Control Loop Foundation for Batch and Continuous Control

06/06/09 Control Loop Foundation for Batch and Continuous Control GREGORY K MCMILLAN use pure black and white option for printing copies

Presenter
Greg is a retired Senior Fellow from Solutia Inc. During his 33 year career with Monsanto Company and its spin off Solutia Inc, he specialized in modeling and control. 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 honored by InTech Magazine in 2003 as one of the most influential innovators in automation. Greg has written a book a year for the last 20 years whether he needed to or not. About half are humorous (the ones with cartoons and top ten lists). Presently Greg contracts via CDI Process and Industrial as a principal consultant in DeltaV Applied R&D at Emerson Process Management in Austin Texas. For more info visit:
http://ModelingandControl.com
http://www.easydeltav.com/controlinsights/index.asp (free E-books)
06/06/09

06/06/09 See Chapter 2 for more info on “Setting the Foundation”
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06/06/09 See Chapters 1-7 for the practical considerations of improving tuning and valve dynamics
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06/06/09 See Appendix C for background of the unification of tuning methods and loop performance
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06/06/09 See Chapter 1 for the essential aspects of system design for pH applications
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Overview
This presentation covers highlights or low lights of current loop performance and how to improve batch and continuous processes:
Pyramid of Technologies
Valve and Flow Meter Performance
Process Control Improvement Examples
Basic Control Opportunities Summary
Reactors and Column Loop Tuning
Facts of Life
Transfer of Variability for Batch
Sources of Disturbances
Transition from Basic to Advanced Regulatory Control of Batch
Online Data Analytics for Batch and Continuous Processes
Virtual Plant
Uses and Fidelities of Dynamic Process Models
What we Need
Columns and Articles in Control Magazine

06/06/09 TS is tactical scheduler, RTO is real time optimizer, LP is linear program, QP is quadratic program Pyramid of Technologies APC is in any technology that integrates process knowledge Foundation must be large and solid enough to support upper levels. Effort and performance of upper technologies is highly dependent on the integrity and scope of the foundation (type and sensitivity of measurements and valves and tuning of loops) The greatest success has been Achieved when the technology closed the loop (automatically corrected the process without operator intervention) Basic Process Control System Loop Performance Monitoring System Process Performance Monitoring System Abnormal Situation Management System Auto Tuning (On-Demand and On-line Adaptive Loop Tuning) Fuzzy Logic Property Estimators Model Predictive Control Ramper or Pusher LP/QP RTO TS

Loops Behaving Badly 06/06/09 1 E i = ------------  T i  E o  K o  K c where: E i = integrated error (% seconds) E o = open loop error from a load disturbance (%) K c = controller gain K o = open loop gain (also known as process gain) (%/%) T i = controller reset time (seconds) (open loop means controller is in manual) A poorly tuned loop will behave as badly as a loop with lousy dynamics (e.g. excessive dead time)! Tune the loops before, during, and after any process control improvements You may not want to minimize the integrated error if the controller output upsets other loops. For surge tank and column distillate receiver level loops you want to minimize and maximize the transfer of variability from level to the manipulated flow, respectively.

Unification of Controller Tuning Settings 06/06/09 Where: K c = controller gain K o = open loop gain (also known as process gain) (%/%)  1  self-regulating process time constant (sec)  max  maximum total loop dead time (sec) All of the major tuning methods (e.g. Ziegler-Nichols ultimate oscillation and reaction curve, Simplified Internal Model Control, and Lambda) reduce to the following form for the maximum useable controller gain

Definition of Deadband and Stick-Slip 06/06/09 Deadband Deadband Stick-Slip Signal (%) 0 Stroke (%) Digital positioner will force valve shut at 0% signal Pneumatic positioner requires a negative signal to close valve The effect of slip is worse than stick, stick is worse than dead band, and dead band is worse than stroking time (except for surge control) Dead band is 5% - 50% without a positioner ! Stick-slip causes a limit cycle for self-regulating processes. Deadband causes a limit cycle in level loops and cascade loops with integral (reset) action. If the cycle is small enough it can get lost in the disturbances, screened out by exception reporting, or attenuated by volumes

