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Opportunity Assessment

andAdvanced Control

GREGORY K MCMILLAN

use pure black and white option for printing copies

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Presenter

– Greg is a retired Senior Fellow from Solutia Inc. During his 33 year career with Monsanto Company and its spin off Solutia Inc, he specialized in modeling and control. Greg received the ISA “Kermit Fischer Environmental” Award for pH control in 1991, the Control Magazine “Engineer of the Year” Award for the Process Industry in 1994, was inducted into the Control “Process Automation Hall of Fame” in 2001, and honored by InTech Magazine in 2003 as one of the most influential innovators in automation. Greg has written a book a year for the last 20 years whether he needed to or not. About half are humorous (the ones with cartoons and top ten lists). Presently Greg contracts via CDI Process and Industrial as a principal consultant in DeltaV Applied R&D at Emerson Process Management in Austin Texas. For more info visit:

– http://ModelingandControl.com

– http://www.easydeltav.com/controlinsights/index.asp (free E-books)

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See Chapter 2 for more info on setting the foundation

Purchase

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See Chapters 2-4 for more info on the application of model predictive control

Purchase

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See Appendix C for background of the unification of tuning methods and loop performance

Purchase

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See Chapter 1 for the essential aspects of system design for pH applications

Purchase

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Overview

This presentation offers examples and a methodology for the identification of the benefits and solutions for advanced control– Pyramid of Technologies– Benchmarking– Opportunity Assessment Methodology– Opportunity Assessment Questions– Mythology– Model Predictive Control Primer– Example of Transition from Conventional to Advanced Control– MPC Valve Rangeability and Sensitivity Solution– MPC Maximization of Low Cost Feed Example– MPC Procedure and Rules of Thumb– Virtual Plant – Lessons Learned– What we Need– Columns and Articles in Control Magazine

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Basic Process Control System

Loop Performance Monitoring System

Process Performance Monitoring System

Abnormal Situation Management System

Auto Tuning (On-Demand and On-line Adaptive Loop Tuning)

Fuzzy Logic

Property Estimators

Model Predictive Control

Ramper or Pusher

LP/QP

RTO

TS

TS is tactical scheduler, RTO is real time optimizer, LP is linear program, QP is quadratic program

Pyramid of Technologies

APC is in any technology that

integrates process knowledgeFoundation must be large and

solid enough to support upper

levels. Effort and performance

of upper technologies is highly

dependent on the integrity and

scope of the foundation (type

and sensitivity of measurements

and valves and tuning of loops)

The greatest success has been

Achieved when the technology

closed the loop (automatically

corrected the process without

operator intervention)

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Loops Behaving Badly

1Ei = ------------ Ti Eo

KoKc

where:Ei = integrated error (% seconds)

Eo = open loop error from a load disturbance (%)

Kc = controller gain

Ko = open loop gain (also known as process gain) (%/%)

Ti = controller reset time (seconds)

(open loop means controller is in manual)

A poorly tuned loop will behave as badly as a loop with lousy dynamics (e.g. excessive dead time)!

Tune the loops before, during, and after any process control improvements

You may not want to minimize the integrated

error if the controller output upsets other loops.

For surge tank and column distillate receiver

level loops you want to minimize and maximize

the transfer of variability from level to the

manipulated flow, respectively.

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Unification of Controller Tuning Settings

max

1*5.0

o

c KK

Where:

Kc = controller gain

Ko = open loop gain (also known as process gain) (%/%)

1 self-regulating process time constant (sec)

max maximum total loop dead time (sec)

All of the major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve,

Simplified Internal Model Control, and Lambda) reduce to the following form for the maximum

useable controller gain

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Categories of Control Used In Benchmarking

1. Basic - Regulatory and Discrete Control (PID, pump, and on-off valve control)2. Basic - Unit Operation Control (batch control and automated startup3. Basic - Advanced Regulatory Control (override control)4. Advanced - Production Management Control (flexible manufacturing)5. Advanced - Advanced Multivariable Control (model predictive control6. Advanced - Global On-Line Optimization (real time optimization)7. Data - Advanced Advisory Systems (multivariate statistical process control)8. Data - Process Data Access (presentation to operations, maintenance, ...)9. Data - Manufacturing Data Integration (integration of business systems)

