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Exceptional Process Control Opportunities - Smart and Wireless Instrumentation, Valves, PID, and Tuning Experitec Kansas City Technology Open House Seminar – March 26, 2010 http://www.modelingandcontrol.com/ Welcome Gregory K. McMillan

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exceptional process control opportunities smart and wireless instrumentation valves pid and tuning

Exceptional Process Control Opportunities - Smart and Wireless Instrumentation, Valves, PID, and Tuning

Experitec Kansas City Technology Open House Seminar – March 26, 2010

http://www.modelingandcontrol.com/

welcome
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, and was honored by InTech Magazine in 2003 as one of the most influential innovators in automation. 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/

newest book the latest on smart and wireless instrumentation
Newest Book - The Latest on Smart and Wireless Instrumentation

Royalties are donated to the

University of Texas Research

Campus for Energy and

Environmental Resources

for Development of Wireless

Instrumentation and Control

top ten ways to make process control enticing
Top Ten Ways to Make Process Control Enticing

(10) Travel programs focusing on the process control systems of cruise ships

(9) Sci-fi flicks devoted to the process control systems in star ships

(8) Reality shows where teams compete to improve process control performance

(7) Entourage shows where groupies use process control to keep their star from self-destruction

(6) Sport analysis programs where commentators and listeners talk about the dynamics and feedforward control opportunities in football

(5) Robot movies where advanced parallel processing robots optimize plants

(4) Detective shows where a special investigator with cute compulsive obsessive habits and an incredibly keen mind for details solves mysterious process control problems

(3) “Who done it” novels where the culprit is a bad acting control valve

(2) Web video with cute animal antics in the foreground and engineers talking about process control opportunities in the background

(1) “Cash for clunkers” programs to replace inefficient old distributed control systems, transmitters, and valves

control valve deadband and stick slip
Control Valve Deadband and Stick-Slip

Deadband is 5% - 50%

without a positioner !

Deadband

Pneumatic positioner

requires a negative %

signal to close valve

Stroke

(%)

Digital positioner

will force valve

shut at 0% signal

Stick-Slip is worse near closed position

0

Signal

(%)

dead band

Deadband (backlash) and stick-slip (sticktion) is greatest near the closed position

slide7

Valve pressure drop ratio (DPR)

for installed characteristic:

Characteristic 1: DPR= 0.5

Characteristic 2: DPR= 0.25

Characteristic 3: DPR= 0.125

Characteristic 4: DPR= 0.0625

Installed Characteristic (Linear Trim)

slide8

Valve pressure drop ratio (DPR)

for installed characteristic:

Characteristic 1: DPR= 0.5

Characteristic 2: DPR= 0.25

Characteristic 3: DPR= 0.125

Characteristic 4: DPR= 0.0625

Installed Characteristic (Equal Percentage Trim)

limit cycle in flow loop from valve stick slip
Limit Cycle in Flow Loop from Valve Stick-Slip

Process Variable (kpph)

Square Wave Oscillation

Controller Output (%)

Saw Tooth Oscillation

limit cycle in level loop from valve deadband
Limit Cycle in Level Loop from Valve Deadband

Level (%)

Controller Output (%)

Rounded Oscillation

Manipulated Flow (kpph)

Clipped Oscillation

slide11

Real Rangeability

Minimum fractional flow coefficient for a linear trim and stick-slip:

Minimum fractional flow coefficient for an equal percentage trim and stick-slip:

Minimum controllable fractional flow for installed characteristic and stick-slip:

Cxmin= minimum flow coefficient expressed as a fraction of maximum (dimensionless)

DPr= valve pressure drop ratio (dimensionless)

Qxmin= minimum flow expressed as a fraction of the maximum (dimensionless)

Rv= rangeability of control valve (dimensionless)

R = range of the equal percentage characteristic (e.g. 50)

Xvmin= maximum valve stroke (%)

Sv= stick-slip near closed position (%)

best practices to improve valve performance
Best Practices to Improve Valve Performance

Actuator, valve, and positioner package from a control valve manufacturer

Digital positioner tuned for valve package and application

Diaphragm actuators where application permits (large valves and high pressure drops may require piston actuators)

Sliding stem (globe) valves where size and fluid permit (large flows and slurries may require rotary valves)

Next best is V-ball or contoured butterfly valve with rotary actuator and positioner

