The document provides an overview of a three-day process control conference in December 2010. Day 1 will include a welcome from keynote speaker Greg McMillan, who has extensive experience in process control. The rest of Day 1 will focus on common mistakes in project definition meetings and the top 10 concepts in process control, including loop deadtime, speed, gain, resonance, attenuation, sensitivity and resolution.
PID Control of Runaway Processes - Greg McMillan DeminarJim Cahill
On-line demo / seminar presented by ModelingAndControl.com's Greg McMillan on August 25, 2010.
Recorded version of presentation will be available post live session at: http://www.screencast.com/users/JimCahill/folders/Deminars
PID Control of True Integrating Processes - Greg McMillan DeminarJim Cahill
Presented August 11, 2010 by Greg McMillan as on-line demo/seminar. Video recording available at: http://www.screencast.com/users/JimCahill/folders/Public
PID Tuning for Near Integrating Processes - Greg McMillan DeminarJim Cahill
Greg McMillan shares how to reduce tuning time for near integrating processes.
Recorded video version available for viewing at: http://www.screencast.com/t/NmUxZTBiNTg
PID Control of Slow Valves and Secondary Loops Greg McMillan Deminar SeriesJim Cahill
Greg McMillan shares ways to address slow valves and different time constants between primary and secondary loops.
Recorded deminar (demo/seminar) available for viewing at: http://www.screencast.com/t/YWYxZGUw
PID Control of Runaway Processes - Greg McMillan DeminarJim Cahill
On-line demo / seminar presented by ModelingAndControl.com's Greg McMillan on August 25, 2010.
Recorded version of presentation will be available post live session at: http://www.screencast.com/users/JimCahill/folders/Deminars
PID Control of True Integrating Processes - Greg McMillan DeminarJim Cahill
Presented August 11, 2010 by Greg McMillan as on-line demo/seminar. Video recording available at: http://www.screencast.com/users/JimCahill/folders/Public
PID Tuning for Near Integrating Processes - Greg McMillan DeminarJim Cahill
Greg McMillan shares how to reduce tuning time for near integrating processes.
Recorded video version available for viewing at: http://www.screencast.com/t/NmUxZTBiNTg
PID Control of Slow Valves and Secondary Loops Greg McMillan Deminar SeriesJim Cahill
Greg McMillan shares ways to address slow valves and different time constants between primary and secondary loops.
Recorded deminar (demo/seminar) available for viewing at: http://www.screencast.com/t/YWYxZGUw
PID Control Of Sampled Measurements - Greg McMillan Deminar SeriesJim Cahill
This presentation, PID Control of Sampled Measurements, is from the first in Greg McMillan's live seminar / demo (a.k.a. deminar) series.
You can watch a recorded version of this presentation at: http://www.screencast.com/t/ODhlOWY4M
For future events and background, visit: http://www.emersonprocessxperts.com/archives/2010/04/free_series_of.html
Greg teaches you about Auto Tuning and Adaptive Control of Nonlinear Processes that are self regulating. Recorded video available for viewing at: http://www.screencast.com/t/NDY1NTQx
PID Control of Valve Sticktion and Backlash - Greg McMillan Deminar SeriesJim Cahill
Greg McMillan shows the limit of PID control options to help with sticky valves and valves with excessive deadband in this live deminar (demo/seminar).
Recorded video at: http://www.screencast.com/t/OTJjYjE1Nz
To gain a basic understanding of the principles of PID Loop Optimisation.
To understand why “Loop Tuning” is often not the solution to achieving (or restoring) stability in a process control loop.
To understand how PROFIBUS PA instrumentation can assist in Loop Optimisation.
On-line Process Control Lab Access and Use DeminarJim Cahill
Recorded demo/seminar of Greg McMillan presenting On-line Process Control Lab with Access and Use Instructions on May 27, 2010.
