04/10/23 1

Control Loop Foundation for

Batch and Continuous Control

GREGORY K MCMILLAN

use pure black and white option for printing copies

04/10/23 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)

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See Chapter 2 for more info on “Setting the Foundation”

Purchase

04/10/23 4

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

Purchase

04/10/23 5

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

Purchase

04/10/23 6

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

Purchase

Overview

This presentation covers highlights or low lights of current loop performance 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

04/10/23 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

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)

04/10/23 9

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

04/10/23 11

Definition of Deadband and Stick-Slip

Deadband

Deadband

Stick-Slip

Signal (%)

0

Stroke (%)

Digital positionerwill force valve shut at 0% signal

Pneumatic positionerrequires a negative signal to close valve

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)

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

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 can

get lost in the disturbances, screened out by exception reporting, or attenuated by volumes

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Controller Output (%)Saw Tooth Oscillation

Controlled Flow (kpph)Square Wave Oscillation

Saw Tooth Flow Controller Output Limit Cycle from Stick-Slip

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Manipulated Flow (kpph)Clipped Oscillation

Controller Output (%)Rounded Oscillation

Controlled Level (%)Saw Tooth Oscillation

Rounded Level Controller Output Limit Cycle from Deadband

04/10/23 14

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 OutputFlow

Dead band ispeak to peakamplitude forsignal reversal

slip

stick

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

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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 loosely

tightened stem packing to minimize seating, sealing, and packing friction Without a representative position feedback in the control room, it is anybody’s

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 time by 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 shaft

position feedback of rotary type isolation valves posing as throttling valves particularly for pinned rather than splined shaft connections due to twisting of the shaft. Field tests show stick-slip of 85 in actual ball or disc movement despite diagnostics and read back indicating a valve resolution of 0.5%

The official definition of valve rangeability is bogus because it doesn’t take into account stick-slip near the seat. Equal percentage valves with minimal stick-slip (excellent resolution and sensitivity) generally offer the best rangeability

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 won’t 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 don’t

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 ¼ -8” 100:1 1/1 solids, alignment, vibration 0.1% of rate Magmeter ¼-78” 25:1 5/1 conductivity, electrical noise 0.5% of rate Vortex ½-12” 9:1* 10/5 profile, viscosity, hydraulics 1.0% of span Orifice ¼-78” 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 offer

direct concentration measurements for 2 component mixtures and

inferential measurements for complex mixtures.

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Neutralizer Control – “Before”

Static Mixer

AC 1-1

Neutralizer

Feed

Discharge

AT 1-1

FT 1-1

FT 2-1

AC 2-1

AT 2-1 FC

1-2

FT 1-2

2pipe

diameters

ReagentStage 1

ReagentStage 2

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Nonlinearity and Sensitivity of pH

pH

Reagent FlowInfluent Flow

6

8

Good valve resolution or fluid mixing does not look

that much better than poor resolution or mixing due

amplification of X axis (concentration) fluctuations

Reagent ChargeProcess Volume

or

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Neutralizer Control – “After”

Static Mixer

AC 1-1

Neutralizer

Feed

Discharge

AT 1-1

FT 1-1

FT 2-1

AT 2-1

FC 1-2

FT 1-2

ReagentStage 1

ReagentStage 2

FC 2-1

AC 2-1

20pipe

diameters

f(x)

FeedforwardSummer RSP

SignalCharacterizer

*1

*1

*1 - Isolation valve closes when control valve closes

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Distillation Column Control – “Before”

FC 3-4

FT 3-4

FC 3-3

FT 3-3

LT 3-1

LC 3-1

TE3-2

TC 3-2

LT 3-2

LC 3-2

Distillate Receiver

Column

Overheads

Bottoms

Steam

Feed

Reflux

PC 3-1

PT 3-1

Vent

Storage Tank

Feed Tank

Tray 10

Thermocouple

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Nonlinearity and Sensitivity of Tray Temperature

Tray 10

Tray 6

Distillate FlowFeed Flow

% Impurity

Temperature

OperatingPoint

Measurement Error

Measurement Error

Impurity Errors

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Distillation Column Control – “After”

