MAXIMIZE CONTROL & OPTIMIZATION ROI - Honeywell · Perry Nordh, P.Eng. MAXIMIZE CONTROL &...

Profit SuiteMAXIMIZE CONTROL & OPTIMIZATION ROIPerry Nordh, P.Eng.

19-June-2017

Honeywell Internal

Honeywell Confidential - © 2017 by Honeywell International Inc. All rights reserved.

Senior VP – Major Oil and Gas Company

1

“I was naive and thought we had a very well run and optimized

business. Some room for improvement maybe, but not huge - or

that would mean I wasn't doing my job well.

Once we dug for data, we found out that the prize was much bigger

than I thought.”

Senior Leadership Perceptions…

“The biggest mistake we’ve seen is the

assumption lot of company leaders have

that they have made such massive

investments in IT that they must have

optimization all over the place”


Improved Profit by

Changing TargetsImproved ControlPoor Control

$

Profit

Specification Limit

MPC Improves Process Stability and pushes the

process to the most profitable operating point

Return on Investment: MPC and Optimization


PID control versus MPC

3

FC

• PID Control (feedback controller)‒ Single Input / Single Output control

‒ Controls to a setpoint

‒ Does not handle interactions well

‒ Little/no optimization

‒ Customisation needed for complex control

‒ Corrects for current error only

‒ (requires an error before it responds)

‒ Like driving while looking in the rearview mirror!

SP

PVOP

• Advanced Regulatory Control (ARC)‒ Typically control that goes beyond Basic PID control

‒ But typically done within the confines of the control

system.

‒ Single Input / Single Output control

‒ Feedforward, Lead/Lag, Decoupling, etc.

‒ Little/no optimization

DV

Corrects for current error only

TC


PID control versus MPC

4

• Multivariable Predictive Control‒ Application residing internal or external to DCS

‒ Contains a model of the process

‒ Controls within a range

‒ Multi-input / Multi-output control

‒ Little / no customization needed

‒ Optimization / constraint handling built-in

‒ Corrects for future errors

‒ Like driving – looking through the windshield

MPC

CV1

CV2

CVn

.

.

.

MV1

MV2

MVm

.

.

.

DV1 DV2

. . .

DVl


Objective: More than just Value Generation

• Even the best operator…can NOT:

- Monitor all operating envelops & change SP each minute to optimize

- Manage disturbances (i.e. rate or composition changes) proactively

• Highest level of MPC… CAN

- Manage changing economics

- Operate consistently & optimally

- Manage disturbances and reduce operator changes

- Improve stability

- …

- Capture “best” operating practice

- Stretch your existing infrastructure

5


Eliminate Process Variability

• Plants with Best Process Control – Have Fewer Slowdowns and Shutdowns

6

Correlation Between Uptime and MPC Implemented2

Analyze constraints and operate within them

Reduces operator manipulation by 80%

2. Birchfield, G., Solomon Associates – “Olefin Plant Reliability”

95%

65%

55%

15%

Plant Uptime

Plant


Why don’t we have MPC everywhere?

7


Agenda

- Why don’t we have MPC everywhere?

- Addressing challenges with Profit Control and Optimization – sustaining ROI

Platforms – now with Experion Profit Controller

Design – new tools to streamline and speed projects

Adding process intelligence with UOP

- Finding more ROI with Profit Suite

Profit Optimization

Profit Opportunities

- What is Honeywell doing differently? Why?

Proactive Monitoring and Honeywell Connected Plant


Sustainment Challenges

• Operator Acceptance

- Operating Philosophy

- Operating Envelopes

• Operations conservatism

• Change Management

• Instrument reliability

9


How can Honeywell technology help?

• Technology inherently addresses the Deploy / Sustain challenges

customers are facing – it lends itself to minimizing lifecycle costs.

• Technology specifically built to reduce implementation effort and lifecycle

burden of profit-generating control applications.

• Enables a lifecycle support program instead of providing a box of tools that

the user must figure out how to configure/use.

• Every development of an extended technology or new tool has a goal to

minimize the lifecycle cost.


