ControlLoopFoundationBatchandContinuousRevD.pdf

8/9/2019 ControlLoopFoundationBatchandContinuousRevD.pdf

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Control Loop Foundation

forBatch and Continuous 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 andIndustrial 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


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See Chapters 1-7 for the practical considerations of improving tuning and valve dynamics


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


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


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Overview

This presentation covers highlights or low lights of current loopperformance and how to improve batch and continuous processes: Pyramid of Technologies

Valve and Flow Meter Performance

Process Control Improvement Examples Basic Control Opportunities Summary

Reactors and Column Loop Tuning

Facts of Life

Transfer of Variability for Batch Sources of Disturbances

Transition from Basic to Advanced Regulatory Control of Batch

Online Data Analytics for Batch and Continuous Processes

Virtual Plant

Uses and Fidelities of Dynamic Process Models

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 Situat ion Management System

Auto Tuning (On-Demand and On-line Adapt ive 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 knowledgeFoundation must be large and

solid enough to suppor t upper

levels. Effort and performance

of upper technologies is highly

dependent on the integrity and

scope of the foundation (typeand sensitivity of measurements

and valves and tun ing of loops)

The greatest success has been

Achieved when the technology

closed the loop (automaticallycorrected the process without

operator intervention)

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


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Loops Behaving Badly

A poorly tuned loop will behave as badly as a loop

with lousy dynamics (e.g. excessive dead time)!

1

Ei = ------------ Ti EoKo Kc

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)

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.

Tune the loops before, during, and after

any process control improvements


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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*5.0

=

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)


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Definition of Deadband and Stick-Slip

Dead band is 5% - 50%without a positioner !

Deadband

Deadband

Stick-Slip

Signal(%)

0

Stroke(%)

Pneumatic positionerrequires a negativesignal to close valve

Digital positionerwill force valveshut at 0% signal

The effect of slip is worse than stick, stick is worse than dead band,

and dead band is worse than stroking time (except for surge control)

Stick-slip causes a limit cycle for self-regulating processes. Deadband causes a limit cycle in

level loops and cascade loops with integral (reset) action. If the cycle is small enough it canget lost in the disturbances, screened out by exception reporting, or attenuated by volumes


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Saw Tooth Flow Controller Output Limit Cycle

from Stick-Slip

Controller Output (%)Saw Tooth Oscillation

Controlled Flow (kpph)

Square Wave Oscillation


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Rounded Level Controller Output Limit Cycle

from Deadband

Manipulated Flow (kpph)

Clipped Oscillation

Controller Output (%)

Rounded Oscillation

Controlled Level (%)

Saw Tooth Oscillation


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Identification of Stick and Slip in

a Closed Loop Response

Time ( Seconds )

Stroke

%

53

53.5

54

54.5

55

55.5

56

56.5

57

57.5

58

58.5

59

0 100 200 300 400 500 600 700 800

3.25 Percent

Backlash + Stiction

Controller Output

Flow

Dead band is

peak to peak

amplitude for

signal reversal

slip

stick

The limit cycle may not be discernable due to frequent disturbances and noise

R Ti f V i P i i


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Response Time of Various Positioners

(small actuators so slewing rate is not limiting)

Response time increase dramatically for steps less than 1%


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Control Valve Facts of Life

Pneumatic positioners are almost always out of calibration

Most tests by valve manufacturers for stick-slip are at 50% with looselytightened stem packing to minimize seating, sealing, and packing friction

Without a representative position feedback in the control room, it is anybodys

guess what the valve is doing unless there is a low noise sensitive flow sensor Not all positioners are equal. Pneumatic positioners, especially the spool or

single amplification stage low gain ones will increase the valve response timeby an order of magnitude (4 -> 40 sec) for small changes in controller output

All valves look good when checking positions for 0, 25, 75, and 100% signals Valve specs do not generally require that the control valve actually move

The tighter the shutoff, the greater the stick-slip for positions less than 20%

Smart positioner diagnostics and position read back are lies for actuator shaftposition feedback of rotary type isolation valves posing as throttling valvesparticularly for pinned rather than splined shaft connections due to twisting ofthe shaft. Field tests show stick-slip of 85 in actual ball or disc movementdespite diagnostics and read back indicating a valve resolution of 0.5%

