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AIChE New Orleans Section Meeting March 15, 2011

Wireless Measurement and Control - Opportunities for Diagnostics, Process Metrics, Inferential Measurements, and Eliminating Oscillations

Welcome• Gregory K. McMillan

– Greg is a retired Senior Fellow from Solutia/Monsanto and an ISA Fellow. Greg was an adjunct professor in the Washington University Saint Louis Chemical Engineering Department 2001-2004. 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 Advanced Temperature Measurement and Control. 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/

Top Ten Things You Don’t Want to Hear on a Startup

• (10) You need the owner to be a little more patient (supplier expert).• (9) Don’t bother with a checkout - just light it up! What is the worst that

can happen?• (8) We didn’t do any simulation or testing. We decided that would spoil

the adventure.• (7) I don’t understand. It fit fine on the drawing.• (6) Cool - This is my first time in a real plant (supplier expert).• (5) I tried to open the valve and nothing happened. Wait! The same valve

on the other reactor just opened.• (4) Should the Variable Frequency Drive smoke like that?• (3) I don’t understand. I am sure I left all your tools and radios in a box

right here.• (2) The CEO is holding on a phone for you.• (1) Boom!!! WHAT was that?!?!

Source: “Final Word on Instrument Upgrade Projects”, Control Talk, Control, Dec 2010http://www.controlglobal.com/articles/2010/InstrumentProjects1012.html

ISA Automation Week - Oct 17-20

Process Automation Hall of Fame Speakers

Charlie CutlerBela LiptakRuss RhinehartGreg McMillanTerry Tolliver

Advances in Smart Measurements Advances in Smart Measurements

• Technological advances in sensing element technology• Integration of multiple measurements• Compensation of application and installation effects• Online device diagnostics• Digital signals with embedded extensive user selected

information• Wireless communication

The out of the box accuracy of modern industrial instrumentation has improved by an order of magnitude. Consider the most common measurement device, the differential pressure transmitter (DP). The 0.25% accuracy of an analog electronic DP has improved to 0.025% accuracy for a smart microprocessor based DP. Furthermore, the analog DP accuracy often deteriorated to 2% when it was moved from the nice bench top setting to service outdoors in a nasty process with all its non-ideal effects of installation, process, and ambient effects [1][16]. A smart DP with its integrated compensation for non-ideal effects will stay close to its inherent 0.025% accuracy. Additionally a smart DP takes 10 years to drift as much as the analog DP did in 1 year.

Smart Transmitter Auxiliary Variables

• The availability of auxiliary process variables in a smart wireless pH transmitter, provide early indicators of performance problems. The use of these variables by online data analytics tools could detect abnormal conditions and predict sensor life.

Smart Transmitter Diagnostic Messages• “Fix Now” and “Fix Soon” alerts are provided along with common

causes and recommended actions

Wireless Opportunities

• Wireless temperatures and differential pressures for packed absorber and distillation column hot spot and flow distribution analysis and control

• Wireless temperatures for finding the column control point with the largest and most symmetrical change in temperature with reflux/feed or steam/feed ratio

• Wireless temperatures for heat transfer coefficient metrics (fouling and frosting)• Wireless temperatures and flows for measurement and control of reaction rate and

crystallization rate from heat transfer (BTU/hr measurement and control)• Wireless temperatures and differential pressures for fluidized bed reactor hot spot

and flow distribution analysis and control• Wireless temperatures and flows to debottleneck coolant systems• Wireless pressures to debottleneck piping systems, monitor process filter operation,

and track down the direction and source of pressure disturbances• Wireless pressures to compute installed control valve characteristic (flow versus

stroke) and variable speed drive installed characteristic (flow versus speed) • Wireless instrumentation to increase the mobility, flexibility, and maintainability of lab

and pilot plant experiments. • Wireless pH and conductivity measurements for

– (1) Selecting the best sensor technology for a wide range of process conditions(2) Eliminating measurement noise(3) Predicting sensor demise(4) Developing process temperature compensation(5) Developing inferential measurements of process concentrations(6) Finding the optimum sensor location

http://www.isa.org/InTechTemplate.cfm?template=/ContentManagement/ContentDisplay.cfm&ContentID=80886