Profit Controller

Model-Based Predictive Control

Inherently More Stable

Minimum MV move – no overcorrections

Optimal response trajectories

Less sensitive to model error

Built in robustness and stability with

On-line Dynamic matrix conditioning protection

Easier to Tune

“One-knob” tuning

No need for move suppression

Independent CV by CV tuning (MVs too)

>90% of applications work with default tuning

Easier to Commission and keep running

Fully supports closed-loop identification

Warm mode enables validation

Predicted Unforced Response

Minimum Effort Move

Past Future

Assumed Values

CVPredicted Unforced Response

MV

T=0

Control Funnel

Optimal Response

Setpoint


Profit Optimization Suite

Consistent Technology – Multiple Platforms - MPC and Dynamic Nonlinear 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 Online Modeler

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)


Experion Profit Controller (CEE – C300 or ACE node) vs

Experion Application Server (URT) Matrix

Function / Benefit Value URT C300 ACE

Redundancy (or Increased Robustness) ❌ ✅ 🔶

Fast Execution Speed 🔶 ✅ 🔶

Reduced Infrastructure (…and therefore Cost) 🔶 ✅ 🔶

CV & MV Transformations ✅ ❌ ❌

Open & Closed Loop Step Testing (Profit Stepper) ✅ ✅ ✅

On-Line Matrix Conditioning ✅ ✅ ✅

One Knob Tuning ✅ ✅ ✅

Optimization (Across Multiple Units or Site-wide) ✅ 🔶* 🔶*

Virtual Sensors (Profit Sensor Pro) ✅ 🔶* 🔶*

Operator Guidance ✅ ✅ ✅

✅ - Good Fit 🔶 - Potential Fit ❌ - Poor Fit

* - Requires EAS/URT based Supervisory Profit Controller


Profit Suite R440

14

Addressing challenges with Profit Control and Optimization – sustaining ROI

Design – new tools to streamline and speed projects

Adding process intelligence with UOP


Profit Suite R440 – Final Scope

• Platform & Infrastructure Compliance

- 64 bit only – follows Experion specifications including support for

Windows Server 2012, 2016, Windows 10

- Upgrading PHD to R321, UPS to R321

- Allow for custom install path

• Honeywell User Experience

- CAB Prioritized enhancements (Stepper, Profit Suite Engineering Studio: back-build)

- Profit Web Viewer

- Profit Suite Operator Station: user configurable colors

- MV Transformations

- Models from historical data in Profit Suite Engineering Studio

• Process Intelligence: UOP Synergy

- Hydroprocessing (hydrocracking) and Oleflex (C4) optimization

• Profit Optimizer Enhancements

- Phase 1 APC Vendor Agnostic parameter mapping

- Optimization engine interior point enhancements TSQP


Faster Setup with Profit Web Viewer

• HTML5 based visualization using Internet Explorer or Chrome web browser

16


Minimize Plant Tests with History ID and Online Modeling

• Automatic segment selection after removal of bad segments

• Checks for controllers in Manual

• Can handle large data as ID does not use all data (~ 6 months)

• Integrated with profit controller PSES infrastructure

- Calculations

- Transformations

- Merge models

- Build controller (xm, xs, xp)

- Simulation

• Identify individual CVs in history ID

nodes and assemble complete model

in Merge model

• Use SEED models and go direct

to online closed loop modeling

17


Manage Nonlinearities with MV Transformations

• Nonlinear transformations

- Extension of the nonlinear transformations to MVs

- Allows Profit Controller to act on a transformed MV whereas the operator sees the real world

values

• This is particularly useful when trying to linearize valve curves.

18


Manage Change with Auto Documentation & PSES Backbuild

• Often running platforms are adjusted/modified

- The variable combinations or logic block calculations which have been designed with PSES are

adjusted by the user in URT

- The changes can be done in the equation or connection or the value of coefficients

- These calculation changes can now be read back into PSES to update the original calculations

and be fully documented with Auto-Documentation in PSES

19


Adding Process Intelligence with UOP

Startup Your Facility Sooner

Reach Target Production Faster

Operate at Peak Performance

UOP Critical Control Applications

Experion® Solution Suites

DynAMo® Process Solution Suites

Profit® Process Solution Suites

UniSim® Process Solution Suites

Modular Automation

Connected Performance Services (CPS)