Low packing, sealing, and seating friction

Booster(s) on positioner output(s) for large valves on fast loops (e.g., compressor anti-surge control)

Valve sizing for a throttle range that provides good linearity [4]:

5% to 75% (sliding stem globe),

10o to 60o (v-ball)

25o to 45o (conventional butterfly)

5o to 65o (contoured and toothed butterfly)

Online diagnostics and step response tests for small changes in signal

Dynamic reset limiting (FRSPID_OPTS) using digital positioner feedback

slide13

Volume Booster with Integral Bypass

(Furnace Pressure and Surge Control)

Signal from

Positioner

Adjustable Bypass

Needle Valve

Air Supply from

Filter-Regulator

Air Loading

to Actuator

slide14

Open bypass just

enough to ensure

a non-oscillatory

fast response

Port A

Terminal Box

Supply

Bypass

Port B

Increase air line size

ZZZZZZZ

Control Signal

1:1

Increase connection size

Volume

Booster

Digital Valve Controller

Air Supply

High Capacity

Filter Regulator

Must be functionally tested

before commissioning!

Booster and Positioner Setup

(Furnace Pressure and Surge Control)

slide15

Open Loop Backup Configuration

SP_Rate_DN and SP_RATE_UP used to insure fast getaway and slow approach

Open Loop Backup Configuration

Open loop backup used for prevention of

compressor surge and RCRA pH violation

slide18

Rise in conductivity

Flow cutback via kicker

Conductivity Kicker for Evaporator

slide19

pH Kicker for Waste Treatment

MPC-1

MPC-2

Waste

RCAS

RCAS

middle selector

ROUT

AC-1

kicker

AC-2

AY

AY

splitter

splitter

AT

AT

AT

AY

AY

Attenuation

Tank

AY

middle selector

middle selector

filter

FT

FT

AY

AY

Stage 2

Stage 1

AT

AT

AT

AT

AT

AT

Mixer

Mixer

FT

question which measurement
Question: Which Measurement
  • Removes the most common nonlinearity in a control loop?
  • Compensates for pressure disturbances?
  • Is used in most cascade control systems?
  • Is used in most feedforward control systems?
  • Is essential to close the material balance for a process?
  • Makes the following advanced control tools more effective?
    • Model Predictive Control (DeltaV PredictPro)
    • Adaptive Control (DeltaV Insight)
    • Neural Network Predictions (DeltaV Neural)
    • Projections to Latent Structures Predictions (DeltaV Analytics)
    • Dynamic Models (MIMIC Advanced Modeling)

Answer: Flow

http://www.isa.org/InTechTemplate.cfm?template=/ContentManagement/ContentDisplay.cfm&ContentID=81073

ratio control examples
Ratio Control Examples
  • Coolant/Feed flow ratio for crystallizer, cooler, extruder, or exothermic reactor temperature control
  • Steam/Feed flow ratio for distillation column, evaporator, heater, dryer, or endothermic reactor temperature control
  • Distillate/Feed or reflux/feed flow ratio for column temperature control
  • Reagent/Feed flow ratio for pH control
  • Reactant/Reactant flow ratio for continuous and fed-batch reactor control
  • Catalyst/Reactant flow ratio for continuous and fed-batch reactor control
  • Makeup/Recycle flow ratio for continuous and fed-batch reactor control
  • Purge/Product flow ratio for continuous and perfusion process contaminant control
  • Stock/Dilution flow ratio control for stock consistency control
  • Additive/Feed flow ratio for blend control (e.g., percent solids)
  • Air/Fuel flow ratio for boiler or furnace combustion control (oxygen control)
  • Feedwater/Steam flow ratio for boiler drum level control (three element control)
  • Blowdown/Feedwater flow ratio for boiler total dissolved solids (conductivity control)
  • Supply/Demand flow ratio for header pressure control
  • Vent/Demand flow ratio for compressor surge control
  • Lime/Liquor flow ratio for slaker control

