11/10/2008 1 Opportunity Assessment and Advanced Control GREGORY K MCMILLAN use pure black and white option for printing copies
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11/10/2008 1
Opportunity Assessmentand
Advanced Control
GREGORY K MCMILLAN
use pure black and white option for printing copies
11/10/2008 2
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)
11/10/2008 3
See Chapter 2 for more info on setting the foundation
11/10/2008 4
See Chapters 2-4 for more info on the application of model predictive control
11/10/2008 5
See Appendix C for background of the unification of tuning methods and loop performance
11/10/2008 6
See Chapter 1 for the essential aspects of system design for pH applications
11/10/2008 7
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
11/10/2008 8
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
Pyramid of Technologies
APC is in any technology that integrates process knowledge
Foundation must be large andsolid enough to support upperlevels. Effort and performanceof upper technologies is highlydependent on the integrity andscope of the foundation (typeand sensitivity of measurementsand valves and tuning of loops)
The greatest success has beenAchieved when the technologyclosed the loop (automaticallycorrected the process withoutoperator intervention)
TS is tactical scheduler, RTO is real time optimizer, LP is linear program, QP is quadratic program
11/10/2008 9
Loops Behaving Badly
A poorly tuned loop will behave as badly as a loop with lousy dynamics (e.g. excessive dead time)!
1Ei = ------------ ∗ Ti ∗ Eo
Ko ∗ Kc
where:Ei = integrated error (% seconds)Eo = open loop error from a load disturbance (%)Kc = controller gainKo = open loop gain (also known as process gain) (%/%) Ti = controller reset time (seconds)(open loop means controller is in manual)
You may not want to minimize the integratederror if the controller output upsets other loops.For surge tank and column distillate receiver level loops you want to minimize and maximizethe transfer of variability from level to themanipulated flow, respectively.
Tune the loops before, during, and after any process control improvements
11/10/2008 10
Unification of Controller Tuning Settings
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
max
1*5.0θ
τ∗
=o
c KK
Where:
Kc = controller gainKo = open loop gain (also known as process gain) (%/%)τ1 = self-regulating process time constant (sec)θmax = maximum total loop dead time (sec)
11/10/2008 11
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
11/10/2008 12
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 approachThis approach appears to give the greatest benefitsFew companies have been able to accomplish thisFor these companies, the total of all categories is 8% of COGS
11/10/2008 13
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
11/10/2008 14
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 technologyUse 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
11/10/2008 15
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 resonanceSeek applications of accurate mass flow ratios for material balance knowledge and controlAsk what would happen if a set point or operating mode is changedPick control technologies to address opportunities and give relative estimate of implementation cost and time (e.g. high, medium, low) and per cent of gap addressedAsk plant process engineers to estimate percentage of gap addressed by each solutionTake 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
11/10/2008 16
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?
11/10/2008 17
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?
11/10/2008 18
Mythology
There were a lot of myths heard in opportunity assessmentHere is the short list of the more humorous ones
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.
11/10/2008 19
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 delaysAfter all the following time honored traditions can’t all be misleading
Professors teach students to think steady stateBooks on process control focus on continuous processesStatisticians analyze snapshots of dataOperations want instantaneous resultsEngineers think the temperatures, compositions and flows in the plant are constant and match what are defined on the Process Flow Diagram (PFD)
11/10/2008 20
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.
11/10/2008 21
Mythology
You need to upset the process to create a modelThe effect of a properly designed PRBS test averages outRelay tuning methods may provide tighter control than loopSoftware 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)
11/10/2008 22
When Process Knowledge is Missing in Action
2-Sigma 2-SigmaRCAS
Set 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
Good engineers can draw straight linesGreat engineers can move straight lines
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)
11/10/2008 23
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
11/10/2008 24
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
11/10/2008 25
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
11/10/2008 26
Batch Control
Variability Transfer from Feeds to pH, and Reactant and Product Concentrations
Feeds Concentrations
Optimum pH
Optimum Product
pH
Product
Optimum Reactant
Reactant
Reagent
Reactant
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.
