The document discusses pH control and measurement. It provides an overview of pH concepts including: - The definition of pH and how it relates to hydrogen and hydroxyl ion concentrations - Details of how a pH sensor works including the glass electrode, reference electrode, and liquid junction - Benefits of smart pH sensors which store calibration data to enable sensor diagnostics and trending - Examples of sensor diagnostics provided by smart sensors such as detecting broken glass, coated sensors, and non-immersed sensors - How sensor parameters change over time and smart sensor trending can identify sensors needing replacement before measurements are compromised

1. Adventures in pH Control
Greg McMillan CDI Process & Industrial
Dave Joseph Rosemount Analytical

2. Photography & Video Recording Policy
Photography and audio/video recording is not permitted in any
sessions or in the exhibition areas without press credentials
or written permission from the Emerson Exchange Board of
Directors. Inquiries should be directed to:
EmersonExchange@Emerson.com
Thank you.

3. Presenters
 Greg McMillan
Principal Consultant
Email: Greg.McMillan@Emerson.com
33 years Monsanto-Solutia Fellow
2 years WU Adjunct Professor
10 years DeltaV R&D Contractor
BS Engineering Physics
MS Control Theory
 Dave Joseph
Sr. Industry Manager
Email: Dave.joseph@emerson.com
24 years with Rosemount Analytical
BS and MS in Chemical Engineering
Member AIChE

4. Key Benefits of Course
 Recognize the opportunity/challenges of pH control
 Learn about modeling and control options
 Optimize hardware implementation
 Understand the root causes of poor performance
 Prioritize improvements based on cost, time, and goal
 Gather insights for applications and solutions
4

5. Section 1: Measuring pH
 Brief theory of pH
 Inside a pH sensor
 The Smart pH sensor
 Diagnostics
5

6. Top Ten Signs of a Rough pH Startup
 Food is burning in the operators’ kitchen
 Only loop mode configured is manual
 Operator puts his fist through the screen
 You trip over a pile of used pH electrodes
 Technicians ask: “what is a positioner?”
 Technicians stick electrodes up your nose
 Environmental engineer is wearing a mask
 Plant manager leaves the country
 Lawyers pull the plugs on the consoles
 President is on the phone holding for you
6

7. The definition of pH
 pH is the unit of measurement for determining
the acidity or alkalinity of a solution.
 The mathematical definition of pH is the
negative logarithm of the molar hydrogen ion
concentration, pH = - log([H+])
 pH is measured by various different sensors, H2O
most common and economical is the glass H+ OH-
OH- H+
electrode/silver reference system.
 pH measurement requires periodic
maintenance to maintain accuracy.

8. pH Scale vs Moles/Liter Ion
Concentration
pH Hydrogen Ion [H+] Hydroxyl Ion [OH-]
0 Acidic 1.0 0.00000000000001
1 0.1 0.0000000000001
2 0.01 0.000000000001
3 0.001 0.00000000001
4 0.0001 0.0000000001
5 0.00001 0.000000001
6 0.000001 0.00000001
7 Neutral 0.0000001 0.0000001
8 0.00000001 0.000001
9 0.000000001 0.00001
10 0.0000000001 0.0001
11 0.00000000001 0.001
12 0.000000000001 0.01
13 0.0000000000001 0.1
14 Basic 0.00000000000001 1.0

9. pH Values of Acids and Bases
14 4.0 % Sodium Hydroxide
12 0.04% Sodium Hydroxide
10 Milk of Magnesia
8 0.84% Sodium Bicarbonate
pH Water @ 25ºC
6
0.00001% Sulfuric Acid
4 0.0001% Hydrochloric Acid
0.01% Sulfuric Acid
2 0.1% Hydrochloric Acid
4.9% Sulfuric Acid
0 1E00 1E-01 1E-02 1E-03 1E-04 1E-05 1E-06 1E-07 1E-08 1E-09 1E-10 1E-11 1E-12 1E-13 1E-14
Hydrogen Ion Concentration (Mole/Liter)

10. What is pH? – technical stuff
 pH = - log([H+])
 Kw = [H+]*[OH-] = 1.0x10-14 at 25ºC
 pH + pOH = pKw
 pH is measured using the Nernst equation
 E(mV) = Ex + 2.3(RT/F)*log aH+
 ~ Ex – (S)*pH in simple form
 Where Ex = calibration constant
 2.3(RT/F) ~ slope (S) in mV/pH units
 aH+ = activity of hydrogen ion ~ [H+]

