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ISA Saint Louis Short Course Dec 6-8, 2010

Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements - Day 3

Welcome

• Gregory K. McMillan – Greg is a retired Senior Fellow from Solutia/Monsanto and an ISA Fellow.

Presently, Greg contracts as a consultant in DeltaV R&D via CDI Process & Industrial. 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 Essentials of Modern Measurements and Final Elements for the Process Industry. 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/

Top Ten Reasons I use a Virtual Plant

• (10) You can’t freeze, restore, and replay an actual plant batch • (9) No separate programs 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) Bioreactor batches are done in 14 minutes instead of 14 days• (3) Plant can be operated on a tropical beach• (2) Last time I checked my wallet I didn’t have $100,000K• (1) Actual plant doesn’t fit in my suitcase

Improving Loops - Part 2

• PID on Error Structure– Maximizes the kick and bump of the controller output for a setpoint change. – Overdrive (driving of output past resting point) is essential for getting slow loops,

such as vessel temperature and pH, to the optimum setpoint as fast as possible.– The setpoint change must be made with the PID in Auto mode.– “SP track PV” will generally maximize the setpoint change and hence the kick and

bump (retaining SP from last batch or startup minimizes kick and bump)• SP Feedforward

– For low controller gains (controller gain less than inverse of process gain), a setpoint feedforward is particularly useful. For this case, the setpoint feedforward gain is the inverse of the dimensionless process gain minus the controller gain.

– For slow self-regulating (e.g. continuous) processes and slow integrating (e.g. batch) processes, even if the controller gain is high, the additional overdrive can be beneficial for small setpoint changes that normally would not cause the PID output to hit a limit.

– If the setpoint and controller output are in engineering units the feedforward gain must be adjusted accordingly.

– The feedforward action is the process action, which is the opposite of the control action, taking into account valve action. In other words for a reverse control action, the feedforward action is direct provided the valve action is inc-open or the analog output block, I/P, or positioner reverses the signal for a inc-close.

Fed-Batch and Startup Time Reduction - 1


• Full Throttle (Bang-Bang Control) - The controller output is stepped to it output limit to maximize the rate of approach to setpoint and when the projected PV equals the setpoint less a bias, the controller output is repositioned to the final resting value. The output is held at the resting value for one deadtime. For more details, check out the Control magazine article “Full Throttle Batch and Startup Response.” http://www.controlglobal.com/articles/2006/096.html – A deadtime (DT) block must be used to compute the rate of change so that new values of

the PV are seen immediately as a change in the rate of approach.– If the total loop deadtime (o) is used in the DT block, the projected PV is simply the current

PV minus the output of the DT block (PV) plus the current PV.– If the PV rate of change (PV/t) is useful for other reasons (e.g. near integrator or true integrating

process tuning), then PV/t = PV/o can be computed. – If the process changes during the setpoint response (e.g. reaction or evaporation), the

resting value can be captured from the last batch or startup – If the process changes are negligible during the setpoint response, the resting value can be

estimated as:– the PID output just before the setpoint change for an integrating (e.g. batch) process– the PID output just before the setpoint change plus the setpoint change divided by the process gain

for a self-regulating (e.g. continuous) process

– For self-regulating processes such as flow with the loop deadtime (o) approaching or less than the largest process time constant (p ), the logic is revised to step the PID output immediately to the resting value. The PID output is held at the resting value for the T98 process response time (T98 o p ).



Structure, SP Feedforward, & Bang-Bang Tests

Structure 3Rise Time = 8.5 min

Settling Time = 8.5 minOvershoot = 0%

Structure 1Rise Time = 1.6 min

Settling Time = 7.5 minOvershoot = 1.7%

Structure 1 + SP FFRise Time = 1.2 min


Structure 1 + Bang-BangRise Time = 0.5 min



• Output Lead-Lag – A lead-lag on the controller output or in the digital positioner can kick the signal

though the valve deadband and sticktion, get past split range points, and make faster transitions from heating to cooling and vice versa.