The best setpoint and process gain is the operating point and slope

on a plot of composition, pH and temperature versus a flow ratio

http://www.modelingandcontrol.com/2009/03/what_have_i_learned_-_ratio_co.html

coriolis flow measurement opportunities
Coriolis Flow Measurement Opportunities
  • Live Process Flow Diagrams (PFD) for Plant Performance Analysis
    • The first document you have on a project is typically a process flow diagram (PFD). The PFD defines the process. It is the ultimate source of info and sets the plant performance and design.
    • What if a plant had a live online PFD? What if we had temperature, pressure, mass flow, and inferential measurements of the composition in every important process stream?
    • What if a plant had online process metrics for yield, efficiency, and production from live PFDs
  • Reactant, Catalyst, Recycle, Dilution, and Reagent Ratio Control
    • True mass flow independent of temperature, density, composition, phases, viscosity, velocity, and installation enables tight control of component concentrations for reaction and neutralization
  • Crystallizer, Evaporator, and Column Product Composition Control
    • Inferential measurement of concentrations or percent solids in feed and product streams enable feedback and feedforward control of composition by manipulation of heat input or temperature
  • Batch Charge Control
    • Coriolis meters can potentially provide more accurate batch charges than weigh tanks because Coriolis meters retain a better long term installed accuracy than load cells since Coriolis does not suffer from drift or installation effects and doesn’t require periodic calibration checks
  • Fermentation Alcohol Yield Optimization
    • Measurement and totalization of carbon dioxide vent flow provides an inferential measurement of conversion of sugars to alcohol that can be used to optimize batch cycle time or efficiency
  • Centrifuge and Dryer Moisture Control
    • Measurement of percent solids in feed enables feedforward control

http://www.modelingandcontrol.com/mt/mt-search.cgi?IncludeBlogs=1&search=Live+Process+Flow

http://www.modelingandcontrol.com/EssentialBookCoriolisExcerpt.pdf

radar level measurement opportunities
Radar Level Measurement Opportunities
  • Raw material and product storage tanks require the best level measurement accuracy, when used in the calculations for:
    • Inventory accounting and optimization
    • Custody transfer
    • Batch charge (rate of change of level)
    • Continuous feed rates (rate of change of level)
    • Material balances (process holdup)
  • Column distillate receivers require the best level measurement resolution, sensitivity, and repeatability for direct material balance control
    • a small change in level must quickly translate to a change in reflux flow to balance a change in vapor flow. This inherent self-regulation provides some internal reflux control and helps decouple the energy balance from the material balance.
    • When the temperature loop makes a change in the distillate flow, the change in controller output has no effect on column temperature until the overhead receiver controller makes a change in the reflux flow
  • Crystallizers and reactors (batch and continuous) require the best level measurement accuracy to control and maximize crystallization and reaction
    • Continuous feed rate and batch cycle time set percent conversion for a given level
    • Level sets production rate for a given residence time and batch cycle time

http://www.isa.org/InTechTemplate.cfm?template=/ContentManagement/ContentDisplay.cfm&ContentID=81073

slide24

tm

tm

tm

tm

Measurement Filter (Transmitter Damping) Effect

For compressor, incinerator pressure, and polymer pressure

control it is critical to make sure transmitter is fast enough!

http://www.modelingandcontrol.com/2007/04/analog_control_holdouts.html

slide25

Transmitter Damping and Signal Filtering Effect

Attenuation of Oscillation Amplitude by Transmitter Damping or Signal Filters:

When a measurement or signal filter time (tf) becomes the largest time

constant in the loop, the above equation can be solved for (Ao) to get the

amplitude of the original process variability from the filtered amplitude (Af)

sample time table typical values
Sample Time Table Typical Values

Practical and Ultimate sample times are for conservative and aggressive tuning, respectively

sample time guideline notes
Sample Time Guideline Notes

The term “sample time” is used in the broadest sense as the time between updates in sampled data from digital measurements and controllers and from analyzers The table should be useful for determining whether DCS scan or module execution times, wireless communication time intervals, model predictive control execution time, and at-line analyzer cycle time will affect control system performance.

* - denotes loop uses a variable speed drive with a negligible dead time, deadband, and resolution limit as the final element. If a control valve or damper is used for these loops, you can multiply the sample times for asterisked items by a factor of 5.

! - denotes an integrating response whose integrating process gain is the inverse of the process time constant shown

!! - denotes a runaway response that can accelerate and reach a point of no return

For surge control, it assumed that a volume booster has been added to the each of the positioner outputs to reduce the pre-stroke dead time to less than 0.2 seconds. A valve with excessive sticktion and backlash will add significant deadtime to the response to unmeasured disturbances that deteriorates the ultimate limit to possible performance.

