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ISA Saint Louis Short Course Dec 9-10, 2010

Advanced pH Measurementand Control - Day 2

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/

TT 6-7

TC 6-7TC 6-7

TT 6-8

TC 6-8TC 6-8

Jacket Water

Bioreactor

AT 6-1

AT 6-4

VSD

AY6-2AY6-2

FT 6-1B

FT 6-4

Substrate

AC 6-1AC 6-1

FT 6-2

Air

pH

AC 6-2AC 6-2

Dissolved O2

Vent

Bioreactor 1 Control Diagram

PT 6-3

PC 6-3PC 6-3

kPa

VSD

VSD

VSD

VSD

AT 6-2

AC 6-4AC 6-4

Batch Drain

AT 6-5

Biomass

Substrate

Basic Reagent

(%)

(%)

(%)

RCAS (g/liter)

(%)

AT 6-6

Product

Splitter

AY6-1AY6-1

Splitter

MC6-1B

MC6-3

MC6-4

MC6-2

VSD

Acidic Reagent

(%)MC6-1A

FT 6-1A

oC

TV6-8 Coolant

MPCbiomass growth rate (kg/hr)

net production rate (kg/hr)

AT 6-9

Dissolved CO2

Estimators from adapted virtual plant:

kPa

kg/sec

kg/sec

kg/sec

kg/sec

FT 6-3

kg/sec

oC

oC

RCAS (g/liter)

Bioreactor Control - 1

Bioreactor

VSD

VSD

TC 41-7

AT 41-4s2

AT 41-4s1

AT 41-2

AT 41-1

TT 41-7

AT 41-6

LT 41-14

Glucose

Glutamine

pH

DO

Product

Heater

VSD

VSD

VSD

AC 41-4s1

AC 41-4s2

Media

Glucose

Glutamine

VSD

Inoculums

VSD

Bicarbonate

AY 41-1

AC 41-1Splitter

AC 41-2

AY 41-2Splitter

CO2

O2

Air

Level

Drain

0.002 g/L

7.0 pH

2.0 g/L

2.0 g/L

37 oC

MFC

MFC

MFC

AT 41-5x2

Viable Cells

AT 41-5x1

Dead Cells

Bioreactor Control - 2

Cardinal pH Model Kinetics

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

6.00 6.20 6.40 6.60 6.80 7.00 7.20 7.40 7.60 7.80 8.00

pH

pH

Gro

wth

Rat

e F

acto

r

pH Growth Rate Factor pH Growth Rate Factor

Convenient pH Model Kinetics

2maxmin

maxmin

)()()(

)()(

optpHpHpHpHpHpH

pHpHpHpH

vHr

pHmax = maximum pH for viable cells (8 pH)

pHmin = minimum pH for viable cells (6 pH)

pHopt = optimum pH for viable cell growth (6.8 pH)

Feed

Reagent

Reagent

ReagentThe period of oscillation (4 x process dead time) and filter time(process residence time) is proportional to volume. To preventresonance of oscillations, different vessel volumes are used.

Small first tank provides a faster responseand oscillation that is more effectively filtered by the larger tanks downstream

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!

Consequences of Poor Dynamics and Tuning

• The peak error is inversely proportional to the controller gain• The integrated error is inversely proportional to the controller gain but is

also proportional to the reset time• The maximum controller gain is proportional to the process time

constant to loop dead time ratio• The minimum reset time is proportional to the dead time • The minimum peak error is inversely proportional to the ratio of the

process time constant to loop dead time• The oscillation period is proportional to the loop dead time• The integrated error is proportional to the loop dead time squared• Most of the process time constant seen by the loop is lost for excursions

on steep slopes of the titration curve• If you can increase the ratio of process time constant to loop dead time,

you can reduce the excursion along the titration curve and hence the change in process gain (slope) seen by the loop. In other words poor control begets poorer control by the introduction of greater nonlinearity

Basic Neutralization System - Before

Static Mixer

AC 1-1

Neutralizer

Feed

Discharge

AT 1-1

FT 1-1

FT 2-1

AC 2-1

AT 2-1 FC

1-2

FT 1-2

2diameters

ReagentStage 1

ReagentStage 2

Can you spot the opportunities forprocess control improvement?

