PROFIT SUITE INNOVATIONS FOR NEXT-GENERATION APC · PDF filePROFIT SUITE INNOVATIONS FOR NEXT-GENERATION APC ... •Gain scheduling for non-linear ... Profit Controller 2 Profit Optimizer

PROFIT SUITE INNOVATIONS

FOR NEXT-GENERATION APC

AND REAL-TIME OPTIMIZATION

Richard Salliss

October 2016

Richard Salliss

25 Oct 2016

Honeywell Proprietary - © 2016 by Honeywell International Inc. All rights reserved.

• 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


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)


Profit Suite R440 – Final Scope

• Profit Optimizer Enhancements

- Phase 1 APC Vendor Agnostic parameter mapping

- Optimization engine interior point enhancements

• UOP Synergy and toolkits

- Hydroprocessing (hydrocracking) and Oleflex (C4) toolkits

• Honeywell User Experience

- PSOS Thin Client and user configurable colors

- CAB Prioritized enhancements (Stepper, PSES backbuild)

- MV Transformations

- Models from historical data in PSES

• Platform & Infrastructure Compliance

- 64 bit only and include support for

Windows Server 2012, 2016

Windows 10

- Upgrading PHD to R321, UPS to R321

- Allow for custom install path


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


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


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



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



MVs

CVs

y y y y y y yOptimization

Constraints

• Optimization CVs - multi-unit steady state CVs (no feed back).

CV x x x


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


Refinery PO Structure Example

PC1

PC2

PC6

PC7

PC5

PC4

PC8

PC3

PC9

PC10

PC11

PC12

PC13

PC

14

PC

15

CC /

aux

Legend:Optimization constraints

Bridge model

Source / clone


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


-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 up to 30%

(depending on process non-linearities)

R440 Optimization Enhancements

*Optimum


Non Linear Optimization Background

• The low hanging fruits have already been taken

- Embedded optimization (e.g., PVOs) within PC and PO

• Remaining opportunities may require nonlinear optimization

- SS RTO is an option, but

Initial and maintenance costs are typically high

It requires a higher level of expertise to implement/maintain

- Honeywell offers a more cost-effective approach

Similar to the existing PO/PC optimization scheme

It allows nonlinear objective functions

Nonlinear constraints are handled at controller level via gain-updating

• Honeywell nonlinear dynamic optimization approach:

- Use nonlinear models where they make the difference and are cost-

effective

- Expand frequent gain updating to frequent Hessian updating

- Use a Time-Sequenced QP algorithm to provide SS solution projections


Starting Point

x2

x1

P-Optimal Point

)x-(1)x-100(x f(x) 2

1

22

12

Rosenbrock's

Banana Function:

• 99+% of nonlinear optimization problems in process industries are

solved by sequential quadratic programing (SQP or sequential QP)

• How does sequential QP work?

- Here is an animated illustration:

Nonlinear Optimization in a Nutshell


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!

HW Approach: modify SQP for real-time use

Update the objective

function, model gains,

future predictions and

constraints at a user-

specified frequency

(or condition)

Actual Optimal

P-Optimal Point


TSQP Versus Gain Updating

17

-5 -4 -3 -2 -1 0 1 2 3 4 5-5

-4

-3

-2

-1

0

1

2

3

4

5

x1

x2Minimization of the JAE function

Start Point

Solution

2

1

01

2

3

4

5

0

6

7

8

3

Starting Point

1

6

5

4

3 2

7

1

6 5

4

3

2

7

0


TSQP Versus SSRTO Ideal World

-5 -4 -3 -2 -1 0 1 2 3 4 5-5

-4

-3

-2

-1

0

1

2

3

4

5

x1

x2Minimization of the JAE function

Start Point

SolutionStarting Point


TSQP Versus SSRTO Real World

Time

Optimum

Operation

Current

Operation

Profit

NLDO TSQP

Path

SSRTO Path

$ Benefits $


Naphtha

Crude Light Distillate VGO Purchase - Column V

Unit C3/C4 to Poly/Alky

A Vacuum LVGOl Gasoline

Light Gasoil to FCC Tower FCC

HVGO LCO

Heavy Gasoil to FCC A

Vacuum Bottoms to FCC Volumetric HCO

Atmos Bottoms Gain

~111%

Atmos Bottoms to FCC Slurry

Naphtha LCO from FCC

Crude

Unit Heavy Distillate Naphtha

Diesel

B HTR

Diesel

Atmos Bottoms Volumetric Cloud = -5 ~-25C Blending

Gain Sulfur = 0~10ppm

104% Final Product

Naphtha

Naphtha

Crude Light Distillate Diesel Cloud = -45 ~ -48C

Unit Unifiner Sulfur = 4-6ppm

Heavy Distillate

C Volumetric

Atmos Gasoil Gain

101% 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)