Field Instrumentation/ Modular Design

20


Adding Process Intelligence with UOP

21

• What is it?- Pre-packaged MPC applications for key UOP units

Best practice from HPS , Expert review and input from UOP,

UOP toolkits as appropriate

- MPC for UOP packages are currently available for:

CCR Platforming, Oleflex

• What is the Value Proposition?- Best in class knowhow from both UOP and HPS

- The inferential calculations enable better estimation of key

variables that are either:

Difficult to measure (e.g., tube wall temperature in furnaces)

Difficult to measure online(e.g., octane or aromatics content))

- Allowing the customer to push their units to their

constraints increasing throughput or yield

- Empirical model gains of the MPC for UOP models can be

kept up to date using the rigorous non-linear optimization

models from UOP CPS/Optimization Advisor


Profit Suite R440

22

Finding more ROI with Profit Suite

Profit Optimization



Truths of Real World Economic Optimization

• No model exactly predicts plant behavior all the time

- Many relationships aren’t measured so can’t be modeled

- Feedback is used to capture missing relationships

• Optimization model requirements differ from control

- Gain needs to be more accurate

- Dynamics are less important

- Non-linear process behavior needs to be accounted for

• All optimizers:

- Use steady state models

- Solve for steady state solution

- Generate steady state “optimal” operating targets

- Require a delivery mechanism to move process

• Value comes from moving the process

- Generating a solution is not enough

- Money is only made after solution is implemented

23


Introduction to Time-Sequenced Quadratic Programming (TSQP)

• Based on the dynamic QP formulation in Profit Optimizer and Profit Controller, Honeywell

has developed a patented technology that extends this to dynamic nonlinear optimization

• Status of SS RTO and its implementation challenges

- SS RTO has been in existence for more than 30 years

- Two heterogeneous solutions – MPC is dynamic, but RTO is steady state.

Two sides have never been seamlessly integrated.

- MPC executes typically once every minute, but steady state RTO cannot execute until the underlying process units all

reach a steady state.

When disturbances come in, which is when a re-optimization is needed the most, an RTO solution must however wait

for a steady state before it can perform any optimization.

• Patented Time-Sequenced Quadratic Programing (TSQP) solves this dichotomy

- It modifies the standard SQP algorithm (used in RTOs) into a TSQP algorithm;

- Nonlinear optimization is formulated within an MPC framework, which

eliminates the burden of having to wait for the steady-state, and

enables the nonlinear optimization to be executed in tandem with the MPC controller(s)

- The optimization result is implemented by the MPCs on a minute to minute basis.


MPC (data-driven) Models SS RTO (first-principle) Models

MVs/DVs CVs Independent Variables Dependent Variables

Shared

Variables

• MVs for each unit

• DVs for each unit

(e.g., connections to

and from other units)

• CVs for each unit

• Some CVs between

the units

• Independent variables

that impact profitability

needle;

• Dependent variables

that represent process

capability

• Unit Feed Rates (as

MVs or DVs)

• Feed Rates always as

optimizing variables

Control-only

Variables

• Transiently changing variables can be modeled

well with the aid of dynamic bias-updating

• These variables must be included in order for

the optimizer to guarantee the APC-feasibility of

its solutions.

• Difficult to model accurately (these variables need to

be dynamically bias-corrected)

• Often glossed over or not modeled -- Examples:

flooding, valve OP, suction pressure,

hydraulic or fluid dynamic constraints

Comments:

• First-principle modeling approach is designed to model the process nonlinearities cost-

effectively (without having to deal with the underlying dynamics)

• Data-driven modeling approach is designed to model APC dynamics cost-effectively

(without having to deal with the embedded nonlinearity)

• TSQP has an unique technical advantage of being able to combine the strength of both

modeling approaches on-the-fly, possibly even within the iterations of TSQP algo.

TSQP combines the strengths of SS RTO and APC!


Pursue MORE Profit without building another unit

Webinar series to further explain:

Think Bigger: consider a larger scope and focus on the final product

quality

Keep it Real: dealing with the reality of nonlinearities and operational

challenges

Keep it Coming: Calculation and Sustainment of benefits

The Math behind the Moves: the math that makes optimization

feasible

26


Profit Opportunities…

27


The 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


The 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


Can we bridge the gap between planning/scheduling and control?