11. Theoretical Response of a
pH Sensor (25ºC)
500
400
300 Slope of 59.16
mV/pH Unit
200
100
0
mV
-100
-200 Zero mV
at 7 pH
-300
-400 pH
-500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

12. pH Sensor Basics
•The pH electrode
produces a potential
(in mV’s) • The reference
proportional to the electrode potential
pH of the solution. must remain stable
regardless of process
or time effects
Glass
Body
Ag/AgCl • Internal element- AgAgCl
Internal Shield
Wire • Electrolyte fill - KCl/AgCl
• Liquid Junctions
pH Sensitive Fill
Glass Solution
12

13. Inside the pH Glass Membrane...
Alkali Metal Ions Anionic Sites
Glass Matrix
(unaffected)
M M M M S
M M Core Glass
S S S
M
Leached Layer +
M H Inner Zone
(not to scale)
+ +
H H
+
+ + H +
H H H
Outer Zone
+ +
H H
Leached Layer
Dissolving
Hydrogen Ions
13

14. The reference electrode
The Reference Cell maintains
a stable potential regardless
of the process pH or changes
in the activities of other ions in
AgCl/KCl solution.
Ag/AgCl Fill
Internal Solution
Wire The Liquid Junction completes
the electrical circuit between
the pH measuring electrode
and the reference cell via the
process solution.
Liquid
Junction
14
pH17

15. The sum of all potentials…
Assuming a preamp with low leakage current, the pH sensor
Ex =
Eoutside of glass (in process solution)
- Einside of glass (in glass fill solution)
- Emeasurement wire (in glass fill solution)
+ Ereference wire (in reference Ag/KCL solution)
+ Ejunction potential (sum of all interface potentials)
 Glass fill solution typically formulated to cancel out effects
so that 7 pH is 0 mV at any temperature.

16. Double Junction Combination pH
Electrode - Circuit Diagram
Em
W
R3
Er
W
R4
solution ground
silver-silver chloride
Dehydration, loss of active sites, internal electrode
chemical attack, and premature
E4
aging reduces efficiency and
second
makes sensor dramatically slow
W
R5
junction
potassium chloride (KCl) electrolyte
Gel layer is used as a term
in salt bridge between junctions
for the glass surface that
has water molecules primary
R6
W
junction
inner E5
gel silver-silver chloride
layer internal electrode
E3
outer
W
R2
gel pH fill solution
layer E2
Process ions may
Measurement R1
W
migrate into porous
becomes slow Ii reference junction
if glass gets coated E1 while electrolyte ions
Process Fluid migrate out
W
W
R10
W
W
R9 R7
R8
High acid or base concentrations can affect glass gel layer and reference junction potential
Increase in noise or decrease in span or efficiency is indicative of glass electrode problem
Shift or drift in pH measurement is normally associated with reference electrode problem
16

17. Life Depends On Process Conditions
Months
>100% increase in life
from new glass designs
for high temperatures
25ºC 50ºC 75ºC 100ºC
Process Temperature
 High pH conditions decrease glass life at any temperature
 Degraded accuracy and response time is also common
 Leads to unreliable feedforward control
17

18. New Glass preserves response time
After 120 hours exposure at 140ºC
200 mV
150
New Glass
100
Other
50
minutes
0
0 50 100 150 200
Glass electrodes get slow as they age
High temperatures cause accelerated aging
New glass formulations can resist this effect
18

19. Review: pH Measurement loop
Analyzer
(not part of the sensor)
4. Solution
ground
2. Reference
electrode
Liquid
junction
1. Glass
electrode 3.Temperature
element

20. What is a SMART sensor?
 SMART sensors store calibration data on an embedded chip.
 SMART sensors record the initial calibration data of the sensor
and all data from the last 5 calibrations
 They allow trending the performance variables of the sensor to
determine how healthy the sensor is and what work is needed
on it before venturing out into the field.
 Trended diagnostics enable Plantweb users to take action
before the reading is compromised without any intimate
knowledge of how a sensor works or what conditions the sensor
may have been exposed to.
 Results are reduced maintenance and increased measurement
uptime.