FC 3-2

FT 3-2

FC 3-4

FT 3-4

FC 3-3

FT 3-3

FC 3-1

FT 3-1

LT 3-1

LC 3-1

TT3-2

TC 3-2

FC 3-5

FT 3-5

LT 3-2

LC 3-2 RSP

RSPRSP

Distillate Receiver

Column

Overheads

Bottoms

Steam

Feed

Reflux

PC 3-1

PT 3-1

Vent

Storage Tank

Feed Tank

Tray 6 f(x)

Signal CharacterizerRTD

FT3-3

FT3-3

Feedforward summer

Feedforward summer

04/10/23 25

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

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 historian’s data compression so that

most changes are screened out as insignificant

04/10/23 27

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 process gain)

Use properly tuned smart positioners, short shafts with tight connections, and low friction packing and seating surfaces to decrease valve slip-stick and dead 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 increase

valve sensitivity Use variable speed drives where appropriate for the best sensitivity

Improve the short and long term reproducibility and reduce the interference and noise in the measurement (Standard Deviation is proportional 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

04/10/23 28

Basic Opportunities in Process Control

Reduce loop dead time (Minimum Integrated Error is proportional to the 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 the controller gain and directly proportional to the controller integral time)

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

Add feed forward control (Standard Deviation is proportional to the root mean 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 automated

sampling 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

04/10/23 29

Reset Gives Them What They Want

SPPVOut

52 44 ?

TC-101Reactor Temperature

steam valveopens

watervalveopens

50%

Proportional and rate action seethe 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

Reset won’t open the water valveUntil the error changes sign, PVgoes above the set point. Reset has no sense of direction.

set point (SP)

temperature

time

PV

Should the steam or

water valve be open?

04/10/23 30

Reactor and Column Loop Tuning

Most reactor and column composition, gas pressure, and temperature loops have too much integral action (reset time too small), not enough proportional 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 time constant . Batch processes often have “true integrators” because of a lack of self-regulation (no steady state). Whether “near integrators” or “true integrators”, these loops require much more gain action to impose self-regulation and provide pre-emptive action. There is a window of allowable gains where too low of a controller gain will result in slow rolling oscillations from reset. (controller gain) * (controller reset time) > 4 / (integrating process gain)

04/10/23 31

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 residence times, 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

04/10/23 32

Modeling and Control Facts of Life

Models (experimental or theoretical) allow you to take the blindfold off Models convey process knowledge and provide insight on what has changed and

what 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 in

most techniques is some model (e.g. reaction curve method) Tight control greatly reduces the operating point nonlinearity (e.g. pH) and

secondary flow loops eliminate the valve nonlinearity for higher level loops Signal characterization on the controller output (based on a model of the

installed valve characteristic) greatly reduces the valve nonlinearity

04/10/23 33

Speed of Various Sources of Disturbances(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).

04/10/23 34

Speed of Various Sources of Disturbances(Speed Kills)

Valves Stick-slip (fast) Split Range (fast) Failures (very fast)

Measurements Noise (very fast) Reproducibility (fast) Failures (very fast)

Controllers Feedback Tuning (fast) * Feed forward Timing (fast) Interaction (fast) Failures (very fast)

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

04/10/23 35

Speed of Various Sources of Disturbances(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

04/10/23 36

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

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

04/10/23 37

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

04/10/23 38

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

04/10/23 39

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

04/10/23 40

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

04/10/23 41

Function Blocks for Online Data Analytics

Function blocks developed to support on-line batch and continuous analytics– PCA Block– PLS Block– Analyzer Block

04/10/23 42

Analyzer Block for Online Data Analytics

History Collection of Lab and Spectral Analyzer Data

Controller

ModuleLab Results

Analyzer Block

Historian Operator Station

Off-line Modeling

OtherData

Processing of Sample Data for Use in Analytics

04/10/23 43

Dynamic Time Warping for Online Batch Data Analytics

Reference trajectory

Trajectory to be synchronized

Synchronized trajectory

04/10/23 44

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

04/10/23 45

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

04/10/23 46

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

04/10/23 47

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

04/10/23 48

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?

04/10/23 49

Key Points

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

04/10/23 50

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