Screencast of presentation available at: http://www.screencast.com/t/Y2Q4NjM0Y
A Unified PID Control Methodology to Meet Plant ObjectivesJim Cahill
Presented at the AIChE 2013 Spring Meeting and 9th Global Congress on Process Safety meeting by Greg McMillan, CDI Process & Industrial and Hector Torres, Eastman Chemical
Split Range Control - Greg McMillan DeminarJim Cahill
Presented March 9, 2011 by Greg McMillan as on-line demo/seminar. Video recording available at: http://www.screencast.com/users/JimCahill/folders/Public
Foundation Fieldbus - Control in the FieldJim Cahill
Presented by Emerson's Travis Hesketh at the 2011 General Assembly in Mumbai, India on March 9-10.
Download the file to get the full effect of the slide builds.
An optimal PID controller via LQR for standard second order plus time delay s...ISA Interchange
An improved tuning methodology of PID controller for standard second order plus time delay systems (SOPTD) is developed using the approach of Linear Quadratic Regulator (LQR) and pole placement technique to obtain the desired performance measures. The pole placement method together with LQR is ingeniously used for SOPTD systems where the time delay part is handled in the controller output equation instead of characteristic equation. The effectiveness of the proposed methodology has been demonstrated via simulation of stable open loop oscillatory, over damped, critical damped and unstable open loop systems. Results show improved closed loop time response over the existing LQR based PI/PID tuning methods with less control effort. The effect of non-dominant pole on the stability and robustness of the controller has also been discussed.
Opportunity Assessment and Advanced ControlJim Cahill
Gregory K. McMillan ( http://www.modelingandcontrol.com ) presents the process of assessing opportunities to apply advanced process control (APC), their potential benefits, and exposes some common myths.
Natural gas operations considerations on process transients design and controlISA Interchange
This manuscript highlights tangible benefits deriving from the dynamic simulation and control of operational transients of natural gas processing plants. Relevant improvements in safety, controllability, operability, and flexibility are obtained not only within the traditional applications, i.e. plant start-up and shutdown, but also in certain fields apparently time-independent such as the feasibility studies of gas processing plant layout and the process design of processes. Specifically, this paper enhances the myopic steady-state approach and its main shortcomings with respect to the more detailed studies that take into consideration the non-steady state behaviors. A portion of a gas processing facility is considered as case study. Process transients, design, and control solutions apparently more appealing from a steady-state approach are compared to the corresponding dynamic simulation solutions.
Control Loop Foundation - Batch And Continous ProcessesEmerson Exchange
This presentation, by Emerson's Terry Blevins and Mark Nixon, is a guide for engineers, managers, technicians, and others that are new to process control or experienced control engineers who are unfamiliar with multi-loop control techniques.
Their book is available in the ISA Bookstore at: http://emrsn.co/1E
Guidelines for Setting Filter and Module Execution RateEmerson Exchange
The is a recording of a presentation given at Emerson Exchange 2010. Basic guidelines are presented on how to set filtering to avoid aliasing. Also, information is provided on how to set control execution rate to achieve best performance.
PID Control Of Sampled Measurements - Greg McMillan Deminar SeriesJim Cahill
This presentation, PID Control of Sampled Measurements, is from the first in Greg McMillan's live seminar / demo (a.k.a. deminar) series.
You can watch a recorded version of this presentation at: http://www.screencast.com/t/ODhlOWY4M
For future events and background, visit: http://www.emersonprocessxperts.com/archives/2010/04/free_series_of.html
Greg teaches you about Auto Tuning and Adaptive Control of Nonlinear Processes that are self regulating. Recorded video available for viewing at: http://www.screencast.com/t/NDY1NTQx
PID Control of Valve Sticktion and Backlash - Greg McMillan Deminar SeriesJim Cahill
Greg McMillan shows the limit of PID control options to help with sticky valves and valves with excessive deadband in this live deminar (demo/seminar).
Recorded video at: http://www.screencast.com/t/OTJjYjE1Nz
To gain a basic understanding of the principles of PID Loop Optimisation.
To understand why “Loop Tuning” is often not the solution to achieving (or restoring) stability in a process control loop.
To understand how PROFIBUS PA instrumentation can assist in Loop Optimisation.
