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Biological-Chemical Reactor ControlPrinciples and Methods forImproving Product Quality and Optimizing Production Rate

22

Presenter

– Greg is a retired Senior Fellow from Solutia/Monsanto and an ISA Fellow. Greg was an adjunct professor in the Washington University Saint Louis Chemical Engineering Department 2001-2004. Presently, Greg contracts as a consultant in DeltaV R&D via CDI Process & Industrial and is a part time employee of Experitec and MYNAH. 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, was honored by InTech Magazine in 2003 as one of the most influential innovators in automation, and received the ISA Life Achievement Award in 2010. Greg is the author of numerous books on process control, his most recent being Advanced Temperature Measurement and Control. Greg has been the monthly “Control Talk” columnist for Control magazine since 2002. Greg’s expertise is available on the web site: http://www.modelingandcontrol.com/

http://www.modelingandcontrol.com/

3

Top Ten Things You Don’t Want to Hear in a Project Definition Meeting

• (10) I don’t want any smart instrumentation talking back to me• (9) Let’s study each loop to see if the valve really needs a positioner• (8) Lets slap an actuator on our piping valves and use them for control

valves• (7) We just need to make sure the control valve spec requires the

tightest shutoff• (6) What is the big deal about process control, we just have to set the

flow per the PFD• (5) Cascade control seems awfully complex• (4) The operators can tune the loops• (3) Let’s do the project for half the money in half the time• (2) Let’s go with packaged equipment and let the equipment supplier

select and design the automation system• (1) Let’s go out for bids and have purchasing pick the best deal

4

Introduction

• Biological and chemical process performance is largely determined by reactor performance. The stage for product quality and process efficiency and capacity is set by reaction yield and selectivity.

• An increase in yield can be used to increase production rate for same feed rate or used to decrease raw material costs for the same production rate.

• For batch reactors, an increase in yield can be taken as shorter cycle time for the same charges or as smaller charges for the same cycle time.

• A higher yield reduces downgraded products, recycle, and waste. • Reactor type, reaction rate, and time available for reaction affect yield.• Temperature, concentration, and sometimes pressure affect reaction rate.• Inventory and feed rate determines the amount of time reactants are in

reactor (residence time), which determines time available for reaction.• Process control of temperature, concentration, pressure, inventory, and feed

rate is essential to achieve reaction rate and time for maximum yield.• Endpoint control inherently prevents the accumulation of excess reactants.• Valve position control increases reactant feed rate to limits of utility systems.• Valve position control increases rangeability of utility systems.

5

Reactor TypeDynamics and Control

ReactorType

DynamicResponse

Residence Time Distribution

ReactionRate

AdditionalControl *

CSTRNear-Regulating

Runaway Wide ModerateLevel

Composition

BatchIntegratingRunaway Very Tight Slow None

Fed-BatchIntegratingRunaway Very Tight Slow Time Profile

Plug FlowSelf-Regulating

Runaway Tight Fast Length Profile

GasSelf-Regulating

Runaway Tight Very Fast Composition

* - Additional control besides temperature and pressure control

6

Liquid Reactants (Jacket CTW) Liquid Product Basic Control

TT1-4

TC1-3

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

CAS

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

reactant A

reactant B

residencetime calc LT

1-8TT1-3

LC1-8

CAS

ratiocalc

product

ventFT1-1

FC1-1

FC1-7

FT 1-7

CAS

PT1-5

PC1-5

FT 1-5

CTW

7

Liquid Reactants (Jacket CTW) Liquid Product Optimization

TT1-4

TC1-3

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

CAS

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

reactant A

reactant B


1-8TT1-3

LC1-8

CAS

ratiocalc

product

ventFT1-1

FC1-1

FC 1-1CAS

ZC1-4OUT

ZC1-4

FC1-7

FT 1-7

PT1-5

PC1-5

FT 1-5

CTW

ZC1-4 is an enhanced PID VPC

Valve position controller (VPC) setpointis the maximum throttle position. TheVPC should turn off integral action toprevent interaction and limit cycles. Thecorrection for a valve position less thansetpoint should be slow to provide a slow approach to optimum. The correction fora valve position greater than setpoint mustbe fast to provide a fast getaway from thepoint of loss of control. Directional velocitylimits in AO with dynamic reset limit in anenhanced PID that tempers integral actioncan achieve these optimization objectives.