Customer Demand

(downstream

supply chain)

Supply Side

(upstream

supply chain)

Raw Materials Execution Products

month ahead days ahead hours ahead now hours ahead days ahead months ahead

Monthly Planning

Rea

l P

lan

t

M

od

els

/Pre

dic

tio

ns

What Feed to buy? What products to sell?

Daily production

average

Daily delivery averageGenerate a month-plan

based on an average model

Product to

be ShippedFeed Received

What Feed to use? What products to deliver?

Translate the plan into production

activities to satisfy the plant logistics

Weekly Schedule

Not effective in

dealing with model

errors and

unplanned events.

More effective in

dealing with model

errors and unplanned

events (as a control

problem)

Optimizers

Control and Optimize to the targets but only on

a “best efforts” basis

Controllers

A set of production activities

(e.g., receipts & blending

activities)

Blending/shipping activities


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


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


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


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



Using real operating data, Honeywell determined that the refiner could save million of

dollars from:

• Feed cost reduction – using cheaper crudes.

• Yield improvements – optimal routing

• Better tradeoff between distillate and naphthas

• Reduce property giveaways

• Better coordination of production and blending

• Reduce overall energy and chemical usage

• Prolonging catalyst life and reduced hydrogen usage


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

Giveaway Estimation

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.


Profit Suite Roadmap

37

What is Honeywell doing differently? Why?

Proactive Monitoring and Honeywell Connected Plant


Profit Control and Optimization Roadmap

38

Control Performance Monitor R600

• Next generation Diagnostic workflow

- Tune/Fix/Investigate (Expert Guidance)

- Completely updated user centered reporting views

• Intuition platform

- Coexistence with DynAMo

- Improved security/user management

Profit Optimization Suite R440

- Optimization engine enhancements

- UOP Oleflex and Hydroprocessing

toolkits

- MV Transforms

- Profit Web Viewer

- Models from Historical Data

CPM R610

- Next generation MPC monitoring

- Optimization monitoring

- Integration with Intuition alerting (Pulse)

Profit R500

- Planning integration

- Enhanced Optimization

- Operator Guidance

- Nonlinear constraints

- Engineering AppStore

- Process Monitoring/modeling with

what-if and support capabilities

- UOP toolkit enhancements

Experion Profit Controller

- Profit Controller in CEE (C300/ACE)

- Part of Experion R500, includes

design and visualization


R441

- Enhancements for

optimization

- Phase 2 APC Agnostic

Multi-Unit Dynamic

optimization

- Proxy Limits

- Extend Profit Web Viewer

- Profit Applications

- Variable combinations

naming & chaining

CPM R601

- Enhanced cross correlation

- Improved displays/trending

- Revised Custom Templates

- Runtime license update


R442

- Process Monitoring and

data analytics

- Extend thin client

- Profit Applications

- L4

- Runtime license update


Profit Suite R500 – Proposed Scope

• Platform & Infrastructure Compliance

- OPC-UA

• Optimization Enhancements

- Operator Guidance for optimization

- Hessian updating – nonlinear constraints

- Profit Executive – Master Controller

• UOP Synergy and toolkits

- Hydroprocessing (hydrotreating) and Oleflex (C3, C3/C4) toolkits

• Engineering APP store

- Downloadable starting points for projects/calculations

• Honeywell Connected Plant: Profit Performance Monitor

- Digital Twin & Shadow Controller in the cloud

What-if analysis

Support/diagnostics

- Enhance Process modeling/monitoring/machine learning


Leveraging Data to Drive Process Performance

40

Challenges

Multiple, inconsistent data sources.

Heavy dependence on spreadsheets and

increasingly scarce skills.

Little use of modern analytical tools as focus is

on historical analysis, not prediction.

Limited embedded intelligence and learning.

Consequences

Decisions are slow and not the best.


Solution: Honeywell Connected Plant

41

Decisions & Actions

Analytics Based Insight at Enterprise Level

Digital Twin ofProcesses & Assets

Cyber Secured Data Stream

Disparate Data Across Enterprise

Plant Availability & Optimization

Equipment Availability Utilizing Partnerships

1

OEM Partner Ecosystem

UOP Process Specialist

2

34

5

6

ERP,Historian,

etc.

