optimizing operations to boost your ability to complete in any market environment
TRANSCRIPT
to Boost your Ability to Compete in any EnvironmentOPTIMIZING OPERATIONSPerry Nordh, P.Eng
20-Jun-2016
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• Profit Stepper
Honeywell’s Advanced Control Solutions
• Profit Controller • Profit Sensor Pro
• Profit Optimizer: Real-time, Dynamic Optimization
*Optimum
Minimum Effort Move
Past Future
Assumed Values
CV
Predicted
Unforced
Response
MV
Control Funnel
Optimal Response
Setpoint • Control Performance Monitor
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Profit Optimization Suite
One Consistent technology platform- MPC and Real Time Optimization
- Flexible Modeling environment
- Unmatched operational awareness
- Lowest lifecycle cost
Seconds Hours/Days
Profit LoopExperion
Profit Controller(C300/ACE)
Profit Suite- Profit Controller
- Profit Sensor
- Profit Stepper
Profit Optimizer(DQP
Multi-Unit
Optimization)
Profit Executive(SuperDQP
Multi-Asset
Optimization)
Continuum of Control Solutions
Single Variable
Linear Control
Multiple-Variable Non –Linear
Control and Optimization
Profit CPM (Control Performance Monitor)
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Profit Control and Optimization Roadmap3
Profit Optimization Suite R431
• Incremental release (simple install)
• CAB enhancements
• Engineering Studio updates
• Updated CV move block
Control Performance Monitor R600
• Next generation Diagnostic workflow
- Tune/Fix/Investigate (Expert Guidance)
• Intuition platform
- Coexistence with DynAMo
- Improved security/user management
Profit Optimization Suite R440
- APC Agnostic Multi-Unit Dynamic optimization
- Optimization engine interior point enhancements
- Operator Guidance enhancements for
optimization
- CAB enhancements
- Engineering Studio updates
- UOP Oleflex and Hydroprocessing toolkits
CPM R610
- Next generation MPC monitoring
- Optimization monitoring
- Integration with Intuition alerting (Pulse)
Profit R500
- Gate to gate Optimization
- Planning integrationExperion Profit Controller
- Profit Controller in CEE (C300/ACE)
- Part of Experion, includes design and visualization
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R440 Focus – More out of the same Assets
• Optimization
- Optimize in a wider range of situations and environments
- Utilize UOP expertise
- Optimize across multiple environments
- Operator Guidance for Profit Optimizer
• User Experience
- New thin client
- Limits repository
- Models from Historical data
• Infrastructure
- Updates
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SS Controller TargetsFeasible SS Controller Targets
SS
Model
Traditional Optimization Overview
• Build SS model for Optimizer
- Linear, NL, large scale
- Represents plant behavior
- Define objective function
• Calculate SS targets to
determine the end goal
• Realize the operating value by
dealing with dynamics
- Ensure feasibility
- Prevent constraint violations
- Push plant to SS
• Update the model, objective
function and dynamic
compensator
- Compensate for changing
conditions
- Repeat cycle
SS Optimizer
Economics and Ranges
SS Targets
Process
Dynamic Compensator
Limits
Process Changes
Feasibility
Feedback
LP Override Target Ramp Rates
Without Profit Optimization,
the LP Override must be engineered to provide
feasible SS controller targets and a realistic ramp
rate to the MPC controller
MPC Controller
Ramped Path
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Profit Optimizer Concepts
Profit
controller
Model matrix
MVs
CVs
Base structure
Optimization Objective Function
Can Have multiple objective functions but only one executing
Profit Optimizer (DQP)
MVs
CVs
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Profit Optimizer Concepts
DV1 DV
x
x
x
MV1 MV2 MV1 MV2 DV1DV
x
x
x
MV1 MV2 MV1 MV2
x
x
x
x
x
x
x
x
x
x
x
x
DV1
DV
x
x
x
MV1 MV2 MV1 MV2
BM
Bridge models add dynamic feed forward dependence
Bridge Model inputs
Bridge Model output
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Profit Optimizer Concepts
MVs
CVs
y y y y y y yCombined
Constraints
and Aux data
• Combined Constraint Models are multi-unit steady state CVs (no feed back).
