EnWin Utilities Reduces Water Main Breaks by 21 Percent
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Transcript of EnWin Utilities Reduces Water Main Breaks by 21 Percent
Copyright © 2014 Rockwell Automation, Inc. All rights reserved.
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EnWin Utilities Reduces Water Main Breaks by 21 Percent
with Model Predictive Control
Garry Rossi, Director, Water Production – EnWin Utilities Edward Scott Canadian Optimization Manager – Rockwell Automation
November 2014
Copyright © 2014 Rockwell Automation, Inc. All rights reserved.
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Agenda
Summary
The Results
The Opportunity
System Overview and Fast Facts
Mission Statement
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Windsor Utilities Commission Mission Statement
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Drinking Water System Overview
• Total daily supply capacity: 349 ML (92MGD)
• Reservoir storage capacity: 118 ML (31MG)
• Number of treatment plants: 2
• Number of pumping stations: 3
• Number of Elevated Storage Tanks: 2
• Length of water main: 1,100 km (690 Miles)
• The Albert H. Weeks Water Treatment Plant supplies an average of 140 ML (37 MG) of water to City residents per day.
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System Fast Facts
• 17% Non-Revenue Water
• 66 psi average operating pressure (at pump station)
• Single pressure zone
• 17 Pressure stations
• Fully redundant SCADA system architecture (2010)
• Fibre communication ring throughout the service area
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The Opportunity
• 238 Main Breaks per year (average)
• 44 average age of distribution water main (one of the oldest in Ontario/Canada)
• Increasing Electricity costs
• Inconsistent system pressures during peak/low demand periods
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The Opportunity
Discovered predictive control
technology applied in
manufacturing
What is it and can the technology
be applied in the drinking water
sector?
What can Model Predictive Control
(MPC) do for WUC?
Entered into a collaborative project
with Rockwell Automation to
leverage this technology
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The Opportunity
Process Variation = inefficiencies = increased customer costs
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The Opportunity
Why the Variation?
Inconsistency in operating
Too many parameters to monitor simultaneously
Changing one variable affects many variables
Previous Controls
Train staff more?
More procedures?
Simple PID Control?
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The Solution
How do we Implement this solution effectively?
Review current control strategy for processes.
Narrow the focus to the highest ROI for the operation.
Implement over several phases to reduce risk.
Advanced Process Control
Real-time Optimization
Model Predictive Control
Nonlinear Multivariable Control
Linear Multivariable Control
Inferential Sensors
Advanced Regulatory Control
Regulatory Control
• Scalable to meet a wide range of advanced process control requirements
• Improve yield, increase throughput, reduce costs, and improve quality
• Economic optimization
• Maximize throughput
• Increase yield
• Reduce variability
• Improve product transition
• Virtual lab readings
• Gain scheduling
• Rule-based control
• Loops with long lag times
• Basic loop PID control Incre
asin
g E
ffo
rt &
In
cre
as
ing
Valu
e
Single variable in & single variable out • Set up a target and control process variable to the target.
What would be a best SP is often unknown
PID
So What Makes MPC Different?
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MPC
Multivariable in & multivariable out • Control strategy based on a cross correlated matrix
of key process variables
Feedback control • The controller will take no action unless the errors show in PV
Predictive control • Dynamic models developed through process step tests
• Future process behavior prediction and feed forward control
Indirect property variable control • Control property variables through temperature, pressure or flow rate,
etc.
Direct property variable control • Property targets could be set and control directly
Poor control quality when there are large time delays or complex dynamic interactions
PID setpoints are not necessarily optimal with respect to plant wide objectives
Optimal PID targets are continuously calculated, set, & modified
Explicit dynamic models leverage computer capabilities to calculate impact to the product
Model Predictive Control (MPC)
MV setpoint
MV measured value
MV prediction
CV prediction
CV measured value Controller optimizes future trajectories
using an internal simulation
Use first step of horizon
MV desired value
CV desired value
MV desired level specified manually
or by steady-state optimizer
Current time indicator
Past Future
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PlantPAx MPC – Solution Development
14
Engineering Workstation
AOI (.L5X)
Programming in the familiar Logix5000 environment
Identification
Control Task
Specs
Monitoring/Tuning Online change monitoring
Logix5000 program download
Logix5000 MPC Builder
Development cycle
1. Identify Process Parameters
2. Specify Objective Function & Constraints
3. Generate MPC AOI
4. Import and instantiate AOI
5. Download project to Logix
6. Monitor and tune
How MPC Generates Benefits
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100
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96
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92
90
Process/Specification Limit
Before
During
After
Variability
reduction with
Pavilion MPC
Variability under
operator control
Benefits
Time
• Reduces Variability
• Achieves “Plant Obedience”
• Manages the process within constraints
• Achieves uplift – operate closer to specifications and
performance limits while maintaining safety margins
Push process
toward limits
Maintain quality
just within
specification
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The Results
Shows pump start/stop to meet flow demand
Fixed demand operator dependant Note the step changes Large variation in system pressure
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The Results
Pump start/stop pressure amplitude reduced
Less operator dependant Reduced step changes Reduction in system pressure variation
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The Results
Pump start/stop improved via FCV’s
Eliminated step changes Reduction in system pressure variation
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The Results
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The Results
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The Results
Pump start/stop cycles
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The Results
When were the pumps started?
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The Results
0
5
10
15
20
25
30
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40
45
50
January February March April May June July August September October November December
2012-2014 Main Breaks
Pre-MPC Post-MPC Avg Breaks
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The Results
-12
-10
-8
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-4
-2
0
2
0
10
20
30
40
50
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January February March
2014 vs Coldest Average Mean Temperature
Pre-MPC
Post-MPC
Pre MPC Avg Temp
Post MPC Avg Temp
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PlantPAx MPC Implementation
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Header Pressure Logix-Based
MPC Controller
VFD Speed SP
VFD Speed SP
FCV SPs
M1 M2 M3 M4 M5 M6 M7 M8
Pressure Meters
Meter Pressure Logix-Based
MPC Controller
Pressure SP
Legend
MPC Controls
Meter Pressure Logix-Based
MPC Controller
Pressure SP
Header Pressure Logix-Based
MPC Controller
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Summary
Lessons Learned
• Pressure station data affected
pressure control model
• Additional VFD’s improve overall
control
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Logix Soft Sensor®
A Range of Advance Control Solutions
REGULATE
VALUE ADD
Single Loop
Multiple Loops
Process Unit
Multiple Units
Plant-wide Area
PREDICT CONTROL OPTIMIZE
27
PlantPAx & Pavilion8® MPC Pavilion™ RTO
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Summary
Next steps
• Add VFD control at the George
high lift station
• Incorporate this new VFD into the
model
• Evaluate opportunities for MPC
control in other areas
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Summary
Operational Improvement Summary
• Reduced average system mean
pressure by 2.8% resulting in $125K
savings
• Reduced standard deviation by 29%
• Added multi-zone control for outlying
areas
• Eliminated pump start/stop variations
• Reduced main breaks by 21%
resulting in $125k savings
• ROI less than one year
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Questions
Garry Rossi, Director, Water Production – EnWin Utilities Edward Scott Canadian Optimization Manager – Rockwell Automation
November 2014