View
213
Download
0
Category
Preview:
Citation preview
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
Recommended