For inline (static mixer) pH control, the largest time constant comes from the sensor lag or the process variable filter time with a nominal value of 5 seconds.

For the vessel pH control it is assumed the mixing time is less than 30 sec and the reagent delivery time delay is negligible by injection of the reagent into a recirculation line just before it enters the vessel. The lower value for the time constant is for a set point on a steep titration curve that cause the pH to move much faster than for a linear response. The response can look like a runaway as the pH accelerates through the neutral region.

For level control set point changes, the deadtime observed is usually about 10 times larger than the actual process deadtime due to level measurement sensitivity limits and noise. For unmeasured disturbances the deadtime observed is often about 20 times larger than the actual process deadtime because of the amount of time it takes the controller output to work through the resolution limit and deadband of the control valve.

http://www.modelingandcontrol.com/2009/09/largest_opportunities_in_proce_1.html

slide28
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

Advances in Smart Measurements

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 d/p. 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 d/p 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 d/p

did in 1 year.

slide29
(10) Reliable from day one

(9) Always on the job

(8) Low maintenance - minimal grooming, clothing, and entertainment

(7) Many programmable features

(6) Stable

(5) Short settling time

(4) No frills or extraneous features

(3) Relies on feedback

(2) Good response to commands and amenable to real time optimization

(1) Readily tuned

Top Ten Reasons Why an Automation Engineer

makes a Great Spouse or at Least a Wedding Gift

wirelesshart network topology
WirelessHART Network Topology
  • 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

Network Manager

wirelesshart features
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%
wireless opportunities
Wireless Opportunities

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

Wireless temperatures and differential pressures for fluidized bed reactor hot spot and flow distribution analysis and control

Wireless pressures to debottleneck piping systems, monitor process filter operation, and track down the direction and source of pressure disturbances

Wireless temperatures and flows to debottleneck coolant systems

Wireless instrumentation to increase the mobility, flexibility, and maintainability of lab and pilot plant experiments.

Wireless pH and conductivity measurements for

Selecting the best sensor technology for a wide range of process conditions

Eliminating measurement noise

Predicting sensor demise

Developing process temperature compensation

Developing inferential measurements of process concentrations

Finding the optimum sensor location

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

top ten signs of a wirelesshart addiction
Top Ten Signs of a WirelessHART Addiction

(10) You try to use the network manager to schedule the activities of your children

(9) You attempt to use RF patterns to explain your last performance review

(8) You use so much resource allocation in your network manager, you eat before you are hungry

(7) You propose your wireless device for the “Miss USA” contest

(6) You develop performance monitoring indices for your spouse

(5) You implement network management on your stock portfolio

(4) You carry pictures of your wireless device in your wallet

(3) You apply mesh redundancy and call three taxis to make sure you get home from your party

(2) You recommend a survivor show where consultants are placed in a plant with no staff or budget and are asked to add wireless to increase plant efficiency

(1) Your spouse has to lure you to bed by offering “expert options” for scheduling

separations research program university of texas ut at austin
Separations Research Program University of Texas (UT) at Austin

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

life depends upon process conditions
Life Depends Upon Process Conditions

Months

>100% increase in life

from new glass designs

for high temperatures

25 C

50 C

75 C

100 C

Process Temperature

new high temperature glass stays fast
New High Temperature Glass Stays Fast

Glass electrodes get slow as they age. High temperatures cause premature aging

slide38

pH / ORP Selection

Preamplifier Location

Type of Reference Used

Ranging

Temperature Comp Parameters

Solution pH Temperature Correction

Isopotential Point Changeable for Special pH Electrodes

Smart Wireless pH Configuration

slide40
The same dynamic control response was observed for SP changes

Filtering of 10 sec was applied to wired measurement, zero filtering for WirelessHART measurement.

Original plant PID tuning was used for both wired and wireless control

GAIN =0.12

RESET = 20.3

RATE = 0

Wired Measurement Used in Control

Wireless Measurement Used in Control

Column Steam Flow Control Performance Wired versus Wireless

slide41
The same dynamic control response was observed for SP changes

Original plant PID tuning was used for both wired and wireless control

GAIN=2.5

RESET=4

RATE=1

Same filtering of 2 sec was applied to wireless and wired input

Wired Measurement Used in Control

Wireless Measurement Used in Control

Column Pressure Control Performance Wired versus Wireless

column steam and pressure control performance wired versus wireless
Column Steam and Pressure Control Performance Wired versus Wireless

Comparable control performance as measured by IAE was achieved using WirelessHART Measurements and DeltaV v11 PID option vs control with wired measurements and PID.