– A lead-lag can potentially provide a faster setpoint response with less overshoot when analyzers are used for closed loop control of integrating processes When combined with the enhanced PID algorithm (PIDPlus) described in: – Deminar #1 http://www.screencast.com/users/JimCahill/folders/Public/media/5acf2135-

38c9-422e-9eb9-33ee844825d3 – White paper http://www.modelingandcontrol.com/DeltaV-v11-PID-Enhancements-for-

Wireless.pdf

• Deadtime Compensation– The simple addition of a delay block with the deadtime set equal to the total loop

deadtime to the external reset signal for the positive feedback implementation of integral action described in Deminar #3 for the dynamic reset limit option http://www.screencast.com/users/JimCahill/folders/Public/media/f093eca1-958f-4d9c-96b7-9229e4a6b5ba .

– The controller reset time can be significantly reduced and the controller gain increased if the delay block deadtime is equal or slightly less than the process deadtime as studied in Advanced Application Note 3 http://www.modelingandcontrol.com/repository/AdvancedApplicationNote003.pdf



Deadtime Compensator Configuration

Insert

deadtime

block

Must enable dynamic reset limit !


• Deadtime is eliminated from the loop. The smith predictor, which created a PV without deadtime, fools the controller into thinking there is no deadtime. However, for an unmeasured disturbance, the loop deadtime still causes a delay in terms of when the loop can see the disturbance and when the loop can enact a correction that arrives in the process at the same point as the disturbance. The ultimate limit to the peak error and integrated error for an unmeasured disturbance are still proportional to the deadtime, and deadtime squared, respectively.

• Control is faster for existing tuning settings. The addition of deadtime compensation actually slows down the response for the existing tuning settings. Setpoint metrics, such as rise time, and load response metrics, such as peak error, will be adversely affected. Assuming the PID was tuned for a smooth stable response, the controller must be retuned for a faster response (see slide 11). For a PID already tuned for maximum disturbance rejection, the gain can be increased by 250%. For deadtime dominant systems where the total loop deadtime is much greater than the largest loop time constant (hopefully the process time constant), the reset time must also be decreased or there will be severe undershoot. If you decrease the reset time to its optimum, undershoot and overshoot are about equal. For the test case where the total loop deadtime to primary process time constant ratio was 10:1, you could decrease the reset time by a factor of 10, smaller than what was noted on slide 11. Further study is needed as to whether the ratio of the old to new reset time is comparable to the ratio of deadtime to time constant and whether the PID module execution time (0.5 sec) is the low limit to the reset time for an accurate deadtime.

Deadtime Myths Busted in Deminar 10


For access to Deminar 10 ScreenCast Recording or SlideShare Presentation go tohttp://www.modelingandcontrol.com/2010/10/review_of_deminar_10_-_deadtim.html

• Compensator works better for loops dominated by a large deadtime. The reduction in rise time is greatest and the sensitivity to per cent deadtime modeling error particularly for an overestimate of deadtime is least for the loop that was dominated by the process time constant. You could have a deadtime estimate that was 100% high before you would see a significant jagged response when the process time constant was much larger than the process deadtime. For a deadtime estimate that was 50% too low, some rounded oscillations developed for this loop. The loop simply degrades to the response that would occur from the high PID gain as the compensator deadtime is decreased to zero. While the magnitude of the error in deadtime seems small, you have to remember that for an industrial temperature control application, the loop deadtime and process time constant would be often at least 100 times larger. For a 400 second deadtime and 10,000 second process time constant, a compensator deadtime 200 seconds smaller or 400 seconds larger than actual would start to cause a problem. In contrast, the deadtime dominant loop developed a jagged response for a deadtime that was high or low by just 10%. I think this requirement is unreasonable in industrial processes. A small filter of 1 second on the input to the deadtime block in the BKCAL path may have helped.

• An underestimate of the deadtime leads to instability. In tuning calculations for a conventional PID, a smaller than actual deadtime can cause an excessively oscillatory response. Contrary to the effect of deadtime on tuning calculations, a compensator deadtime smaller than actual deadtime will only cause instability if the controller is tuned aggressively after the deadtime compensator is added.

• An overestimate of the deadtime leads to sluggish response and greater stability. In tuning calculations for a conventional PID, a larger than actual deadtime can cause an excessively slow response. Contrary to the effect of deadtime on tuning calculations, a compensator deadtime greater than actual deadtime will cause jagged irregular oscillations.