The official definition of valve rangeability is bogus because it doesnt take into

account stick-slip near the seat. Equal percentage valves with minimal stick-slip (excellent resolution and sensitivity) generally offer the best rangeability


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Top Ten Signs of a Valve Problem

(10) The pipe fitters are complaining about trying to fit a 1 inch

valve into a 10 inch pipe.(9) You bought the valve suppliers monthly special.(8) A butterfly disc wont open because the ID of the lined pipe is

smaller than the OD of the disc.(7) The maintenance department personally put the valve on your

desk.(6) A red slide ruler was used to size a green valve.(5) Your latest valve catalog is dated 1976.(4) The maintenance department said they dont want a double

seat A body.(3) The valve was specified to have 0% leakage for all conditions

including all signals.(2) The fluid field in the sizing program was left as water.(1) The valve is bigger than the pipe.


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Flow Meter Performance

Type Sizes Range Piping Interferences Reproducibility

Coriolis -8 100:1 1/1 solids, alignment, vibration 0.1% of rate

Magmeter -78 25:1 5/1 conductivity, electrical noise 0.5% of rate

Vortex -12 9:1* 10/5 profile, viscosity, hydraulics 1.0% of span

Orifice -78 4:1 10/5 profile, Reynolds Number 5.0% of span

* - assumes a minimum and maximum velocity of about 1 and 9 fps, respectively

Coriolis flow meters via their accurate density measurement offerdirect concentration measurements for 2 component mixtures and

inferential measurements for complex mixtures.


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Neutralizer Control Before

Static Mixer

AC1-1

Neutralizer

Feed

Discharge

AT1-1

FT1-1

FT2-1

AC2-1

AT2-1FC

1-2

FT1-2

Reagent

Stage 2

ReagentStage 1

2

pipe

diameters


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Nonlinearity and Sensitivity of pH

pH

Reagent FlowInfluent Flow

6

8

Reagent Charge

Process Volume

orGood valve resolution or fluid mixing does not look

that much better than poor resolution or mixing dueamplification of X axis (concentration) fluctuations


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Neutralizer Control After

Static Mixer

AC1-1

Neutralizer

Feed

Discharge

AT1-1

FT1-1

FT2-1

AT2-1

FC1-2

FT1-2

Reagent

Stage 1

ReagentStage 2

FC2-1

AC2-1

20

pipe

diameters

f(x)

Feedforward

Summer RSP

Signal

Characterizer

*1

*1

*1 - Isolation valve closes when control valve closes


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Distillation Column Control Before

FC3-4

FT3-4

FC3-3

FT3-3

LT3-1

LC3-1

TE3-2

TC3-2

LT3-2

LC3-2

DistillateReceiver

Column

Overheads

Bottoms

Steam

Feed

Reflux

PC3-1

PT3-1

Vent

Storage Tank

Feed Tank

Tray 10

Thermocouple


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Nonlinearity and Sensitivity of Tray Temperature

Tray 10

Tray 6

Distil late FlowFeed Flow

% Impuri ty

Operating

Point

Temperature

Impurity Errors

Measurement Error

Measurement Error


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Distillation Column Control After

FC

3-2

FT3-2

FC3-4

FT3-4

FC3-3

FT3-3

FC

3-1

FT3-1

LT3-1

LC3-1

TT3-2

TC3-2

FC3-5

FT3-5

LT3-2

LC3-2 RSP

RSPRSP

DistillateReceiver

Column

Overheads

Bottoms

Steam

Feed

Reflux

PC3-1

PT3-1

Vent

Storage Tank

Feed Tank

Tray 6 f(x)

Signal CharacterizerRTD

FT3-3

FT3-3

Feedforward summer

Feedforward summer


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When Process Knowledge is Missing in Action

2-Sigma 2-Sigma

RCASSet Point

LOCAL

Set Point

2-Sigma 2-Sigma

Upper L imitPV distribution fororiginal control

PV distribution forimproved control

Extra margin when

war stories ormythology rules

value

Good engineers can draw straight lines

Great engineers can move straight lines

Benefits are not realized until the set point is moved!