The number of measurement samples used in control with WirelessHART and v11 PID option versus Wired transmitter and PID was reduced by a factor of 10X for flow control and 6X for pressure control – accounting for differences in test duration.

installation at broadley james
Installation at Broadley James
  • 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
slide44

Elimination of Ground Noise Spikes by Wireless

Incredibly tight pH control via 0.001 pH wireless resolution

setting still reduced the number of communications by 60%

Temperature compensated wireless pH controlling at 6.9 pH set point

Wired pH ground noise spike

traditional and wireless pid pidplus
Traditional and Wireless PID (PIDPLUS)
  • PID integral mode is restructured to provide integral action to match the process response in the elapsed time (reset time is 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
  • PID algorithm with enhanced reset and rate action is termed PIDPLUS

http://www.modelingandcontrol.com/repository/WirelessPrimeTime.pdf

slide48

Control Studies of pH Resolution and Feedforward(Bioreactor batch running 500x real time)

Feedforward

Feedforward

Batch 1

Batch 2

Batch 1

Batch 2

Batches 1 and 2 have 0.00 pH resolution and standard PID

Feedforward

Feedforward

Batch 3

Batch 4

Batch 3

Batch 4

Batches 3 and 4 have 0.01 pH resolution and standard PID

slide49

Control Studies of pH Resolution and Feedforward(Bioreactor batch running 500x real time)

Feedforward

Feedforward

Batch 5

Batch 6

Batch 5

Batch 6

Batches 5 and 6 have 0.02 pH resolution and standard PID

Feedforward

Feedforward

Batch 7

Batch 8

Batch 7

Batch 8

Batches 7 and 8 have 0.04 pH resolution and standard PID

slide50

Control Studies of pH Refresh Time and Feedforward(Bioreactor batch running 500x real time)

Feedforward

Feedforward

Batch 9

Batch 10

Batch 9

Batch 10

Batches 9 and 10 have 30 sec x 500refresh time and standard PID

Feedforward

Feedforward

Batch 11

Batch 11

Batch 12

Batch 12

Batches 11 and 12 have 30 sec x 500 refresh time and wireless PID

slide51

Glucose

Concentration

Batch 3

Batch 6

Batch 1

Batch 2

Batch 5

Batch 4

11 hr Sample FF-Yes

Wireless PID

11 hr Sample FF-Yes

Standard PID

Continuous FF-No

Standard PID

Continuous FF-Yes

Standard PID

11 hr Sample FF-No

Wireless PID

11 hr Sample FF-No

Standard PID

Control Studies of Glucose Sample Time and Feedforward (Bioreactor batch running 1000x real time)

x1000

Batch 1: Glucose Probe (Continuous - No Delay) + Feed Forward - No + Standard PID

Batch 2: Glucose Probe (Continuous - No Delay) + Feed Forward - Yes + Standard PID

Batch 3: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - No + Standard PID

Batch 4: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - Yes + Standard PID

Batch 5: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - No + Wireless PID

Batch 6: Glucose Analyzer (11 Hr Sample Delay) + Feed Forward - Yes + Wireless PID

slide52

Control Studies of Reset Factor & Wireless PID for Real Time Integrating Process (20 sec analyzer sample time)

Standard PID

Standard PID

Standard PID

Reset Factor = 0.5

Reset Factor = 2.0

Reset Factor = 1.0

Wireless PID

Wireless PID

Wireless PID

Reset Factor = 1.0

Reset Factor = 2.0

Reset Factor = 0.5

Improvement in stability is significant for any integrating process with analyzer delay

slide53

Control Studies of Lambda Factor & Wireless PID for Real Time Integrating Process (20 sec analyzer sample time)

Standard PID

Standard PID

Standard PID

Lambda Factor = 2.5

Lambda Factor = 2.0

Lambda Factor = 1.5

Wireless PID

Wireless PID

Wireless PID

Lambda Factor = 2.5

Lambda Factor = 2.0

Lambda Factor = 1.5

Improvement in stability is significant for any integrating process with analyzer delay

slide54

Control Studies of Reset Factor & Wireless PID for Real Time Self-Regulating Process (40 sec analyzer sample time)

