ensuring apc optimization with profit suite
TRANSCRIPT
2015 Honeywell Users Group
Europe, Middle East and Africa
Ensuring APC Optimization with Profit Suite
Richard Salliss, Honeywell
2 © 2015 Honeywell International All Rights Reserved
Profit Optimization Suite Overview
Profit Optimization Suite Roadmap
How is Honeywell advancing optimization?
The UOP connection…
Optimization Case Studies
Agenda
1
2
3
4
5
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Profit Optimization Suite
Profit Controller
Minimum Effort Move
Past Future
Assumed Values
CV
Predicted
Unforced
Response
MV
Control Funnel
Optimal Response
Setpoint
Profit Sensor Pro
Profit Stepper
*Optimum
Profit Optimizer: Real-time, Dynamic
Optimization
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 Loop Experion
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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• CAB Prioritized enhancements
• Profit Suite Engineering Studio ‒ Profit Optimizer integration
‒ Enhanced DMCplus conversion tools
‒ Support for Predict Back
• Updated CV Move Function Block (account for
subcontrollers)
Profit Suite R431 – Proposed Scope
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• Platform & Infrastructure Compliance
• Profit Optimizer Enhancements ‒ APC Vendor Agnostic
‒ Operator Guidance for optimization
• Honeywell User Experience ‒ Engineer/Operator Feedback
‒ CAB Prioritized enhancements
• APC monitoring / benefits sustainment improvement ‒ Improved support for BGP Performa
‒ Mobility and alignment with CPM and BGP Performa
• Operator Training Tools ‒ Automate/simplify model builds for USO training
infrastructure
Profit Suite R440 – Proposed Scope
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• Most APC applications have two components ‒ Constraint control
‒ Optimization
• Key to successful application ‒ Design for responsive control action
‒ Use optimizer to take economic advantage of degrees of freedom
• Small scope applications are straight forward ‒ Distillation column, reactor, furnace
‒ Good control and Opt scope inherent
‒ Few design issues
• Large scope Applications are more challenging ‒ Multiple columns, Multiple equipment processing unit
‒ Trade off typically requires choosing either good control or optimization scope
Optimization – Control Engineer’s Dilemma
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SS Controller Targets Feasible SS Controller Targets
SS
Model
• 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
Traditional Optimization Overview
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 Optimization Suite provides best of both big and
multiple small applications ‒ Model pairing for good local control
‒ Global objective function to fully utilize degrees of freedom
‒ Reduce impact of week and less certain models
• A robust solution ‒ Flexes with changing process conditions
Accepts what can realistically be achieved and rejects what can’t
‒ Ensures feasible optimization path across entire process
scope
• Easy-to-use optimization/coordination solution ‒ Accommodates both SS and dynamic optimization schemes
Solving The Dilemma
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Honeywell’s Layered Optimization
• Dynamic application
• Multi-unit optimization
• Non-linear optimization
Profit Optimizer coordinates
the economics of multiple
Profit Controllers for multi-unit
plant-wide optimization
Profit
Controller
Profit
Controller
Profit Optimizer
Profit
Controller
Economics and Ideal
Target Values
Disturbance
Values
Control
Variables
Manipulated
Variables
Product Value
Optimization
RMPCT
Algorithm
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Profit Optimizer
Profit Optimizer
Profit Controller 1
Global Economics and Ranges
SS MV Targets
Gains
MV Economics
Process 1
Profit Controller 3
Process 3
Profit Controller 2
Process 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
Interface block
Other Controller 2
Ensures Optimal Dynamic Global Path
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Multi-Unit Dynamic Global Coordination
$
Cont
50%
$
Cont
30%
$
Cont
20%
Dynamic Paths
Dynamic Paths
20-30% additional benefits over unit-based optimization
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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
6 5
4 3 2
7
• Profit Optimizer will be enhanced to include ‘Hessian
Updating’ which will ‒ allow optimizer to use
‘rate-of-change’ information
compared to simple gain updating
‒ allow faster convergence on the
optimum solution and improved
ability to deal with process
non-linearities
‒ provide additional benefits of around 10%, depending on
process non-linearities
• Profit Optimizer will be deployable across all major
APC solutions on the market, not just Profit Controller
R440 Optimization Enhancements
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Plant-wide Optimization
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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Stra
igh
t-Ru
n a
nd
/or B
len
din
g
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
Plant-wide Optimization
Refinery Model – An APC View
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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
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
Refinery Model – A Planner’s View
Plant-wide Optimization
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App 1-1
App 1-2
App 1-3
App 1-n
DQP -1
App 2 -1
App w -1
App m-1
App m-p
DQP -m
A copy of yield model
+ economics from
planning/scheduling
Master Optimizer
Master MPC Controller
SDQP
Returned values can
include the predicted