Deadtime Myths Busted in Deminar 10


• Feed Maximization– Model Predictive Control described in Application Note 1

http://www.modelingandcontrol.com/repository/AdvancedApplicationNote001.pdf – Override control (next slide) is used to maximize feeds to limits of operating constraints via

valve position control (e.g. maximum vent, overhead condenser, or jacket valve position with sufficient sensitivity per installed characteristic).

– Alternatively, the limiting valve can be set wide open and the feeds throttled for temperature or pressure control. For pressure control of gaseous reactants, this strategy can be quite effective.

– For temperature control of liquid reactants, the user needs to confirm that inverse response from the addition of cold reactants to an exothermic reactor and the lag from the concentration response does not cause temperature control problems.

– All of these methods require tuning and may not be particularly adept at dealing with fast disturbances unless some feedforward is added. Fortunately the prevalent disturbance that is a feed concentration change is often slow enough due to raw material storage volume to be corrected by temperature feedback.

• Profile Control– If you have a have batch measurement that should increase to a maximum at the batch end

point (e.g. maximum reaction temperature or product concentration), the slope of the batch profile of this measurement can be maximized to reduce batch cycle time. For application examples checkout “Direct Temperature Rate of Change Control Improves Reactor Yield” in a Funny Thing Happened on the Way to the Control Room http://www.modelingandcontrol.com/FunnyThing/ and the Control magazine article “Unlocking the Secret Profiles of Batch Reactors” http://www.controlglobal.com/articles/2008/230.html .



Identified Responses for Fed-Batch Profile Model Predictive Control (MPC)


Product Formation Rate

Biomass Growth rate

Substrate

Dissolved Oxygen

Model Predictive Control (MPC) of Growth Rate and Product Formation Rate


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) Reduces Fed-Batch Cycle Time


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) Improves Batch Predictions


• Reduce wait times, operator attention requests, and manual actions by automation.• Reduce excess hold times (e.g. heat release can confirm reaction start/end). • Improve charge times and accuracy by better sensor design (e.g. mass flow meters

and valve location (e.g. minimize dribble time and holdup).• Minimize acquire time by improved prioritization of users (e.g. unit operation with

biggest effect on production rate gets access to feeds and utilities).• Reduce failure expression activation by better instruments, redundancy and signal

selection, and more realistic expectations of instrument performance.• Improve failure expression recovery by configuration and displays.• Eliminate steps by simultaneous actions (e.g. heat-up and pressurization).• Increase feed and heat transfer rate by an increase in pump impeller size.• Minimize non constrained processing time by all out run, cutoff, and coast.• Minimize processing time by better end point detection (inferential measurements

by neural networks and online or at-line analyzers).• Mid batch correction based on adapted online virtual plant model or batch analytics

projection to latent structures (PLS) and first principle relationships.

Batch Sequence Time Reduction


Open Loop Backup Configuration

SP_Rate_DN and SP_RATE_UP used to insure fast getaway and slow approach

Open loop backup used for prevention of compressor surge and RCRA pH violation

Open Loop Backup Configuration


PID Controller Disturbance Response


Open Loop Backup Disturbance Response

Open Loop Backup


Conductivity Kicker for Evaporator


Mixer

AttenuationTank

AY

AT

middle selector

AY

splitter

AT

FT

FT

AT

AY

ATAT AT

AY

ATAT AT

Mixer

AY

FT

Stage 2Stage 1

middle selector

Wastemiddle selectorRCAS RCAS

splitter

AY

filter

AYROUT

kickerAC-1 AC-2

MPC-2

MPC-1

pH Kicker for Waste Treatment (Pensacola Plant)


Virtual Plant Opportunities Beyond Operator Training Systems (OTS)

• Dynamic simulations offer the opportunity to explore, quantify, demonstrate, detail, and prototype process control improvements (PCI).