Advanced process control (APC) is any control system higher than basic loop and batch control that offers additional benefits (categories 3-9)

– incorporates process knowledge – uses direct or implied economic objective(s)

Ten companies who are leaders in process control were benchmarked

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Benefits from Process Control Improvement by Top Three Companies Benchmarked

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1 2 3 4 5 6 7 8 9

Benefits% COGS

basic advanced data

Categories of Controls

Closer to balanced approach This approach appears to give the greatest benefits Few companies have been able to accomplish this For these companies, the total of all categories is 8% of COGS

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Advanced Process Control Benefits

Improved yield (better selectivity)* Less blending, scrap, and rework or higher price for higher grade * Lower utility costs (energy minimization) Higher production rate (feed maximization) Increased on stream time (fewer shutdowns) Reduced maintenance (less stress on equipment) Safer Operation (fewer shutdowns and less stress on equipment)

* The benefits for improved yield and less scrap or rework can be taken as an increase in capacity or a reduction in raw materials

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Opportunity Sizing and Assessment(2% of COGs on the average in 50 processes)

Do a thorough opportunity sizing (OS) before the opportunity assessment using cost sheets, product prices, historian trends, business plans, research reports, technical studies, and simulations to establish actual, practical, and theoretical performance (e.g. yield, capacity) with operations and technology

Use plant process engineers to go through process, identify constraints, and offer ideas on opportunities to reduce gaps identified in OS to see and work way out of the current process box Avoid temptation of canned solution or for consultants to come to conclusions

before the plant people thoroughly discuss peculiarities and special problems. Get knowledgeable people to speak first and ask questions – hold off on solutions but offer concepts that people can use to generate solutions and be a good listener

Use historian to find loops in manual, limit cycles, slow or oscillatory set point and load responses, and controller outputs running near limits

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Opportunity Sizing and Assessment(2% of COGs on the average in 50 processes)

Look for opportunities to infer compositions from fast lower maintenance measurements such as density, viscosity, mass spectrometers, microwave, and nuclear magnetic resonance

Seek applications of accurate mass flow ratios for material balance knowledge and control

Ask what would happen if a set point or operating mode is changed Pick control technologies to address opportunities and give relative

estimate of implementation cost and time (e.g. high, medium, low) and per cent of gap addressed

Ask plant process engineers to estimate percentage of gap addressed by each solution

Take advantage of momentum and group enthusiasm – start on “quick hits” immediately and set definitive schedule and assignments for others (avoid inertia of waiting for quote or study) All the people you need to get started should be in the meeting,

otherwise you have the wrong people Tune the loops and improve the loops

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Opportunity Assessment Questions

Are there limits to operating values that are important for product quality, efficiency, or for environmental, personnel, and property protection?

Can these limits be measured online, analyzed in the lab, or calculated? Has there been down time attributed to violations of these limits? This can

show up as an increase in the maintenance cost or number of failures of equipment, a decrease in the run time between catalyst replacement or regeneration, a decrease in the run time between clean outs or defrosts from a faster rate of fouling or coating, and trips from interlocks for personnel and property protection.

Has product been downgraded, recycled, returned, or pitched as the result of excursions beyond these limits?

Would operation closer to a limit significantly decrease utility or raw material use or increase production rate?

Have there been any environmental violations or near misses? Does the operator pick set points to keep operating points away from limits? Is there a batch operation with a feed rate that depends upon a process

variable where the batch time could be reduced by increase in a feed rate by operating closer to process or equipment limits?

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Opportunity Assessment Questions

Are there more than two controlled or constraint variables affected by more than one manipulated variable?

Are these controlled or constraint variables important? Do the controlled variables have the same order of magnitude lag and delay? Can the PID controllers effectively use rate action? Is the time delay more than ¼ of the total time to steady state or time to reach

98% of the change (T98)?

Is a chromatograph used for a controlled or constraint variable? Can you measure or calculate the upsets? Do these upsets affect more than one controlled or constraint variable? Are equations or parameters not known completely enough to calculate the feed

forward gain and timing requirements? Are there any loops where the initial response is opposite of the final response?