06/06/09 Controller Output (%) Saw Tooth Oscillation Controlled Flow (kpph) Square Wave Oscillation Saw Tooth Flow Controller Output Limit Cycle from Stick-Slip

06/06/09 Manipulated Flow (kpph) Clipped Oscillation Controller Output (%) Rounded Oscillation Controlled Level (%) Saw Tooth Oscillation Rounded Level Controller Output Limit Cycle from Deadband

Identification of Stick and Slip in a Closed Loop Response 06/06/09 The limit cycle may not be discernable due to frequent disturbances and noise Time ( Seconds ) Stroke % 53 53.5 54 54.5 55 55.5 56 56.5 57 57.5 58 58.5 59 0 100 200 300 400 500 600 700 800 3.25 Percent Backlash + Stiction Controller Output Flow Dead band is peak to peak amplitude for signal reversal slip stick

Response Time of Various Positioners (small actuators so slewing rate is not limiting) 06/06/09 Response time increase dramatically for steps less than 1%

Control Valve Facts of Life
Pneumatic positioners are almost always out of calibration
Most tests by valve manufacturers for stick-slip are at 50% with loosely tightened stem packing to minimize seating, sealing, and packing friction
Without a representative position feedback in the control room, it is anybody’s guess what the valve is doing unless there is a low noise sensitive flow sensor
Not all positioners are equal. Pneumatic positioners, especially the spool or single amplification stage low gain ones will increase the valve response time by an order of magnitude (4 -> 40 sec) for small changes in controller output
All valves look good when checking positions for 0, 25, 75, and 100% signals
Valve specs do not generally require that the control valve actually move
The tighter the shutoff, the greater the stick-slip for positions less than 20%
Smart positioner diagnostics and position read back are lies for actuator shaft position feedback of rotary type isolation valves posing as throttling valves particularly for pinned rather than splined shaft connections due to twisting of the shaft. Field tests show stick-slip of 85 in actual ball or disc movement despite diagnostics and read back indicating a valve resolution of 0.5%
The official definition of valve rangeability is bogus because it doesn’t take into account stick-slip near the seat. Equal percentage valves with minimal stick-slip (excellent resolution and sensitivity) generally offer the best rangeability
06/06/09

Top Ten Signs of a Valve Problem
(10) The pipe fitters are complaining about trying to fit a 1 inch valve into a 10 inch pipe.
(9) You bought the valve suppliers’ “monthly special.”
(8) A butterfly disc won’t open because the ID of the lined pipe is smaller than the OD of the disc.
(7) The maintenance department personally put the valve on your desk.
(6) A red slide ruler was used to size a green valve.
(5) Your latest valve catalog is dated 1976.
(4) The maintenance department said they don’t want a double seat “A” body.
(3) The valve was specified to have 0% leakage for all conditions including all signals.
(2) The fluid field in the sizing program was left as water.
(1) The valve is bigger than the pipe.

Flow Meter Performance
Type Sizes Range Piping Interferences Reproducibility
Coriolis ¼ -8” 100:1 1/1 solids, alignment, vibration 0.1% of rate
Magmeter ¼-78” 25:1 5/1 conductivity, electrical noise 0.5% of rate
Vortex ½-12” 9:1* 10/5 profile, viscosity, hydraulics 1.0% of span
Orifice ¼-78” 4:1 10/5 profile, Reynolds Number 5.0% of span
* - assumes a minimum and maximum velocity of about 1 and 9 fps, respectively
06/06/09 Coriolis flow meters via their accurate density measurement offer direct concentration measurements for 2 component mixtures and inferential measurements for complex mixtures.