8

Liquid Reactants (Jacket BFW) Liquid Product Basic Control

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

CAS

residencetime calc

CAS

ratiocalc

AC1-6

steam

PT1-4

TC1-3

PC1-4

LY 1-8

FY 1-6

reactant A

reactant B


1-8TT1-3

LC1-8

CAS

ratiocalc

product

ventFT1-1

FC1-1

LT1-9

LC1-9

FC1-7

FT 1-7

PT1-5

PC1-5

FT 1-5

BFW FT 1-9

9

Liquid Reactants (Jacket BFW) Liquid Product Optimization

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

CAS

residencetime calc

CAS

ratiocalc

AC1-6

steam

PT1-4

TC1-3

PC1-4

LY 1-8

FY 1-6

reactant A

reactant B


1-8TT1-3

LC1-8

CAS

ratiocalc

product

ventFT1-1

FC1-1

LT1-9

LC1-9

BFW

ZC1-4

ZC1-9

ZY 1-1

FC1-1CAS

ZY1-1OUT

low signalselector

FC1-7

FT 1-7

PT1-5

PC1-5

FT 1-5

FT 1-9

ZC1-4 & ZC-9 are enhanced PID VPC

10

Gas Reactants (Jacket BFW) Gas Product Optimization

AT 1-6

FT1-2

FC1-2

CAS

AC1-6

TC1-3

FY 1-6

gas reactant A

TT1-3b

TT1-3a

CAS

ratiocalc

product

FT1-1

FC1-1

steamBFW

gas reactant B

TT1-3c

steamBFW

steamBFW

TY 1-3

high signalselector

average bedtemperatures

Fluidized BedCatalytic Reactor

PT1-5

PC1-5

FT 1-5

Fast reaction, short residence time, and high heat release prevents inverse response in manipulation of reactantfeed rate for temperature control.

Temperature controller inherentlymaximizes reactant feed rate toamount permitted by the numberof BFW coils in service

11

Gas & Liquid Reactants (Jacket CTW) Liquid Product End Point Control

TT1-4

PC1-5

TC1-3

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

gas reactant A

liquid reactant B

CAS

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

residencetime calc

CAS

ratiocalc

product

purgeFT1-1

FC1-1

FC1-5

FT 1-5

LT1-8

TT1-3

PT1-5

LC1-8

CAS

FC1-7

FT 1-7

CTW

Pressure control inherentlyprevents an excess of gas reactant providing endpointcontrol for liquid product in continuous andfed-batch reactors.

12

Gas & Liquid Reactants (Jacket CTW) Gas Product End Point Control

TT1-4

TC1-3

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

liquid reactant A

gas reactant B

CAS

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6


1-8TT1-3

LC1-8

CAS

ratiocalc

purge

product

FT1-1

FC1-1

CAS

FC1-7

FT 1-7

PC1-5

FT 1-5

PT1-5

CTW

Level control inherentlyprevents an excess of liquidreactant providing endpointcontrol for gas product incontinuous reactors.

13

Liquid Reactants (Jacket CTW) Gas & Liquid Products Basic Control

TT1-4

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

reactant A

reactant B

CAS


1-8

LC1-8

CAS

ratiocalc

product

FT1-1

FC1-1

FC1-7

FT 1-7

CAS

TC1-3

TT1-3

product

PC1-5

FT 1-5

W

PT1-5

TT1-10

TC1-10

CTW

14

Liquid Reactants (Jacket CTW) Gas & Liquid Products Optimization

TT1-4

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

reactant A

reactant B

CAS


1-8

LC1-8

CAS

ratiocalc

product

FT1-1

FC1-1

FC1-7

FT 1-7

CAS

TC1-3

TT1-3

product

PC1-5

FT 1-5

W

PT1-5

TT1-10

TC1-10

ZC1-4

ZC1-10

ZC1-5

ZY 1-1

FC1-1CAS

ZY1-1OUT

low signalselector

ZC-10OUT

ZC-4OUT

ZC-5OUT

ZY-1IN1

ZY-1IN2

ZY-1IN3

CTW

ZC1-4, ZC-5, & ZC-10 are enhanced PID VPC

15

Liquid Reactants (Jacket CTW) Liquid Product with Recycle Basic Control

TT1-4

TC1-3

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

reactant A

reactant B


1-8TT1-3

LC1-8

CAS

ratiocalc

product

ventFT1-1

FC1-1

FC1-7

FT 1-7

PT1-5

PC1-5

FT 1-5

Recycle fromrecovery column distillate receiver

CAS CAS

CTW

A flow controller in the recyclepath from reactant in product back as recovered reactant is necessary to prevent a snowballing effect and divergenceof reactant concentration. In thiscase the flow controller is on thereactor discharge and recovery column distillate receiver level controller provides fresh makeup of recycled reactant as needed.