Basic Neutralization System - AfterFeedforward

Summer

Static Mixer

AC 1-1

Neutralizer

Feed

Discharge

AT 1-1

FT 1-1

FT 2-1

AT 2-1

FC 1-2

FT 1-2

10-20diameters

ReagentStage 1

ReagentStage 2

FC 2-1

AC 2-1

10-20diameters

f(x)

RSP

SignalCharacterizer

*1

*1

*1 - Isolation valve closes when control valve closes

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

Methods of Reducing Reagent Delivery Delays

• Locate reagent throttle valve at the injection point

• Mount automatic on-off isolation valve at the injection point

• Reduce diameter and length (volume) of injector or dip tube

• Dilute the reagent upstream to increase reagent flow rate

• Inject reagent into vessel side just past baffles

• Inject reagent into recirculation line just before vessel entry

• Inject reagent into feed line just before vessel entry

• Reduce reagent control valve sticktion and deadband

The benefits of feedforward are realized only if the correction arrives at aboutthe same time as the disturbance at the point of the pH measurement. Since the disturbance is usually in a high flow influent stream, any reagent delivery delays severely diminish the effectiveness of feedforward besides feedback control because the disturbance hits the pH measurement before the correction.

High Uniformity Reagent Dilution Control

Water

FC 1-2

FT 1-2

DilutedReagent

DC 1-1

DT 1-1

FT 1-1

Rea

gen

t

High Recirculation Flow

Any Old Tank

LT 1-3

LC 1-3

Eductor

FC 1-1

Ratio

Density

RSP

RSP

Big old tank acts an effective filter ofreagent concentration fluctuations

Cascade pH Control to Reduce Downstream Offset

M

AT 1-2

Static Mixer

Feed

AT 1-1

FT 1-1

FT 1-2

Reagent

10 to 20pipe

diameters

Sum FC 1-1

Filter

Coriolis MassFlow Meter

f(x)

AC 1-1

AC 1-2

PV signalCharacterizer

RSP

f(x)

Flow Feedforward

SP signalcharacterizer

Trim of Inline Set Point

IntegralOnly

Controller

Linear ReagentDemand Controller

Any Old Tank

Full Throttle Batch pH Control

Batch Reactor

AT 1-1

10 to 20 pipe

diameters

Filter

Delay

Sub Div

Sum

t

Cutoff

PastpH

Rate ofChangepH/t

Mul

Total System Dead Time

ProjectedpHNew pH

Old pH

Batch pH End Point

Predicted pHReagent

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

Linear Reagent Demand Batch pH Control

Batch Reactor

AC 1-1

AT 1-1

10 to 20 pipe

diameters

f(x)

Master Reagent DemandAdaptive PID Controller

Static Mixer

AC 1-1

AT 1-1

10 to 20 pipe

diameters

Secondary pHPI Controller

Signal CharacterizerUses Online

Titration Curve

FT 1-1

FC 1-1

FQ 1-1

FT 1-2

Online Curve Identification

Influent #1

Reduces injection and mixing delays and enables some crossneutralization of swings between acidic and basic influent. It issuitable for continuous control as well as fed-batch operation.

Influent #2

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)

Pulse Width and Amplitude Modulated Reagent

Neutralizer

AC 1-1

Reagent

AT 1-1

10 to 20 diameters

PWM

Faster cheaperon-off valve ispulse width modulated

PD Controller

Cycle Time = SystemDead Time

Throttle valve positionsets pulse amplitude

Pulse width modulation is linear. The additionof pulse amplitude modulation introduces asevere nonlinearity but greatly increases thesensitivity and rangeability of reagent addition

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 - 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

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:– 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)

Benefit depends more upon on slopes rather than accuracy of points of titration curve (more robust than feedforward)

Case History 2 - Reagent Demand Control Case History 2 - Reagent Demand Control

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

Adapted Reagent Demand Control

Neutralizer

AC 1-1

AT 1-1

10 to 20 diameters

f(x)