• However– The investment in software and time to learn and develop simulations typically limits the

creation of models to specialists who have significant simulation and DCS expertise.– Process deadtime, measurement dynamics, and valve response is often not modeled (not

understood by traditional process simulation software suppliers)– The emulation of the basic and advanced control in a DCS by process simulators is unrealistic

• What is needed is a virtual plant that uses the actual DCS with all of its capability and uses dynamics of all parts of the process and automation systems in a friendly control room environment by the use of the DCS operator interface

• The virtual plant should be useable by any one who wants to learn the best of the practical control technologies for the process industry and to find, demonstrate, estimate, and convince people of the benefits of PCI

– Automation Engineers– Local Business Partners– Process Engineers – Students– System Integrators– Suppliers

• The virtual plant offers the ability to develop, prototype, and demo the dynamic advantages of solutions, products, and services


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

EmbeddedPAT Tools

Process Knowledge

Virtual Plant Synergy


PCI and OTS Virtual Plants

Dynamic

Process

Simulators

Virtual

Process

Virtual

Sensors

Virtual

Valves

Virtual

I/O

MiMiC

PCI

DeltaV

SimulatePro

Virtual

DCS

Virtual

Process

Virtual

Sensors

Virtual

Valves

Virtual

I/O Module

Actual

DCS

MiMiC

OTS

DeltaV

ProPlusVIM

Configuration

Graphics

Trends


Virtual Plant Essentials

Feed 1

Feed 2

Condenser

Cooling water Fcw

Reflux Drum

Lc, Vc_out

Reflux L_R

Distillate product L_D

CW Out

V_DA_VD1

A_Vlv1

Reboiler

A_v

L_B + V_B

V_B

Buttom product L_B

Heating steam

HE condensate

Side withdraw 2

Side withdraw 1

Heavy liquid L_HvLiq

Vnt

V_D1

DeltaV Simulate Product Family

MiMiC Simulation Software


Smart Bang-Bang LabSmart Bang-Bang Lab

• Objective – Show how to reduce batch and startup time by a full throttle setpoint response (bang-bang control)

• Activities:1. Go to Main Display and select Single Loop Lab01

2. Click on PID faceplate and click on magnifying glass icon to get Detail display

3. Enter tuning settings: Gain = 1.7, Reset = 210 sec, Rate = 2 sec

4. Click on any block in block diagram and then on Process tab detail

5. Set primary process Delay = 9 sec, Lag 2 Inc & Lag 2 Dec = 100 sec

6. Set primary process Type = Integrating

7. Enable setpoint metrics

8. Make PID setpoint change from 50% to 60%

9. Wait for setpoint response to complete and note metrics

10. In PID detail, set Bang-Bang Bias = 4%

11. Make PID setpoint change from 60% to 50%

12. Wait for setpoint response to complete and note metrics


Nonlinearity - Graphical Deception

0.00000000

2.00000000

4.00000000

6.00000000

8.00000000

10.00000000

12.00000000

14.00000000

0.00000000 0.00050000 0.00100000 0.00150000 0.00200000

3.00000000

4.00000000

5.00000000

6.00000000

7.00000000

8.00000000

9.00000000

10.00000000

11.00000000

0.00099995 0.00099996 0.00099997 0.00099998 0.00099999 0.00100000 0.00100001 0.00100002 0.00100003 0.00100004 0.00100005

14

12

10

8

6

4

2

0

pH

Reagent Influent Ratio

11

10

9

8

7

6

5

4

3

pH

Reagent Influent Ratio

Despite appearances there are no straight lines in a titration curve (zoom in reveals another curve if there are enough data points - a big “IF” in neutral region)

For a strong acid and base the pKa are off-scale and the slope continually changes by a factor of ten for each pH unit deviationfrom neutrality (7 pH at 25 oC)

Yet titration curves are essential for every aspect of pH systemdesign but you must get numerical values and avoid mistakessuch as insufficient data points in the area around the set point

Improving Neutralizer pH Control

Effect of Acid and Base Type

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0.000 0.500 1.000 1.500 2.000

Reagent / Influent

pH Calculated pH

Weak Acid and Strong Base

pka = 4

Figure 3-1d: Weak Acid Titrated with a Weak Base

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0.000 0.500 1.000 1.500 2.000

Reagent / Influent

pH

Weak Acid and Weak Base

pka = 4

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0.000 0.500 1.000 1.500 2.000

Reagent / Influent

pH

Strong Acid and Weak Base

pka = 10

Figure 3-1e: Weak 2-Ion Acid Titrated with a Weak 2-Ion Base

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

0.000 0.500 1.000 1.500 2.000

Reagent / Influent

pH

Multiple Weak Acids and Weak Bases

pka = 3

pka = 5

pka = 9

Slope moderatednear each pKa !