WirelessHART Network Topology

FieldDevice

FieldDevice

FieldDevice

RouterDevice

RouterDevice

FieldDevice

FieldDevice

FieldDevice

GatewayDevice

Plant Automation Network

Plant Automation Application Host

Wireless HART

Handheld

• Wireless Field Devices– Relatively simple - Obeys Network

Manager– All devices are full-function (e.g., must

route)• Adapters

– Provide access to existing HART-enabled Field Devices

– Fully Documented, well defined requirements

• Gateway and Access Points – Allows access to WirelessHART Network

from the Process Automation Network– Gateways can offer multiple Access

Points for increased Bandwidth and Reliability

– Caches measurement and control values– Directly Supports WirelessHART Adapters– Seamless access from existing HART

Applications• Network Manager

– Manages communication bandwidth and routing

– Redundant Network Managers supported – Often embedded in Gateway– Critical to performance of the network

• Handheld– Supports direct communication to field

device– For security, one hop only communication

WirelessHART Features

• Wireless transmitters provide nonintrusive replacement and diagnostics

• Wireless transmitters automatically communicate alerts based on smart diagnostics without interrogation from an automated maintenance system

• Wireless transmitters eliminate the questions of wiring integrity and termination

• Wireless transmitters eliminate ground loops that are difficult to track down

• Network manager optimizes routing to maximize reliability and performance

• Network manager maximizes signal strength and battery life by minimizing the number of hops and preferably using routers and main (line) powered devices

• Network manager minimizes interference by channel hopping and blacklisting

• The standard WirelessHART capability of exception reporting via a resolution setting helps to increase battery life

• WirelessHART control solution, keeps control execution times fast but a new value is communicated as scheduled only if the change in the measurement exceeds the resolution or the elapsed time exceeds the refresh time

• PIDPLUS and new communication rules can reduce communications by 96%

Broadley-James Corporation Bioreactor Setup

• Hyclone 100 liter Single Use Bioreactor (SUB)

• Rosemount WirelessHART gateway and transmitters for measurement and control of pH and temperature. (pressure monitored)

• BioNet lab optimized control system based on DeltaV

Elimination of Ground Noise by Wireless pH Elimination of Ground Noise by Wireless pH

Wired pH ground noise spike

Temperature compensated wireless pH controlling at 6.9 pH set point

Incredibly tight pH control via 0.001 pH wireless resolution

setting still reduced the number of communications by 60%

Wireless Bioreactor Adaptive pH Loop TestWireless Bioreactor Adaptive pH Loop Test

University of Texas Pilot Plant for CO2 Research

The Separations Research Program was established at the J.J. Pickle Research Campus in 1984

This cooperative industry/university program performs fundamental research of interest to chemical, biotechnological, petroleum refining, gas processing, pharmaceutical, and food companies.

CO2 removal from stack gas is a focus project for which WirelessHART transmitters are being installed

Wireless Conductivity and pH Lab Setup

• In the UT lab that supports the pilot plant, solvent concentration and loading were varied and the conductivity and pH were wirelessly communicated to the DCS in the control room

Effect of Ions on Conductivity• Conductivity measures the concentration and mobility of ions. Plots

of conductivity versus ion concentration will increase from zero concentration to a maximum as the number of ions in solution increases. The conductivity then falls off to the right of the maximum as the ions get crowded and start to interact or associate (group) reducing the ion mobility.

Effect of Solvent on Conductivity

• Conductivity in the operating range of 25% to 30% by weight solvent is relatively unaffected by solvent concentration

Conductivity Dependence on Solvent Concentration at Constant CO2 Load

0.000

10.000

20.000

30.000

40.000

50.000

60.000

15% 20% 25% 30% 35% 40% 45%

Solvent Concentration (wt%)

Co

nd

uct

ivit

y (m

illiS

iem

ens/

cm)

20 oC

30 oC

40 oC

Effect of CO2 Load on Conductivity

• Conductivity shows good sensitivity to CO2 loading that can be fitted by a straight line whose slope depends upon temperature above 30 oC