Standard PID

Standard PID

Standard PID

Reset Factor = 0.5

Reset Factor = 2.0

Reset Factor = 1.0

Wireless PID

Wireless PID

Wireless PID

Reset Factor = 1.0

Reset Factor = 2.0

Reset Factor = 0.5

Improvement in stability and control is dramatic for any self-regulating process with analyzer delay

slide55

Control Studies of Lambda Factor & Wireless PID for Real Time Self-Regulating Process (40 sec analyzer sample time)

Standard PID

Standard PID

Standard PID

Lambda Factor = 2.0

Lambda Factor = 2.5

Lambda Factor = 1.5

Wireless PID

Wireless PID

Wireless PID

Lambda Factor = 1.5

Lambda Factor = 2.0

Lambda Factor = 2.5

Improvement in stability and control is dramatic for any self-regulating process with analyzer delay

conclusions from wireless pid control tests
Conclusions from Wireless PID Control Tests
  • Wireless PID and new communication rules can increase battery life
  • Wireless pH eliminates spikes form ground noise
  • Wireless PID provides tight control for set point changes
  • Feedforward of ammonia formation rate and oxygen uptake rate (OUR) offers significant improvement. OUR decouples interaction between pH and DO loops
  • Wireless PIDPLUS dramatically improves the control and stability of any self-regulating process with large measurement delay (sample delay). The wireless PID is a technological breakthrough for the use at-line analyzers for control
    • The Wireless PIDPLUS set point overshoot is negligible for self-regulating processes with large sample delays if controller gain is less than the inverse of process gain
  • Wireless PIDPLUS is stable for self-regulating process with large sample delay if controller gain is less than twice the inverse of the process gain
    • As the analyzer sample time decreases and approaches the module execution time, it is expected that the wireless PID behaves more like a standard PID
  • Wireless PIDPLUS significantly reduces the oscillations of integrating processes but the improvement is not as dramatic as for self-regulating processes
  • Integrating processes are much more sensitive than self-regulating processes to increases in sample time, decreases in reset time, and increases in gain
  • Detuned controllers (large Lambda Factors), makes loops less sensitive to sample time (see Advanced Application Note 005 “Effect of Sample Time ….”)
  • If the controller gain is increased or the wireless resolution setting is made finer, the PIDPLUS can provide tighter control. For a loss of communication, the PIDPLUS offers significantly better performance than a wired traditional PID particularly when rate action and actuator feedback (readback) is used
slide57

Response to change in controller output with controller in manual

% Controlled Variable (CV)

or

% Controller Output (CO)

CV

Kp = DCV / DCO

Self-regulating process gain (%/%)

CO

0.63*DCV

DCV

DCO

Time (seconds)

tp

qo

observed

process

deadtime

self-regulating process

time constant

Self-Regulating Process Response

Most continuous processes have a self-regulating response (PV lines out in manual)

Lambda (closed loop time constant) is defined in terms of a Lambda factor (lf):

Closed loop time constant

for setpoint change

slide58

Response to change in controller output with controller in manual

% Controlled Variable (CV)

or

% Controller Output (CO)

CV

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

Integrating process gain (%/sec/%)

CO

DCO

ramp rate is

DCV2/ Dt2

ramp rate is

DCV1 / Dt1

Time (seconds)

qo

Integrating Process Response

observed

process

deadtime

Most batch processes have an integrating response (PV ramps in manual)

Lambda (closed loop arrest time) is defined in terms of a Lambda factor (lf):

Closed loop arrest time

for load disturbance

slide59

Response to change in controller output with controller in manual

% Controlled Variable (CV)

or

% Controller Output (CO)

Kp = DCV / DCO

Runaway process gain (%/%)