Asset Data

Process Data

Sensors

Connected Plant

Plant and Enterprise

Level

Customer SME

Optimize

Monitor

Troubleshoot

What if?

Planning Vectors

Benchmark (future)


Refinery

Crude

FCCU

Honeywell | CAM Process Engineer3

Profit Suite Operation Performance

Profit Web Viewer

Key Process Variables

Feed

View All

GO Flowrate

Naphtha Flowrate

Energy

Avg. Gap

17%

20%

33%

27%

01 Apr 2017 – 07 Apr

2017

S M T W T F S

Long Term Performance JAN-FEB-MAR-PRESENT

Compare

from:

2

1

Ja

nFeb Current

MonthWee

kYea

r

3

An

nu

al V

alu

e $

M

APC Off

M

ar

Start

Date

Equipment Limit Protection View All01 Apr 2017 – 07 Apr

2017Relative shift performance

100

75

50

25

Shift-A Shift-B Shift-C Shift-D

Metrics SelectWee

k

Day

Pending Action NotesSoft Sensor Performance

Lost Opportunity Cost of Constraints

Fuel Gas Pressure

Chg. Heater COT

Over flash flow

Naphtha 95%

Splitter reflux0 25 50 75 100

Limit 6 psi <benchmark

Limit 5 °F < benchmark

Variance 1.5 x benchmark

Soft sensor performance ± 4.2 °f

Valve in manual (long term)

10 20 30 40 50 60 70 80 90 100

Naphtha D86 90% Pnt.

Jet Flash Pnt.

Jet D86 90% Pnt.

LGO Flash Pnt.

LGO D86 90% Pnt.

APC Off

LE Compressor surge margin

Chg. Heater O2

Chg. Heater Skin Temp.

20

1. Review drop in unit profitability and feedrate decrease with Tammi.

Specific reference to why fuel gas pressure limit is a lost

opportunity.

2. Review status of C103 (Splitter) reflux valve maintenance.

3. Review with shift B training with Tammi and Frank B.

4. Clarify COT upper limit with Rod.

Profit Loss $K / yr

Indications on

the operations

monitoring

dashboard

show a

reduction in

financial

performance

AND lost

opportunity…

Sustaining the Profit Opportunity


Drill Down From Lost Opportunity

43

Preheat Temp

Fired Heater Variables

Fuel Gas Pressure

Heater Outlet (COT)

01 Apr 2017 – 07 Apr 2017

S M T W T F S

Heater Inlet Temperature

F101 Fuel Gas (Burner) Pressure

Limit Controller Reference Units

High 36.0 40.0 psig

Low 3.0 2.0 psig

Profit Loss $K / yr

Lost opportunity identified with the charge

heater not firing as hard as expected,

resulting in reduced plant capacity. Cost is

$75k/year.


Digital Twin

44

Experience: Detailed first

principle model for entire plant

or complex across plant life

Empirical: APC model

generated from plant data or

control models

Equipment: Library - first

principle; regressed; rule-

based

Types of Digital Twins

Augmented with process and

control knowhow


Why Is Honeywell Different?

45

Data

Not just a “DIY”

platform and toolbox

Embedded models.

Embedded knowledge.

Results

Solve real-world

problems to deliver

improvements fast

Not just “data scientists”

keen to “have a go”

Leverages extensive

experience along with

state of the art analytics.

Analysis


What is the difference between CPS and APC?

• Strategic decisions

- Open Loop

- Rigorous UOP UniSim models (first principles)

- Updates several times a day

- UOP Process Expert consultation

- Catalyst life versus throughput maximization

• Operational decisions

- Closed Loop

- UOP Toolkits (correlations from UOP rigorous models)

- Execution 10sec-5 mins (nominal ~1min)

- Respond to instantaneous changes in raw materials,

utilities, ambient, catalyst and equipment condition

CPS

APC

MAXIMIZE CONTROL & OPTIMIZATION ROI - Honeywell · Perry Nordh, P.Eng. MAXIMIZE CONTROL &...

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Transcript of MAXIMIZE CONTROL & OPTIMIZATION ROI - Honeywell · Perry Nordh, P.Eng. MAXIMIZE CONTROL &...