ab t 78% of users believe APC i necessary t remain competitive
About f b li is to i titi
Leaders: APC culture & Adopt BP
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12. Organizational Aspects – APC Implementation
APC Outsourcing Strategy
Leaders
Competitors
Followers
0% 20% 40% 60% 80% 100%
Less than 25% Between 25 and 50% Between 50 and 75% Over 75%
Leaders: View APC as competitive Advantage, they have more
internal resources and outsource less
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13. Organizational Aspects – APC Implementation
Use of Internal Resources during Implementation
Leaders
Competitors
Followers
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Dedicated Teams Manufacturing Staff Engineering Staff Other
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14. APC Maintenance Methodology
APC Outsourcing Strategy
Leaders
Competitors
p
Followers
0% 20% 40% 60% 80% 100%
Less than 25% Between 25 and 50% Between 50 and 75% Over 75%
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15. APC Maintenance Methodology
Internal Resources
Emerging Best Practice Criteria
APC Applications/ APC Expert
APC Expert Certificaion
0% 20% 40% 60% 80% 100%
Yes No
APC Applications/Expert range typically range
pp / p g yp y g
between 5 and 10 depending upon its complexity
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16. APC Maintenance Methodology
Use of Internal Resources for Maintenance
Leaders
Competitors
Followers
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Dedicated Teams Manufacturing Staff Engineering Staff
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17. RTO Implementation Methodology
Number of RTO Applications
Leaders
Competitors
Followers
F ll
0% 20% 40% 60% 80% 100%
0 1-2 3-5 over 5
User’s Perspective
• About 30 % of users believe RTO provides a competitive advantage
• About 70% of users believe RTO enables more effective manufacturing
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18. RTO Implementation Methodology
RTO Outsourcing Strategy
Leaders
Competitors
Followers
0% 20% 40% 60% 80% 100%
Less than 25% Between 25 and 50% Between 50 and 75% Over 75%
Leaders more heavily involved in Implementing RTO
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19. Focus Areas for APC Current and Future Use
APC Focus Areas for Various Situations
Large Cont. Units Large Cont. Units
Small Cont. Units Small Cont. Units
Competitiors
Leaders
Large Units: Tranisitions Large Units: Tranisitions
Small Units: Transitions Small Units: Transitions
Large Units: Abnormal Large Units: Abnormal
Small Units: Abnormal Small Units: Abnormal
0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Deployed Being Deployed Short-term Long-term No Plans Deployed Being Deployed Short-term Long-term No Plans
Large Cont. Units
Small Cont. Units
• Note: These charts do
not i di
indicate percent
Followers
s
Large Units: Tranisitions
saturation. The charts
Small Units: Transitions show only that users
are deploying or plan
Large Units: Abnormal to deploy APC on some
process units in the
Small Units: Abnormal indicated category
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Deployed Being Deployed Short-term Long-term No Plans
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20. Adoption Level of APC
Current Level of APC Adoption for Continuous Processes
Leaders
Competitors
Followers
All
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Large Units/Plants
g Small-Midsize Units/Plants
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21. Adoption Level of APC
Current Level of APC Adoption for Transition
Management in Continuous Processes
Leaders
Competitors
Followers
All
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Large Units/Plants Small-Midsize Units/Plants
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22. Adoption Level of APC
Current Level of APC Adoption for Abnormal Situations
in Continuous Processes
Leaders
Competitors
Followers
All
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Large Units/Plants Small-Midsizde Units/Plants
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23. APC Techniques vs. Objectives for Non-Steady State
Transition Management is Becoming Competitive Advantage
Operator Guidance Sequence Control
Safety C id
S f Consideration
i Safety Consideration
Increase Energy Savings Increase Energy Savings
Increase Throughput Increase Throughput
Improve Quality Improve Quality
Faster Swith Over Faster Swith Over
Man-Hour Saving Man-Hour Saving
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Very Important Important Somewhat Important Less Important Not Important Very Important Important Somewhat Important Less Important Not Important
Model Predictive Control
Safety Consideration
Increase Energy Savings
gy g
Increase Throughput
Improve Quality
Faster Swith Over
Man-Hour Saving
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Very Important Important Somewhat Important Less Important Not Important
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24. Focus Areas for Online Opt. Current and Future Use
Online Optimization Focus Area for Various Situations
Large Units Large Units
Competitiors
Small Units Small Units
Leaders
Dyn Opt Dyn Opt
Batch Batch
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
Deployed Being Deployed Short-term Long-term No Plans Deployed Being Deployed Short-term Long-term No Plans
Large Units
• Note: These charts do
Small Units not i di
indicate percent
Followers
s
saturation. The charts
Dyn Opt
show only that users
are deploying or plan
to deploy Online
Batch Optimization on some
process units in the
indicated
i di t d category
t
0% 20% 40% 60% 80% 100%
Deployed Being Deployed Short-term Long-term No Plans
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25. Adoption Level of Online Optimization
Current Level of Adoption for Online Optimization for
Various Situations
Leaders
Competitors
Followers
All
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Large Units Small Units Dynamic Optimization Batch Optimization
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26. Adoption Level of APC
Current Level of APC Adoption for Batch Processes
Leaders
Competitors
Followers
All
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Soft Sensors Profile Control Run to Run
Run-to-Run Control MPC
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27. Controller Performance
Control Performance Deteriorates Over Time
10% Continuous Improvement
with Online Monitoring
25% Good Support
Benefits
With No Online Monitoring
APC Performance
degradation
40% Poor Support
pp
No Online Monitoring
APC Turned On
Time
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28. Adoption Level and Focus Areas for Control
Performance Monitoring
Control Performance Monitoring Focus
APC APC
Competitors
Leaders
Regulatory
g y Regulatory
0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Deployed Being Deployed Short-term Long-term No Plans Deployed Being Deployed Short-term Long-term No Plans
• About 40% of users • Note: These charts do
currently use APC
not indicate that users
Regulatory Performance deploy performance
Followers
Monitoring Applications monitoring tools on all
of its regulatory and
APC applications, but
Regulatory
rather a portion of its
• Approximately 30% of
control applications
users currently use APC
Performance Monitoring
Applications 0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
Deployed Being Deployed Short-term Long-term No Plans
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29. Adoption Level and Focus Areas for Control
Performance Monitoring
Performance Monitoring: COTS vs. In-House Development
APC APC
Competitors
Leaders
Regulatory Regulatory
0.0% 20.0% 40.0% 60.0% 80.0% 100.0% 0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
COT In-House Combination COT In-House Combination
• About 40% of users
currently use APC • Note: These charts do
Regulatory Performance not indicate that users
Followers
Monitoring Applications deploy performance
monitoring tools on all
of its regulatory and
Regulatory APC applications, but
• Approximately 30% of rather a portion of its
users currently use APC control applications
Performance Monitoring
Applications 0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
COT In-House Combination
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30. Plant Database Applications
Current Level of Automation Integration for Plant Applications and Databases
Used for Process Control and Operations
PAM PAM
LIMS LIMS
Competitiors
Leaders
MOC MOC
Alarm History Alarm History
Process Data Storage Process Data Storage
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
Fully Integrates Being Integrated Short-term Long-term No Plans Fully Integrates Being Integrated Short-term Long-term No Plans
PAM
LIMS
ers
Followe
MOC
Alarm History
Process Data Storage
0% 20% 40% 60% 80% 100%
Fully Integrates Being Integrated Short-term Long-term No Plans
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© ARC Advisory Group