Conductivity Dependence on CO2 Load at Constant Solvent Concentration

0.000

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

45.000

50.000

0.0 0.5 1.0 1.5 2.0

CO2 Molarity (mol/L)

Co

nd

uct

ivit

y (m

illiS

iem

ens/

cm)

20 oC30 oC

40 oC

Effect of Solvent on pH

• pH measures the activity of the hydrogen ion, which is the ion concentration multiplied by an activity coefficient. An increase in solvent concentration increases the pH by a decrease in the activity coefficient and a decrease in the ion concentration from a decrease per the water dissociation constant.

• pH is also affected by CO2 weight percent since pH changes with the concentration of carbonic acid.

• Density measurements by Micromotion meters provide an accurate inference of CO2 weight percent.

Effect of MEA Solvent on pH

Correlation of pH to CO2 Weight Percent in Methyl Ethyl Amine (MEA)

MEA2% =( 0.2573)(pH) - 2.4727R² = 0.9641

MEA6.5% = (0.2047)(pH) - 1.75R² = 0.9445

MEA11% = (0.0598)(pH) - 0.1864R² = 0.9785

0.300

0.320

0.340

0.360

0.380

0.400

0.420

8.00 8.50 9.00 9.50 10.00 10.50 11.00 11.50

pH

MEA

(CO

2 fre

e w

iegh

t %)

2%

6.50%

11%

Correlation of pH to CO2 Weight % in Piperazine (PZ)

PZ10%= (0.1573)(pH) - 1.1783R² = 0.9407

PZ12.5% = (0.118)(pH) - 0.7192R² = 0.9969

PZ15% = (0.0776)(pH) - 0.2664R² = 0.9897

0.35

0.37

0.39

0.41

0.43

0.45

0.47

8 8.5 9 9.5 10 10.5

pH

PZ w

iegh

t % (C

O2 f

ree)

10% CO2

12.5% CO2

15% CO2

Effect of PZ Solvent on pH

Enhanced PID Algorithm for Wireless (PIDPlus) PID integral mode is

restructured to provide integral action to match the process response in the elapsed time (reset time set equal to process time constant)

PID derivative mode is modified to compute a rate of change over the elapsed time from the last new measurement value

PID reset and rate action are only computed when there is a new value

If transmitter damping is set to make noise amplitude less than sensitivity limit, valve packing and battery life is dramatically improved

Enhancement compensates for measurement sample time suppressing oscillations and enabling a smooth recovery from a loss in communications further extending packing -battery life

+

+

+

+

Elapsed Time

Elapsed Time

TD

Kc

Kc

TD

http://www2.emersonprocess.com/siteadmincenter/PM%20DeltaV%20Documents/

Whitepapers/WP_DeltaV%20PID%20Enhancements%20for%20Wireless.pdf

Link to PIDPlus White Paper

Flow Setpoint Response - PIDPlus vs. Traditional PID

Traditional PID Sensor PV

Enhanced PID Sensor PV

Flow Load Response - PIDPlus vs. Traditional PID

Traditional PID Sensor PV

Enhanced PID Sensor PV

Flow Failure Response - PIDPlus vs. Traditional PID

Enhanced PID Sensor PV

Traditional PID Sensor PV

pH Setpoint Response - PIDPlus vs. Traditional PID

Enhanced PID Sensor PV

Traditional PID Sensor PV

pH Load Response - PIDPlus vs. Traditional PID

Traditional PID Sensor PV

Enhanced PID Sensor PV

pH Failure Response - PIDPlus vs. Traditional PID

Traditional PID Sensor PV

Enhanced PID Sensor PV

• The PID enhancement for wireless (PIDPlus) offers an improvement wherever there is an update time in the loop. In the broadest sense, an update time can range from seconds (wireless updates and valve or measurement sensitivity limits) to hours (failures in communication, valve, or measurement). Some of the sources of update time are:– Wireless measurement default update rate for periodic reporting (default update rate)– Wireless measurement trigger level for exception reporting (trigger level)– Wireless communication failure – Broken pH electrode glass or lead wires (failure point is about 7 pH)– Large valve operating on upper part of installed characteristic (low sensitivity)– Valve with backlash (deadband) and stick-slip (resolution and sensitivity limit)– Operating at split range point (discontinuity of no response to abrupt response)– Valve with solids, high temperature, or sticky fluid that causes plugging or seizing – Plugged impulse lines– Analyzer sample processing delay and analysis or multiplex cycle time– Analyzer resolution and sensitivity limit