16

Liquid Reactants (Jacket CTW) Liquid Product with Recycle Optimization

TT1-4

TC1-3

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

reactant A

reactant B


1-8TT1-3CAS

ratiocalc

product

ventFT1-1

FC1-1

FC1-7

FT 1-7

PT1-5

PC1-5

FT 1-5

Recycle fromrecovery column distillate receiver

ZC1-4

LC1-8

CAS CAS

CTW


The product discharge flowcontroller setpoint, whichsets reactor production ratecan be increased by a VPC to the maximum throttle limit of coolant system valve.

17

Jacket CTWOutlet Temperature Control

TT1-4

TC1-4

makeup

return

CTW

18

Jacket CTWInlet Temperature Control

TT1-4

TC1-4

makeup

return

CTW

19

Jacket CTW with Steam Injection Outlet Temperature Control

TT1-4

TC1-4

TY1-4splitter

stea

m

steaminjectionheater CTW

makeup

return

20

Reactor Heat Exchanger Recirc Temperature Control

TT1-4

product

W

TT1-4

TC1-4

CTW

21

Reactor Heat Exchanger Recirc Temperature Bypass Control

TT1-4

TC1-4

product

W

CTW

22

Reactor Heat Exchanger Recirc Temperature Bypass Optimization

TT1-4

TC1-4

product

W

ZC1-4

CTW


23

Jacket Steam & CTW Heat Exchanger Inlet Temperature Control

TT1-4

TC1-4

W

W

TY1-4

CTW

steam

splitter

24

Jacket CTW Heat ExchangerInlet Temperature Bypass Control

TT1-4

TC1-4

WCTW

25

Jacket CTW Heat Exchanger Inlet Temperature Bypass Optimization

TT1-4

TC1-4

Wchilledwater

ZC1-4


26

Reactor Inferential Measurements ofHeat Transfer Rate and Conversion

TT1-4a

makeup

return

TT1-4bDT

SUB

FI 1-4MUL

SpecificHeat

CapacityCp

Heat Transfer Rate

INTIntegration

Period Total Heat Transferred (Inferential Measurement of Conversion)

DIVJacketVolume

JacketFlow

Integration Period is continuous reactor residence time or batch reactor cycle time