31. Control Performance Monitoring
Key Performance Indicators for Control Monitoring
Regulatory Control Advanced Process Control
% Utiliztion % Utiliztion
Prediction Accuracy Prediction Accuracy
Control Accuracy Control Accuracy
% Loops at Limit % Loops at Limit
% Loops Normal % Loops Normal
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Very Important Important Somewhat Important Less Important Not Important Very Important Important Somewhat Important Less Important Not Important
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© ARC Advisory Group

32. Control Performance Monitoring
Key Performance Indicators for Control Monitoring
Advanced Process Control
Leaders
Competitors
Followers
0.0% 20.0% 40.0% 60.0% 80.0% 100.0%
APC % Utilization
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© ARC Advisory Group

33. Plant Database Applications
Current Level of Adoption and Functionality of Alarm Management
Applications
Reducing Peak Alarms Reducing Peak Alarms
Reducing Standing Alarms Reducing Standing Alarms
Alarm Analysis: Root Cause Alarm Analysis: Root Cause
Competitiors
Leaders
State Based Alarming State Based Alarming
Alarm Filtering Alarm Filtering
Alarm Analysis: Combination Alarm Analysis: Combination
Alarm Analysis: Frequency Alarm Analysis: Frequency
Alarm History Alarm History
0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%
Deployed Being Integrated Short-term
Short term Long-term
Long term No Plans Deployed Being Integrated Short term
Short-term Long term
Long-term No Plans
Reducing Peak Alarms
Reducing Standing Alarms
Alarm Analysis: Root Cause
Followers
State Based Alarming
Alarm Filtering
Alarm Analysis: Combination
Alarm Analysis: Frequency
Alarm History
0% 20% 40% 60% 80% 100%
Deployed Being Integrated Short-term Long-term No Plans
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34. Plant Database Applications
Plant Database Integration
Plant Database Integration for Operator
Assistance and Decision Support Adoption of Data Analysis Technologies
QC
Estimate Key Properties
Performance Monitoring
ID Key Process Factors
Sensor Diagnostics
Improve QC
Efficiency M it i
Effi i Monitoring
Improve Batch Ops
PV-MV Correlation
Control Monitoring Abnormal Situation Detection
0% 20% 40% 60% 80% 100% 0%
% 20%
% 40%
% 60%
% 80%
% 100%
%
Deployed Being Integrated Short-term Long-term No Plans Deployed Being Deployed Short-term Long-term No Plans
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35. Thank Y
Th k You
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