On-line Process Control Lab Access and Use DeminarJim Cahill
Recorded demo/seminar of Greg McMillan presenting On-line Process Control Lab with Access and Use Instructions on May 27, 2010.
Screencast of presentation available at: http://www.screencast.com/t/Y2Q4NjM0Y
A Unified PID Control Methodology to Meet Plant ObjectivesJim Cahill
Presented at the AIChE 2013 Spring Meeting and 9th Global Congress on Process Safety meeting by Greg McMillan, CDI Process & Industrial and Hector Torres, Eastman Chemical
Split Range Control - Greg McMillan DeminarJim Cahill
Presented March 9, 2011 by Greg McMillan as on-line demo/seminar. Video recording available at: http://www.screencast.com/users/JimCahill/folders/Public
Foundation Fieldbus - Control in the FieldJim Cahill
Presented by Emerson's Travis Hesketh at the 2011 General Assembly in Mumbai, India on March 9-10.
Download the file to get the full effect of the slide builds.
An optimal PID controller via LQR for standard second order plus time delay s...ISA Interchange
An improved tuning methodology of PID controller for standard second order plus time delay systems (SOPTD) is developed using the approach of Linear Quadratic Regulator (LQR) and pole placement technique to obtain the desired performance measures. The pole placement method together with LQR is ingeniously used for SOPTD systems where the time delay part is handled in the controller output equation instead of characteristic equation. The effectiveness of the proposed methodology has been demonstrated via simulation of stable open loop oscillatory, over damped, critical damped and unstable open loop systems. Results show improved closed loop time response over the existing LQR based PI/PID tuning methods with less control effort. The effect of non-dominant pole on the stability and robustness of the controller has also been discussed.
Opportunity Assessment and Advanced ControlJim Cahill
Gregory K. McMillan ( http://www.modelingandcontrol.com ) presents the process of assessing opportunities to apply advanced process control (APC), their potential benefits, and exposes some common myths.
Natural gas operations considerations on process transients design and controlISA Interchange
This manuscript highlights tangible benefits deriving from the dynamic simulation and control of operational transients of natural gas processing plants. Relevant improvements in safety, controllability, operability, and flexibility are obtained not only within the traditional applications, i.e. plant start-up and shutdown, but also in certain fields apparently time-independent such as the feasibility studies of gas processing plant layout and the process design of processes. Specifically, this paper enhances the myopic steady-state approach and its main shortcomings with respect to the more detailed studies that take into consideration the non-steady state behaviors. A portion of a gas processing facility is considered as case study. Process transients, design, and control solutions apparently more appealing from a steady-state approach are compared to the corresponding dynamic simulation solutions.
Control Loop Foundation - Batch And Continous ProcessesEmerson Exchange
This presentation, by Emerson's Terry Blevins and Mark Nixon, is a guide for engineers, managers, technicians, and others that are new to process control or experienced control engineers who are unfamiliar with multi-loop control techniques.
Their book is available in the ISA Bookstore at: http://emrsn.co/1E
Guidelines for Setting Filter and Module Execution RateEmerson Exchange
The is a recording of a presentation given at Emerson Exchange 2010. Basic guidelines are presented on how to set filtering to avoid aliasing. Also, information is provided on how to set control execution rate to achieve best performance.
Wireless Measurement and Control - AIChE New OrleansJim Cahill
Wireless Measurement and Control - Opportunities for Diagnostics Process Metrics Inferential Measurements and Eliminating Oscillations
Presented by Greg McMillan on March 15, 2011.
This is a general idea discussion, how we can improve our control methods by adding some control elements in conventional control loops (specially in solid fuel boilers)
Master-Trol is a electronic water management software system used to regulate water usage, provide complete control over the use of water fixtures in a facility and provide reports on water consumption.
For more information, please visit www.acorneng.com
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems.
New Kids on the I/O Block - Transferring Process Control Knowledge to Millenn...Jim Cahill
Presented at 2014 Emerson Exchange conference by Danaca Jordan and Jim Cahill.
