Your SlideShare is downloading. ×
Isa saint-louis-exceptional-opportunities-short-course-day-2
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Isa saint-louis-exceptional-opportunities-short-course-day-2

1,678

Published on

Presented by Greg McMillan on December 7, 2010 to the ISA St. Louis section.

Presented by Greg McMillan on December 7, 2010 to the ISA St. Louis section.

0 Comments
3 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
1,678
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
116
Comments
0
Likes
3
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide
  • The self-organizing network and automatic reporting of alerts makes it easy to maintain and revise a control system for flexibility and adaptability to changing experimental conditions and requirements in the dynamic environment of a process development lab.
  • Battery Life is extended by using exception reporting (signal is transmitted only when it exceeds the resolution setting) unless the elapsed time since the last transmission exceeds the refresh time, which can be set to large value.
  • Transcript

    • 1. ISA Saint Louis Short Course Dec 6-8, 2010 Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements - Day 2
    • 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/
    • 3. 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 2010
    • 4. Block Diagram of “Series”, “Real”, or “Interacting” PID Form Nearly all analog controllers used the “Series” or “Real” Form   SP   proportional integral derivative  Gain     Reset (1  T i )  Rate    CO filter filter CV filter Filter Time   Rate Time Improving Controllers
    • 5. Block Diagram of “Standard”, “Ideal”, or “Non-interacting” PID Form Nearly all digital controllers have the “standard” or “Ideal” form as the default Improving Controllers   SP   proportional integral derivative  Gain     Reset (1  T i )  Rate    CO filter filter CV filter Filter Time   Rate Time
    • 6. Positive Feedback Implementation of Integral Mode Improving Controllers   SP   proportional derivative  Gain     Rate    CO filter filter CV filter Filter Time   Rate Time  filter Filter Time = Reset Time ER is external reset (e.g. secondary PV) Dynamic Reset Limit ER Positive Feedback
    • 7. PID Structure Choices
      • PID action on error (  = 1 and  = 1)
      • PI action on error, D action on PV (  = 1 and  = 0)
      • I action on error, PD action on PV (  = 0 and  = 0)
      • PD action on error (  = 1 and  = 1) (no I action)
      • P action on error, D action on PV (  = 1 and  = 0) (no I action)
      • ID action on error (  = 1) (no P action)
      • I action on error, D action on PV (  = 0) (no P action)
      • Two degrees of freedom controller (  and  adjustable 0 to 1)
      The  and  factors do not affect the load response of a control loop ! Improving Controllers
    • 8. PID Controller Forms PB = 100%  K c CO n = P n  I n + D n + CO i CO i = controller output at transition to AUTO, CAS, or RCAS modes (%) CO n = controller output at execution n (%) CV n = controlled variable at execution n (%) D n = contribution from derivative mode for execution n (%) I n = contribution from integral mode for execution n (%) K c = controller gain (dimensionless) P n = contribution from proportional mode for execution n (%) R i = reset setting (repeats/minute) PB = controller proportional band (%) SP n = set point at execution n (%) T d = derivative (rate) time setting (seconds) T i = integral (reset) time setting (seconds/repeat)  = rate time factor to set derivative filter time constant (1/8 to 1/10)  = set point weight for proportional mode (0 to 1)  = set point weight for derivative mode (0 to 1) R i = (60  T i ) Conversion of settings: Improving Controllers (K c  T d )  SP n – SP n-1 ) – (CV n  CV n-1 ) ]  T d  D n-1 D n = -------------------------------------------------------------------------------  T d  t P n = K c  SP n – CV n ) I n = (K c  T i )  SP n – CV n )  t  I n-1 Standard P n = K c  SP n – D n ) I n = (K c  T i )  SP n – D n )  t  I n-1 Series
    • 9. PIDPlus Solution - Algorithm
      • 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
      http://www2.emersonprocess.com/siteadmincenter/PM%20DeltaV%20Documents/ Whitepapers/WP_DeltaV%20PID%20Enhancements%20for%20Wireless.pdf Improving Controllers Link to PIDPlus White Paper + + + + Elapsed Time Elapsed Time T D K c K c T D
    • 10. Flow Setpoint Response - PIDPlus vs. Traditional PID Improving Controllers
    • 11. Flow Load Response - PIDPlus vs. Traditional PID Improving Controllers
    • 12. Flow Failure Response - PIDPlus vs. Traditional PID Improving Controllers
    • 13. pH Setpoint Response - PIDPlus vs. Traditional PID Improving Controllers
    • 14. pH Load Response - PIDPlus vs. Traditional PID Improving Controllers
    • 15. pH Failure Response - PIDPlus vs. Traditional PID Improving Controllers
    • 16.