yields and proxy limits
Manage intermediate and final:
• Inventory (volumes)
• Properties (quality)
• Timeline (just in time)
Manage:
• Unit operation
• Limits
• Constraints
Profit Executive is the “Integration Scheme”
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Profit Executive Components
Planning Execution
Optimizer What-If steady state analysis using
current operating conditions
Controller Dynamic operating plan moves provided
to simulator
Optimizer Generates optimal steady state operating
plan based on actual costs
Controller Moves plant toward optimal while
accounting for immediate constraints
Predictor Computes yield/property model biases
using operating data
APC Extensions Bounds optimization problem based on
APC constraints
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UOP-HPS Integrated Solutions for APC Process Models Example - Hydrocracking
Performance
Calculations
System data Process
models
• Mass balance
• Catalyst space velocity
• Catalyst life
• Catalyst average bed
temperatures
• %HDS, %HDN, gross and net
conversion
• H2 consumption
• H2 partial pressure
• As-produced product yields
• Product yields at standard cut
points
• Product TBP cut points
• Process data
• Lab data
• Online analyzers
• Yield
• Activity
• Deactivation
• Product quality
Economically optimize
manufacturing complex:
• Unit product rates
• Unit product qualities
• Provides operating
responses for optimum
Practical constraints:
• Catalyst temperatures
• Equipment design
• Cycle length
System
Constraints
Leverages process expertise for attainable optima
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Optimization Case Studies
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• Profit Optimizer coordinates Profit Controllers (APC controllers) to
maximize profit by solving collaborative optimization problem
• Scope: coordinate Profit Controllers of FCC and HFA units
• Optimization task: maximize most valuable product yield for different
scenarios (gasoline / alkylate / propylene) with compliance to each
Profit Controller constraints
Multi-unit Optimization Case Study
Profit Controller-1
Profit Optimizer (DQP)
Profit Controller-2 Profit Controller-9
Production
plan
Optimization
target
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FCC Reactor Temperature
Gasoline Butane-
butene Gasoline Propylene Profit
Date
Multi-unit Optimization Case Study
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Optimization scheme
Results (change of yield %
to FCCU feed) Comments
Maximize gasoline yield
+0.6% gasoline
–0.2% propylene
–0.3% alkylate
Constrains: propylene yield, butan-butylene yield,
gasoline RVP, gasoline RON, olefins in butan-
butylene, min T of reactor
Maximize propylene yield
+0.15% propylene
+0.05 % alkylate
–0.1% gasoline
Constrains: gasoline yield, max T of reactor, gas
plant capacity
Maximize alkylate yield
+0.4% alkylate
–0.6% gasoline
propylene – no change
Constrains: gasoline yield, max T of FCC reactor,
max T of HFA reactor, alkylation capacity
Net profit maximization 78 000 Rubbles per day,
(= 28 mln. Rubbles per year) Constrains: all above
Multi-unit Optimization Case Study
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Naphtha
Crude Light Distillate VGO Purchase - Column V
Unit C3/C4 to Poly/Alky
Heavy Distillate
A Vacuum LVGOl Gasoline
Light Gasoil to FCC Tower FCC
HVGO LCO
Heavy Gasoil to FCC A
Vacuum Bottoms to FCC HCO
Atmos Bottoms
Atmos Bottoms to FCC Slurry
Naphtha LCO from FCC
Crude
Unit Distillate Naphtha
Diesel
B HTR
Diesel
Atmos Bottoms Cloud = -5 ~-25C Blending
Sulfur = 0~10ppm
Final Product
Naphtha
Naphtha
Crude Light Distillate Diesel Cloud = -45 ~ -48C
Unit Unifiner Sulfur = 4-6ppm
Heavy Distillate
C
Atmos Gasoil
Debut Overhead
Light Naphtha Debutanizer
Hydro Heavy Naphtha
Atmos Bottoms Vacuum Light Vacuum Gasoil Cracker
Tower Light Distillate
C Heavy Vacuum Gasoil
Heavy Distillate (to FCC)
Vacuum Tower Bottoms Bottoms
HCO from FCC
Component
Tank A
Tank B
Component
FeedTanks 3
Fd tank 2
Feed
Tank 2
4%
1%
20%
10%
Gate-to-gate optimization of oil refinery distillate production
Plant-wide Optimization Case Study
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0 50 100 150 200 250 300 350 400-5
-4
-3
-2
-1
0
1
2
3
4Incremental Changes in Crude Unit Feed Rate
Crude Unit 1 deltaFeed
Crude Unit 2 deltaFeed
Crude Unit 3 deltaFeed
Combined Crude deltaFeed
0 50 100 150 200 250 300 350 400-5
-4
-3
-2
-1
0
1
2
3
4Incremental Changes in Crude Unit Feed Rate
Crude Unit 1 deltaFeed
Crude Unit 2 deltaFeed
Crude Unit 3 deltaFeed
Combined Crude deltaFeed
Average crude
saving is ~ 0.5%
for meeting the
same product
demand.
This is worth
$22M/year
Plant-wide Optimization Case Study
Feed Cost Saving from Optimizing the Volumetric Gains
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Pursue Profit with Honeywell’s Profit Suite
Unified Technology Single platform from Experion-embedded
to plant-wide control & optimization
Faster Realization
of Benefits Designed for minimal effort to
achieve the first Euro of
benefits and build to larger
benefits as ROI is justified
More Benefits Over Time The most comprehensive offering to
transform business needs and objectives
into real-time operations
Flexible
Licensing Honeywell’s flexible approach
to software licensing ensures
maximum returns and
protection of your investment
Layered Optimization Honeywell’s unique solution for
leveraging existing models and
increasing operator effectiveness
while driving max benefits
through large-scale
optimization
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www.honeywell.com/ps
Questions?