CV x x x
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Profit Optimizer Concept
• Multiple applications built for good control and optimization performance
• Global objective function to fully utilize degrees of freedom
Time
Controller
Local optimizer
App 1
Controller
Local optimizerApp 2
Controller
Local optimizer App 3
Controller
Local optimizer App 4
Global SS
Overall Optimization
Horizon
Local optimizer
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Profit Controller 2
Profit Optimizer
Ensures Optimal Dynamic Global Path
Profit Optimizer
Profit Controller 1
Global Economics and Ranges
SS MV Targets
Gains
MV Economics
Process 1
Profit Controller 3
Process 3Process 2
Limits
Optimal
Dynamic Path
Local Feasible SS MV targets
QP Override 1 QP Override 2 QP Override 3
Feedback
SS
Model
Profit Bridge
Gains
Profit Optimizer Link
Other Controller 2
Profit Optimizer coordinates
the economics of multiple
Profit Controllers for multi-unit
plant-wide optimization
Optimal
Dynamic Path
Optimal
Dynamic Path
Local Feasible SS MV targets Local Feasible SS MV targets
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-5 -4 -3 -2 -1 0 1 2 3 4 5-5
-4
-3
-2
-1
0
1
2
3
4
5
x1
x2
Minimization of the JAE function
Start Point
Solution
1
65
43 2
7
Profit Optimizer will be enhanced to include
‘Hessian Updating’ which will:
- Allow optimizer to use
curvature-of-the-surface information
compared to simple gain updating
- Allow superior handling of unconstrained
(interior point) solutions
- Improve ability to deal with process non-linearities
- Faster convergence on the optimum solution
- Provide additional benefits of around 10%
(depending on process non-linearities)
R440 Optimization Enhancements
*Optimum
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Starting Point
x2
x1
)x-(1)x-100(x f(x) 2
1
22
12
Rosenbrock's
Banana Function:
• Change Sequential QP (SQP) to Time-Sequenced QP (TSQP)
• Update the Hessian matrix and model-predicted constraints along the sequence
• Solve the time-sequenced QP successively inside the control feedback loop- If the model error or disturbance is negligible, the TSQP gets the same solution as SQP (or SS RTO)
- If the model error or disturbance is modest, the TSQP gets feedback corrections along the way
- If the model error or disturbance is/becomes large (over time), the TSQP should do better.
- We all know: Disturbances are part of life in process industries!
Our Approach: modify SQP for real-time use12
Update the objective
function, model gains,
future predictions and
constraints at a user-
specified frequency
(or condition)
Actual Optimal
P-Optimal Point
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Honeywell Optimization Solutions
-5 -4 -3 -2 -1 0 1 2 3 4 5-5
-4
-3
-2
-1
0
1
2
3
4
5
x1
x2
Minimization of the JAE function
Start Point
Solution
Profit Optimizer – Dynamic Optimization
• Dynamic optimization
– no steady-state detection is needed
– 3-5 minute execution frequency
– Full formulation objective function w/ QP solver
• Cooperative optimization approach
– Shares common models with Profit Controller application(s)
– Calculates optimization speed for each controller depending on underlying process dynamics & settling time
– Patented bridge model technology: full dynamic relationships across the optimization scope.