27

Batch Reactor ConcentrationProfile Slope Control

TT1-4

TC1-3

TC1-4

AT 1-6

LY 1-8

FY 1-6

FT1-2

FC1-2

reactant A

reactant B

CAS

residencetime calc

CAS

ratiocalc

AC1-6

makeup

return

LY 1-8

FY 1-6

reactant A

reactant B


1-8TT1-3

LC1-8

CAS

ratiocalc

product

ventFT1-1

FC1-1

FC1-7

FT 1-7

CAS

PT1-5

PC1-5

FT 1-5

DT

SUB

DIV

LoopDeadtime

ΔPV

SlopeΔPV/Δt

2828

Innovative PID System to Optimize Ethanol Yield and Carbon Footprint

AT 1- 4

AC 1- 4

DC 2- 4

SC 1-4

FT 1- 5

FC 1- 5

AY 1- 4

Corn

NIR-T

Production RateEnhanced PID

DT 2- 4

Slurry SolidsEnhanced PID

DX 2- 4

Feedforward

FT 1- 6

FC 1- 6

Backset Recycle

Dilution Water

XC 1- 4

XY 1- 4

Average Fermentation TimeEnhanced PID

Fermentable Starch Correction

SlurryTank 1

SlurryTank 2

CoriolisMeter

setpoint

DY 2- 4

Lag and Delay

RCAS

Predicted Fermentable Starch

29

Bioreactor

VSD

VSD

TC 1-7

AT 1-4s2

AT 1-4s1

AT 1-2

AT 1-1

TT 1-7

AT 1-6

LT 1-8

Glucose

Glutamine

pH

DO

Product

HeaterVSD

VSD

Media

Glucose

Glutamine

Inoculums

VSDBicarbonate

AY 1-1

AC 1-1

Splitter

AC 1-2

AY 1-2

Splitter

CO2

O2

Air

0.002 g/L

7.0 pH

37 oC

MFC

MFC

AT 1-5x1

ViableCells

AT 1-5x2DeadCells

MFC - Mass Flow ControllerVSD - Variable Speed Drive

MFC

wireless

wireless

spargers

Mammalian Bioreactor WithEnhanced DO and pH Control

AT 1-9

Off-gas

Calc

OURMass Spec

AC1-1 and AC1-2are Enhanced PID

Product

30

Bioreactor

VSD

VSD

TC 1-7

AT 1-4s2

AT 1-4s1

AT 1-2

AT 1-1

TT 1-7

AT 1-6

LT 1-8

Glucose

Glutamine

pH

DO

Product

HeaterVSD

VSD

AC 1-4s1

AC 1-4s2

Media

Glucose

Glutamine

Inoculums

VSDBicarbonate

AY 1-1

AC 1-1

Splitter

AC 1-2

AY 1-2

Splitter

CO2

O2

AirProduct

0.002 g/L

7.0 pH

2.0 g/L

2.0 g/L

37 oC

MFC

MFC

AT 1-5x1

AT 1-5x2


MFC

wireless

wireless

spargers

Mammalian Bioreactor WithEnhanced Substrate Control

Substrate (Glutamine/Glucose) RatioControl with OUR Feedforward

AT 1-9

Off-gas

Mass Spec

Calc

FF

RatioOUR


AC1-4s1 and AC1-4s2are Enhanced PID

ViableCells

DeadCells

31

Top Ten Reasons Why an Automation Engineer Makes a Great Spouse or at Least a Wedding Gift

• (10) Reliable from day one• (9) Always on the job• (8) Low maintenance (minimal grooming, clothing, and entertainment costs• (7) Many programmable features• (6) Stable• (5) Short settling time• (4) No frills or extraneous features• (3) Relies on feedback• (2) Good response to commands and amenable to real time optimization• (1) Readily tuned

32

Enhanced PID Algorithm Originally Developed for Wireless

PID integral mode is restructured to provide integral action to match the process response in the elapsed time (reset time set equal to process time constant)PID derivative mode is modified to compute a rate of change over the elapsed time from the last new measurement valuePID reset and rate action are only computed when there is a new valueIf transmitter damping is set to make noise amplitude less than communication trigger level, valve packing and battery life is dramatically improvedEnhancement compensates for measurement sample time suppressing oscillations and enabling a smooth recovery from a loss in communications further extending packing -battery life

+

+

+

+

Elapsed Time

Elapsed Time

TD

Kc

Kc

TD

http://www2.emersonprocess.com/siteadmincenter/PM%20DeltaV%20Documents/Whitepapers/WP_DeltaV%20PID%20Enhancements%20for%20Wireless.pdf

Link to Enhanced PID White Paper

33

Broadley-James Corporation Wireless Bioreactor Setup

• Hyclone 100 liter Single Use Bioreactor (SUB)

• Rosemount WirelessHART gateway and transmitters for measurement and control of pH and temperature. (pressure monitored)

• BioNet lab optimized control system based on DeltaV

34

Wireless Bioreactor pHGain 30 Reset 1200

35

Gain 40 Reset 500

Output comes off high limit at 36.8 oC

0.30 oC overshoot

Wireless Bioreactor Temperature

36

Gain 40 Reset 5000


0.12 oC overshoot


37

Gain 40 Reset 10000

0.13 oC overshoot



38

Gain 40 Reset 15000

0.20 oC overshoot



39

Gain 80 Reset 15000

0.11 oC overshoot



Best Tuning: < 0.01 oC overshoot for Gain = 80 Reset = 5000

40

Bioreactor

VSD

VSD

TC 1-7

AT 1-4s2

AT 1-4s1

AT 1-2

AT 1-1

TT 1-7

AT 1-6

LT 1-8

Glucose

Glutamine

pH

DO

Product

HeaterVSD

VSD

AC 1-4s1

AC 1-4s2

Media

Glucose

Glutamine

Inoculums

VSDBicarbonate

AY 1-1

AC 1-1

Splitter

AC 1-2

AY 1-2

Splitter

CO2

O2

AirProduct

0.002 g/L

7.0 pH

37 oC

MFC

MFC

AT 1-5x2

AT 1-5x2


MFC

wireless

wireless

spargers

Mammalian Bioreactor WithViable Cell and Product Profile Control

AT 1-9

Off-gas

Mass Spec

Calc

FF

RatioOUR


MPCCalc

Calc

ProductFormation

Rate

GrowthRate

ViableCells

DeadCells

AC1-4s1 and AC1-4s2are Enhanced PID

41

Model Predictive Control (MPC) Identified Responses for Profile

42

Product Formation Rate

Biomass Growth rate

Substrate

Dissolved Oxygen

Model Predictive Control (MPC) Profile Control Test

43

Batch Basic Fed-Batch APC Fed-BatchBatch

Inoculation Inoculation

Dissolved Oxygen (AT6-2)

pH (AT6-1)