      • 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 Improving Controllers
    • 17.
      • 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
      PIDPlus Benefits Extend Far Beyond Wireless - 2 Improving Controllers 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 Wireless PID Benefits
    • 18. PIDPlus Fast Wireless Loop Lab
      • Objective – See how PIDPlus can achieve the ultimate performance limit for a wireless refresh time of 16 sec in a fast secondary loop
      • Activities:
        • On Main Display, select Restore Labs to Initial State
        • On Main Display, select Cascade Loop Lab02
        • Click on secondary loop AC1-2 PID Faceplate and put PID in Auto
        • Click on magnifying glass icon to get Detail display
        • Click on any block in block diagram
        • In Measurement tab Detail set Refresh = 16 sec (set periodic reporting) and set Sensitivity = 100% (eliminate exception reporting) for secondary measurement
        • Change secondary PID setpoint from 50% to 60%
        • Wait for oscillations to develop
        • In PID tab detail, Enable PIDPlus for secondary loop
        • Wait for oscillations to decay
        • Change secondary PID setpoint from 60% to 50%
        • In AC1-2 PID Detail display, change PID gain to 1.0
        • Change secondary PID setpoint from 50% to 60%
        • Return Refresh to 0 sec, Sensitivity to 0%, and PID Gain = 0.5 and Disable PIDPlus
      Improving Controllers
    • 19. Control Valve Watch-outs dead band Deadband Stick-Slip is worse near closed position Signal (%) 0 Stroke (%) Digital positioner will force valve shut at 0% signal Pneumatic positioner requires a negative % signal to close valve The dead band and stick-slip is greatest near the closed position Deadband is 5% - 50% without a positioner ! Plugging and laminar flow can occur for low Cv requirements and throttling near the seat Consider going to reagent dilution. If this is not possible checkout out a laminar flow valve for an extremely low Cv and pulse width modulation for low lifts Improving Valves
    • 20. Direct Connection Piston Actuator Less backlash but wear of piston O-ring seal from piston pitch is concern Improving Valves
    • 21. Significant backlash from link pin points 1 and 2 Link-Arm Connection Piston Actuator Improving Valves
    • 22. Stick-slip from rack and gear teeth - particularly bad for worn teeth Rack & Pinion Connection Piston Actuator Improving Valves
    • 23. Lots of backlash from slot Scotch Yoke Connection Piston Actuator Improving Valves
    • 24. Diaphragm Actuator with Solenoid Valves Improving Valves Port A Port B Supply ZZZZZZZ Control Signal Digital Valve Controller Must be functionally tested before commissioning! SV Terminal Box
    • 25. Piston Actuator with Solenoid Valves Improving Valves Port A Port B Supply Digital Valve Controller SV SV Volume Tank Must be functionally tested before commissioning! Piston W Check Valve Air Supply Terminal Box
    • 26. Size of Step Determines What you See Improving Valves Maintenance test of 25% or 50% steps will not detect dead band - all valves look good for 10% or larger steps
    • 27. Effect of Step Size Due to Sensitivity Limit Improving Valves
    • 28. Response to Small Steps (No Sensitivity Limit) Improving Valves Stroke (%) Time (sec)
    • 29. Response to Large Steps (Small Actuator Volume) Improving Valves Time (sec) Stroke (%)
    • 30. Installed Characteristic (Linear Trim) Improving Valves Valve pressure drop ratio (  P R ) for installed characteristic: Characteristic 1:  P R  0.5 Characteristic 2:  P R  0.25 Characteristic 3:  P R  0.125 Characteristic 4:  P R  0.0625
    • 31. Installed Characteristic (Equal Percentage Trim) Improving Valves Valve pressure drop ratio (  P R ) for installed characteristic: Characteristic 1:  P R  0.5 Characteristic 2:  P R  0.25 Characteristic 3:  P R  0.125 Characteristic 4:  P R  0.0625