Effect of Mixing Uniformity and Valve Resolution

pH

Reagent to Feed Flow Ratio

4

10

6

8

pH Set Point

Fluctuations or OscillationsIn Flows or Concentrations

Control valve resolution (stick-slip) andmixing uniformity requirements areextraordinary on the steepest slope


Control Valve Size and ResolutionpH

Reagent FlowInfluent Flow

6

8

Influent pH

B

A

Control BandSet point

BEr =100% Fimax Frmax

Frmax =A Fimax

BEr =100% A

Ss = 0.5 Er

A = distance of center of reagent error band on abscissa from originB = width of allowable reagent error band on abscissa for control band Er = allowable reagent error (%)

Frmax = maximum reagent valve capacity (kg per minute)

Fimax = maximum influent flow (kg per minute)

Ss = allowable stick-slip (resolution limit) (%)

Most reagent control valves are oversized, which increases the limit cycle amplitudefrom stick-slip (resolution) and deadband(integrating processes and cascade loops)


Feed

Reagent

Reagent

ReagentThe period of oscillation (total loop dead time) must differ by morethan factor of 5 to prevent resonance (amplification of oscillations)

Small first tank provides a faster responseand oscillation that is more effectively filtered by the larger tanks downstream per Eq. 5-3j

Big footprintand high cost!

Traditional System for Minimum Variability


Reagent

Reagent

Feed

Reagent

Traditional System for Minimum Reagent Use Traditional System for Minimum Reagent Use

The period of oscillation (total loop dead time) must differ by morethan factor of 5 to prevent resonance (amplification of oscillations)

The large first tank offers more cross neutralization of influents

Big footprintand high cost!


Tight pH Control with Minimum Capital Investment

Influent

FC 1-2

FT 1-2

Effluent AC 1-1

AT 1-1

FT 1-1

10 to 20 pipe

diameters

f(x)

*IL#1

Re

ag

en

t

High Recirculation Flow

Any Old TankSignal

Characterizer

*IL#2

LT 1-3

LC 1-3

IL#1 – Interlock that prevents back fill ofreagent piping when control valve closes

IL#2 – Interlock that shuts off effluent flow untilvessel pH is projected to be within control band

Eductor


Linear Reagent Demand Control

• Signal characterizer translates loop PV and SP from pH to % Reagent Demand

– PV is abscissa of the titration curve scaled 0 to 100% reagent demand– Piecewise segment fit normally used to go from ordinate to abscissa of curve– Fieldbus block offers 21 custom space X,Y pairs (X is pH and Y is % demand)– Closer spacing of X,Y pairs in control region provides most needed

compensation– If neural network or polynomial fit used, beware of bumps and wild extrapolation

• Special configuration is needed to provide operations with pH interface to:– See loop PV in pH and signal to final element– Enter loop SP in pH– Change mode to manual and change manual output

• Set point on steep part of curve shows biggest improvements from – Reduction in limit cycle amplitude seen from pH nonlinearity– Decrease in limit cycle frequency from final element resolution (e.g. stick-slip)– Decrease in crossing of split range point– Reduced reaction to measurement noise– Shorter startup time (loop sees real distance to set point and is not detuned)– Simplified tuning (process gain no longer depends upon titration curve slope)– Restored process time constant (slower pH excursion from disturbance)


Case History 1- Existing Control System

Mixer

AttenuationTank

AY

AT

middle selector

AY

splitter

AC

AT

FT

FT

AT

AY

ATAT AT

AY

ATAT AT

Mixer

AY

FT

Stage 2Stage 1

middle selector

AC

Waste

Waste

middle selector

FuzzyLogic

RCAS RCAS

splitter

AY

filter

AYROUT

kicker


Case History 1 - New Control System

Mixer

AttenuationTank

AY

AT

middle selector

AY

splitter

AT

FT

FT

AT

AY

ATAT AT

AY

ATAT AT

Mixer

AY

FT

Stage 2Stage 1

middle selector

Waste

Waste

middle selectorRCAS RCAS

splitter

AY

filter

AYROUT

kickerAC-1 AC-2

MPC-2

MPC-1


Case History 1 - Opportunities for Reagent Savings

pH

Reagent to Waste Flow Ratio

Reagent Savings

2

12

Old Set Point

New Set Point

Old RatioNew Ratio


Case History 1 - Online Adaptation and Optimization

Actual PlantOptimization(MPC1 and MPC2)