As retirement rates accelerate in Western nations, efficiently transferring knowledge and lessons learned to new instrumentation and automation professionals grows in importance. Given generational differences in learning styles and limited spare time to develop training, what are some effective ways to accomplish this? A Boomer and a Millennial collaborate to share practical methods to take back with you.
Social Media and Collaboration in Automation and ManufacturingJim Cahill
Presented at the ARC Industry Forum in Orlando, Florida. The presentation highlights the important of surfacing expertise to make it findable and expanding your social network connections.
Social Media for Process Automation - Why?Jim Cahill
Some reasons process automation suppliers may want to consider the use of social media in their business efforts. Presented by Jim Cahill at the 2011 Valve Manufacturers Association Market Outlook Workshop (http://jimc.me/p5uOFC)
A presentation given to the ISA Executive Board on February 25, 2010. It describes opportunities for social media for businesses and institutions, the Emerson social media story, other companies successes, and pitfalls.
Process Profiling: Investigation And Prediction Of Process Upsets With Advanc...Jim Cahill
2009 HART Plant of the Year Award winner Mitsubishi Chemical Corporation Uses HART Technology to Detect Abnormal Situations and Failures before they Affect the Process
http://hartcomm.org/protocol/realworld/realworld_success_mitsubishi09.html
Advances In Digital Automation Within RefiningJim Cahill
Emerson's Tim Olsen presents to Argentinean refiners on the changes in automation technologies and how they are being applied to improve efficiency and reduce costs.
At Emerson Exchange 2009, Martin Berutti presents on the business benefits, requirements, and steps for building a Virtual DeltaV system with a virtual plant and I/O.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Thesis Statement for students diagnonsed withADHD.ppt
Isa saint-louis-exceptional-opportunities-short-course-day-1
1. ISA Saint Louis Short Course Dec 6-8, 2010 Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements - Day 1
2.
3.
4.
5.
6.
7.
8. (4) - Resonance Top Ten Concepts For all of you frequency response and Bode Plot Fans 1 Ultimate Period 1 1 Faster Tuning Log of Ratio of closed loop amplitude to open loop amplitude Log of ratio of disturbance period to ultimate period no attenuation of disturbances resonance (amplification) of disturbances amplitude ratio is proportional to ratio of break frequency lag to disturbance period 1 no better than manual worse than manual improving control
9.
10. The attenuation of oscillations can be estimated from the expression of the Bode plot equation for the attenuation of oscillations slower than the break frequency where ( f ) is the filter time constant, electrode or thermowell lag, or a mixed volume residence time Equation is also useful for estimating original process oscillation amplitude from filtered oscillation amplitude to better know actual process variability (measurement lags and filters provide a attenuated view of real world) (5) Attenuation Top Ten Concepts
11.
12. (6) Sensitivity- Resolution Top Ten Concepts Sensitivity o x x o x o o o o o o o o o x x x x x x x x Actual Transmitter Response True Process Variable Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0.00% 0.09% 0.08% 0.07% 0.06% 0.05% 0.04% 0.03% 0.02% 0.01% 1.00%
13. (6) Sensitivity- Resolution Top Ten Concepts Resolution Digital Updates o o o o o o o o o o x x x x x x x x x x o x Actual Transmitter Response True Process Variable 0 1 2 3 4 5 6 7 8 9 10 0.00% 0.09% 0.08% 0.07% 0.06% 0.05% 0.04% 0.03% 0.02% 0.01% 1.00% Process Variable and Measurements
14.
15. (7) Hysteresis-Backlash Top Ten Concepts Hysteresis Hysteresis Digital Updates Process Variable and Measurements Actual Transmitter Response True Process Variable 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 0 1 2 3 4 5 6 7 8 9 10 0 x x x x x x x x x x x x x x x x x x x x
16. (7) Hysteresis-Backlash Top Ten Concepts Backlash (Deadband) Deadband is 5% - 50% without a positioner ! Deadband Signal (%) 0 Stroke (%) Digital positioner will force valve shut at 0% signal Pneumatic positioner requires a negative % signal to close valve
17.