Estimated Substrate Concentration (AT6-4)

Estimated Biomass Concentration (AT6-5)

Estimated Product Concentration (AT6-6)

Estimated Net Production Rate (AY6-12)

Estimated Biomass Growth Rate (AY6-11)

MPC in Auto

Model Predictive Control (MPC) Profile Control Cycle Time Reduction

44

Current Product Yield (AY6-10D)

Current Batch Time (AY6-10A)

Predicted Batch Cycle Time (AY6-10B)

Predicted Cycle Time Improvement (AY6-10C)

Predicted Final Product Yield (AY6-10E)

Predicted YieldImprovement (AY6-10F)

Batch Basic Fed-Batch APC Fed-BatchBatchInoculation

Inoculation

MPC in Auto

Predicted Final Product Yield (AY6-10E)

Predicted Batch Cycle Time (AY6-10B)

Model Predictive Control (MPC) Profile Control Improved Predictions

45

τp1 θp2 τp2 Kpθp1

τc1 τm2 θm2 τm1 θm1Kmθcτc2

Kc Ti Td

Valve Process

Controller Measurement

Kvτvθv

KLτLθL

Load Upset

Δ%PV

Δ%CO

ΔMVΔPV

PID

Delay Lag

Delay Delay Delay

Delay

Delay

Delay

Lag Lag Lag

LagLagLag

Lag

Gain

Gain

Gain

Gain

LocalSet Point

ΔDV

First Order Approximation: θo ≅ θv + θp1 + θp2 + θm1 + θm2 + θc + τv + τp1 + τm1 + τm2 + τc1 + τc2(set by automation system design for flow, pressure, level, speed, surge, and static mixer pH control)

%

%%

Delay <=> Dead TimeLag <=>Time Constant

For integrating processes: Ki = Kv ∗ (Kp / τp2 ) ∗ Km

100% / span

Loop Block Diagram (First Order Approximation)

Hopefully τp2 is the largest lag in the loop

½ of Wireless Default Update Rate

46

Time (seconds)

% Controlled Variable (%PV) or

% Controller Output (%CO)

Δ%CO

Δ%PV

θo

Ko = Δ%PV / Δ%CO

0.63∗Δ%PV

%CO

%PV

Self-regulating process open loopnegative feedback time constant

Self-regulating process gain (%/%)

Response to change in controller output with controller in manual

observed total loopdeadtime

ideally τp2τo

Maximum speedin 4 deadtimesis critical speed

Open Loop Response ofSelf-Regulating Process

Noise Band

For CSTR τo >> θo processresponse appears to ramp for 10 θo and is termed a “near-integrating” process

For plug flow reactor andthe manipulation of feed θo >> τo process responseis a transport delay and istermed “deadtime dominant”

47

Time (seconds)θo

Ki = { [ %PV2 / Δt2 ] − [ %PV1 / Δt1 ] } / Δ%CO

Δ%CO

ramp rate isΔ%PV1 / Δt1

ramp rate isΔ%PV2 / Δt2

%CO

%PV

Integrating process gain (%/sec/%)

Response to change in controller output with controller in manual% Process Variable (%PV)

or% Controller Output (%CO)

observed total loopdeadtime


Open Loop Response ofIntegrating Process

Wireless Trigger Level > Noise

Wireless DefaultUpdate Rate

Wireless default update rate must be fastenough that excursion for maximum ramprate is less than wireless trigger level that is set just larger than measurement noise

48

Response to change in controller output with controller in manual

Noise Band

Acceleration

Δ%PV

Δ%CO

1.72∗Δ%PV

Ko = Δ%PV / Δ%CO Runaway process gain (%/%)

% Process Variable (%PV) or

% Controller Output (%CO)

Time (seconds)observed total loopdeadtime runaway process open loop

positive feedback time constant

For safety reasons, tests are terminated after 4 deadtimes


Open Loop Response ofRunaway Process

Wireless presently notadvisable for runaway

τ’o must be τ’p2θo

Tests are terminatedbefore a noticeableacceleration leadingto characterization asan integrating process

49

COtPVMaxKKo

oi %/)/%( ΔΔΔ==

τ

For “Near Integrating” gain approximation use maximum ramp rate divided by change in controller output