Acceleration

1.72*DCV

DCV

DCO

Noise Band

Time (seconds)

t

q

observed

process

deadtime

p

p

runaway process

time constant

Runaway Process Response

Exothermic reactors, strong acid-base pH systems, and compressor surge

can exhibit a runaway response (PV accelerates in manual)

adaptive control gain 40 reset 500
Adaptive ControlGain 40 Reset 500

0.30 oC overshoot

Output comes off high limit at 36.8 oC

adaptive control gain 40 reset 5000
Adaptive ControlGain 40 Reset 5000

0.12 oC overshoot

Output comes off high limit at 35.9 oC

Zero overshoot found to occur for Gain = 66 and Reset = 5000

integrating and runaway process tuning
Integrating and Runaway Process Tuning

It is difficult to prevent overshoot in processes without self-regulation

Controller gain adds self-regulation via closed loop response

Examples of integrating processes (ramping response) are

Liquid and solids level

furnace, column, or vessel pressure

batch composition, pH, or temperature

Examples of runaway processes (accelerating response) are

exothermic reactor temperature

strong acid - strong base pH

compressor speed during surge

An overdrive of the controller output beyond its resting value is needed to reach a set point or compensate for a disturbance (achieved by high controller gain)

The maximum allowable controller gain for many integrating processes is well beyond the comfort level of most users. Measurement noise and resolution often sets the practical high limit to the controller gain rather than process dynamics

Too much reset action (too small of a reset time) causes severe overshoot

A higher controller gain creates more overdrive for small setpoint changes and gets controller off it’s output limit sooner for large setpoint changes

There is a window of allowable controller gains.

Instability from too high of a controller gain (not likely for industrial processes)

Slow rolling oscillations from too low of a controller gain (common case) that slowly decay for integrating processes but can grow for runaway processes till it hits physical limits

top ten reasons i use a virtual plant
Top Ten Reasons I use a Virtual Plant

(10) You can’t freeze, restore, and replay an actual plant batch

(9) No separate programs to learn, install, interface, and support

(8) No waiting on lab analysis

(7) No raw materials

(6) No environmental waste

(5) Virtual instead of actual problems

(4) Batches are done in 14 minutes instead of 14 days

(3) Plant can be operated on a tropical beach

(2) Last time we checked our wallet we didn’t have $100,000K

(1) Actual plant doesn’t fit in our suitcase

virtual plant synergy
Virtual Plant Synergy

DCS batch and loop

configuration, displays,

and historian

Embedded

PAT Tools

Embedded

Advanced Control 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

virtual plant continuity
Virtual Plant Continuity

Train

Deploy

Prototype

Discover

Explore

The consistent platform offered by the virtual plant

can insure maximum flow of knowledge gained at

each step in the commercialization process from

bench top to pilot plant to industrial plant operation

demographic time bomb
Demographic Time Bomb
  • Average age of energy industry worker over 50
  • Half of the current work force will retire (more than 500,000 workers) in 5 to 10 years
  • Irreplaceable knowledge loss
  • Newer generation of workers with less mechanical inclination and exposure
  • Scenario for control engineers and technicians may be more severe due to suspension in hiring in 1980s
  • Petrochemical / energy plants in danger of closing due to lack of qualified operators
  • Delayed retirement plans will be accelerated as equity markets recover
components deltav simulate
Components – DeltaV Simulate
  • The DeltaV Simulate Experience
slide72

Complex Dynamic Modeling

Process Model Development

  • Dynamic, Accurate Simulations “High Fidelity”
  • Advanced IEC Objects and Functions with Streams
  • Thermo / Flash / Stream Property Functions
  • Advanced Modeling Core Objects
    • Vessel, Valve, Pump, PRV, HX, DHX, Stream T
    • PF Solver
  • Energy Management Objects
    • Boiler with Furnace, Steam Header, Desuperheater, Fuel, Turbine
  • Distillation Objects
    • Column, Reboiler, Stripper
  • Separator Objects
    • 2-phase, 3-phase Separator, Physical Absorber
where to get more information
Where To Get More Information
  • MYNAH Website – www.mynah.com
    • DeltaV Operator Training Systems with MiMiC
    • DeltaV Software Acceptance Testing with MiMiC
    • Pre-recorded E-Seminars
    • Understanding Simulation Fidelity Paper and Podcast
    • Simulation System Integrity Paper and Podcast
    • Delivering the Virtual Plant Paper and Podcast
    • Simulation Objections Answered Paper and Podcast
    • Using Simulation to Optimize the Results of Automation Projects, Dr. Tom Fiske, ARC
    • MYNAH YouTube Series
  • Martin Berutti, MYNAH Technologiesmartin.berutti@mynah.com, +1.636.681.1567Skype: mberutti