PIDPlus Benefits Extend Far Beyond Wireless - 1

• The PIDPlus executes when there a change in setpoint, feedforward, or remote output to provide an immediate reaction based on PID structure

• The improvement in control by the PIDPlus is most noticeable as the update time becomes much larger than the 63% process response time (defined in the white paper as the sum of the process deadtime and time constant). When the update time becomes 4 times larger than this 63% process response time that roughly corresponds to the 98% response time frequently cited in the literature, the feedforward and controller gains can be set to provide a complete correction for changes in the measurement and setpoint. – Helps ignore inverse response and errors in feedforward timing– Helps ignore discontinuity (e.g. steam shock) at split range point– Helps extend packing life by reducing oscillations and hence valve travel

• Since the PIDPlus can be set to execute only upon a significant change in user valve position, the PIDPlus as a valve position controller offers less interaction and cycling for optimization of unit operations by increasing reactor feed, column feed or increasing refrigeration unit temperature, or decreasing compressor pressure till feed, vent, coolant, and/or steam, valves are at maximum good throttle position.

PIDPlus Benefits Extend Far Beyond Wireless - 2

http://www.modelingandcontrol.com/2010/08/wireless_pid_benefits_extend_t.html http://www.modelingandcontrol.com/2010/10/enhanced_pid_for_wireless_elim.html

http://www.modelingandcontrol.com/2010/11/a_delay_of_any_sorts.html

Website entries on Enhanced PID Benefits

Enhanced PID Can Eliminate Valve Limit Cycles

PID PV

PID Output

Enhanced PIDTraditional PID

Limit Cycles from Valve Stick-Slip

feed A

feed B

coolantmakeup

CAS

ratio

CAS

reactor

vent

product

maximum productionrate

condenser

CTW

PT

PC-1

TT

TT

TC-2

TC-1

FC-1

FT

FT

FC-2

<

TC-3

RC-1

TT

ZC-1

ZC-2CAS

CAS

CAS

ZC-3 ZC-4<

Valve Position Controllers (VPC)ZC-1,2,3,4 are enhanced PID with

directional output velocity limiting and position noise band set to reduce

interactions and limit cycling

Enhanced PID Can Maximize Production Rate

Time (seconds)

% Controlled Variable (CV) or

% Controller Output (CO)

DCO

DCV

qo tp2

Kp = DCV / DCO

0.63*DCV

CO

CV

Self-regulating processopen loop

negative feedback time constant

Self-regulating process gain (%/%)

Response to change in controller output with controller in manual

observed total loopdeadtime

toor

Maximum speedin 4 deadtimes

is critical speed

Self-Regulating Process Open Loop Response

Time (seconds)qo

Ki = { [ CV2 / Dt2 ] - [ CV1 / Dt1 ] } / DCO

DCO

ramp rate isDCV1 / Dt1

ramp rate isDCV2 / Dt2

CO

CV

Integrating process gain (%/sec/%)

Response to change in controller output with controller in manual% Controlled Variable (CV)

or% Controller Output (CO)

observed total loopdeadtime

Maximum speedin 4 deadtimes

is critical speed

Integrating Process Open Loop Response

Wireless Trigger Level > noise

Wireless DefaultUpdate

Rate

Response to change in controller output with controller in manual

qo t’p2

Noise Band

Acceleration

DCV

DCO

1.72*DCV

Kp = DCV / DCO

Runaway process gain (%/%)

% Controlled Variable (CV) or

% Controller Output (CO)