Tank pH and 2nd Stage Valves

Stage 1 and 2 Set Points

Virtual Plant

Inferential Measurement(Waste Concentration)

and Diagnostics

Adaptation(MPC3)

Actual Reagent/Waste Ratio

(MPC SP)

ModelInfluent Concentration

(MPC MV)

Model Predictive Control (MPC)For Optimization of Actual Plant

Model Predictive Control (MPC)For Adaptation of Virtual Plant

Virtual Reagent/Influent Ratio

(MPC CV)

Stage 1 and 2 pH Set Points


Case History 1 - Online Model Adaptation Results

Adapted Influent Concentration(Model Parameter)

Actual Plant’sReagent/Influent

Flow Ratio

Virtual Plant’sReagent/Influent

Flow Ratio


Case History 2 - Existing Neutralization System

Water93%

Acid

50%

Caustic

Pit

Cation Anion

To EO

Final acid

adjustment

Final caustic

adjustment

AT


Case History 2 - Project Objectives

• Safe• Responsible• Reliable

– Mechanically– Robust controls, Operator friendly– Ability to have one tank out of service

• Balance initial capital against reagent cost• Little or no equipment rework


Case History 2 - Cost Data

• 93%H2SO4 spot market price $2.10/Gal

• 50% NaOH spot market price $2.30/Gal

2k Gal 5k Gal 10k Gal 20k Gal 40k Gal

Tank $20k $30k $50k $80k $310k

Pump $25k $35k $45k $75k $140k


Case History 2 - Challenges

• Process gain changes by factor of 1000x• Final element rangeability needed is 1000:1• Final element resolution requirement is 0.1%• Concentrated reagents (50% caustic and 93% sulfuric)• Caustic valve’s ¼ inch port may plug at < 10% position• Must mix 0.05 gal reagent in 5,000 gal < 2 minutes• Volume between valve and injection must be < 0.05 gal • 0.04 pH sensor error causes 20% flow feedforward error• Extreme sport - extreme nonlinearity, sensitivity, and

rangeability of pH demands extraordinary requirements for mechanical, piping, and automation system design


Really big tank and thousands of miceeach with 0.001 gallon of acid or caustic

or

modeling and control

Case History 2 - Choices Case History 2 - Choices


Case History 2 - Demineralized pH Titration Curve

Slope

pH


Case History 2 - Demineralized pH Control System

Signal characterizers linearize loop via reagent demand control

AY 1-4

AC 1-1

AY 1-3

splitter

AT 1-3

AT 1-2

AT 1-1

AY 1-1

AY 1-2

middlesignal

selector

signalcharacterizer

signalcharacterizer

pH set point

Eductors

FT 1-1

FT 1-2

NaOH Acid

LT 1-5

Tank

Static Mixer

Feed

To other Tank

Downstream system

LC 1-5

From other Tank

To other Tank


Case History 2 - Tuning for Conventional pH Control Case History 2 - Tuning for Conventional pH Control


Gain 10x larger

Case History 2 - Tuning for Reagent Demand Control Case History 2 - Tuning for Reagent Demand Control


One of many spikes from stick-slip of water valve

Tank 1 pH for Reagent Demand Control

Tank 1 pH for Conventional pH Control

Start of Step 2(Regeneration)

Start of Step 4(Slow Rinses)

Case History 2 - Process Test Results Case History 2 - Process Test Results


• If Tank pH is within control band, reduce tank level rapidly to minimum. (CL#1a). If Tank pH is out of control band, close valve to downstream system and send effluent to the other tank if it has more room (CL#1b).