(may get benefits by better set point based on processknowledge even if variability has not been reduced)


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Top Ten Ways to Impress Your Management

with the Trends of a Control System

(10) Make large set point changes that will zip past valve dead

band and local nonlinearities(9) Change the set point to operate on the flat part of the titration

curve(8) Select the tray with minimum process sensitivity for column

temperature control

(7) Pick periods when the unit was down(6) Decrease the time span so that just a couple data points are

trended(5) Increase the reporting interval so that just a couple data points

are trended

(4) Use really thick line sizes(3) Add huge signal filters(2) Increase the process variable scale span so it is at least ten times

the control region of interest(1) Increase the historians data compression so that most changes

are screened out as insignificant

O C


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Basic Opportunities in Process Control

Decrease stick-slip and improve the sensitivity of the final element(Standard Deviation is the product of stick-slip, valve gain, and processgain) Use properly tuned smart positioners, short shafts with tight connections,

and low friction packing and seating surfaces to decrease valve slip-stick anddead band (do not use isolation valves for throttling valves)

If high friction packing must be used, aggressively tune the smart positioner

Improve valve type and sizing and add signal characterization to increasevalve sensitivity

Use variable speed drives where appropriate for the best sensitivity

Improve the short and long term reproducibility and reduce theinterference and noise in the measurement (Standard Deviation isproportional to reproducibility and noise) Use magnetic and Coriolis mass flow meters to eliminate sensing lines,

improve rangeability, and reduce effect of Reynolds Number and piping

Use smart transmitters to reduce process and ambient effects Use RTDs and digital transmitters to decrease temperature noise and drift

B i O t iti i P C t l


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Basic Opportunities in Process Control

Reduce loop dead time (Minimum Integrated Error is proportional tothe dead time squared) Decrease valve dead time (stick and dead band)

Decrease transport (plug flow volume) and mixing delay (turnover time)

Decrease measurement lags (sensor lag, dampening, and filter time)

Decrease discrete device delays (scan or update time) Decrease analyzer sample transport and cycle time

Tune the controllers (Integrated Error is inversely proportional to thecontroller gain and directly proportional to the controller integral time)

Add cascade control (Standard Deviation is proportional to the ratio of theperiod of the secondary to the process time constant of the primary loop)

Add feed forward control (Standard Deviation is proportional to the rootmean square of the measurement, feed forward gain, and timing errors)

Eliminate or slow down disturbances (track down source and speed)

Add inline analyzers (probes) and at-line analyzers with automatedsampling since ultimately what you want to control is a composition

Optimize set points (based on process knowledge and variability) To realize the benefit of reduced variability, often need to change a set point

R t Gi Th Wh t Th W t


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Reset Gives Them What They Want

SPPVOut

5244?

TC-101Reactor Temperature

steamvalveopens

water

valveopens

50%

Reset wont open the water valve

Until the error changes sign, PV

goes above the set point. Reset

has no sense of direction.

set point (SP)

temperature

time

PV

Should the steam or

water valve be open?

Proportional and rate action see

the trajectory visible in a trend!

Both would work to open the

water valve to prevent overshoot.

Reset action integrates the numeric

difference between the PV and SP

seen by operator on a loop faceplate

Reset works to open the steam valve

R t d C l L T i


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Reactor and Column Loop Tuning

Most reactor and column composition, gas pressure, and temperatureloops have too much integral action (reset time too small), not enoughproportional action (gain too small), and not enough derivative action(rate time too small). Rate time should be 0.1x process time constant or 0.1x reset time with a

minimum value of sensor lag time.

Rate action is essential for exothermic reactors that can runaway

Often these loops are near integrators due to a large process timeconstant . Batch processes often have true integrators because of alack of self-regulation (no steady state). Whether near integrators ortrue integrators, these loops require much more gain action to imposeself-regulation and provide pre-emptive action. There is a window of

allowable gains where too low of a controller gain will result in slowrolling oscillations from reset.