    • 32. Improving Valves Installed Characteristic (Modified Parabolic Trim) Valve pressure drop ratio (  P R ) for installed characteristic: Characteristic 1:  P R  0.5 Characteristic 2:  P R  0.25 Characteristic 3:  P R  0.125 Characteristic 4:  P R  0.0625
    • 33. Limit Cycle in Flow Loop from Valve Stick-Slip Improving Valves Controller Output (%) Saw Tooth Oscillation Process Variable (kpph) Square Wave Oscillation
    • 34. Limit Cycle in Level Loop from Valve Deadband Improving Valves Manipulated Flow (kpph) Clipped Oscillation Controller Output (%) Rounded Oscillation Level (%)
    • 35. Real Rangeability Improving Valves 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: C xmin  minimum flow coefficient expressed as a fraction of maximum (dimensionless)  P r  valve pressure drop ratio (dimensionless) Q xmin  minimum flow expressed as a fraction of the maximum (dimensionless) R v  rangeability of control valve (dimensionless) R  range of the equal percentage characteristic (e.g. 50) X vmin  maximum valve stroke (%) S v  stick-slip near closed position (%)
    • 36. 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 Vee-ball or contoured butterfly with rotary digital positioner
      • Low stem packing friction
      • Low sealing and seating friction of the closure components
      • 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),
        • 10 o to 60 o (Vee-ball)
        • 25 o to 45 o (conventional butterfly)
        • 5 o to 65 o (contoured and toothed butterfly)
      • Online diagnostics and step response tests for small changes in signal
      • Dynamic reset limiting using digital positioner feedback [2]
      Improving Valves
    • 37. Volume Booster with Integral Bypass (Furnace Pressure and Surge Control) Improving Valves Signal from Positioner Air Supply from Filter-Regulator Air Loading to Actuator Adjustable Bypass Needle Valve
    • 38. Booster and Positioner Setup (Furnace Pressure and Surge Control) Improving Valves Port A Port B Supply ZZZZZZZ Control Signal Digital Valve Controller Must be functionally tested before commissioning! 1:1 Bypass Volume Booster Open bypass just enough to ensure a non-oscillatory fast response Air Supply High Capacity Filter Regulator Increase air line size Increase connection size Terminal Box
    • 39. PIDPlus Valve Stick-Slip Lab
      • Objective – See how PIDPlus can eliminate limit cycles from stick-slip
      • Activities:
        • On Main Display, select Cascade Loop Lab02
        • Click on secondary loop AC1-2 PID Faceplate and put PID in Auto
        • Change secondary PID setpoint from 50% to 60%
        • Click on magnifying glass icon to get Detail display
        • Click on any block in block diagram
        • In Control Valve tab, set stick-slip = 4%
        • Change secondary PID setpoint from 60% to 50%
        • Wait for oscillations to develop
        • In PID tab detail, Enable PIDPlus for secondary loop
        • Wait for oscillations to decay
      Improving Valves
    • 40. Top Ten Reasons Why an Automation Engineer Makes a Great Spouse or at Least a Wedding Gift
      • (10) Reliable from day one
      • (9) Always on the job
      • (8) Low maintenance - minimal grooming, clothing, and entertainment costs
      • (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
    • 41.
      • 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 Improving 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 d/p has improved to 0.025% accuracy for a smart microprocessor based DP. Furthermore, the analog d/p 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 d/p takes 10 years to drift as much as the analog DP did in 1 year.
    • 42. 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.