The above equation can be solved for the process time constant by taking the process gain to be 1.0 or for more sophistication as the

average ratio of the controlled variable to controller output

Tuning test can be done for a setpoint change if the PID gain is > 2 and the PID structure is

“PI on Error D on PV” so you see a step changein controller output from the proportional mode

Near Integrator Gain Approximation

The maximum ramp rate is found by passing filtered process variable (PV) through a deadtime (DT) block to create an old process variable. The deadtime block uses the total loop deadtime (θo) for the time interval (Δt ). The old process variable is subtracted from the new process variable and divided by the time interval to get the ramp rate. The maximum of a continuous train of ramp rates updated each module execution over A period of 3 or more deadtimes is selected to compute the near integrating process gain. For an inverseresponse or large secondary time constant, the computation may need to continue for 10 or more deadtimes.

50

SRoo

oNI TT ∗

∗+∗

=τθ

θ4

3

The near integrating test time (3 deadtimes) as a fraction of the self-regulating test (time to steady state is taken as 98% response time TSR = T98 = θo + 4 το ) is:

If the process time constant is greater than 6 times the deadtime

oθτ ∗≥ 6o

Then the near integrating tuning test time is reduced by > 90%:

SRNI TT ∗≤ 1.0

For example:

sec100o =τ sec4o =θThe near integrator tuning time is reduced by 97%!

SRNI TT ∗≤ 03.0

Reduction inIdentification Test Time

51

4

PVSUB

First Principle Parameters = f (Ki)

CO0Initial Controller Output at time 0

∆COθo

Ko

Ki

∆PV Switch

ODE (Ki)

∆PV

∆PV

Sum

PV0Initial Controlled Variable at time 0

Ko = PV0 / CO0 process gain approximationτo = Ko / Ki negative feedback time constantτ’o = Ko / Ki positive feedback time constant

Methodology extends beyond loops to anyprocess variable that can be measured

and any variablethat can be changed

CO

τo

Ko τ’o

1

2

3

∆PV

For the manipulation of jacket temperature to control vessel temperature, the near integrator gain is

)(/)( opi MCAUK ∗∗=Since we generally know vessel volume (liquid mass), heat transfer area, and process heat capacity,

We can solve for overall heat transfer coefficient (least known parameter) to provide a usefulordinary differential equation (ODE) for a first principle model (1).

Rapid Process Model Identification and Deployment Opportunity

52

The observed deadtime (θo ) and integrator gain (Ki) are identified after a change in any controller output (e.g. final control element or setpoint) or any disturbance measurement. The identification of the integrator gain uses the fastest ramp rate over a short time period (e.g. 2 dead times) at the start of the process response.

The models are not restricted to loops but can be used to identify the relationship between any variable that can be changed and any affected process variable that can be measured.

The models are used for processes that are have a true integrating response or slow processes with a “near integrating” response (τo > 5∗θo ). The process deadtime and integrating process gain can be used for controller tuning and for plant wide simulations including but not limited to the following types of models:

Model 1: Hybrid ordinary differential equation (ODE) first principle and experimental model Model 2: Integrating process experimental modelModel 3: Slow self-regulating experimental modelModel 4: Slow non-self-regulating positive feedback (runaway) experimental model

Patent disclosure filed on 3-1-2010

Rapid Process Model Identification and Deployment Opportunity

53

ooo

ox EE ∗

+=

)( τθθ

ooo

oi EE ∗

+=

)(

2

τθθ

Peak error is proportional to the ratio of loop deadtime to 63% response time(Important to prevent SIS trips, relief device activation, surge prevention, and RCRA pH violations)

Integrated error is proportional to the ratio of loop deadtime squared to 63% response time(Important to minimize quantity of product off-spec and total energy and raw material use)

Loop Performance Ultimate Limit

Total loop deadtimethat is often set byautomation design

Largest lag in loopthat is ideally set by

large process volume

Wireless default update rate affects ultimate performance limitbecause ½ of default update rate is additional loop deadtime

54

oco

x EKK

E ∗∗+

=)1(

1

oco

fxii E

KKtT

E ∗∗

++=

τΔ

Peak error decreases as the controller gain increases but is essentially the open loop error for systems when total deadtime >> process time constant

Integrated error decreases as the controller gain increases and reset time decreases but is essentially the open loop error multiplied by the reset time plus signal delays and lags for systems when total deadtime >> process time constant