21. SMART loop: instrument-cable-sensor
4-wire Models
56 and 1056 are
smart-enabled
2-wire, FF
Model 1066 is
smart-enabled Smart pH sensorused
sensor used
OR in “Smart” mode
VP8 (or cable)
Model 6081pH
Smart-enabled
Wireless Transmitter

22. SMART pH Sensors
 Plug and Play
- Factory pre-calibrated
- Calibrate in lab instead of in field
- Can restore to factory values
 SMART technology
- Automatically trend diagnostics
- Capture intermittent sensor problems
- SMART signal superimposed on mV signal (like HART)
 Simple Migration path
- Compatible with previous analyzers
- Compatible with previous sensors

23. Calibration history
Advanced diagnostics
 Last 5 calibration data
sets for troubleshooting

24. Calibration data set – diagnostics
Current readings!
Calibration Data
Time stamp between calibrations
Calibration method
Slope
Offset
Temperature at the time of calibration
Glass impedance
Reference impedance

25. Calibration History

26. Plug & Play Convenience
Conventional approach: Field calibration with buffers
 SMART approach: Cal in the lab, Plug & Play in the field
Conventional sensor Field Equipment Smart Sensor Field Equipment

27. Siemens Water Technologies, WI
• Application: spent caustic, pH ~10-12
• pH sensor: 3500HTVP and 396PVP
• User comment:
“The SMART is somewhat fool proof. I do like
the backward compatibility with it, because
initially we had the wrong probes hooked to the
wrong boards, and everything still worked. The
SMART features obviously didn't, but the
probes themselves all functioned fine. “

28. Key Indicators of Sensor Performance
Plantweb pH measurements
provide a complete view of
the operational parameters:
 pH reading
 raw sensor output
 temperature
 reference impedance
 Glass impedance
 RTD resistance

29. Diagnostics - Broken Glass
3K 150 M
0-5
 
Broken
Glass!
Reference
Electrode Glass
Solution Electrode
Ground
Broken Glass Fault
 pH Glass electrode normally has high impedance of 50-500 Megohm
 Recommended setting of 10 Megohm will detect even hairline cracks
 Glass can be cracked at the tip or further back inside the sensor (and
not easily visible)
29

30. Diagnostics - Coated Sensor
3K
40k 150 M
 
Coated
Sensor!
Reference
Electrode Glass
Solution Electrode
Ground
Coated Sensor Fault (Ref Z Too High)
 pH Reference electrode normally has low impedance of 1-10 KilOhm
 Reference coating slowly builds up around the junction
 Setting of 20 KilOhm should not generally cause false alarms
30

31. Diagnostics - Non-Immersed Sensor
60K 1500 M
 
Dry
Sensor!
Reference
Electrode Glass
Solution Electrode
Ground
Dry Sensor Fault (Glass Z Too High)
 pH Glass electrode normally has impedance of 50-500 Megohm
 When sensor is dry there is no continuity between the electrode(s) and
the solution ground so impedance reading is very high
 Recommended setting of 1000 Megohm will not cause false alarms
31

32. More Advanced Diagnostics
 pH parameters slope, reference offset, glass, and reference
impedances change little over bulk of operational life.
 When parameters start to change, they indicate that more
frequent calibrations will be necessary.
 Diagnostics are at their most powerful when they can be
compared to the original properties of the sensor.
 Example: a pH slope of 54 may not indicate a problem, but
a sudden drop in slope from 58 to 54 may indicate a 9
month old sensor will not last much longer.
 Trending the electrode slope, reference offset, and
reference impedance will show the first sign of problems.