(controller gain) * (controller reset time) > 4 / (integrating process gain)

M d li d C t l F t f Lif


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Modeling and Control Facts of Life

Timing is Everything

In life, business, and process control (especially feedforward)

Without Dead Time I would be Out of Job If the dead time was zero, the only limit to how high you can set the controller

gain or how tight you can control is measurement noise

Unlike aerospace, the process industry has large and variable time delays and

time lags from batch cycle times, vessel mixing times, volume residencetimes, transportation delays, resolution limits, dead band, and measurements

Total dead time is sum of time delays and all time lags smaller than largest

Best possible integrated absolute error is proportional to dead time squared



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Models (experimental or theoretical) allow you to take the blindfold off

Models convey process knowledge and provide insight on what has changed andwhat should be improved (e.g. largest source of dead time)

War stories rule where there are no models

Mythology rules where there are no models

Benefits are hearsay where there are no models

Nonlinearity is a reason to build models rather than avoid models

Unless you want job security for constantly retuning controllers. Also, implied inmost techniques is some model (e.g. reaction curve method)

Tight control greatly reduces the operating point nonlinearity (e.g. pH) andsecondary flow loops eliminate the valve nonlinearity for higher level loops

Signal characterization on the controller output (based on a model of theinstalled valve characteristic) greatly reduces the valve nonlinearity

Speed of Various Sources of Disturbances


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p

(Speed Kills)

Process Flow (fast)

Gas pressure (fast)

Liquid Pressure (very fast)

Raw Materials (slow)

Recycle (very slow)

Temperature (slow)

Catalyst (slow)

Steam (fast) Coolant (fast)

Equipment

Fouling (slow)

Failures (fast)

Environmental

Day to Night (slow)

Rain Storms and fronts (fast)

Season to Season (very slow)

A loop can catch up to a slow

disturbance. Liquid pressure

Is the fastest upset (travels at

the speed of sound in liquid).



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(Speed Kills)

Valves

Stick-slip (fast)

Split Range (fast)

Failures (very fast)

Measurements

Noise (very fast)

Reproducibility (fast)


Controllers

Feedback Tuning (fast) *

Feed forward Timing (fast)

Interaction (fast)


* Most frequent culprit is an oscillating level loop primarily due to excessive reset action



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(Speed Kills)

Market* Rate changes (fast)

Product transitions (fast)

Operators Manual operation (fast)

Sweet spots (fast)

Inventory control (fast)

Discrete On-off control (very fast)

Sequences (fast)

Batch operations (fast)

Startup and shutdown (very fast)

Interlocks (very fast)

*For minimized inventory, changes in market demand can result in

fast production rate changes and product grade or type transitions

Batch Control


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Batch Control

Variabili ty Transfer from Feeds topH, and Reactant and Product Concentrations

Feeds Concentrations

Optimum pH

OptimumProduct

pH

Product

OptimumReactant

Reactant

Reagent

Reactant

Most published cases of multivariate statistical process control (MSPC) use the processvariables and this case of variations in process variables induced by sequenced flows.

PID Control


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PID Control

Variabili ty Transfer from pH and ReactantConcentration to Feeds

Concentrations

Optimum pH

OptimumProduct

Feeds

pH

Product

Reagent

Reactant

OptimumReactant

Reactant

The story is now in the controller outputs

(manipulated flows) yet MSPC still focuseson the process variables for analysis



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

Optimum pH

OptimumProduct

Feeds Concentrations

pH

Product

Reagent

Reactant

OptimumReactant

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

Example of Basic PID Control


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feed A

feed B

coolantmakeup

CAS

ratio

cont ro l

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


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feed A

feed B

coolantmakeup

CAS

ratio

CAS

reactor

vent

product

maximumproductionrate

condenser

CTW

PT

PC-1

TT

TT

TC-2

TC-1

FC-1

FT

FT

FC-2

10 biological)

Product development, process design, real time optimization, advanced controlprototyping 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 atlimits, % 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, dataanalytics, 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

Key Points


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Tune the loops

Use digital positioners and throttle valves to get resolution better than 0.5%

Use Coriolis and Magmeters to get accuracy better than 0.5% of rate Tune the loops

Add cascade and feed forward control for disturbances

Model the process to dispel myths and build on process knowledge

Improve the set points Add composition control

Reduce the size and speed of disturbances

Transfer variability from most important process outputs

Add online data analytics (multivariate statistical process control) Add online metrics to spur competition, and to adjust, verify, and justify controls

Control Magazine Columns and Articles


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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

ControlLoopFoundationBatchandContinuousRevD.pdf

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