      Improving Measurements
    • 43. Smart Transmitter Diagnostic Messages
      • “ Fix Now” and “Fix Soon” alerts are provided along with common causes and recommended actions
      Improving Measurements
    • 44. Dynamic Response to Step Improving Measurements Time (seconds) Theoretical Transmitter Response Actual Transmitter Response True Process Variable  m  m deadtime measurement time constant Process Variable and Measurement
    • 45. Dynamic Response to Ramp Improving Measurements Time (seconds) Actual Transmitter Response True Process Variable  m  m Process Variable and Measurement
    • 46. Attenuation of Oscillation Amplitude by Transmitter Damping or Signal Filters: When a measurement or signal filter time (  f ) becomes the largest time constant in the loop, the above equation can be solved for (A o ) to get the Amplitude of the original process variability from the filtered amplitude (A f ) Improving Measurements Effect of Transmitter Damping and Signal Filters
    • 47. Effect of Transmitter Damping or Filter for Surge Improving Measurements  m  m  m  m
    • 48.
      • What you really want most often is a mass flow measurement. However this depends upon the density of the mixture. The fluid density variation not only depends upon temperature and pressure but also composition. The effect of composition can be estimated based on the pure component densities and concentrations via Amagat's law, which works well for liquid mixtures despite being technically based on ideal gas partial volumes. The Coriolis meter uses Amagat's law for two components to provide a relatively accurate inferential measurement of fluid composition. Thus, the mass flow measurement provided by pressure and temperature compensation of a differential head, magmeter, or vortex meter can have an unknown error due to variations in process composition. Many users probably don't realize that even the volumetric flow measurement by a differential head meter is affected by fluid density, and thus composition.
      • Everyone is probably cognizant of the effect of velocity profile and hence the upstream piping system and know not to put a control valve upstream of a flowmeter. Swirl is particularly detrimental. Less known is that variations of a percent or more in the discharge or meter coefficient can occur from changes in Reynold's number and orifice edge wear. A transition to laminar flow is disastrous. The tolerance of inside pipe diameter and surface roughness can introduce several percent uncertainty. Flow conditioners, honed meter runs, flow nozzles, and venturi tubes and smart transmitters help considerably.
      • Upstream piping and changes in kinematic viscosity affect the vortex meter coefficient. The effects of installation on Coriolis meters are typically negligible. The noise from vibration and dissolved gas has been essentially eliminated by new Coriolis designs.
      Flow Measurement Improving Measurements Amagat’s Law (4 components)  X     X     X     X     ]
    • 49.
      • Changes in process pressure and temperature can introduce an error of several percent in 1980s and earlier vintage instrumentation DP transmitters used for level measurement.
      • Changes in ambient temperature can affect capillary and diaphragm seals. Solutions are equal length capillary with the same sun exposure on the high and low side or separate smart transmitters mounted on the equipment or piping with digital computation of the differential pressure. Reducing diaphragm seal and capillary diameter reduces this error but reduces measurement sensitivity and increases response time.
      • Often not recognized are the transient errors in DP measurements that use bubblers and purged lines from the changes in process pressure that cause changes in the purge gas compression. The bubbler tip can get coated from the drying action of the purge gas.
      • For DP level measurements, fluid density and bubbles and hence the composition besides the temperature and pressure of the process affect the measurement. For ultrasonic level measurements, changes in the velocity of sound and scattering cause errors. Thus changes in vapor composition and temperature, entrainment of liquid droplets, and foam can be a problem. For radar, if the dielectric constant is large enough and geometry for the vessel and source installation is defined properly, the installation and process effects are typically negligible.
      Level Measurement Improving Measurements See Nov-Dec 2010 Control Talk for information useful for Instrument Upgrade Projects http://www.controlglobal.com/articles/2010/RetrofitProjects1011.html
    • 50.
      • New pH electrode designs are much less sensitive to sodium ion error, contamination, plugging and the loss of efficiency and response time from premature aging of the glass from high temperature. New high temperature designs has doubled the life expectancy of the electrode and returned the response time from an hour or more to a matter of seconds. I didn't know the response time could get so bizarrely slow at temperatures above 50 degrees centigrade until new technology solved the problem. Similarly I did not know a drift of several tenths of a pH could occur after sterilization until an electrode design essentially eliminated the drift. Smart transmitters now have process temperature compensation built in to account for the changes in solution pH from changes in the dissociation constants with temperature. Most users only know about the standard electrode temperature compensation for the changes in the millivolt potential developed by the glass electrode per the Nernst equation. Velocity errors are still largely unknown and the error introduced by changes in the activity of the hydrogen ion with ionic strength is largely ignored but quantified in Chapter 2 of Advanced pH Measurement and Control - 3rd Edition.