Master Reagent DemandAdaptive PID Controller

Static Mixer

AC 1-1

AT 1-1

10 to 20 diameters

Secondary pHPI Controller

Signal CharacterizerUses Online

Titration Curve

FT 1-1

FC 1-1

FQ 1-1

FT 1-2

Online Curve Identification

Influent

Reduces injection and mixing delays and enables somecross neutralization in continuous and batch operations

Speed of Response Seen by pH Loop

pH

Reagent FlowInfluent Flow

6

8

10

12

2

4

pH2

pH1

pH3

Fastest process response seen byLoop at inflection point (e.g. 7 pH)

Slow

Slow

(1) Excursion from pH1 to pH2 acceleration makes response look like a runaway to loop

(2) Excursion from pH2 to pH3 deceleration is not enough to show true process time constant Apparent loss of investment in large well mixed volume can be restored

by signal characterization of pH to give abscissa as controlled variable!

Speed of Response Seen by pH Loop

d o

0

1

2

curve 0 = Self-Regulatingcurve 1 = Integratingcurve 2 = Runaway

Time(minutes)

pH

0

pH

Ramp

Acceleration

Open Loop Time Constant

Total LoopDead Time

CO(% step inController

Output)

Batch processes have less self-regulation because there is no discharge flow.If there is no consumption of reagent in the batch by a reaction, the pH response is only in one direction for a given reagent. If there is no split ranged acid and base reagent in the batch, PD instead of PID and predictive strategies are used.

• For a first order plus deadtime process, only nine (9) models are evaluated each sub-iteration, first gain is determined, then deadtime, and last time constant.

• After each iteration, the bank of models is re-centered using the new gain, time constant, and deadtime

First Order Plus Deadtime Process

Estimated Gain, time constant, and deadtime

Multiple Model parameterInterpolation with re-centering

Changing process input

sKe DT

1

Gain

Tim

e C

onst

ant

Dead time

1

2

3

First Order plus Dead Time Model Identification

Changes in the process model can be used to diagnose changesin the influent and the reagent delivery and measurement systems

Model and tuning is scheduled based on pH

Scheduling of Learned Dynamics and Tuning

Adaptive Control Efficiently Achieves Optimum

hourly cost of excess reagent

hourly cost of excess reagent

total cost ofexcess reagent

total cost ofexcess reagent

pH

pH

Adaptive Control Efficiently Rejects Loads

hourly costof excess

hourly cost of excess

pH

total costof excessreagent

total cost of excessreagent

pH

Adaptive Control is Stable on Steep Slopes

pH

pH

Recently Developed Adaptive Control

• Anticipates nonlinearity by recognizing old territory– Model and tuning settings are scheduled per operating region

– Remembers what it has learned for preemptive correction

• Demonstrates efficiency improvement during testing– Steps can be in direction of optimum set point– Excess reagent useage rate and total cost can be displayed online

• Achieves optimum set point more efficiently– Rapid approach to set point in new operating region

• Recovers from upsets more effectively– Faster correction to prevent violation

– More efficient recovery when driven towards constraint

• Returns to old set points with less oscillation – Faster and smoother return with less overshoot

PID Valve Sensitivity and Rangeability Solution 1

Neutralizer AC 1-1a

AT 1-1

PID Controller

Large(Coarse)

Small(Fine)

AC 1-1b

P only Controller

Reagent

Difficult to tune

Neutralizer AC 1-1

AT 1-1

PID Controller

Large Small

ZC 1-1

I only Controller

Reagent

PID Valve Sensitivity and Rangeability Solution 2 PID Valve Sensitivity and Rangeability Solution 2

Difficult to tune

manipulated variables

Small (Fine)Reagent Valve SP

NeutralizerpH PV

Small (Fine)Reagent Valve SP

cont

rolled

va

riab

le

MPC Large (Coarse)Reagent Valve SP

cont

rolled

va

riab

le

null

Model Predictive Controller (MPC) setup for rapid simultaneous throttling of a fine and coarse control valves that addressesboth the rangeability and resolution issues. This MPC canpossibly reduce the number of stages of neutralization needed