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Mythology

Auto tuners can compute controller tuning settings with an accuracy of more than one digit. Act surprised when unmeasured disturbances, load changes, valve stick-slip,

and noise cause each result to be different. Look forward to the opportunity to play bingo with the second digit.

You can just dump all your historical data into an artificial neural network and get wonderful results. Forget about the same stuff that cause auto tuners to have problems. Use

variables drawing straight lines because anything that smooth or well controlled must be important. Use the controlled variables (process variables) instead of the manipulated variables (controller outputs). Don’t try to avoid extraneous inputs or identification of the control algorithm instead of the process. If you want to purse a career in data processing, use every input you can find.

There were a lot of myths heard in opportunity assessmentHere is the short list of the more humorous ones

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Mythology

Models can predict a process variable that is not measured in the field or lab. Great way to spur creativity in training an ANN, developing a PLS model, and

validating a first principal model plus it has the added bonus of the model never being wrong. Wait till your customers figure out something is wrong with the composition of your product. Discount as hearsay any suggestions that even the best models need periodic correction

Models don’t need to include process and measurement time delays After all the following time honored traditions can’t all be misleading

Professors teach students to think steady state Books on process control focus on continuous processes Statisticians analyze snapshots of data Operations want instantaneous results Engineers think the temperatures, compositions and flows in the plant are

constant and match what are defined on the Process Flow Diagram (PFD)

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Mythology

Process control does not apply to batch processes. Use that time tested fixed sequence. After all, that batch cycle time is a

tradition and the golden batch sure looks shiny.

Positioners should not be used on fast loops. What was true for the good old days of pneumatic positioners and analog

controllers must still be true today. Surely, digital positioners with tuning settings and digital control system scan times can’t make the original theoretical concerns less important than the practical issues of real valves. If you would rather believe the controller outputs are the actual valve positions, and just want valve problems to slip by, save some bucks on your project and only put positioners on slow loops. Just don’t stick around for start up.

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Mythology

You need to upset the process to create a model The effect of a properly designed PRBS test averages out Relay tuning methods may provide tighter control than loop Software can automatically identify models from the normal set point changes

made during startup and operation To reduce variability in process outputs (temperatures and compositions),

keep all the process inputs (flows) constant. Keep believing that you can fix both the process inputs and outputs and don’t

accept the notion that process control must transfer variability from process outputs to process inputs to compensate for disturbances. Just make the variability disappear.

Use process outputs for principal component analysis, neural network and partial least squares models regardless of control system design Use the same process outputs (e.g. composition, temperature) after the loop is

closed and variability has been transferred to process inputs (e.g. flows)

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When Process Knowledge is Missing in Action

2-Sigma 2-Sigma RCASSet Point

LOCALSet Point

2-Sigma 2-Sigma

Upper LimitPV distribution for original control

PV distribution forimproved control

Extra margin when “war stories” or mythology rules

value

Benefits are not realized until the set point is moved!(may get benefits by better set point based on process knowledge even if variability has not been reduced)

Good engineers can draw straight lines

Great engineers can move straight lines

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Common Misconceptions

You need an advanced degree to do advanced control. Not so anymore. New software packages used to form a virtual plant

automate much of the expertise needed and eliminate the need for special interfaces. The user can now focus mostly on the application and the goal.

Models only apply to continuous processes. Since most of the applications are in the continuous industry, this is a

common misconception. While it is true that steady state simulations are not valid for batch operations since there is by definition no steady state, dynamic simulations can follow a batch as long as the software can handle zero flows and empty vessels. Model based control (MPC), which looks at trajectories is suitable for optimization of fed batch processes. The opportunities to improve a process’s efficiency by the use of models add up to be about 25% for batch compared to 5% for continuous operations

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Common Misconceptions

You need consultants to maintain experimental models. No longer true. The ease of use of new software allows the user to get

much more involved, which is critical to make sure the plant gets the most value out of the models. Previously, the benefits started to drop as soon as the consultant left the job site. Now the user should be able to adjust,

troubleshoot, and update the models. You don’t need good operator displays and training for well designed

advanced control systems. The operators are the biggest constraint in most plants. Even if the models

used for real time optimization (RTO) and model based control (MPC) are perfect, operators will take these systems offline if they don’t understand them. The new guy in town is always suspect, so the first time there is an operational problem and there is no one around to answer questions, the RTO and MPC systems are turned off even if they are doing the right thing. Training sessions and displays should provide an understanding of the effect of future trajectories on actions taken by controller