11/10/2008 27
PID Control
Variability Transfer from pH and ReactantConcentration to Feeds
Concentrations
Optimum pH
Optimum Product
Feeds
pH
Product
Reagent
Reactant
Optimum Reactant
Reactant
The story is now in the controller outputs(manipulated flows) yet MSPC still focuseson the process variables for analysis
11/10/2008 28
Model Predictive Control
Variability Transfer from Product Concentrationto pH, reactant Concentration, and Feeds
Optimum pH
Optimum Product
Feeds Concentrations
pH
Product
Reagent
Reactant
Optimum Reactant
Reactant
TimeTime
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
11/10/2008 30
Types of Process Responses
τd τo
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)
Self-Regulating Process Gain Kp = ∆CV / ∆CO Integrating Process Gain Ki = ∆CV / t / ∆CO
The temperature and composition of batch processes tend to have an integrating response since there is no self-regulation from a discharge flow
11/10/2008 31
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
11/10/2008 32
Linear Superposition of MPC
time
CV1 = f(∆ MV1)set point
time
time
CV1 = f(∆ MV2)
CV1 = f(∆ MV1 + ∆ MV2)
set point
set point
Nomenclature: CV is controlled variable (PV) and MV is manipulated variable (IVP)
11/10/2008 33
Feedback Correction of Process Vectorand Mirror Image Control Vector
set point
process vector
actual CV
predicted CV
time
time
time
set point
set pointcontrol vector
process vector
process vector
shift vectorto correctmodel error
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)
11/10/2008 34
Situations Where Model Predictive Control May be Beneficial
Process and Measurement NoiseErratic or Stepped Measurement ResponseInverse ResponseLarge Dead TimesMove Size Limits and Penalty on Move (Move Suppression)*Measured DisturbancesMultiple Manipulated VariablesInteractionsConstraintsOptimization No PID Control Expertise
* Enables regulation of the transfer of variability from CV to MV
11/10/2008 35
Automated PRBS Test for Fed-Batch Reactor
Non-stationary Behavior(operating point is not constant)
Test Data During Fed-Batch Operation
11/10/2008 36
Linear Program (LP) Optimizer
MV1
MV2 CV2max
CV2min
MV2max
MV2min
MV
1max
MV
1min
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.
11/10/2008 37
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
11/10/2008 38
Example of Basic PID Control
feed A
feed B
coolantmakeup
CAS
ratiocontrol
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
11/10/2008 39
Example of Advanced Regulatory Control
feed A
feed B
coolantmakeup
CAS
ratio
CAS
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 respectivecontrol valves at a max throttle position with goodsensitivity and room for loop to maneuver. ZC-2 will raise TC-1 SP if FC-1 feed rate is maxed out
11/10/2008 40
Example of Model Predictive Control
feed A
feed B
coolantmakeup
CAS
ratio
RCAS
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
11/10/2008 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 IVP
TV-3 condenser coolantvalve IVP
PV-1 vacuum systemvalve IVP
FV-1 feed A valve IVP
cont
rolle
d va
riab
les
(CVs
)co
nstr
aint
var
iabl
es (A
Vs)
null nullmaximize
MPC
Top Ten Signs You Have a Dysfunctional MPC Team
The recommended sizes of controllers range from 0x0 to 100x100The models for the first controller fill up the hard driveThe model after 4 months of PRBS testing looks suspiciously likethe model from the first bump testThe completion of the project is tied to the “Second Coming”Food fights break out in the cafeteria over matrix designMeetings kick off with kick boxing between consultants More than one consultant onsite at a time is ruled a health hazardA psychiatrist is chosen as the best possible project managerThe project over runs it’s Prozac budgetThe creators of “South Park” request movie rights to the project
11/10/2008 43
MPC Valve Sensitivity and Rangeability Solution
manipulated variables
Small (Fine)Reagent Valve SP
NeutralizerpH PV
Small (Fine)Reagent Valve SP
cont
rolle
d va
riab
le
MPC Large (Coarse)Reagent Valve SP
cont
rolle
d 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
11/10/2008 44
MPC Valve Sensitivity and Rangeability Solution
11/10/2008 45
MPC Valve Sensitivity and Rangeability Solution
11/10/2008 46
Successive Load Upsets Process Set Point Change Trim Valve Set Point Change
CriticalProcess Variable
CoarseValve
TrimValve
MPC Valve Sensitivity and Rangeability Solution
11/10/2008 47
MPC Maximization of Low Cost Feed Example
manipulated variables
High Cost FastFeed SP
Critical PV(normal PE)
Low Cost SlowFeed SP
(lowered PE)
cont
rolle
d va
riab
le
Maximize
MPC Low Cost SlowFeed SP
null
opti
miz
atio
n va
riab
le
11/10/2008 48
MPC Maximization of Low Cost Feed Example
11/10/2008 49
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 SPIncreased
Low Cost MV Maximum SPDecreased
Critical CV
MPC Maximization of Low Cost Feed Example
11/10/2008 50
MPC Procedure and Rules of Thumb