      • For temperature measurements, there are thermal errors from heat conduction from the thermowell tip to the flanged or threaded process connection, dynamic error from thermowell lags, nonlinearity error (solved by sensor matching and smart transmitters), lead wire errors, insulation errors, radiation errors in furnaces, velocity errors in high flow gas streams, and sensor de-calibration errors as detailed in Chapter 2 of Advanced Temperature Measurement and Control - 2nd Edition.
      • For pH and temperature, non ideal mixing introduces significant process measurement errors. The concentrations and temperatures in a vessel or over the cross section of a pipe are not uniform. Very little attention is paid to this. The effect is thought to be more significant for highly viscous flows. The significant effect of composition and viscosity on temperature profile has been studied for extruders.
      • The effect of coatings is sketchy. We know it can be profound for pH electrodes where an almost imperceptible coating can increase the response time from 12 to 120 seconds. Similar but not as dramatic effects should occur for coatings on thermowells depending upon the conductivity of the coating. Low velocities increase the response time for both pH and temperature besides increasing the likelihood and rate of coating formation.
      pH and Temperature Measurement Improving Measurements
    • 51. Temperature Sensor Performance Improving Measurements
    • 52. Temperature Sensor Lag Improving Measurements
    • 53. Thermowell Lags Improving Measurements
    • 54. 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
      Improving Measurements
    • 55. 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%
      Improving Measurements
    • 56. Elimination of Ground Noise by Wireless pH Improving Measurements 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%
    • 57. 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
        • (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
      Improving Measurements
    • 58. University of Texas Pilot Plant for CO 2 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
      Improving Measurements
    • 59. 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
      Improving Measurements
    • 60. 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.
      Improving Measurements
    • 61. Effect of Solvent on Conductivity
      • Conductivity in the operating range of 25% to 30% by weight solvent is relatively unaffected by solvent concentration
      Improving Measurements Conductivity (milliSiemens/cm) 20 o C 30 o C 40 o C
    • 62. Effect of CO 2 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 o C
      Improving Measurements Conductivity (milliSiemens/cm) 20 o C 30 o C 40 o C
    • 63. 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 CO 2 weight percent since pH changes with the concentration of carbonic acid.
      • Density measurements by Micromotion meters provide an accurate inference of CO 2 weight percent.
      Improving Measurements
    • 64. Effect of MEA Solvent on pH Improving Measurements
    • 65. Effect of PZ Solvent on pH Improving Measurements
    • 66. Measurement Lag Lab
      • Objective – Understand relative effects of large measurement lag
      • Activities:
        • Go to Main Display, select Single Loop Lab01,
        • Click on AC1-1 PID Faceplate and Click on magnifying glass icon to get Detail display
        • Click on Duncan icon for “Tune with Insight” and click on top tab “On Demand Tuning”
        • Set Step Size = 10% and click on “Test” for “On Demand Tuning”
        • Note ultimate period and “ Ziegler-Nichols - PI” tuning settings
        • Update PID tuning settings
        • Set Desired Run Time to 300 sec and change mode from Explore to Run
        • Click on any block in block diagram
        • In Measurement detail, set Primary Measurement Lag = 100 sec
        • Set Step Size = 10% and click on “Test” for “On Demand Tuning”
        • Note ultimate period and “ Ziegler-Nichols - PI” tuning settings
        • Update PID tuning settings and change mode from Explore to Run
      Improving Measurements