Open loop error forfastest and largestload disturbance

ocffi

r SPKSPCOKSPT θ+

∗+=

)|,min(|( max ΔΔΔ

Rise time (time to reach a new setpoint) is inversely proportional to controller gain

Loop PerformancePractical Limit

55

oo

oc K

Kθ

τ∗

∗= 4.0 oiT θ∗= 4 1d pT τ=

For runaway processes:

For self-regulating processes:

oic K

Kθ∗

∗=15.0 oiT θ∗= 4 1d pT τ=

oic K

Kθ∗

∗=16.0

oiT θ∗= 40 1d 2 pT τ∗=

For integrating processes:

oo

oc K

Kθ

τ∗

∗='6.0

oic K

Kθ∗

∗=14.0

Near integrator (τp2 >> θo):

oiT θ∗= 5.0

Near integrator (τ’p2 >> θo):

Deadtime dominant (τp2 << θo):

0d =To

c KK 14.0 ∗=

Fastest Controller Tuning(Reaction Curve Method*)

These tuning equations provide maximumdisturbance rejection but will cause

some overshoot of setpoint response

* - Ziegler Nichols method closed loop modifiedto be more robust and less oscillatory

1.0 for Enhanced PID if Wireless Default Update Rate > Process Response Time !

Wireless default update rate affects fastest controller tuningbecause ½ of default update rate is additional loop deadtime

56

Effect of Wireless MeasurementUpdate Time and Interval on Performance

o

omS E

S τθ ∗∗=

5.0

ovwo

x ET

E ∗++

=63

θθθo

vwoi E

TE ∗

++=

63

2)( θθθowoT τθθ ++=63

),( STw Min θθθ Δ= wT TΔΔ ∗= 5.0θmax)/%(

5.0tPV

SmS ΔΔ

∗=θ

)/()/%( max ooi KEKtPV ∗=ΔΔo

oi

KKτ

=o

oE

tPVτ

=max)/%( ΔΔ

57

Additional Deadtime fromValve Stick-Slip, Resolution, or Deadband

ox

oovv EK

KS∗

∗∗∗=

θθ 5.0

max)/%(5.0

tCOSv

v ΔΔ∗

=θ maxmax )/%()/%( tPVKtCO c ΔΔΔΔ ∗=

oo

x

KEK

tCO o

θ∗

∗=max)/%( ΔΔ

[ ]⎥⎦⎤

⎢⎣

⎡−∗

∗=

002.0),(max,min

mm

v

oo

oxc SN

SKKK

θτ

o

oE

tPVτ

=max)/%( ΔΔ

Increase in process gain from elimination of controller reaction to noise by wireless trigger level or PID threshold sensitivity setting decreases deadtime from valve stick-slip, resolution, or deadband

58

ΔCV = change in controlled variable (change in process variable in % of scale)ΔCO = change in controller output (%)Kc = controller gain (dimensionless)Ki = integrating process gain (%/sec/% or 1/sec)Kp = process gain (dimensionless) also known as open loop gainDV = disturbance variable (engineering units)MV = manipulated variable (engineering units)PV = process variable (engineering units)ΔSP = change in setpoint (engineering units)SPff = setpoint feedforward (engineering units)Δt = change in time (sec)Δtx = execution or update time (sec)θo = total loop dead time (sec)τf = filter time constant or well mixed volume residence time (sec)τm = measurement time constant (sec) Τp2 = primary (large) self-regulating process time constant (sec) τ’p2 = primary (large) runaway process time constant (sec) τp1 = secondary (small) process time constant (sec) Ti = integral (reset) time setting (sec/repeat)Td = derivative (rate) time setting (sec)Tr = rise time for setpoint change (sec)to = oscillation period (sec)λ = Lambda (closed loop time constant or arrest time) (sec)λf = Lambda factor (ratio of closed to open loop time constant or arrest time)

Nomenclature(Process Dynamics & Performance)

59

Ei = integrated error for unmeasured load disturbance (% sec)Ex = peak error for unmeasured load disturbance (%)Eo = open loop error (loop in manual) for unmeasured load disturbance (%)Ki = near integrator process gain (% per % per sec)Ko = open loop gain (product of valve, process, and measurement gains) (dimensionless)Kx = detuning factor for controller gain (dimensionless)Nm = measurement noise (%)Δ%CO/Δt = rate of change in PID % controller output (% per sec)Δ%PV/Δt = rate of change in PID % process variable (% per sec)ΔTw = wireless default update rate (update time interval) (sec)Sm = wireless measurement trigger level (threshold sensitivity) (%)Sv = valve stick-slip, resolution, or deadband (%)T63 = 63% process response time (sec)θo = original loop deadtime (sec)θΔt = additional deadtime from default update rate (sec)θs = additional deadtime from wireless trigger level (sec)θv = additional deadtime from valve (sec)θw = additional deadtime from wireless measurement (sec)τo = self-regulating open loop time constant (largest time constant in loop) (sec)τ’o = runaway open loop time constant (largest time constant in loop) (sec)

Nomenclature(Wireless Dynamics & Performance)

60

Key Insights

• A liquid or solids phase reaction without a continuous liquid or solids discharge flow is the distinguishing characteristic of batch and fed-batch.