Define control/economic scope and objectivesTune and improve the loopsInstall flow loops or secondary loops to avoid direct manipulation of a valveReduce the data compression and increase the update rate of the data historianDefine and document baseline of operating conditionsDefine and implement performance indicesFor self-regulating responses, steady state = dead time plus 4 time constantsFor integrating processes, time horizon is at least 5 dead timesCalculate the integrating process gain for level from vessel geometry and flowsChoose a step size that is at least 5x the noise level or resolution limit Conduct a simple bump test for each manipulated and disturbance variableRevise estimates of time to steady state or time horizon and step sizeConduct a Pseudo Random Binary Sequence (PRBS) test if neededChoose simplest model (fluctuations of 10% in fit or parameters are insignificant)Simulate the response for changes in targets, economics, and disturbance variablesIncrease the penalty on move (move suppression) to reduce oscillationDecrease the penalty on error and/or priority for less important controlled variablesProvide displays that show future predictions and process metricsTrain operations and engineering on use and benefits
11/10/2008 51
Virtual Plant Setup
Advanced Control Modules
Process Models(first principal
and experimental)
Virtual PlantLaptop or Desktop
or Control System Station
This is where I hang out
11/10/2008 52
Virtual Plant Integration
DCS batch and loopconfiguration, displays,
and historian
EmbeddedModeling Tools
Embedded Advanced Control Tools
Dynamic Process Model
Loop MonitoringAnd Tuning
OnlineData Analytics
Model PredictiveControl
Virtual PlantLaptop or DesktopPersonal Computer
OrDCS Application
Station or Controller
Process Knowledge
11/10/2008 53
Model Predictive Control and LPFor Optimization of Actual Plant
Actual PlantOptimization
Temperature Set Point
Reactant Ratio Correction
Virtual Plant
Online KPI:Yield and Capacity
Adaptation
Inferential Measurements:Reaction Rates
Key Actual Process Variables
Key VirtualProcess Variables
Actual BatchProfiles
Multi-way Principal
Component Analysis
Super Model Based Principal
Component Analysis
Adaptation and Optimization
Model Parameters
Error between virtual and actual process variablesare minimized by correction of model parameters Model Predictive Control and
Neural NetworkFor Adaptation of Virtual Plant
Optimum and Reference
Batch Profiles
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
11/10/2008 55
Typical Uses and Fidelities of Process Models(Fidelity Scale 0 - 10)
Process DevelopmentMedia or reactant optimization and identification of kinetics on the bench top - 10Optimization of process conditions in pilot plant - 9Agitation and mass transfer rates - 8* Process scale-up – 8
* - assumes computational fluid dynamics (CFD) program provides necessary inputs
Process DesignInnovative reactor designs or single use bioreactors (SUB) - 7Vessel, feed, and jacket system size and performance - 6
Automation DesignReal Time Optimization (RTO) - 7Model Predictive Control (MPC) - 6Controller tuning (PID) - 5Control strategy development and prototyping - 4Batch sequence (e.g. timing of feed schedules and set point shifts) – 3
11/10/2008 56
Typical Uses and Fidelities of Process Models(Fidelity Scale 0 - 10)
Online DiagnosticsRoot cause analysis - 5Data analytics development and prototyping - 4
Operator Training SystemsDeveloping and maintaining troubleshooting skills - 4Understanding process relationships - 3Gaining familiarity with interface and functionality of automation system - 2
Configuration CheckoutVerifying configuration meets functional specification - 2Verifying configuration has no incorrect or missing I/O, loops, or devices - 1
11/10/2008 57
Loops that are not islands of automationUnit operation control for integrated objectives, performance, and diagnosticsHigh speed local control of pressure with ROUT, CAS, and RCAS signals
Engineer with process, configuration, control, measurement, and valve skillsVirtual 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 graphicsOnline 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 oscillationsProcess 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 everywhereReal time analysis via probes or automated low maintenance sample systemsAutomated time stamped entry of lab results into data historianOnline material, energy, and component balances
Control valves with < 0.25% resolution and < 0.5% dead band
What Do We Need?
11/10/2008 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 doMakes 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 plantIf the process is not modeled, meetings can go around in circlesGet involved in configuration and implementation“Camp out” with the operators during tests, trials, and commissioningStay “in touch” with everyone in the opportunity assessmentReport benefits and distribute credit
Models can help distinguish benefits from noise or other effectsRemember MMM and PPP
Measurements (especially density and mass flow), models, and momentum (MMM)Process knowledge, performance indicators, and people psychology (PPP)
11/10/2008 59
Key Points
Conduct an open minded opportunity sizing and assessmentTune the loops and improve the loopsAdd model predictive controlModel the process to dispel myths and build on process knowledgeImprove the set points Add composition control (add inferential measurements and analyzers)Transfer variability from most important process outputsAdd online data analytics (add online multivariate statistical process control)Add online metrics to spur competition, and to adjust, verify, and justify controlsMaintain 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
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