Neutralizer Control – “Before” 06/06/09 Static Mixer Neutralizer Feed Discharge AT 1-1 FT 1-1 FT 2-1 AT 2-1 FT 1-2 2 pipe diameters Reagent Stage 1 Reagent Stage 2 AC 1-1 AC 2-1 FC 1-2

Nonlinearity and Sensitivity of pH 06/06/09 pH Reagent Flow Influent Flow 6 8 Good valve resolution or fluid mixing does not look that much better than poor resolution or mixing due amplification of X axis (concentration) fluctuations Reagent Charge Process Volume or

Neutralizer Control – “After” 06/06/09 Static Mixer Neutralizer Feed Discharge AT 1-1 FT 1-1 FT 2-1 AT 2-1 FT 1-2 Reagent Stage 1 Reagent Stage 2 20 pipe diameters f(x)  Feedforward Summer RSP Signal Characterizer *1 *1 *1 - Isolation valve closes when control valve closes AC 1-1 FC 1-2 FC 2-1 AC 2-1

Distillation Column Control – “Before” 06/06/09 FC 3-4 FT 3-4 FC 3-3 FT 3-3 LT 3-1 LC 3-1 TE 3-2 TC 3-2 LT 3-2 LC 3-2 Distillate Receiver Column Overheads Bottoms Steam Feed Reflux PC 3-1 PT 3-1 Vent Storage Tank Feed Tank Tray 10 Thermocouple

Nonlinearity and Sensitivity of Tray Temperature 06/06/09 Tray 10 Tray 6 Distillate Flow Feed Flow % Impurity Temperature Operating Point Measurement Error Measurement Error Impurity Errors

Distillation Column Control – “After” 06/06/09 FC 3-2 FT 3-2 FC 3-4 FT 3-4 FC 3-3 FT 3-3 FC 3-1 FT 3-1 LT 3-1 LC 3-1 TT 3-2 TC 3-2 FC 3-5 FT 3-5 LT 3-2 LC 3-2 RSP RSP RSP Distillate Receiver Column Overheads Bottoms Steam Feed Reflux PC 3-1 PT 3-1 Vent Storage Tank Feed Tank Tray 6 f(x) Signal Characterizer RTD   FT3-3 FT3-3 Feedforward summer Feedforward summer

When Process Knowledge is Missing in Action 06/06/09 2-Sigma 2-Sigma RCAS Set Point LOCAL Set Point 2-Sigma 2-Sigma Upper Limit PV distribution for original control PV distribution for improved control Extra margin when “ war stories” or mythology rules value Benefits are not realized until the set point is moved! (may get benefits by better set point based on process knowledge even if variability has not been reduced) Good engineers can draw straight lines Great engineers can move straight lines

Top Ten Ways to Impress Your Management with the Trends of a Control System
(10) Make large set point changes that will zip past valve dead band and local nonlinearities
(9) Change the set point to operate on the flat part of the titration curve
(8) Select the tray with minimum process sensitivity for column temperature control
(7) Pick periods when the unit was down
(6) Decrease the time span so that just a couple data points are trended
(5) Increase the reporting interval so that just a couple data points are trended
(4) Use really thick line sizes
(3) Add huge signal filters
(2) Increase the process variable scale span so it is at least ten times the control region of interest
(1) Increase the historian’s data compression so that most changes are screened out as insignificant

Basic Opportunities in Process Control
Decrease stick-slip and improve the sensitivity of the final element (Standard Deviation is the product of stick-slip, valve gain, and process gain)
Use properly tuned smart positioners, short shafts with tight connections, and low friction packing and seating surfaces to decrease valve slip-stick and dead band (do not use isolation valves for throttling valves)
If high friction packing must be used, aggressively tune the smart positioner
Improve valve type and sizing and add signal characterization to increase valve sensitivity
Use variable speed drives where appropriate for the best sensitivity
Improve the short and long term reproducibility and reduce the interference and noise in the measurement (Standard Deviation is proportional to reproducibility and noise)
Use magnetic and Coriolis mass flow meters to eliminate sensing lines, improve rangeability, and reduce effect of Reynolds Number and piping
Use smart transmitters to reduce process and ambient effects
Use RTDs and digital transmitters to decrease temperature noise and drift
06/06/09