18. (8) Repeatability-Noise Top Ten Concepts Official definition of repeatability obtained from calibration tests Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 Repeatability 0 0 0 0 0 0 0 0 0 0 Actual Transmitter Response True Process Variable
19. (8) Repeatability-Noise Top Ten Concepts Practical definition of repeatability as seen on trend charts Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% Repeatability 0 x 0 0 0 0 0 0 0 0 0 0 x x x x x x x x x x Actual Transmitter Response True Process Variable
20. (8) Repeatability-Noise Top Ten Concepts Noise as seen on trend charts Process Variable and Measurements Digital Updates 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 0 x x x x x x x x x x x Noise Actual Transmitter Response True Process Variable
21.
22. (9) Offset-Drift Top Ten Concepts Offset (Bias) 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 Digital Updates 0 1 2 3 4 5 6 7 8 9 10 Process Variable and Measurements Bias Actual Transmitter Response True Process Variable x x x x x x x x x x 0
23. (9) Offset-Drift Top Ten Concepts Drift (Shifting Bias) Process Variable and Measurements Months 0 1 2 3 4 5 6 7 8 9 10 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 0 Actual Transmitter Response True Process Variable x Drift = 1% per month x x x x x x x x x x
24.
25. (10) Nonlinearity Top Ten Concepts 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 0 0 0 0 0 0 0 0 0 0 Digital Updates 0 1 2 3 4 5 6 7 8 9 10 Process Variable and Measurements Nonlinearity Actual Transmitter Response True Process Variable x x x x x x x x x x x 0
26. Accuracy and Precision Top Ten Concepts Good Accuracy and Good Precision 2-Sigma Bias 2-Sigma True and Measured Values Frequency of Measurements True Value Measured Values Good Accuracy and Poor Precision 2-Sigma 2-Sigma Bias True and Measured Values True Value Measured Values Frequency of Measurements Poor Accuracy and Good Precision 2-Sigma Bias 2-Sigma True and Measured Values True Value Measured Values Frequency of Measurements Poor Accuracy and Poor Precision 2-Sigma 2-Sigma Bias True and Measured Values True Value Measured Values Frequency of Measurements
27. Self-Regulating Process Open Loop Response Time (seconds) % Controlled Variable (CV) or % Controller Output (CO) CO CV o p2 K p = CV CO CV CO CV Self-regulating process open loop negative feedback time constant Self-regulating process gain (%/%) Response to change in controller output with controller in manual observed total loop deadtime o or Maximum speed in 4 deadtimes is critical speed Improving Dynamics
28. Integrating Process Open Loop Response Maximum speed in 4 deadtimes is critical speed Improving Dynamics Time (seconds) o K i = { [ CV 2 t 2 ] CV 1 t 1 ] } CO CO ramp rate is CV 1 t 1 ramp rate is CV 2 t 2 CO CV Integrating process gain (%/sec/%) Response to change in controller output with controller in manual % Controlled Variable (CV) or % Controller Output (CO) observed total loop deadtime
29. Runaway Process Open Loop Response Response to change in controller output with controller in manual o Noise Band Acceleration CV CO CV K p = CV CO Runaway process gain (%/%) % Controlled Variable (CV) or % Controller Output (CO) Time (seconds) observed total loop deadtime runaway process open loop positive feedback time constant For safety reasons, tests are terminated after 4 deadtimes or Maximum speed in 4 deadtimes is critical speed Improving Dynamics ’ p2 ’ o
30.