33. SMART and Trending Diagnostics
SMART pH sensors
automatically record their ^mV/ pH
59 58.9 58.8
Max pH error per calibration cycle
Change in Slope
58.7 58.6 58.5 58.4 58.3 58.2 58.1 58 57.9 57.8 57.7 57.6 57.5 57.4 57.3
initial conditions and the
^pH 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7
0.1 # 0.02% # 0.03% # 0.05% # 0.07% # 0.09% # 0.10% # 0.12% # 0.14% # 0.15% # 0.17% # 0.19% # 0.21% # 0.23% # 0.24% # 0.26% # 0.28% # 0.30%
0.2 # 0.03% # 0.07% # 0.10% # 0.14% # 0.17% # 0.21% # 0.24% # 0.27% # 0.31% # 0.34% # 0.38% # 0.42% # 0.45% # 0.49% # 0.52% # 0.56% # 0.59%
0.3 # 0.05% # 0.10% # 0.15% # 0.20% # 0.26% # 0.31% # 0.36% # 0.41% # 0.46% # 0.52% # 0.57% # 0.62% # 0.68% # 0.73% # 0.78% # 0.84% # 0.89%
0.4 # 0.07% # 0.14% # 0.20% # 0.27% # 0.34% # 0.41% # 0.48% # 0.55% # 0.62% # 0.69% # 0.76% # 0.83% # 0.90% # 0.97% # 1.04% # 1.11% # 1.19%
0.5 # 0.08% # 0.17% # 0.26% # 0.34% # 0.43% # 0.51% # 0.60% # 0.69% # 0.77% # 0.86% # 0.95% # 1.04% # 1.13% # 1.22% # 1.30% # 1.39% # 1.48%
last 5 calibrations to make
0.6 # 0.10% # 0.20% # 0.31% # 0.41% # 0.51% # 0.62% # 0.72% # 0.82% # 0.93% # 1.03% # 1.14% # 1.25% # 1.35% # 1.46% # 1.57% # 1.67% # 1.78%
0.7 # 0.12% # 0.24% # 0.36% # 0.48% # 0.60% # 0.72% # 0.84% # 0.96% # 1.08% # 1.21% # 1.33% # 1.45% # 1.58% # 1.70% # 1.83% # 1.95% # 2.08%
0.8 # 0.14% # 0.27% # 0.41% # 0.55% # 0.68% # 0.82% # 0.96% # 1.10% # 1.24% # 1.38% # 1.52% # 1.66% # 1.80% # 1.94% # 2.09% # 2.23% # 2.37%
0.9 # 0.15% # 0.31% # 0.46% # 0.61% # 0.77% # 0.92% # 1.08% # 1.24% # 1.39% # 1.55% # 1.71% # 1.87% # 2.03% # 2.19% # 2.35% # 2.51% # 2.67%
1 # 0.17% # 0.34% # 0.51% # 0.68% # 0.85% # 1.03% # 1.20% # 1.37% # 1.55% # 1.72% # 1.90% # 2.08% # 2.25% # 2.43% # 2.61% # 2.79% # 2.97%
1.1 # 0.19% # 0.37% # 0.56% # 0.75% # 0.94% # 1.13% # 1.32% # 1.51% # 1.70% # 1.90% # 2.09% # 2.28% # 2.48% # 2.67% # 2.87% # 3.07% # 3.26%
trending easier. 1.2
1.3
1.4
1.5
1.6
# 0.20%
# 0.22%
# 0.24%
# 0.25%
# 0.27%
#
#
#
#
#
0.41% # 0.61%
0.44% # 0.66%
0.48% # 0.72%
0.51% # 0.77%
0.54% # 0.82%
#
#
#
#
#
0.82%
0.89%
0.96%
1.02%
1.09%
#
#
#
#
#
1.03%
1.11%
1.20%
1.28%
1.37%
#
#
#
#
#
1.23%
1.34%
1.44%
1.54%
1.64%
#
#
#
#
#
1.44%
1.56%
1.68%
1.80%
1.92%
#
#
#
#
#
1.65%
1.79%
1.92%
2.06%
2.20%
#
#
#
#
#
1.86%
2.01%
2.17%
2.32%
2.48%
#
#
#
#
#
Beginning Slope - Ending Slope =
2.07%
2.24%
2.41%
2.59%
#
#
#
#
2.28%
2.47%
Slope Change
2.66%
2.85%
#
#
#
#
2.49%
2.70%
2.91%
3.11%
#
#
#
#
Slope change * Max change in pH =
2.76% # 3.04% # 3.32% #
2.70%
2.93%
3.15%
3.38%
3.60%
#
#
#
#
#
2.92%
3.16%
3.40%
3.65%
3.89%
#
#
#
#
#
3.13%
3.39%
3.65%
3.91%
4.17%
#
#
#
#
#
3.34%
3.62%
3.90%
4.18%
4.46%
#
#
#
#
#
3.56%
3.86%
4.15%
4.45%
4.75%
• Predictive maintenance
1.7 # 0.29% # 0.58% # 0.87% # 1.16% # 1.45% # 1.75% # 2.04% # 2.34% # 2.63% # 2.93% # 3.23% # 3.53% # 3.83% # 4.13% # 4.43% # 4.74% # 5.04%
1.8 # 0.31% # 0.61% # 0.92% # 1.23% # 1.54% # 1.85% # 2.16% # 2.47% # 2.79% # 3.10% # 3.42% # 3.74% # 4.06% # 4.38% # 4.70% # 5.02% # 5.34%
1.9 # 0.32% # 0.65% # 0.97% # 1.30% # 1.62% # 1.95% # 2.28% # 2.61% # 2.94% # 3.28% Max mV deviation per
# 3.61% # 3.94% # 4.28% # 4.62% # 4.96% # 5.30% # 5.64%
2 # 0.34% # 0.68% # 1.02% # 1.37% # 1.71% # 2.05% # 2.40% # 2.75% # 3.10% # 3.45% # 3.80% # 4.15% # 4.51% # 4.86% # 5.22% # 5.57% # 5.93%