Basic Opportunities in Process Control
Reduce loop dead time (Minimum Integrated Error is proportional to the dead time squared)
Decrease valve dead time (stick and dead band)
Decrease transport (plug flow volume) and mixing delay (turnover time)
Decrease measurement lags (sensor lag, dampening, and filter time)
Decrease discrete device delays (scan or update time)
Decrease analyzer sample transport and cycle time
Tune the controllers (Integrated Error is inversely proportional to the controller gain and directly proportional to the controller integral time)
Add cascade control (Standard Deviation is proportional to the ratio of the period of the secondary to the process time constant of the primary loop)
Add feed forward control (Standard Deviation is proportional to the root mean square of the measurement, feed forward gain, and timing errors)
Eliminate or slow down disturbances (track down source and speed)
Add inline analyzers (probes) and at-line analyzers with automated sampling since ultimately what you want to control is a composition
Optimize set points (based on process knowledge and variability)
To realize the benefit of reduced variability, often need to change a set point
06/06/09

Reset Gives Them What They Want 06/06/09 Proportional and rate action see the trajectory visible in a trend! Both would work to open the water valve to prevent overshoot. Reset action integrates the numeric difference between the PV and SP seen by operator on a loop faceplate Reset works to open the steam valve Reset won’t open the water valve Until the error changes sign, PV goes above the set point. Reset has no sense of direction. Should the steam or water valve be open? SP PV Out 52 44 ? TC-101 Reactor Temperature steam valve opens water valve opens 50% set point (SP) temperature time PV

Reactor and Column Loop Tuning
Most reactor and column composition, gas pressure, and temperature loops have too much integral action (reset time too small), not enough proportional action (gain too small), and not enough derivative action (rate time too small).
Rate time should be 0.1x process time constant or 0.1x reset time with a minimum value of sensor lag time.
Rate action is essential for exothermic reactors that can runaway
Often these loops are “near integrators” due to a large process time constant . Batch processes often have “true integrators” because of a lack of self-regulation (no steady state). Whether “near integrators” or “true integrators”, these loops require much more gain action to impose self-regulation and provide pre-emptive action. There is a window of allowable gains where too low of a controller gain will result in slow rolling oscillations from reset.
(controller gain) * (controller reset time) > 4 / (integrating process gain)
06/06/09

Modeling and Control Facts of Life
“ Timing is Everything”
In life, business, and process control (especially feedforward)
“ Without Dead Time I would be Out of Job”
If the dead time was zero, the only limit to how high you can set the controller gain or how tight you can control is measurement noise
Unlike aerospace, the process industry has large and variable time delays and time lags from batch cycle times, vessel mixing times, volume residence times, transportation delays, resolution limits, dead band, and measurements
Total dead time is sum of time delays and all time lags smaller than largest
Best possible integrated absolute error is proportional to dead time squared
06/06/09

Modeling and Control Facts of Life
Models (experimental or theoretical) allow you to take the blindfold off
Models convey process knowledge and provide insight on what has changed and what should be improved (e.g. largest source of dead time)
“ War stories rule” where there are no models
“ Mythology rules” where there are no models
“ Benefits are hearsay” where there are no models
Nonlinearity is a reason to build models rather than avoid models
Unless you want job security for constantly retuning controllers. Also, implied in most techniques is some model (e.g. reaction curve method)
Tight control greatly reduces the operating point nonlinearity (e.g. pH) and secondary flow loops eliminate the valve nonlinearity for higher level loops
Signal characterization on the controller output (based on a model of the installed valve characteristic) greatly reduces the valve nonlinearity
06/06/09