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Common Misconceptions

Simple step (bump) tests are never enough. You must do a PRBS test. A complete pseudo random binary sequence (PRBS) test may take too long. The

plant may have moved to an entirely different state, tripped, or in the case of a batch operation finished, before a PRBS test is complete. As a minimum, there should be one step in each direction held to steady state. The old rule is true, if you can see the model from a trend, it is there. Sometimes, the brain can estimate the process gain, time delay, and lag better than a software package.

You need to know your process before you start a MPC application. This would be nice, but often the benefits from a model stems from the knowledge

discovery during the systematic building and identification procedures. Frequently, the understanding gained from developing models leads to immediate benefits in terms of better set points and instruments. The commissioning of the MPC is the icing on the cake and locks in benefits

Optimization by pushing constraints will decrease on-stream time. Not true. MPC sees future violations of constraints to increase on-stream time

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Batch Control

Reagent

Optimum pH

Optimum Product

Feeds Concentrations

pH

Product

Optimum Reactant

Reactant

Reactant

Variability Transfer from Feeds to pH, and Reactant and Product Concentrations

Most published cases of multivariate statistical process control (MSPC) use the process

outputs and this case of variations in process variables induced by sequenced flows.

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PID Control

Optimum pH

Optimum Product

Feeds Concentrations

pH

Product

Reagent

Reactant

Optimum Reactant

Reactant

Variability Transfer from pH and ReactantConcentration to Feeds

The story is now in the controller outputs

(manipulated flows) yet MSPC still focuses

on the process variables for analysis

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Optimum pH

Optimum Product

Feeds Concentrations

pH

Product

ReagentOptimum Reactant

Reactant

Reactant

Time Time

Variability Transfer from Product Concentrationto pH, reactant Concentration, and Feeds

Model Predictive Control

Model Predictive Control of product concentration batch profile uses slope for CV which makes the integrating response self-regulating and enables negative besides positive corrections in CV

Top Ten Signs of an Advanced Control Addiction

(10) You try to use Neural Networks to predict the behavior of your children.

(9) You attempt to use Fuzzy Logic to explain your last performance review.

(8) You use so much Feedforward, you eat before you are hungry.

(7) You propose Model Predictive Control for the “Miss USA” contest.

(6) You develop performance monitoring indices for your spouse.

(5) You implement adaptive control on your stock portfolio.

(4) You carry wallet photos of Auto Tuner trend results.

(3) You apply dead time compensation by drinking before you go to a party.

(2) You recommend a survivor show where consultants are placed in a stressed out old pneumatic plant with no staff or budget and are asked to add advanced control to increase plant efficiency.

(1) Your spouse has to lure you to bed by offering “expert options” for advanced control

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Types of Process Responses

d o

0

1

2

curve 0 = Self-Regulatingcurve 1 = Integratingcurve 2 = Runaway

Time(minutes)

CV

0

CV

Ramp

Acceleration

Open Loop Time Constant

Total LoopDead Time

CO(% step inController

Output)

The temperature and composition of batch processes tend to have an integrating response since there is no self-regulation from a discharge flow

Self-Regulating Process Gain Kp = CV CO

Integrating Process Gain Ki = CV t CO

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What Does PID and MPC See of Future?(Long Term versus Short Term View)

time

controlled variable (CV)

set point

manipulated variable (MV)

PIDloop onlyseesthis

presenttime

MPC sees whole future trajectoryloop dead timecompensatorsees one deadtime ahead

response

PID is best if high gain or rate action is needed for immediate action to correct frequent fast unmeasured disturbances or a prevent runaway

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Linear Superposition of MPC

time

time

time

CV1 = f(MV1)

CV1 = f(MV2)

CV1 = f(MV1 MV2)

set point

set point

set point

Nomenclature: CV is controlled variable (PV) and MV is manipulated variable (IVP)