2.1 # 0.36% # 0.71% # 1.07% # 1.43% # 1.79% # 2.16% # 2.52% # 2.89% # 3.25% # 3.62% calibration cycle
# 3.99% # 4.36% # 4.73% # 5.10% # 5.48% # 5.85% # 6.23%
2.2 # 0.37% # 0.75% # 1.12% # 1.50% # 1.88% # 2.26% # 2.64% # 3.02% # 3.41% # 3.79% # 4.18% # 4.57% # 4.96% # 5.35% # 5.74% # 6.13% # 6.53%
2.3 # 0.39% # 0.78% # 1.18% # 1.57% # 1.97% # 2.36% # 2.76% # 3.16% # 3.56% # (Max mV deviation / Beginning
3.97% # 4.37% # 4.78% # 5.18% # 5.59% # 6.00% # 6.41% # 6.82%
with reference Impedance 2.4
2.5
2.6
2.7
2.8
# 0.41%
# 0.42%
# 0.44%
# 0.46%
# 0.48%
#
#
#
#
#
0.82% # 1.23%
0.85% # 1.28%
0.88% # 1.33%
0.92% # 1.38%
0.95% # 1.43%
#
#
#
#
#
1.64%
1.71%
1.77%
1.84%
1.91%
#
#
#
#
#
2.05%
2.14%
2.22%
2.31%
2.39%
#
#
#
#
#
2.47%
2.57%
2.67%
2.77%
2.88%
#
#
#
#
#
2.88%
3.00%
3.12%
3.24%
3.36%
#
#
#
#
#
3.30%
3.44%
3.57%
3.71%
3.85%
#
#
#
#
#
3.72%
3.87%
4.03%
4.18%
4.34%
#
#
#
#
#
4.14%
4.31%
4.48%
4.66%
4.83%
#
#
#
#
#
4.56%
4.75%
4.94%
5.13%
5.32%
#
#
#
#
#
4.98%
Slope) = Max pH error
5.19%
5.40%
5.61%
5.81%
#
#
#
#
#
5.41%
5.63%
5.86%
6.08%
6.31%
#
#
#
#
#
5.83%
6.08%
6.32%
6.56%
6.81%
#
#
#
#
#
6.26%
6.52%
6.78%
7.04%
7.30%
#
#
#
#
#
6.69%
6.97%
7.25%
7.53%
7.80%
#
#
#
#
#
7.12%
7.42%
7.71%
8.01%
8.31%
trending
2.9 # 0.49% # 0.99% # 1.48% # 1.98% # 2.48% # 2.98% # 3.48% # 3.99% # 4.49% # 5.00% # 5.51% # 6.02% # 6.53% # 7.05% # 7.57% # 8.08% # 8.60%
3 # 0.51% # 1.02% # 1.53% # 2.05% # 2.56% # 3.08% # 3.60% # 4.12% # 4.65% # 5.17% # 5.70% # 6.23% # 6.76% # 7.29% # 7.83% # 8.36% # 8.90%
3.1 # 0.53% # 1.05% # 1.58% # 2.12% # 2.65% # 3.18% # 3.72% # 4.26% # 4.80% # 5.34% # 5.89% # 6.44% # 6.98% # 7.53% # 8.09% # 8.64% # 9.20%
3.2 # 0.54% # 1.09% # 1.64% # 2.18% # 2.74% # 3.29% # 3.84% # 4.40% # 4.96% # 5.52% # 6.08% # 6.64% # 7.21% # 7.78% # 8.35% # 8.92% # 9.49%
3.3 # 0.56% # 1.12% # 1.69% # 2.25% # 2.82% # 3.39% # 3.96% # 4.54% # 5.11% # 5.69% # 6.27% # 6.85% # 7.44% # 8.02% # 8.61% # 9.20% # 9.79%
• Determine optimum
3.4 # 0.58% # 1.16% # 1.74% # 2.32% # 2.91% # 3.49% # 4.08% # 4.67% # 5.27% # 5.86% # 6.46% # 7.06% # 7.66% # 8.26% # 8.87% # 9.48% # 10.09%
3.5 # 0.59% # 1.19% # 1.79% # 2.39% # 2.99% # 3.60% # 4.20% # 4.81% # 5.42% # 59mV/pH - 55mV/pH = 4mV/pH * 6pH =
6.03% # 6.65% # 7.27% # 7.89% # 8.51% # 9.13% # 9.76% # 10.38%
3.6 # 0.61% # 1.22% # 1.84% # 2.46% # 3.08% # 3.70% # 4.32% # 4.95% # 5.58% # 6.21% # 6.84% # 7.47% # 8.11% # 8.75% # 9.39% # 10.03% # 10.68%
3.7 # 0.63% # 1.26% # 1.89% # 2.53% # 3.16% # 3.80% # 4.44% # 5.09% # 5.73% # 6.38% 24mV / 59mV/pH = 0.41pH
# 7.03% # 7.68% # 8.34% # 8.99% # 9.65% # 10.31% # 10.98%
3.8 # 0.65% # 1.29% # 1.94% # 2.59% # 3.25% # 3.90% # 4.56% # 5.22% # 5.89% # 6.55% # 7.22% # 7.89% # 8.56% # 9.24% # 9.91% # 10.59% # 11.27%
3.9 # 0.66% # 1.33% # 1.99% # 2.66% # 3.33% # 4.01% # 4.68% # 5.36% # 6.04% # 6.72% # 7.41% # 8.10% # 8.79% # 9.48% # 10.17% # 10.87% # 11.57%
4 # 0.68% # 1.36% # 2.04% # 2.73% # 3.42% # 4.11% # 4.80% # 5.50% # 6.20% # 6.90% # 7.60% # 8.30% # 9.01% # 9.72% # 10.43% # 11.15% # 11.87%
calibration frequency and Change in Process
= 1% = 2% = 3%
Beginning Slope - Ending Slope = Slope Change
= 4% = 5%
predict probe life with pH Slope Change * Maximum Change in Process pH = Maximum pH Deviation
For a typical application ranging from 4 to
slope trending 10 pH the error from assuming 59 slope
instead of 55 could be 0.41 pH units
>>> need to recalibrate