Speed of Various Sources of Disturbances (Speed Kills)
Process
Flow (fast)
Gas pressure (fast)
Liquid Pressure (very fast)
Raw Materials (slow)
Recycle (very slow)
Temperature (slow)
Catalyst (slow)
Steam (fast)
Coolant (fast)
Equipment
Fouling (slow)
Failures (fast)
Environmental
Day to Night (slow)
Rain Storms and fronts (fast)
Season to Season (very slow)
06/06/09 A loop can catch up to a slow disturbance. Liquid pressure Is the fastest upset (travels at the speed of sound in liquid).

Valves
Stick-slip (fast)
Split Range (fast)
Failures (very fast)
Measurements
Noise (very fast)
Reproducibility (fast)
Controllers
Feedback Tuning (fast) *
Feed forward Timing (fast)
Interaction (fast)
06/06/09 * Most frequent culprit is an oscillating level loop primarily due to excessive reset action

Market*
Rate changes (fast)
Product transitions (fast)
Operators
Manual operation (fast)
Sweet spots (fast)
Inventory control (fast)
Discrete
On-off control (very fast)
Sequences (fast)
Batch operations (fast)
Startup and shutdown (very fast)
Interlocks (very fast)
06/06/09 *For minimized inventory, changes in market demand can result in fast production rate changes and product grade or type transitions

Batch Control 06/06/09 Reagent Optimum pH Optimum Product Feeds Concentrations pH Product Optimum Reactant Reactant Reactant Variability Transfer from Feeds to pH, and Reactant and Product Concentrations Most published cases of multivariate statistical process control (MSPC) use the process variables and this case of variations in process variables induced by sequenced flows.

PID Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Reactant Optimum Reactant Reactant Variability Transfer from pH and Reactant Concentration to Feeds The story is now in the controller outputs (manipulated flows) yet MSPC still focuses on the process variables for analysis

Model Predictive Control 06/06/09 Optimum pH Optimum Product Feeds Concentrations pH Product Reagent Optimum Reactant Reactant Reactant Time Time Variability Transfer from Product Concentration to pH, reactant Concentration, and Feeds Model Predictive Control of product concentration batch profile uses slope for CV which makes the integrating response self-regulating and enables negative besides positive corrections in CV

Example of Basic PID Control 06/06/09 feed A feed B coolant makeup CAS ratio control reactor vent product condenser CTW PT PC-1 TT TT TC-2 TC-1 FC-1 FT FT FC-2 TC-3 RC-1 TT CAS cascade control Conventional Control

Example of Advanced Regulatory Control 06/06/09 feed A feed B coolant makeup CAS ratio CAS reactor vent product maximum production rate 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-2 CAS CAS CAS ZC-3 ZC-4 < Override Control override control ZC-1, ZC-3, and ZC-4 work to keep their respective control valves at a max throttle position with good sensitivity and room for loop to maneuver. ZC-2 will raise TC-1 SP if FC-1 feed rate is maxed out

Function Blocks for Online Data Analytics
Function blocks developed to support on-line batch and continuous analytics
PCA Block
PLS Block
Analyzer Block
06/06/09

Analyzer Block for Online Data Analytics 06/06/09 History Collection of Lab and Spectral Analyzer Data Controller Processing of Sample Data for Use in Analytics Module Lab Results Analyzer Block Historian Operator Station Off-line Modeling Other Data

Dynamic Time Warping for Online Batch Data Analytics 06/06/09 Reference trajectory Trajectory to be synchronized Synchronized trajectory

Virtual Plant Setup 06/06/09 Advanced Control Modules Process Models (first principal and experimental) Virtual Plant Laptop or Desktop or Control System Station This is where I hang out