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Feedback Correction of Process Vector and Mirror Image Control Vector

time

time

time

set point

set point

set point

control vector

process vector

process vector

process vector

shift vectorto correctmodel error

actual CV

predicted CV

compute futuremoves for amirror image vector to bringprocess to setpoint trajectory

Most MPC packages use standard matrix math and methods (e.g. matrix summation and inversion)

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Situations Where Model Predictive Control May be Beneficial

Process and Measurement Noise Erratic or Stepped Measurement Response Inverse Response Large Dead Times Move Size Limits and Penalty on Move (Move Suppression)* Measured Disturbances Multiple Manipulated Variables Interactions Constraints Optimization No PID Control Expertise

* Enables regulation of the transfer of variability from CV to MV

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Automated PRBS Test for Fed-Batch Reactor

Non-stationary Behavior(operating point is not constant)

Test Data During Fed-Batch Operation

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Linear Program (LP) Optimizer

MV1

MV2 CV2max

CV2min

MV2max

MV2min

MV

1m

ax

MV

1m

in

CV1max

CV1min

Region of feasible solutions Optimal solution

is in one of the vertexes

For a minimization of maximization of a MV as a CV, a simpleramper or pusher is sufficient. If the constraint intersectionsmove or the value of type of optimal CV changes, real timeOptimization is needed to provide a more optimal solution.

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How Well Can Coincident Constraints Be Handled?

Number of % Time % Time - % TimeCoincident Operator Override MPCConstraints Can Hold Can Hold Can Hold

One 30% 90% 98% Two 20% 45% 90% Three 0% 30% 80%

MPC can hold constraints twice as tight as override and ten times as tight as operator if measurements and final elements precision is not an issue

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feed A

feed B

coolantmakeup

CAS

ratiocontrol

Example of Basic PID Control

reactor

vent

product

condenser

CTW

PT

PC-1

TT

TT

TC-2

TC-1

FC-1

FT

FT

FC-2

TC-3

RC-1

TT

CAS

cascade control

Conventional Control

04/12/23 39

feed A

feed B

coolantmakeup

CAS

ratio

CAS

Example of Advanced Regulatory Control

reactor

vent

product

maximum productionrate

condenser

CTW

PT

PC-1

TT

TT

TC-2

TC-1

FC-1

FT

FT

FC-2

<

TC-3

RC-1

TT

ZC-1

ZC-2CAS

CAS

CAS

ZC-3 ZC-4<

Override Control

override control

ZC-1, ZC-3, and ZC-4 work to keep their respective

control valves at a max throttle position with good

sensitivity and room for loop to maneuver. ZC-2

will raise TC-1 SP if FC-1 feed rate is maxed out

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feed A

feed B

coolantmakeup

CAS

ratio

RCAS

Example of Model Predictive Control

reactor

vent

product

condenser

CTW

PT

PC-1

TT

TT

TC-2

FC-1

FT

FT

FC-2RC-1

TT

RCAS

MPC

MPC

MPC

Maximizefeed rate

Model Predictive Control (MPC)

set point

set point

04/12/23 41

Example of MPC (Responses)

manipulated variables (MVs)

TC-2 jacket exittemperature SP

TV-1 condensercoolant valve IVP

FC-1 reactor feed A SP

TC-1 reactortemperature PV

TC-3 condensertemperature PV

FC-1 reactor feed A SP

TV-2 reactor coolant valve IVPTV-3 condenser coolantvalve IVPPV-1 vacuum systemvalve IVP

FV-1 feed A valve IVP

con

trolled

vari

ab

les (

CV

s)

con

str

ain

t vari

ab

les (

AV

s)

null nullmaximize

MPC

Top Ten Signs You Have a Dysfunctional MPC Team

The recommended sizes of controllers range from 0x0 to 100x100

The models for the first controller fill up the hard drive The model after 4 months of PRBS testing looks suspiciously

like the model from the first bump test The completion of the project is tied to the “Second Coming” Food fights break out in the cafeteria over matrix design Meetings kick off with kick boxing between consultants More than one consultant onsite at a time is ruled a health

hazard A psychiatrist is chosen as the best possible project manager The project over runs it’s Prozac budget The creators of “South Park” request movie rights to the

project

04/12/23 43

manipulated variables

Small (Fine)Reagent Valve SP

NeutralizerpH PV

Small (Fine)Reagent Valve SP

cont

rolled

va

riab

le

MPC Large (Coarse)Reagent Valve SP

cont

rolled

va

riab

le

null

Model Predictive Controller (MPC) setup for rapid simultaneous throttling of a fine and coarse control valves that addressesboth the rangeability and resolution issues. This MPC canpossibly reduce the number of stages of neutralization needed