34. Using Diagnostics
 Instruments ship with the
diagnostics turned off
 When enabled, default setpoints
will generally be ok
 Few false alarms when
correctly configured
 Some problems may not be
detectable with online
diagnostics
 When in doubt, check with
buffers

35. Section 2: Modeling and Control
 Virtual plant and embedded process models
 Online identification of titration curve
 Minimization of project capital cost
 Cascade pH control
 Batch pH control
 Linear reagent demand control
 Elimination of split range control
 Model predictive control
35

36. Embedded Process Model for pH
36

37. Titration Curves can Vary
Weak Acid and Strong Base Strong Acid and Weak Base
pka = 10
pka = 4
Slope moderated
near each pKa
Weak Acid and Weak Base Multiple Weak Acids and Weak Bases
pKa and curve
change with
pka = 10
temperature!
pka = 9
pka = 5
pka = 4
pka = 3
37

38. Nonlinearity can cost big money
pH measurement error may look smaller on the flatter portion of a titration
curve but the associated reagent delivery error is larger
10
pH
4
Reagent to Feed
Reagent Flow Ratio
Optimum Savings
Original
set point
set point
Oscillations could be due to non-ideal mixing, control valve stick-slip. or pressure fluctuations
38

39. Titration Curve Matched to Plant
pH
Slope
39

40. Modeled pH Control System
AY signal
pH set point characterizer
1-3
Signal characterizers linearize loop
via reagent demand control
AC
1-1
LC LT
1-5 1-5
signal splitter
characterizer
AY Feed
1-4
AY
1-2
NaOH Acid To other Tank
middle
signal
selector FT FT
Tank 1-1 1-2
AY
1-1
AT AT AT
Eductors 1-1 1-2 1-3
Static Mixer From other Tank
To other Tank
Downstream system
40