    • 67. Cascade Loop Block Diagram  p1  p2  p2 K p2  p1  m2  m2 K m2  c2  f2 Primary Process K v  v  v K L2  L2  L2 Primary Load Upset  CV p  CO p  MV  PV p2 Delay Lag Delay Delay Delay Delay Delay Lag Lag Lag Lag Lag Gain Gain Gain Gain Local Set Point  DV p2 % % % Delay <=> Dead Time Lag <=>Time Constant K L1  L1  L1 Delay Lag Gain  DV p1 Secondary Load Upset  CO s Secondary PID Cascade Set Point % % K p1 Gain  CV s  m2  m2 K m2 Delay Lag Gain  c2  f2 Delay Lag Secondary Process Primary PID Primary:  o2  v  p1  p2  m2  c2  f2  v  p1  Secondary:  o1  v  p1  m1  c1  f1  v Improving Loops - Part 1 K c2 T i2 T d2 K c1 T i1 T d1
    • 68. Cascade Control Benefit (self-regulating process) Improving Loops - Part 1  i   o   i   o   i   o   i  inner loop process time constant  o  outer loop process time constant  i  inner loop process deadtime  o  outer loop process deadtime
    • 69. Cascade Control Benefit (integrating process) Improving Loops - Part 1  i  inner loop process time constant  o  outer loop process time constant  i  inner loop process deadtime  o  outer loop process deadtime  i   o   i   o   i   o 
    • 70. Cascade Control Benefit (runaway process) Improving Loops - Part 1  i   o   i   o   i   o   i  inner loop process time constant  o  outer loop process time constant  i  inner loop process deadtime  o  outer loop process deadtime
    • 71. Secondary loop slowed down by a factor of 5 Secondary SP Secondary CO Primary PV Secondary SP Primary PV Secondary CO Effect of Slow Secondary Tuning (cascade control) Improving Loops - Part 1
    • 72. Triple Cascade Loop Block Diagram Improving Loops - Part 1 Control Valve AO PID PID AI AI Flow Meter Process Process Sensor Secondary (Inner) Loop Feedback Primary (Outer) Loop Feedback Process SP Flow SP Out PV PV Relay PID * Position Loop Feedback DCS Valve Positioner Position (Valve Travel) I/P Drive Signal * most positioners use proportional only
    • 73. Feedforward Applications
      • Feedforward is the most common advanced control technique used - often the feedforward signal is a flow or speed for ratio control that is corrected by a feedback process controller ( Flow is the predominant process input that is manipulated to set production rate and to control process outputs (e.g. temperature and composition))
          • Blend composition control - additive/feed (flow/flow) ratio
          • Column temperature control - distillate/feed, reflux/feed, stm/feed, and bttms/feed (flow/flow) ratio
          • Combustion temperature control - air/fuel (flow/flow) ratio
          • Drum level control - feedwater/steam (flow/flow) ratio
          • Extruder quality control - extruder/mixer (power/power) ratio
          • Heat exchanger temperature control - coolant/feed (flow/flow) ratio
          • Neutralizer pH control - reagent/feed (flow/flow) ratio
          • Reactor reaction rate control - catalyst/reactant (speed/flow) ratio
          • Reactor composition control - reactant/reactant (flow/flow) ratio
          • Sheet, web, and film line machine direction (MD) gage control - roller/pump (speed/speed) ratio
          • Slaker conductivity control - lime/liquor (speed/flow) ratio
          • Spin line fiber diameter gage control - winder/pump (speed/speed) ratio
      • Feedforward is most effective if the loop deadtime is large, disturbance speed is fast and size is large, feedforward gain is well known, feedforward measurement and dynamic compensation are accurate
      • Setpoint feedforward is most effective if the loop deadtime exceeds the process time constant and the process gain is well known
      For more discussion of Feedforward see May 2008 Control Talk http://www.controlglobal.com/articles/2008/171.html Improving Loops - Part 1
    • 74. Feedforward Implementation - 1
      • Feedforward gain can be computed from a material or energy balance ODE * & explored for different setpoints and conditions from a plot of the controlled variable (e.g. composition, conductivity, pH, temperature, or gage) vs. ratio of manipulated variable to independent variable (e.g. feed) but is most often simply based on operating experience
        • * http://www.modelingandcontrol.com/repository/AdvancedApplicationNote004.pdf
        • Plots are based on an assumed composition, pressure, temperature, and/or quality
          • For concentration and pH control, the flow/flow ratio is valid if the changes in the composition of both the manipulated and feed flow are negligible.