Virtual Plant Integration 06/06/09 Dynamic Process Model Online Data Analytics Model Predictive Control Loop Monitoring And Tuning DCS batch and loop configuration, displays, and historian Virtual Plant Laptop or Desktop Personal Computer Or DCS Application Station or Controller Embedded Advanced Control Tools Embedded Modeling Tools Process Knowledge

Typical Uses and Fidelities of Process Models (Fidelity Scale 0 - 10)
Process Development
Media or reactant optimization and identification of kinetics on the bench top - 10
Optimization of process conditions in pilot plant - 9
Agitation and mass transfer rates - 8*
Process scale-up – 8
* - assumes computational fluid dynamics (CFD) program provides necessary inputs
Process Design
Innovative reactor designs or single use bioreactors (SUB) - 7
Vessel, feed, and jacket system size and performance - 6
Automation Design
Real Time Optimization (RTO) - 7
Model Predictive Control (MPC) - 6
Controller tuning (PID) - 5
Control strategy development and prototyping - 4
Batch sequence (e.g. timing of feed schedules and set point shifts) – 3
06/06/09

Typical Uses and Fidelities of Process Models (Fidelity Scale 0 - 10)
Online Diagnostics
Root cause analysis - 5
Data analytics development and prototyping - 4
Operator Training Systems
Developing and maintaining troubleshooting skills - 4
Understanding process relationships - 3
Gaining familiarity with interface and functionality of automation system - 2
Configuration Checkout
Verifying configuration meets functional specification - 2
Verifying configuration has no incorrect or missing I/O, loops, or devices - 1
06/06/09

Loops that are not islands of automation
Unit operation control for integrated objectives, performance, and diagnostics
High speed local control of pressure with ROUT, CAS, and RCAS signals
Engineer with process, configuration, control, measurement, and valve skills
Virtual plants with increasing Fidelity (3 -> 7 chemical, 3->10 biological)
Product development, process design, real time optimization, advanced control prototyping and justification, process control improvement, diagnostics, training
Smart wireless integrated process and operations graphics
Online process, loop, and advanced control metrics for plants, trains, and shifts
Yield, on-stream time, production rate, utility cost, raw material cost, maintenance cost*
Variability, average % of max speed (Lambda), % time process variable or output is at limits, % time in highest mode, % deadband, % resolution, number of oscillations
Process control improvement (PCI) benefits ($ of revenue and costs)
3-D, XY, future trajectories of process and performance metrics response, data analytics, worm plots, and trends of automatically selected correlated variables
Coriolis flow meters, RTDs, and online and at-line analyzers everywhere
Real time analysis via probes or automated low maintenance sample systems
Automated time stamped entry of lab results into data historian
Online material, energy, and component balances
Control valves with < 0.25% resolution and < 0.5% dead band
06/06/09 What Do We Need?

Key Points
Tune the loops
Use digital positioners and throttle valves to get resolution better than 0.5%
Use Coriolis and Magmeters to get accuracy better than 0.5% of rate
Tune the loops
Add cascade and feed forward control for disturbances
Model the process to dispel myths and build on process knowledge
Improve the set points
Add composition control
Reduce the size and speed of disturbances
Transfer variability from most important process outputs
Add online data analytics (multivariate statistical process control)
Add online metrics to spur competition, and to adjust, verify, and justify controls
06/06/09

Control Magazine Columns and Articles
“ Control Talk” column 2002-2008
“ Has Your Control Valve Responded Lately?” 2003
“ Advanced Control Smorgasbord” 2004
“ Fed-Batch Reactor Temperature Control” 2005
“ A Fine Time to Break Away from Old Valve Problems” 2005
“ Virtual Plant Reality” 2005
“ Full Throttle Batch and Startup Responses” 2006
“ Virtual Control of Real pH” 2007
“ Unlocking the Secret Profiles of Batch Reactors” 2008
06/06/09

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Control Loop Foundation for Batch and Continuous Control

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Control Loop Foundation for Batch and Continuous Control — Presentation Transcript