41. Conventional vs. Reagent Demand
One of many spikes of recirculation pH
spikes from stick-slip of water valve
Influent pH
Tank 1 pH for Reagent Demand Control
Tank 1 pH for Conventional pH Control
Start of Step 4
(Slow Rinses)
Start of Step 2
(Regeneration)
41

42. Traditional System for
Minimum Variability
The period of oscillation (4 x process dead time) and filter time
(process residence time) is proportional to volume. To prevent Reagent
resonance of oscillations, different vessel volumes are used.
Major overlooked Reagent
Reagent problem is reagent
Deliver delay from
dip tube design
Feed
Small first tank provides a faster response
and oscillation that is more effectively filtered Big footprint
by the larger tanks downstream and high cost!
42

43. Traditional System for
Minimum Reagent Use
Reagent
The period of oscillation (total loop dead time) must differ by more
than factor of 5 to prevent resonance (amplification of oscillations)
Feed Reagent
Reagent
Big footprint
and high cost!
The large first tank offers more cross neutralization of influents
43

44. Tight pH Control with
Minimum Capital
IL#1 – Interlock that prevents back fill of
reagent piping when control valve closes
IL#2 – Interlock that shuts off effluent flow until
vessel pH is projected to be within control band
Eductor
High Recirculation Flow
Reagent
Any Old Tank
Signal
Characterizer
LC LT
1-3 1-3
*IL#2 f(x)
FT
1-1
Effluent
AC
1-1
FC
1-2
AT
1-1
*IL#1
Influent FT
1-2
10 to 20
pipe
diameters
44

45. Linear Reagent Demand Control
 Signal characterizer converts 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 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)
45

46. Cascade Control to Reduce
Downstream Offset
Linear Reagent
Demand Controller
Flow Feedforward
FT
1-1
RSP
FC AC Trim of Inline
1-1 Sum Set Point
1-1
Reagent
AT f(x)
Filter f(x)
1-1
FT
Static Mixer PV signal
1-2 SP signal
Characterizer characterizer
Feed
Coriolis Mass
10 to 20
Flow Meter
pipe
diameters
M
AC
1-2
Any Old Tank
Enhanced PID
Controller
AT
1-2
46

47. Full Throttle Batch pH Control
Batch pH
End Point
Predicted pH
Reagent
Cutoff Sum
Rate of
Projected
Past Change DpH
New pH DpH DpH/Dt
Sub Div Mul
Old pH
Delay Dt Total System
Dead Time
Batch Reactor
Filter
AT
1-1
10 to 20
pipe
diameters
Section 3-5 in New Directions in Bioprocess Modeling and Control
shows how this strategy is used as a head start for a PID controller
47

48. Linear Reagent Demand
Batch pH Control
FQ FT
Secondary pH
1-1 1-1
PI Controller
AC FC
1-1 1-1
Influent #1
AT Online Curve
1-1 Identification
Static Mixer
10 to 20
pipe FT
diameters 1-2
Influent #2
AC
f(x)
1-1
Batch Reactor
Signal
Master Reagent Demand
Characterizer
Adaptive PID Controller
AT Uses Online
1-1 Titration Curve
10 to 20 Reduces injection and mixing delays and enables some cross
pipe neutralization of swings between acidic and basic influent. It is
diameters suitable for continuous control as well as fed-batch operation.
48

49. Conventional Fine and
Coarse Valve Control
Large Small
(Coarse) (Fine)
ZC CV
1-1
Integral only Controller
(CV is Implied Fine
Control Valve Position)
Neutralizer
AC
ZC speed of response must
1-1
be slow and tuning is difficult
Must add feedforward for fast
and large influent disturbance PID Controller
AT
1-1
49

50. Advanced Fine and
Coarse Valve Control
manipulated
variables
Small (Fine) Large (Coarse)
MPC Reagent Valve SP Reagent Valve SP
controlled
variable
Small (Fine) null
Reagent Valve SP
controlled
variable
Neutralizer
pH PV
Model Predictive Controller (MPC) setup for rapid simultaneous
throttling of a fine and coarse control valves that addresses
both the rangeability and resolution issues. This MPC can
possibly reduce the number of stages of neutralization needed
50