31. Phase Shift ( ) and Amplitude Ratio (B/A) A B time phase shift oscillation period T o If the phase shift is -180 o between the process input A and output B , then the total shift for a control loop is -360 o and the output is in phase with the input (resonance) since there is a -180 o from negative feedback (control error = set point – process variable). This point sets the ultimate gain and period that is important for controller tuning. Improving Dynamics For frequency response and Bode plot fans
32. Basis of First Order Approximation = Tan -1 ( ) negative phase shift (as approaches infinity, approaches -90 o phase shift) t = (-360 T o time shift B 1 AR = ---- = ----------------------- amplitude ratio A [1 + ( ] 1/2 Amplitude ratios are multiplicative (AR = AR 1 AR 2 ) and phase shifts are additive ( ) asis of first order approx method where gains are multiplicative and dead times are additive Improving Dynamics For a self-regulating process
33. Loop Block Diagram (First Order Approximation) p1 p2 p2 K pv p1 c1 m2 m2 m1 m1 K cv c c2 Valve Process Controller Measurement K mv v v K L L L Load Upset CV CO MV PV PID Delay Lag Delay Delay Delay Delay Delay Delay Lag Lag Lag Lag Lag Lag Lag Gain Gain Gain Gain Local Set Point DV First Order Approximation : o v p1 p2 m1 m2 c v p1 m1 m2 c1 c2 (set by automation system design for flow, pressure, level, speed, surge, and static mixer pH control) % % % Delay <=> Dead Time Lag <=>Time Constant For integrating processes: K i = K mv (K pv / p2 ) K cv 100% / span Hopefully p2 is the largest lag in the loop Improving Dynamics K c T i T d
34.
35. Ultimate Limit to Loop Performance Peak error is proportional to the ratio of loop deadtime to 63% response time (Important to prevent SIS trips, relief device activation, surge prevention, and RCRA pH violations) Integrated error is proportional to the ratio of loop deadtime squared to 63% response time (Important to minimize quantity of product off-spec and total energy and raw material use) For a sensor lag (e.g. electrode or thermowell lag) or signal filter that is much larger than the process time constant, the unfiltered actual process variable error can be found from the equation for attenuation Total loop deadtime that is often set by automation design Largest lag in loop that is ideally set by large process volume Improving Dynamics
36. Disturbance Speed and Attenuation Effect of load disturbance lag ( L ) on peak error can be estimated by replacing the open loop error with the exponential response of the disturbance during the loop deadtime For E i (integrated error), use closed loop time constant instead of deadtime Improving Dynamics
37. Effect of Disturbance Lag on Integrating Process Periodic load disturbance time constant increased by factor of 10 Adaptive loop Baseline loop Adaptive loop Baseline loop Primary reason why bioreactor control loop tuning and performance for load upsets is a non issue! Improving Dynamics
38. Accessing On-Demand and Adaptive Tuning Click on magnifying glass to get detail view of limits and tuning Click on Duncan to get DeltaV Insight for “On-Demand” and “Adaptive” tuning Improving Dynamics
39.
40.
41.
42. Contribution of Each PID Mode Improving Tuning - Part 1 Contribution of Each PID Mode for a Step Change in the Set Point ( and ) CO 2 = CO 1 SP seconds/repeat CO 1 Time (seconds) Signal (%) 0 kick from proportional mode bump from filtered derivative mode repeat from integral mode
43. Reset Gives Operations What They Want SP PV IVP 52 48 ? TC-100 Reactor Temperature steam valve opens water valve opens 50% set point temperature time PV Should steam or water valve be open ? Improving Tuning - Part 1
44. Open Loop Time Constant (controller in manual) CO Time (seconds) Signal (%) 0 o Dead Time (Time Delay) p Open Loop (process) Time Constant (Time Lag) CV SP Controller is in Manual Open Loop Error E o (%) 0.63 E o Improving Tuning - Part 1
45. Closed Loop Time Constant (controller in auto) CO Time (seconds) Signal (%) 0 o Dead Time (Time Delay) c Closed Loop Time Constant (Time Lag) Lambda ( ) CV SP Controller is in Automatic SP (%) 0.63 SP Improving Tuning - Part 1
46. Conversion of Signals for PID Algorithm To compute controller tuning settings, the process variable and controller output must be converted to % of scale and time units of deadtimes and time constants must be same as time units of reset time and rate time settings! Improving Tuning - Part 1 Sensing Element Control Valve AO PID SCLR AI SCLR SCLR % % % SUB CV SP % CO OUT (e.u.) Process Equipment Smart Transmitter PV - Primary Variable SV - Second Variable* TV - Third Variable* FV - Fourth Variable* PV (e.u.) PID DCS MV (e.u.) The scaler block (SCLR) that convert between engineering units of application and % of scale used in PID algorithm is embedded hidden part of the Proportional-Integral-Derivative block (PID) Final Element Measurement * - additional HART variables PV (e.u.)