• There is generally no level control except perhaps in terms of a high level override or high level shutdown of feeds in batch and fed-batch reactors.

• In batch reactors, the reactants are fed sequentially and shutoff when charge tank weight or flow totals indicate the total charged is complete.

• In fed-batch operation, the reactants are fed simultaneously under flow control at a rate determined by a control system.

• Many of the same controls used for continuous reactors are applicable to fed-batch except typically there is no level or residence time control.

• There is a profile of temperature, physical properties, and composition with respect to length for a plug flow reactor and with respect to batch time for a fed-batch vessel. In both types of reactors opportunities exist for profile control and optimization. The temperature may be controlled at various setpoints depending upon length and time. Since composition generally goes in one direction only with length and time, the slope is controlled at points in length and time for composition profile control.

61

Key Insights

• Gas phase reactors are generally continuous with a short tight residence time.• The process deadtime from transportation delay of gas reactants is small

compared to the lags from catalyst heat capacity and thermowell design.• Fast temperature control is possible by manipulation of gas reactant flows.• Mature high capacity products (e.g. oil, gas, and petrochemicals) tend to use

continuous reactors whereas new high value processes (e.g. specialty chemical and biological) primarily use batch and fed-batch reactors.

• The fastest and simplest implementation is batch with quantities charged sequentially mimicking lab experiments. As knowledge is gained batch reactors can become fed-batch reactors and eventually continuous reactors if there is enough demand and chemistry permits a variable residence time.

• Candidates for continuous reactors are products with a low profit margin, high volume requirement, fast reaction, minimal adverse reactions, preventable buildup of inhibitors and inactive components, and an extensive R&D history.

• Candidates for batch reactors are products with a high profit margin, low volume requirement, slow reaction, significant side effects, and minimal R&D.

62

Key Insights

• Reactors have 3 types of dynamic responses observed when the PID is put in manual and a step change is made in PID output with no disturbances.

– If the response lines out at a steady state, the process is “self-regulating.” – If the response continues to ramp (no steady state), the process is “integrating.” – If the response continues to accelerate, the process is “runaway.”

• Inverse response can occur in any of the above responses when the initial response is in the opposite direction of eventual response.

– Temperature control by manipulation of cold feed can exhibit an inverse response• A CSTR has a slow self-regulating response.• A batch and fed-batch reactor has a slow integrating response. • A plug flow and gas phase reactor has a fast self-regulating response.• All of these reactors can develop a runaway response when the increase in

reaction heat release with temperature exceeds the cooling capability.

63

Key Insights

• Reaction rate depends upon temperature and composition. • To prevent an excess or deficiency of reactants, the reactant concentration

must be in the ratio set by the stoichiometric equation for the reaction.• High capacity products such as petrochemicals and intermediates greatly

depend upon the added value of chromatographs because even a fractional percent increase in production rate is millions of dollars.

• Biological reactions utilize a wide variety of composition measurements including potentiometric and amperiometric electrodes for substrates and waste products, and dielectric spectroscopy and digital imagery for viable cell concentration, and near infrared (NIR) spectroscopy for compounds.

• The average amount of time reactants stay in contact is called residence time in continuous operations and is simply volume divided by flow rate.

• For fed-batch and batch, batch cycle time is used instead of residence time.• In batch reactors, the batch cycle time is often longer than necessary.• The process dynamics for vessels offer incredibly tight concentration, level,

pH, pressure, and temperature control.

64

Resource

Advanced Temperature Measurement and Control - 2nd Ed has a lot of detailson how to improve temperature sensoraccuracy and temperature control of heat exchangers, reactors, and kilns

Biological and Chemical Reactor Controlis an ISA book whose kernel is the ISAAutomation Week tutorial. The book will beavailable in December just in time for the holidays. Surprise your spouse with the bookas a “gift that keeps on giving.” Your spousewill never let you forget it.