MPC Valve Sensitivity and Rangeability Solution

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MPC Valve Sensitivity and Rangeability Solution

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MPC Valve Sensitivity and Rangeability Solution

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Successive Load Upsets Process Set Point Change Trim Valve Set Point Change

CriticalProcess Variable

CoarseValve

TrimValve

MPC Valve Sensitivity and Rangeability Solution

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manipulated variables

High Cost FastFeed SP

Critical PV(normal PE)

Low Cost SlowFeed SP

(lowered PE)

contr

olled

vari

able

Maximize

MPC Low Cost SlowFeed SP

null

opti

miz

ati

on

vari

able

MPC Maximization of Low Cost Feed Example

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MPC Maximization of Low Cost Feed Example

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Riding Max SPon Lo Cost MV

Riding Min SPon Hi Cost MV

Critical CV

Lo Cost Slow MV

Hi Cost Fast MV

LoadUpsets

Set PointChanges

LoadUpsets

Set PointChanges

Low Cost MV Maximum SP Increased

Low Cost MV Maximum SP Decreased

Critical CV

MPC Maximization of Low Cost Feed Example

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MPC Procedure and Rules of Thumb

Define control/economic scope and objectives Tune and improve the loops Install flow loops or secondary loops to avoid direct manipulation of a valve Reduce the data compression and increase the update rate of the data historian Define and document baseline of operating conditions Define and implement performance indices For self-regulating responses, steady state = dead time plus 4 time constants For integrating processes, time horizon is at least 5 dead times Calculate the integrating process gain for level from vessel geometry and flows Choose a step size that is at least 5x the noise level or resolution limit Conduct a simple bump test for each manipulated and disturbance variable Revise estimates of time to steady state or time horizon and step size Conduct a Pseudo Random Binary Sequence (PRBS) test if needed Choose simplest model (fluctuations of 10% in fit or parameters are insignificant) Simulate the response for changes in targets, economics, and disturbance variables Increase the penalty on move (move suppression) to reduce oscillation Decrease the penalty on error and/or priority for less important controlled variables Provide displays that show future predictions and process metrics Train operations and engineering on use and benefits

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Advanced Control Modules

Process Models(first principal

and experimental)

Virtual Plant

Laptop or Desktopor Control System Station

Virtual Plant Setup

This is where I hang out

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Virtual Plant Integration

Dynamic Process Model

OnlineData Analytics

Model PredictiveControl

Loop MonitoringAnd Tuning

DCS batch and loopconfiguration, displays,

and historian

Virtual PlantLaptop or DesktopPersonal Computer

OrDCS Application

Station or Controller

Embedded Advanced Control Tools

EmbeddedModeling Tools

Process Knowledge

04/12/23 53

Actual PlantOptimizationReactant Ratio Correction

Temperature Set Point

Virtual Plant

Online KPI:Yield and Capacity

Inferential Measurements:Reaction Rates

Adaptation

Key Actual Process Variables

Key VirtualProcess Variables

Model Parameters

Error between virtual and actual process variablesare minimized by correction of model parameters

Model Predictive Control and LPFor Optimization of Actual Plant

Model Predictive Control andNeural Network

For Adaptation of Virtual Plant

Optimum and Reference

Batch Profiles

Actual BatchProfiles

Multi-way Principal

Component Analysis

Super Model Based Principal

Component Analysis

Adaptation and Optimization

Top Ten Reasons I Use a Virtual Plant

(10) You can’t freeze, restore, and replay an actual plant run

(9) No software to learn, install, interface, and support

(8) No waiting on lab analysis

(7) No raw materials

(6) No environmental waste

(5) Virtual instead of actual problems

(4) Runs are done in minutes instead of days

(3) Plant can be operated on a tropical beach

(2) Last time I checked my wallet I didn’t have $1,000K

(1) Actual plant doesn’t fit in my suitcase

04/12/23 55

Typical Uses and Fidelities of Process Models(Fidelity Scale 0 - 10)