47. Practical Limit to Loop Performance Peak error decreases as the controller gain increases but is essentially the open loop error for systems when total deadtime >> process time constant Integrated error decreases as the controller gain increases and reset time decreases but is essentially the open loop error multiplied by the reset time plus signal delays and lags for systems when total deadtime >> process time constant Peak and integrated errors cannot be better than ultimate limit - The errors predicted by these equations for the PIDPlus and deadtime compensators cannot be better than the ultimate limit set by the loop deadtime and process time constant Open loop error for fastest and largest load disturbance Improving Tuning - Part 1
48. Implied Deadtime from Slow Tuning Slow tuning (large Lambda) creates an implied deadtime where the loop performs about the same as a loop with fast tuning and an actual deadtime equal to the implied deadtime ( i ) For most aggressive tuning Lambda is set equal to observed deadtime (implied deadtime is equal to observed deadtime) Money spent on improving measurement and process dynamics (e.g. reducing measurement delays and process deadtimes) will be wasted if the controller is not tuned faster to take advantage of the faster dynamics You can prove most any point you want to make in a comparison of control system performance, by how you tune the PID. Inventors of special algorithms as alternatives to the PID naturally tend to tune the PID to prove their case. For example Ziegler-Nichols tuning is often used to show excessive oscillations that could have be eliminated by cutting gain in half Improving Tuning - Part 1
49. Effect of Implied Deadtime on Allowable Digital or Analyzer Delay In this self-regulating process the original process delay (dead time) was 10 sec. Lambda was 20 sec and the sample time was set at 0, 5, 10, 20, 30, and 80 sec (Loops 1 - 6) The loop integrated error increased slightly by 1%*sec for a sample time of 10 sec which corresponded to a total deadtime (original process deadtime + 1/2 sample time) equal to the implied deadtime of 15 seconds. http://www.modelingandcontrol.com/repository/AdvancedApplicationNote005.pdf sample time = 0 sec sample time = 5 sec sample time = 10 sec sample time = 20 sec sample time = 30 sec sample time = 80 sec Effect depends on tuning, which leads to miss-guided generalities based on process dynamics Improving Tuning - Part 1
50. Lambda Tuning for Self-Regulating Processes Self-Regulation Process Gain: Controller Gain Controller Integral Time Lambda (Closed Loop Time Constant) Lambda tuning excels at coordinating loops for blending, fixing lower loop dynamics for model predictive control, and reducing loop interaction and resonance Improving Tuning - Part 1
51. Lambda Tuning for Integrating Processes Integrating Process Gain: Controller Gain: Controller Integral (Reset) Time: Lambda (closed loop arrest time) is defined in terms of a Lambda factor ( f ): Closed loop arrest time for load disturbance Controller Derivative (Rate) Time: To prevent slow rolling oscillations: secondary lag Improving Tuning - Part 1
52. Fastest Possible Tuning (Lambda Tuning Method) For max load rejection set lambda equal to deadtime Substitute Into Tuning for max disturbance rejection (Ziegler Nichols reaction curve method gain factor would be 1.0 instead of 0.5) For setpoint response to minimize overshoot Improving Tuning - Part 1
53. Near Integrator Approximation (Short Cut Tuning Method) For “Near Integrating” gain approximation use maximum ramp rate divided by change in controller output The above equation can be solved for the process time constant by taking the process gain to be 1.0 or for more sophistication as the average ratio of the controlled variable to controller output Tuning test can be done for a setpoint change if the PID gain is > 2 and the PID structure is “ PI on Error D on PV” so you see a step change in controller output from the proportional mode Improving Tuning - Part 1