MPC Valve Sensitivity and Rangeability Solution MPC Valve Sensitivity and Rangeability Solution

MPC Valve Sensitivity and Rangeability Solution MPC Valve Sensitivity and Rangeability Solution

MPC Valve Sensitivity and Rangeability Solution

MPC Valve Sensitivity and Rangeability Solution

Successive Load Upsets Process Set Point Change Trim Valve Set Point Change

CriticalProcess Variable

CoarseValve

TrimValve

MPC Maximization of Low Cost Reagent

manipulated variables

High Cost FastFeed SP

Critical PV(normal PE)

Low Cost SlowFeed SP

(lowered PE)

contr

olled

vari

able

Maximize

MPC Low Cost SlowFeed SP

null

opti

miz

ati

on

vari

able

MPC Maximization of Low Cost Reagent

MPC Maximization of Low Cost Reagent

Riding Max SPon Lo Cost MV

Riding Min SPon Hi Cost MV

Critical CV

Lo Cost Slow MV

Hi Cost Fast MV

LoadUpsets

Set PointChanges

LoadUpsets

Set PointChanges

Low Cost MV Maximum SP Increased

Low Cost MV Maximum SP Decreased

Critical CV

MPC Maximization of Low Cost Reagent

manipulated variables

SupplementalReagent Flow SP

Cheap Reagent Flow PV

Neutralizer pH PV

Acidic Feed Flow SP

Supplemental Reagent

Valve Position

con

trolled

v

ari

ab

lecon

str

ain

t

vari

ab

le

MPC

disturbance variable

Acid Feed Flow SP

null

op

tim

izati

on

v

ari

ab

le

nullMaximize

Note that cheap reagent valve is wide open and feed is maximized to keep supplemental reagent valve at minimum throttle position for minimum stick-slip

Review of Key Points

• More so than for any other loop, it is important to reduce dead time for pH control because it reduces the effect of the nonlinearity

• The effectiveness of feedforward control greatly depends upon the ability to eliminate reagent delivery delays

• If there is a reproducible influent flow measurement use flow feedforward, otherwise use a head start or full throttle logic for startup

• The reliability and error of a pH feedforward is unacceptable if the influent pH measurement is on the extremities of the titration curve

• Except for fast inline systems, use cascade control of pH to reagent flow to compensate for pressure upsets and enable flow feedforward

• Use adaptation of the charge balance model pH or online identification of the titration curve to compensate for a distortion of the curve

• Linear reagent demand can restore the time constant and capture the investment in well mixed vessels, provide a unity gain for the process variable, simply and improve controller tuning, suppress oscillations and noise on the steep part of the curve, and speed up startup and recovery from the flat part of the curve

Review of Key Points

• Nearly all the previously develop adaptive controllers are playing catch up and do not reveal the process model or the imbedded tuning rules

• New adaptive controllers will remember changes in the process model as a function of operating point and preemptively schedule tuning

• Changes in the process model can be used to predict and analyze changes in the influent, reagent, valve, and sensor

• The use of reagent demand control can free up the adaptive controller to find the changes in titration curve and make a MPC more effective

• Use a wide open reagent valve that is shut based on a predicted online pH measurement to provide the fastest pH batch or startup

• Use pulse width and amplitude modulation of a proportional plus derivative controller output to mimic lab titration for batch pH control

• Use online titration curve identification and linear reagent demand control for extremely variable and sharp titration curvature

• Model predictive control (MPC) can adapt online process models and improve reagent resolution and rangeability and minimize reagent costs

Advanced Application Notes

A Funny Thing Happened (E-Book Online)

Elimination of Lime Delay and Lag Times

FC 1-1

FT 1-1

AC 3-1

AT 3-1

LC 1-1

LT 1-1Liquid Waste

Storage

Lime Conveyor

< HC 2-1

Delay Lag Sum

RSP

Rotary Valve Speed

ConveyorTransportation

Delay

LimeDissolution Lag Time

FeedforwardSummer

LowSignal

Selector

Neutralizer

LimeHopper

Manual Loader