Improving pH System Design and Performance

Improving pH System Design and Performance

Roger Reedy - Principal Engineer

Greg McMillan - Principal Consultant

John Moulis - Principal Engineer

PresentersPresenters

Roger Reedy

Greg McMillan

John Moulis

IntroductionIntroduction

Use of modeling in DeltaV control studio to optimize design and prototype control of neutralization process

Topics to be covered– Existing system– Project drivers and objectives– Project cost– Challenges– Control system and equipment design– Process model– Virtual plant– Business results– Summary

The Luling PlantThe Luling Plant

The Luling PlantThe Luling Plant

Existing Production SystemExisting Production System

Cation Anion

Filtered Water

De-mineralized Water

Existing Neutralization SystemExisting Neutralization System

Water

93%

Acid

50%

Caustic

Pit

Cation Anion

To EO

Final acid

adjustment

Final caustic

adjustment

AT

Project DriversProject Drivers

Old pit needs significant upgrades Corporate objective for secondary containment

Project ObjectivesProject Objectives

Safe Responsible Reliable

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

Balance initial capital against reagent cost

Cost DataCost Data

Acid market price $.75/Gal Caustic market price $.75/Gal

All other equipment $150k Total project cost (2 10k Gal tanks) $550k

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

Tank $20k $30k $48k $81k $120k

Pump $26k $35k $45k $74k $105k

Top Ten Signs of a Rough pH StartupTop Ten Signs of a Rough pH Startup

Food is burning in the operators’ kitchen The only loop mode configured is manual An operator puts his fist through the screen You trip over a pile of used pH electrodes The technicians ask: “what is a positioner?” The technicians stick electrodes up your nose The environmental engineer is wearing a

mask The plant manager leaves the country Lawyers pull the plugs on the consoles The president is on the phone holding for you

Titration Curve Titration Curve

BEr = 100% Fimax Frmax

Frmax = A Fimax

Er =

Ss = 0.5 Er

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

Frmax = maximum reagent flow (kg per minute)

Fimax = maximum influent flow (kg per minute)

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

12

pH

Reagent FlowInfluent Flow

6

9

InfluentB

A

Control BandSet point

2

Slope is 1,000xsteeper at 7 pHthan at 2 or 12 pHfrom small buffereffect at 6 and 9 pH(slope would be100,000x steeperfor pure strongacid and base)

Mistake in equipment, piping, valve, and sensordesign can cause thesystem not only to failbut to fail miserably

ChallengesChallenges

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 98% 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

ChoicesChoices

Really big tank and thousands of miceeach with 0.01 gallon of acid or caustic

or

modeling and control

New Control System New Control System

FT 1-1

FT 1-2

AT 1-3

AT 1-2

AT 1-1

AY 1-1

AY 1-2

AY 1-4

AC 1-1

AY 1-3

splitter

middlesignal

selector

signalcharacterizer

signalcharacterizer

pH set point

CL#1

eductors

NaOH Acid

LT 1-5

LC1-5

FT 2-1

FT 2-2

AT 2-3

AT 2-2

AT 2-1

AY 2-1

AY 2-2

AY 2-4

AC 2-1

AY 2-3

splitter

middlesignal

selector

signalcharacterizer

signalcharacterizer

pH set point

CL#2

eductors

NaOH Acid

LT 2-5

LC2-5

Tank 1 Tank 2

Static mixer

Anion

Cation

CL#3 CL#4

AT 3-3

AT 3-2

AT 3-1

AY 3-1

middlesignal

selector

FT 3-4

CL#5

Control Logic CL# 1,2,3,4,5,6,7,8 optimizes system operation

Signal characterizers provide Linear Reagent Demand Control

CL#6 CL#6

CL#7 CL#7

CL#8 CL#8

Influent

Control LogicControl Logic

If influent pH more than 2 pH units away from control band and flow has just started, head start tank 1 pH loop for 2 minutes to move 2 pH closer (CL#1)

If Tank 1 pH more than 2 pH units away from control band and flow has just started, head start tank 2 pH loop for 2 minutes to move 2 pH closer (CL#2)

If Tank 1 pH within control band, reduce its level rapidly to minimum (CL#3) If Tank 2 pH within control band, reduce its level rapidly to minimum (CL#4) If Tank 2 pH outside control band, and level in Tank 2 is higher than Tank 1,

level control recirculation of Tank 2 back to Tank 1 (CL#5) If caustic reagent valve signal is less than 10%, use pulse width modulation

and increase pH loop filter time and reset time to smooth out pulses (CL#6) Shut reagent valves periodically for 30 seconds to get tank pH reading to

optimize the recirculation pH set point (CL#7) If feed is negligible and tank pH within control band, shut off pump (CL#8)

Middle Signal Selection AdvantagesMiddle Signal Selection Advantages

Inherently ignores single measurement failure of any type including the most insidious PV failure at set point

Inherently ignores slowest electrode Reduces noise and spikes particularly for steep curves Offers online diagnostics on electrode problems

– Slow response indicates coated measurement electrode– Shortened response indicates aged measurement electrode– Drift indicates coated or contaminated reference electrode– Noise indicates dehydrated measurement electrode

Facilitates online calibration of a measurement

Linear Reagent Demand Control Linear Reagent Demand Control

Signal characterizer translates 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 needed compensation

Special configuration is needed to provide operations with pH interface to:– See loop PV in pH and enter loop SP in pH

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)

Dynamic Process Model in DeltaV Dynamic Process Model in DeltaV

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

DeltaV Virtual PlantDeltaV Virtual Plant

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

Top Ten Reasons We Use a Virtual PlantTop Ten Reasons We Use a Virtual Plant

You can’t freeze, restore, and replay an actual plant batch

No software to learn, install, interface, and support No waiting on lab analysis No raw materials No environmental waste Virtual instead of actual problems Batches are done in 5 minutes instead of 5 hours Plant can be operated on a tropical beach Last time we checked our wallet we didn’t have

$1,000K Actual plant doesn’t fit in our suitcase

Business Results AchievedBusiness Results Achieved

Results of modeling indicate all objectives will be met Rework savings on $550k project potentially significant Equipment savings of $132k per tank (10k vs. 40k Gal)

SummarySummary

Study shows potential project savings overwhelm reagent savings– Reagent savings is < $1K per year– Equipment savings is $132K per tank

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 oscillations from PV on steep part of titration curve

– Control logic optimizes pH loops to minimize inventory to maximize availability and turn off pumps to reduce energy use

• Initialization of pH loop when feed flow starts provides head start for upset• Pulse width modulation of caustic at low valve positions minimizes plugging• Periodic optimization of loop set point keeps tank pH within control band

Feedback? Questions?

Where To Get More InformationWhere To Get More Information

McMillan, Gregory and Cameron, Robert, Advanced pH Measurement and Control, 3rd edition, ISA, 2005

McMillan, Gregory K., A Funny Thing Happened on the Way to the Control Room, 1989 http://www.easydeltav.com/controlinsights/FunnyThing/default.asp

McMillan, Gregory K., Plant Design and Education Categories, http://ModelingandControl.com

McMillan, Gregory, K. and Sowell, Mark. S., “Virtual Control of Real pH”, Control, November 2007

McMillan, Gregory, K. and Sowell, Mark. S., “Advances in pH Modeling and Control”, ISA 54th IIS Paper IIS08-P044, 2008