54. Fastest Controller Tuning ( ultimate oscillation method*) K c K u T i = 1.0 * u T d = 0.1 u For integrating processes or for self-regulating processes where p >> o , double the factor for reset time (0.5 => 1.0) and add rate time if the process noise is negligible. The oscillations associated with quarter amplitude decay is about ½ the ultimate gain. Thus if we use quarter amplitude decaying oscillations for the test, we take ½ of the controller gain that caused these oscillations to get ¼ of the ultimate gain These tuning equations provide maximum disturbance rejection but will cause some overshoot of setpoint response Improving Tuning - Part 1 * - Ziegler Nichols method closed loop modified to be more robust and less oscillatory
55. Fastest Controller Tuning (reaction curve method*) For runaway processes: For self-regulating processes: For integrating processes: Near integrator ( p2 >> o ): Near integrator ( ’ p2 >> o ): Deadtime dominant ( p2 << o ): Improving Tuning - Part 1 These tuning equations provide maximum disturbance rejection but will cause some overshoot of setpoint response * - Ziegler Nichols method closed loop modified to be more robust and less oscillatory
56. Ultimate Period and Ultimate Gain Improving Tuning - Part 1 Time (min) Measurement (%) Ultimate Gain is Controller Gain that Caused these Nearly Equal Amplitude Oscillations (K u ) Set Point Ultimate Period T u 0 If p o then T u If p o then u
57. Damped Oscillation - (Proportional Only Control) Time (min) Measurement (%) Offset 110% of o Quarter Amplitude Period T q 0 Improving Tuning - Part 1 Set Point
58.
59.
60.
61.
62. On-Demand Tuning Algorithm Time (min) Ultimate Period T u 0 Set Point d a Ultimate Gain 4 d K u = e n e = sq rt (a 2 - n 2 ) If n = 0, then e = a alternative to n is a filter to smooth PV Signal (%) Improving Tuning - Part 2
71. Output comes off high limit at 36.8 o C 0.30 o C overshoot Bioreactor Adaptive Tuning Gain 40 Reset 500 Improving Tuning - Part 2
72. Output comes off high limit at 35.9 o C 0.12 o C overshoot Bioreactor Adaptive Tuning Gain 40 Reset 5,000 Improving Tuning - Part 2
73. 0.13 o C overshoot Output comes off high limit at 36.1 o C Bioreactor Adaptive Tuning Gain 40 Reset 10,000 Improving Tuning - Part 2
74. 0.20 o C overshoot Output comes off high limit at 36.4 o C Bioreactor Adaptive Tuning Gain 40 Reset 15,000 Improving Tuning - Part 2
75. 0.11 o C overshoot Output comes off high limit at 36.1 o C Bioreactor Adaptive Tuning Gain 80 Reset 15,000 Improving Tuning - Part 2
76.
77. MIT Anna India University Lab Setup Improving Tuning - Part 2 http://www.controlglobal.com/articles/2010/LevelControl1002.html
78. Improving Tuning - Part 2 Gravity discharge flow makes the level response self-regulating (increase in level head increases flow through discharge valve) Increase in cross sectional area with level increases process time constant making process response slower Conical Tank Detail
79. Improving Tuning - Part 2 Conical Tank Linear Level Controller Performance
85. Nonlinear Control Valve Lab Improving Tuning - Part 2 Process gain is approximately proportional to flow for equal percentage flow characteristic
86. Nonlinear Control Valve Lab Improving Tuning - Part 2 Identification Out Limit that sets deadzone should be set approximately equal to valve deadband and stick-slip near closed position
If the temperature is below set point, should the steam or water valve be open? Based on looking at a faceplate or digital value of temperature on a graphic display, the operator will expect the steam valve to be open. Reset will work towards this end. However, the proper position of the control valves depends upon the trajectory of the process variable (PV). If the temperature is rapidly increasing with a sharp upward slope, the coolant valve should be open. Gain and rate action will recognize the approach to the set point and position the valves correctly to prevent overshoot. In contrast, reset has no sense of direction, and sacrifices long term results for short term gratification, much like corporate policies that cater to Wall Street. The human tendency to be impatient and not visualize the projected response (especially if it is slow or noisy) results in too much reset and not enough gain and rate action.
The offset from proportional only control is inversely proportional to the controller gain. For level and temperature loops, where the controller gain can be set high, very little reset action is need to eliminate offset..