Process Development Media or reactant optimization and identification of kinetics on the bench top - 10 Optimization of process conditions in pilot plant - 9 Agitation and mass transfer rates - 8* Process scale-up – 8* - assumes computational fluid dynamics (CFD) program provides necessary inputs

Process Design Innovative reactor designs or single use bioreactors (SUB) - 7 Vessel, feed, and jacket system size and performance - 6

Automation Design Real Time Optimization (RTO) - 7 Model Predictive Control (MPC) - 6 Controller tuning (PID) - 5 Control strategy development and prototyping - 4 Batch sequence (e.g. timing of feed schedules and set point shifts) – 3

04/12/23 56

Typical Uses and Fidelities of Process Models (Fidelity Scale 0 - 10)

Online Diagnostics Root cause analysis - 5 Data analytics development and prototyping - 4

Operator Training Systems Developing and maintaining troubleshooting skills - 4 Understanding process relationships - 3 Gaining familiarity with interface and functionality of automation system - 2

Configuration Checkout Verifying configuration meets functional specification - 2 Verifying configuration has no incorrect or missing I/O, loops, or devices - 1

04/12/23 57

Loops that are not islands of automation Unit operation control for integrated objectives, performance, and diagnostics High speed local control of pressure with ROUT, CAS, and RCAS signals

Engineer with process, configuration, control, measurement, and valve skills Virtual plants with increasing Fidelity (3 -> 7 chemical, 3->10 biological)

Product development, process design, real time optimization, advanced control prototyping and justification, process control improvement, diagnostics, training

Smart wireless integrated process and operations graphics Online process, loop, and advanced control metrics for plants, trains, and shifts

Yield, on-stream time, production rate, utility cost, raw material cost, maintenance cost* Variability, average % of max speed (Lambda), % time process variable or output is at

limits, % time in highest mode, % deadband, % resolution, number of oscillations Process control improvement (PCI) benefits ($ of revenue and costs)

3-D, XY, future trajectories of process and performance metrics response, data analytics, worm plots, and trends of automatically selected correlated variables

Coriolis flow meters, RTDs, and online and at-line analyzers everywhere Real time analysis via probes or automated low maintenance sample systems Automated time stamped entry of lab results into data historian Online material, energy, and component balances

Control valves with < 0.25% resolution and < 0.5% dead band

What Do We Need?

04/12/23 58

Lessons Learned

Let process people see and work their way out the process box otherwise you will get the conclusion there is nothing better to do

Makes sure business, maintenance, E&I, configuration, operations, process, analyzer specialists, and research people are in the opportunity assessment

Ask “can we trial a change in set point or operating mode” If best, do it first in a model, on a bench-top, or in a pilot plant

If the process is not modeled, meetings can go around in circles Get involved in configuration and implementation “Camp out” with the operators during tests, trials, and commissioning Stay “in touch” with everyone in the opportunity assessment Report benefits and distribute credit

Models can help distinguish benefits from noise or other effects Remember MMM and PPP

Measurements (especially density and mass flow), models, and momentum (MMM) Process knowledge, performance indicators, and people psychology (PPP)

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Key Points

Conduct an open minded opportunity sizing and assessment Tune the loops and improve the loops Add model predictive control Model the process to dispel myths and build on process knowledge Improve the set points Add composition control (add inferential measurements and analyzers) Transfer variability from most important process outputs Add online data analytics (add online multivariate statistical process control) Add online metrics to spur competition, and to adjust, verify, and justify controls Maintain the momentum

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Control Magazine Columns and Articles

“Control Talk” column 2002-2008 “Has Your Control Valve Responded Lately?” 2003 “Advanced Control Smorgasbord” 2004 “Fed-Batch Reactor Temperature Control” 2005 “A Fine Time to Break Away from Old Valve Problems” 2005 “Virtual Plant Reality” 2005 “Full Throttle Batch and Startup Responses” 2006 “Virtual Control of Real pH” 2007 “Unlocking the Secret Profiles of Batch Reactors” 2008