• Gain scheduling for non-linear enhancement (via Profit Controller/Optimizer Gain Mappers)
– Direct move in the most profitable direction
– Global optimum when possible
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OptimizationFuture Direction: Gate to gate
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Refinery Model – An APC View
Stra
igh
t-Run
an
d/o
r Ble
nd
ing
Blending Components
Straight-run products
APC model has a greater resolution but limited scope• It captures essential unit or area operating constraints
• Optimizes unit production to possible detriment of plant
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Refinery Model – A Planner’s View
The model has a high level of abstraction• It captures essential material and energy balances in holistic view of the refinery
• It leaves out many unnecessary or even obscuring details for the economic optimization
Stra
igh
t-Ru
n a
nd
/or B
len
din
g
Blending Components
Straight-run products
Feed
y
y
y
p
p
p
nn
2
1
2
1
Product
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Planning – Execution Gap
Plan Execute
Planner uses aggregate yield models to predict
product makes for raw materials.
Detailed unit model is generally not required
and can lead to deleterious effects for solver
Planning model required to make complex
decisions for which raw materials to purchase
and which product to produce
Current plan may be infeasible for current
operating conditions
Mismatched operation can lead to imbalances
in plant inventories
Today’s yields may not match the planner’s
yield model resulting in different operation
Assumed planning constraints for unit
performance may be too aggressive or too
conservative compared to actual performance
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Key Challenge:
Plantwide economics
Local economics
Optimal feasible
Control & Optimization
Schedule & Optimization
Production Planning
Business Planning
Planning (Months)
Scheduling (Day/Weeks)
Integration Scheme? How?
App -1 App -2 App -3 App -n
Real Time Dynamic Optimization - DQP (hr)
How to get the solution layers to stay consistent and
reach the global optimum jointly?
Manage intermediate and final:
• Inventory (volumes)
• Properties (quality)
• Timeline (just in time)
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Implementation of the plan today
MPC 1-1
MPC 1-2
MPC1-3
MPC1-n
Optimizer -1
MPC2 -1
MPCw -1
MPCm-1
MPCm-p
Optimizer -m
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Implementation of the plan tomorrow
Profit Executive
MPC 1-1
MPC 1-2
MPC1-3
MPC1-n
Optimizer -1
MPC2 -1
MPCw -1
MPCm-1
MPCm-p
Optimizer -m
Returned values can include
the actual versus predicted
yields and proxy limits
Manage intermediate and final:
• Inventory (volumes)
• Properties (quality)
• Timeline (just in time)
Manage:
• Unit operation
• Limits
• Constraints
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Profit Executive Components
Planning Execution
OptimizerWhat-If steady state analysis using current
operating conditions
ControllerDynamic operating plan moves provided to
simulator
OptimizerGenerates optimal steady state operating
plan based on actual costs
ControllerMoves plant toward optimal while accounting
for immediate constraints
PredictorComputes yield/property model biases using
operating data
APC ExtensionsBounds optimization problem based on APC
constraints
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1 2 3 4 5 6 7 8 9 10 11 12 130
500
1000
1500
2000Monthly Diesel Production in a More Profitable Product Mix
KB
arr
els
/Month
1 2 3 4 5 6 7 8 9 10 11 12 130
500
1000
1500
2000Historical (Acutal) Monthly Diesel Production
Month in the Study Year
KB
arr
els
/Month
Product A
Product B
Product C
Product D
Product E
Product F
Quality Giveaway
A more profitable product mix could have been produced in the study year
The actual product mix was produced and sold in the study year
Over-Qualification: $65M/year
~$3/Barrel of Product
The purpose for estimating the quality giveaway is to show:
how less optimally the components were produced at a potentially higher cost.
the potential room for reducing the component quality while still meeting the
same demand.
Actu
al
Optim
al
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Plantwide Optimization - conclusions
• The planning model is fleshed out with dynamics and used in closed-loop control
• This Profit Executive solution provides better:
- Scalability – Suitable for different plant size, small or large
- Operability – Decentralized control is retained while providing centralized, Plantwide optimization
- Real-time responsiveness – The master runs like a regular MPC controller
• Benefits of better closed-loop Plantwide control
- Refineries can give away a significant amount of product quality due to inadequate closed loop control – ~$65M/year (~$3/barrel)
- The Profit Executive solution can capture ~$22M/year (~$1/barrel) without changing the product orders.