Improving pH System Design and Performance

1
Improving pH System Design and Performance

• Roger Reedy - Principal Engineer
• Greg McMillan - Principal Consultant
• John Moulis - Principal Engineer

2
Presenters

• Roger Reedy
• Greg McMillan
• John Moulis

3
Introduction

• 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

4
The Luling Plant
5
The Luling Plant
6
Existing Production System
Filtered Water
Cation
Anion
De-mineralized Water
7
Existing Neutralization System
Water
93 Acid
50 Caustic
AT
Cation
Anion
To EO
Final caustic adjustment
Final acid adjustment
Pit
8
Project Drivers

• Old pit needs significant upgrades
• Corporate objective for secondary containment

9
Project Objectives

• Safe
• Responsible
• Reliable
• Mechanically
• Robust controls, Operator friendly
• Ability to have one tank out of service
• Balance initial capital against reagent cost

10
Cost 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
11
Top 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

12
Titration Curve
Slope is 1,000x steeper at 7 pH than at 2 or 12
pH from small buffer effect at 6 and 9 pH (slope
would be 100,000x steeper for pure strong acid
and base)
B Er 100 Fimax
---- Frmax Frmax A
Fimax Er B / A Ss 0.5
Er Where A distance of center of
reagent error band on abscissa from origin B
width of allowable reagent error band on
abscissa for control band Er allowable
reagent error () Frmax maximum reagent flow
(kg per minute) Fimax maximum influent flow (kg
per minute) Ss allowable stick-slip
(resolution limit) ()
Mistake in equipment, piping, valve, and
sensor design can cause the system not only to
fail but to fail miserably
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Challenges

• Process gain changes by factor of 1000x
• Final element rangeability needed is 10001
• Final element resolution requirement is 0.1
• Concentrated reagents (50 caustic and 98
  sulfuric)
• Caustic valves ¼ inch port may plug at lt 10
  position
• Must mix 0.05 gal reagent in 5,000 gal lt 2
  minutes
• Volume between valve and injection must be lt 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

14
Choices
Really big tank and thousands of mice each with
0.01 gallon of acid or caustic
or
modeling and control
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New Control System
middle signal selector
AY 3-1
Anion
AT 3-2
AT 3-1
AT 3-3
Control Logic CL 1,2,3,4,5,6,7,8 optimizes
system operation
Cation
FT 3-4
Static mixer
Influent
Signal characterizers provide Linear Reagent
Demand Control
signal characterizer
signal characterizer
AY 2-3
LT 2-5
AY 1-3
LC 2-5
LT 1-5
LC 1-5
pH set point
pH set point
signal characterizer
signal characterizer
CL2
CL1
AY 2-2
AC 2-1
AY 1-2
AC 1-1
middle signal selector
middle signal selector
CL7
CL7
splitter
splitter
Tank 1
Tank 2
AY 2-4
AY 2-1
AY 1-1
AY 1-4
NaOH
Acid
NaOH
Acid
AT 2-3
AT 2-2
AT 2-1
AT 1-2
AT 1-1
FT 2-1
AT 1-3
FT 2-2
FT 1-1
FT 1-2
eductors
eductors
CL3
CL4
CL6
CL6
CL8
CL8
CL5
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Control 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 (CL1)
• 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 (CL2)
• If Tank 1 pH within control band, reduce its
  level rapidly to minimum (CL3)
• If Tank 2 pH within control band, reduce its
  level rapidly to minimum (CL4)
• 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 (CL5)
• 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
  (CL6)
• Shut reagent valves periodically for 30 seconds
  to get tank pH reading to optimize the
  recirculation pH set point (CL7)
• If feed is negligible and tank pH within control
  band, shut off pump (CL8)

17
Middle 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

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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)

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Dynamic Process Model in DeltaV
Streams, pumps, valves, sensors, tanks, and
mixers are modules from DeltaV composite
template library.
Each wire is a pipe that is a process stream
data array (e.g. pressure, flow, temperature,
density, heat capacity, and concentrations)
First principle conservation of material,
energy, components, and ion charges
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DeltaV Virtual Plant
DCS batch and loop configuration, displays, and
historian
Embedded Advanced Control Tools
Embedded Modeling Tools
Dynamic Process Model
Loop Monitoring And Tuning
Virtual Plant Laptop or Desktop Personal
Computer Or DCS Application Station or Controller
Online Data Analytics
Model Predictive Control
Process Knowledge
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Top Ten Reasons We Use a Virtual Plant

• You cant 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 didnt have
  1,000K
• Actual plant doesnt fit in our suitcase

22
Business 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)

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Summary

• Study shows potential project savings overwhelm
  reagent savings
• Reagent savings is lt 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?

24
Where 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/FunnyThi
  ng/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