• For caustic reagent valve signals of 0-10%, set control valve at 10%, pulse width modulate isolation valve proportional to loop output, and increase loop filter time and reset time to smooth out pulses (CL#2)

• If reagent valves are near the split range point, periodically (e.g. every 5 minutes) shut the reagent valves and divert feed to other tank for 15 seconds to get tank pH reading (CL#3).

• Coordinate opening and closing of reagent isolation valves with the opening and closing of reagent control valves (CL#4)

• If feed is negligible and tank pH is within control band, shut off the recirculation pump (CL#5)

Case History 2 - Control Logic Case History 2 - Control Logic


Streams, pumps, valves, sensors, tanks, and mixersare modules from DeltaV composite template library.

Each wire is a pipe that is a processstream data array(e.g. pressure, flow,temperature, density,heat capacity, and concentrations)

First principleconservation ofmaterial, energy,components, and ion charges

Case History 2 - Dynamic Model in the DCS Case History 2 - Dynamic Model in the DCS


• Study shows potential project savings overwhelm reagent savings• Modeling removes uncertainty from design

– First principle relationships show how well mechanical, piping, and automation system deal with nonlinearity, sensitivity, and rangeability

• Modeling enables prototyping of control improvements– Linear reagent demand control speeds up response from PV on flat and

reduces oscillations from the PV on steep part of titration curve– Control logic optimizes pH loops to minimize downtime and inventory to

maximize availability and minimize energy use– Pulse width modulation of caustic at low valve positions minimizes plugging– Recirculation within tank and between tanks offers maximum flexibility and

continuous, semi-continuous, and batch modes of operation– Periodic observation of tank pH to determine best mode of operation

Case History 2 - Summary Case History 2 - Summary


Neutralizer pH Control LabNeutralizer pH Control Lab

• Objective – See how optimizing setpoint can reduce reagent use

• Activities:1. Go to Main Display, select pH Lab02b

2. Set Desired Run time = minimum run time

3. Change from Explore to Run Mode

4. Note process metrics when done

5. Click on AC1-1 PID Faceplate and change pH setpoint from 7 to 4.5 pH



The Top Ten Signs You are Ready for a Hawaiian Vacation

• (10) You give your boss the “hang loose” hand gesture• (9) You day dream about hula dancers in hardhats• (8) Your cubicle has a mosquito net with tropical sounds• (7) You bring a kayak to the company’s waste pond• (6) You ask “where is the company’s pupu stand”?• (5) You tell your secretary she is wearing a nice muumuu• (4) You play a ukulele in your office• (3) You show up to a meeting in a Hawaiian shirt, shorts and sandals• (2) You start answering your phone saying "Aloha“• (1) You wear a snorkeling mask instead of glasses

Improving Reactor Temperature Control

Reactor Control Strategies Reactor Control Strategies


Reactor Cascade Control Reactor Cascade Control


Exothermic Reactions Exothermic Reactions


Reactor Valve Position Control Reactor Valve Position Control


Reactor Equilibrium Control Reactor Equilibrium Control


A low

Reactor Rate of Change Control Reactor Rate of Change Control


Reactor Override Control Reactor Override Control

Reactor Temperature Control LabReactor Temperature Control Lab

• Objective – See how optimizing setpoint can reduce coolant use

• Activities:1. Go to Main Display, select Temperature Lab02a

2. Set Desired Run time = minimum run time



5. Click on TC1-1 PID Faceplate and change pH setpoint from 35 to 40 deg C




Improving Unit Op Temperature Control

Heat Exchanger Coolant Control Heat Exchanger Coolant Control


Heat Exchanger By-Pass Control Heat Exchanger By-Pass Control


Heat Exchanger Feedforward ControlHeat Exchanger Feedforward Control


Column Control by Manipulation of Distillate Column Control by Manipulation of Distillate


Column Control by Manipulation of Reflux Column Control by Manipulation of Reflux


Column Control by Manipulation of Steam Column Control by Manipulation of Steam


Column Control by Manipulation of Bottoms Column Control by Manipulation of Bottoms


Kiln Feedforward and Valve Position Control Kiln Feedforward and Valve Position Control


Kiln Differential Temperature Control Kiln Differential Temperature Control


Kiln Oxygen Control Kiln Oxygen Control


Crystallizer Control Crystallizer Control


Extruder Specific Energy Control Extruder Specific Energy Control