Time (seconds)observed total loopdeadtime

runaway processopen loop

positive feedback time constant

For safety reasons, tests are terminated after 4 deadtimes

t’oor

Maximum speedin 4 deadtimes

is critical speed

Runaway Process Open Loop Response

tp1 qp2 tp2 Kpvqp1

tc1 tm2 qm2 tm1 qm1Kcvqctc2

Kc Ti Td

Valve Process

Controller Measurement

Kmvtvqv

KLtLqL

Load Upset

CV

CO

MVPV

PID

Delay Lag

Delay Delay Delay

Delay

Delay

Delay

Lag Lag Lag

LagLagLag

Lag

Gain

Gain

Gain

Gain

LocalSet Point

DV

First Order Approximation: qo @ qv + qp1 + qp2 + qm1 + qm2 + qc + tv + tp1 + tm1 + tm2 + tc1 + tc2

(set by automation system design for flow, pressure, level, speed, surge, and static mixer pH control)

%

%

%

Delay <=> Dead TimeLag <=>Time Constant

For integrating processes: Ki = Kmv * (Kpv / tp2 ) * Kcv

100% / span

Loop Block Diagram (First Order Approximation)

Hopefully tp2 is the largest lag in the loop

½ of Wireless Default Update Rate

opo

ox EE

)(

opo

oi EE

)(

2

Peak error is proportional to the ratio of loop deadtime to 63% response time(Important to prevent SIS trips, relief device activation, surge prevention, and RCRA pH violations)

Integrated error is proportional to the ratio of loop deadtime squared to 63% response time(Important to minimize quantity of product off-spec and total energy and raw material use)

Ultimate Limit to Loop Performance

Total loop deadtimethat is often set byautomation design

Largest lag in loopthat is ideally set bylarge process volume

½ of Wireless Default Update Rateis additional deadtime

ocp

x EKK

E

)1(

1

ocp

fxii E

KK

tTE

Peak error decreases as the controller gain increases but is essentially the open loop error for systems when total deadtime >> process time constant

Integrated error decreases as the controller gain increases and reset time decreases but is essentially the open loop error multiplied by the reset time plus signal delays and lags for systems when total deadtime >> process time constant

Practical Limit to Loop Performance

Open loop error forfastest and largestload disturbance

ocffi

r SPKSPCOK

SPT

)|,min(|( max

Rise time (time to reach a new setpoint) is inversely proportional to controller gain

op

pc KK

24.0oiT 4 1d pT

For runaway processes:

For self-regulating processes:

oic KK

15.0

oiT 4 1d pT

oic KK

16.0

oiT 40 1d 2 pT

For integrating processes:

op

pc KK

2'6.0

oic KK

14.0

Near integrator (tp2 >> qo):

oiT 5.0

Near integrator (t’p2 >> qo):

Deadtime dominant (tp2 << qo):

0d Tp

c KK

14.0

Fastest Controller Tuning (reaction curve method*)

These tuning equations provide maximumdisturbance rejection but will cause

some overshoot of setpoint response

* - Ziegler Nichols method closed loop modifiedto be more robust and less oscillatory

1.0 for Enhanced PID if Wireless Default Update Rate > Process Response Time !

DCV = change in controlled variable (%) DCO = change in controller output (%) Kc = controller gain (dimensionless) Ki = integrating process gain (%/sec/% or 1/sec) Kp = process gain (dimensionless) also known as open loop gain DV = disturbance variable (engineering units) MV = manipulated variable (engineering units) PV = process variable (engineering units) DSP = change in setpoint (engineering units) SPff = setpoint feedforward (engineering units) Dt = change in time (sec) Dtx = execution or update time (sec) qo = total loop dead time (sec) tf = filter time constant or well mixed volume residence time (sec) tm = measurement time constant (sec) tp2 = primary (large) self-regulating process time constant (sec) t’p2 = primary (large) runaway process time constant (sec) tp1 = secondary (small) process time constant (sec) Ti = integral (reset) time setting (sec/repeat) Td = derivative (rate) time setting (sec) Tr = rise time for setpoint change (sec) to = oscillation period (sec) l = Lambda (closed loop time constant or arrest time) (sec) lf = Lambda factor (ratio of closed to open loop time constant or arrest time)

Nomenclature