          • For column and reactor temperature control, the flow/flow ratio is valid if the changes in the composition and temperature of both the manipulated and feed flow are negligible.
          • For reactor reaction rate control, the speed/flow is valid if changes in catalyst quality and void fraction and reactant composition are negligible.
          • For heat exchanger control, the flow/flow ratio is valid if changes in temperatures of coolant and feed flow are negligible.
          • For reactor temperature control, the flow/flow ratio is valid if changes in temperatures of coolant and feed flow are negligible.
          • For slaker conductivity (effective alkali) control, the speed/flow ratio is valid if changes in lime quality and void fraction and liquor composition are negligible.
          • For spin or sheet line gage control, the speed/speed ratio is valid only if changes in the pump pressure and the polymer melt quality are negligible.
      • Dynamic compensation is used to insure the feedforward signal arrives at same point at same time in process as upset
        • Compensation of a delay in the feedforward path > delay in upset path is not possible
      Improving Loops - Part 1
    • 75.
      • Feedback correction is essential in industrial processes
        • While technically, the correction should be a multiplier for a change in slope and a bias for a change in the intercept in a plot of the manipulated variable versus independent variable (independent from this loop but possibly set by another PID or MPC), a multiplier creates scaling problems for the user, consequently the correction of most feedforward signal is done via a bias.
        • The bias correction must have sufficient positive and negative range for worst case.
        • Model predictive control (MPC) and PID loops get into a severe nonlinearity by creating a controlled variable that is the ratio. It is important that the independent variable be multiplied by the ratio and the result be corrected by a feedback loop with the process variable (composition, conductivity, gage, temperature, or pH) as the controlled variable.
      • Feedforward gain is a ratio for most load upsets.
      • Feedforward gain is the inverse of the process gain for setpoint feedforward.
        • Process gain is the open loop gain seen by the PID (product of manipulated variable, process variable, and measurement variable gain) that is dimensionless.
      • Feedforward action must be in the same direction as feedback action for upset.
      • Feedforward action is the opposite of the control action for setpoint feedforward.
      • Feedforward delay and lag adjusted to match any additional delay and lag, respectively in path of upset so feedforward correction does not arrive too soon.
      • Feedforward lead is adjusted to compensate for any additional lag in the path of the manipulated variable so the feedforward correction does not arrive too late.
      • The actual and desired feedforward ratio should be displayed along with the bias correction by the process controller. This is often best done by the use of a ratio block and a bias/gain block instead of the internal PID feedforward calculation.
      Feedforward Implementation - 2 Improving Loops - Part 1
    • 76. Bias Correction of Ratio Control For information on Ratio Control, see April 7, 2009 Post on website http://www.modelingandcontrol.com/2009/04/what_have_i_learned_-_ratio_co_1.html Improving Loops - Part 1
    • 77. Feedforward Lab 1
      • Objective – Show the effect of a feedforward correction arriving too early
      • Activities:
        • Go to Main Display, select Feedforward Loop Lab03, and click on any block
        • In Measurements detail set primary feedforward gain = 1.0 and delay = 0 sec
        • Change Lab03 Run time to 300 sec and change mode from Explore to Run
        • Go to Main Display, select Cascade Loop Lab02, and click on any block
        • Click on Trend icon next to faceplate icon and open Lab02 & Lab03 charts
        • Change Lab02 Run time to 300 sec and change mode from Explore to Run
        • Compare the results of Lab02 & Lab03 trend charts
        • In Lab02 note peak error & IAE
        • In Lab03 note peak error & IAE
      Improving Loops - Part 1
    • 78.
      • Objective – Show the effect of a feedforward correction arriving too late
      • Activities:
        • In Lab03 Measurements detail set primary feedforward delay = 40 sec
        • Change Lab03 Run time to 300 sec and change mode from Explore to Run
        • Change Lab02 Run time to 300 sec and change mode from Explore to Run
        • Compare the results of Lab02 & Lab03 charts
        • In Lab02 note peak error & IAE
        • In Lab03 note peak error & IAE
      Feedforward Lab 2 Improving Loops - Part 1

    ×