Volumetric

Vacuum Tower Bottoms Gain Bottoms

HCO from FCC 120%

Component

Tank A

Tank B

Component

FeedTanks 3

Fd tank 2

Feed

Tank 2

Opportunities for nonlinear optimization (1)

• Multiple feed choices for a unit

Swing Cut

Purchased VGO

Crude

Blending

Crude

Blending

Crude

Blending


Multiple feed choices for a unit

A

B

Products

A unit (or a pool of units)

0 1

A/B Ratio

Profit

1) linearly increasing

0 1

A/B Ratio

Profit

n) linearly decreasing

0 1

A/B Ratio

Profit

2) humpback

0 1

A/B Ratio

Profit

3) camelback

• • •

Profit Curve Shape Categories:

Nonlinear

Feed Ratio

(A/B)


Naphtha

Crude Light Distillate VGO Purchase - Column V

Unit C3/C4 to Poly/Alky

A Vacuum LVGOl Gasoline

Light Gasoil to FCC Tower FCC

HVGO LCO

Heavy Gasoil to FCC A

Vacuum Bottoms to FCC Volumetric HCO

Atmos Bottoms Gain

~111%

Atmos Bottoms to FCC Slurry

Naphtha LCO from FCC

Crude

Unit Heavy Distillate Naphtha

Diesel

B HTR

Diesel

Atmos Bottoms Volumetric Cloud = -5 ~-25C Blending

Gain Sulfur = 0~10ppm

104% Final Product

Naphtha

Naphtha

Crude Light Distillate Diesel Cloud = -45 ~ -48C

Unit Unifiner Sulfur = 4-6ppm

Heavy Distillate

C Volumetric

Atmos Gasoil Gain

101% 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)

Volumetric

Vacuum Tower Bottoms Gain Bottoms

HCO from FCC 120%

Component

Tank A

Tank B

Component

FeedTanks 3

Fd tank 2

Feed

Tank 2

Opportunities for nonlinear optimization (2)

• Pooled product streams – nonlinear property blending


Opportunities for nonlinear optimization

• Nonlinear process yield relationship

Products

A reactive unit

Feed

Product Value Optimization:

However,

Thus,

iiii feedtproductpriceObjFun cos

),...,( etc convfproducti

appears linear

nonlinear function

)( variables operatingunNonlinearFObjFun


Future development: Profit Executive

Plantwide economics

Local economics

Optimal feasible

Control & Optimization

Schedule & Optimization

Production Planning

Business Planning

Planning

(Months)

Scheduling (Day/Weeks)

Profit Executive

App -1 App -2 App -3 App -n

Real Time Dynamic Optimization - Profit Optimizer (hr)

Key Challenge: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)


Pursue Profit with Honeywell’s Profit Suite

Unified TechnologySingle platform from Experion-embedded

to plant-wide control & optimization

Faster Realization

of BenefitsDesigned for minimal effort to

achieve the first Euro of

benefits and build to larger

benefits as ROI is justified

More Benefits Over TimeThe most comprehensive offering to

transform business needs and objectives

into real-time operations

Flexible LicensingHoneywell’s flexible approach

to software licensing ensures

maximum returns and

protection of your investment

Layered OptimizationHoneywell’s unique solution for

leveraging existing models and

increasing operator effectiveness

while driving max benefits

through large-scale

optimization

Documents

PROFIT SUITE INNOVATIONS FOR NEXT-GENERATION APC · PDF filePROFIT SUITE INNOVATIONS FOR NEXT-GENERATION APC ... •Gain scheduling for non-linear ... Profit Controller 2 Profit Optimizer