Opportunity Assessment and Advanced Control

06/06/09 Opportunity Assessment and Advanced Control GREGORY K MCMILLAN use pure black and white option for printing copies

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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06/06/09 See Chapter 2 for more info on setting the foundation Purchase

06/06/09 See Chapters 2-4 for more info on the application of model predictive control Purchase

06/06/09 See Appendix C for background of the unification of tuning methods and loop performance Purchase

06/06/09 See Chapter 1 for the essential aspects of system design for pH applications Purchase

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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06/06/09 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 knowledge Foundation 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) 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

Loops Behaving Badly 06/06/09 1 E i = ------------  T i  E o  K o  K c where: E i = integrated error (% seconds) E o = open loop error from a load disturbance (%) K c = controller gain K o = open loop gain (also known as process gain) (%/%) T i = 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.

Unification of Controller Tuning Settings 06/06/09 Where: K c = controller gain K o = 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

Categories of Control Used In Benchmarking

Basic - Regulatory and Discrete Control (PID, pump, and on-off valve control)

Basic - Unit Operation Control (batch control and automated startup

Basic - Advanced Regulatory Control (override control)

Advanced - Production Management Control (flexible manufacturing)

Advanced - Advanced Multivariable Control (model predictive control

Advanced - Global On-Line Optimization (real time optimization)

Data - Advanced Advisory Systems (multivariate statistical process control)

Data - Process Data Access (presentation to operations, maintenance, ...)

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 06/06/09 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

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)

06/06/09 * The benefits for improved yield and less scrap or rework can be taken as an increase in capacity or a reduction in raw materials

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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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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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 (T 98 )?

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.

06/06/09 There were a lot of myths heard in opportunity assessment Here is the short list of the more humorous ones

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 06/06/09 2-Sigma 2-Sigma RCAS Set Point LOCAL Set Point 2-Sigma 2-Sigma Upper Limit PV distribution for original control PV distribution for improved 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

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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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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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 06/06/09 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.

PID Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Reactant Optimum Reactant Reactant Variability Transfer from pH and Reactant Concentration to Feeds The story is now in the controller outputs (manipulated flows) yet MSPC still focuses on the process variables for analysis

Model Predictive Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Optimum Reactant Reactant Reactant Time Time Variability Transfer from Product Concentration to pH, reactant Concentration, and Feeds 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

Types of Process Responses 06/06/09 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 K p =  CV  CO Integrating Process Gain K i =  CV  t  CO  d  o 0 1 2 curve 0 = Self-Regulating curve 1 = Integrating curve 2 = Runaway Time (minutes) CV 0  CV Ramp Acceleration Open Loop Time Constant Total Loop Dead Time  CO (% step in Controller Output)

What Does PID and MPC See of Future? (Long Term versus Short Term View) 06/06/09 time controlled variable (CV) set point manipulated variable (MV) PID loop only sees this present time MPC sees whole future trajectory loop dead time compensator sees one dead time 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

Linear Superposition of MPC 06/06/09 time time time CV 1 = f(  MV 1 ) CV 1 = f(  MV 2 ) CV 1 = f(  MV 1  MV 2 ) set point set point set point Nomenclature: CV is controlled variable (PV) and MV is manipulated variable (IVP)

Feedback Correction of Process Vector and Mirror Image Control Vector 06/06/09 time time time set point set point set point control vector process vector process vector process vector shift vector to correct model error actual CV predicted CV compute future moves for a mirror image vector to bring process to set point trajectory Most MPC packages use standard matrix math and methods (e.g. matrix summation and inversion)

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

06/06/09 * Enables regulation of the transfer of variability from CV to MV

Automated PRBS Test for Fed-Batch Reactor 06/06/09 Non-stationary Behavior (operating point is not constant) Test Data During Fed-Batch Operation

Linear Program (LP) Optimizer 06/06/09 For a minimization of maximization of a MV as a CV, a simple ramper or pusher is sufficient. If the constraint intersections move or the value of type of optimal CV changes, real time Optimization is needed to provide a more optimal solution. MV1 MV2 CV2max CV2min MV2max MV2min MV1max MV1min CV1max CV1min Region of feasible solutions Optimal solution is in one of the vertexes

How Well Can Coincident Constraints Be Handled?

Number of % Time % Time - % Time

Coincident Operator Override MPC

Constraints 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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Example of Basic PID Control 06/06/09 feed A feed B coolant makeup CAS ratio 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

Example of Advanced Regulatory Control 06/06/09 feed A feed B coolant makeup CAS ratio CAS reactor vent product maximum production rate 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-2 CAS 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

Example of Model Predictive Control 06/06/09 feed A feed B coolant makeup CAS ratio RCAS reactor vent product condenser CTW PT PC-1 TT TT TC-2 FC-1 FT FT FC-2 RC-1 TT RCAS MPC MPC MPC Maximize feed rate Model Predictive Control (MPC) set point set point

Example of MPC (Responses) 06/06/09 manipulated variables (MVs) TC-2 jacket exit temperature SP TV-1 condenser coolant valve IVP FC-1 reactor feed A SP TC-1 reactor temperature PV TC-3 condenser temperature PV FC-1 reactor feed A SP TV-2 reactor coolant valve IVP TV-3 condenser coolant valve IVP PV-1 vacuum system valve IVP FV-1 feed A valve IVP controlled variables (CVs) constraint variables (AVs) null null maximize 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

06/06/09 Model Predictive Controller (MPC) setup for rapid simultaneous throttling of a fine and coarse control valves that addresses both the rangeability and resolution issues. This MPC can possibly reduce the number of stages of neutralization needed MPC Valve Sensitivity and Rangeability Solution

06/06/09 MPC Valve Sensitivity and Rangeability Solution

06/06/09 MPC Maximization of Low Cost Feed Example

06/06/09 MPC Maximization of Low Cost Feed Example Riding Max SP on Lo Cost MV Riding Min SP on Hi Cost MV Critical CV Lo Cost Slow MV Hi Cost Fast MV Load Upsets Set Point Changes Load Upsets Set Point Changes Low Cost MV Maximum SP Increased Low Cost MV Maximum SP Decreased Critical CV

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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Virtual Plant Setup 06/06/09 Advanced Control Modules Process Models (first principal and experimental) Virtual Plant Laptop or Desktop or Control System Station This is where I hang out

Virtual Plant Integration 06/06/09 Dynamic Process Model Online Data Analytics Model Predictive Control Loop Monitoring And Tuning DCS batch and loop configuration, displays, and historian Virtual Plant Laptop or Desktop Personal Computer Or DCS Application Station or Controller Embedded Advanced Control Tools Embedded Modeling Tools Process Knowledge

06/06/09 Actual Plant Optimization Reactant Ratio Correction Temperature Set Point Virtual Plant Online KPI: Yield and Capacity Inferential Measurements: Reaction Rates Adaptation Key Actual Process Variables Key Virtual Process Variables Model Parameters Error between virtual and actual process variables are minimized by correction of model parameters Model Predictive Control and LP For Optimization of Actual Plant Model Predictive Control and Neural Network For Adaptation of Virtual Plant Optimum and Reference Batch Profiles Actual Batch Profiles 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

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

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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

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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

06/06/09 What Do We Need?

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

06/06/09

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

06/06/09

Opportunity Assessment and Advanced Control

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