Modeling and Control

Exceptional Opportunities in Process Control - Articles and Books

Mon, 09 Nov 2009 17:44:36 -0600

After all is said and done, articles and books have been the main method of advancing and sharing the technology for industrial process control.

I don't know of an undergraduate degree in process automation. Chemical, electrical, mechanical, and systems engineering programs offer an undergraduate course or two on process control. However, the typical university control course needs to spend most of the time on Laplace transforms, frequency response, and state-space to provide a theoretical understanding and groundwork for graduate courses. Outside of chemical engineering the focus is more on set point response and signal noise for servo mechanism and aerospace control. Consequently, the student doesn't learn about the critical characteristics of control for the process industry where nonlinearities, deadtime, valve stick-slip, unmeasured load disturbances, and incredibly long time frames are the cause of most tuning and control loop performance problems. Throw into the mix the unknown features of proprietary PID algorithms, and you have a script for islands of expertise. I personally like tropical islands so maybe this is OK. I could retire to one and conduct web based courses instead of doing cross word puzzles.

Courses may not be the whole answer considering that more than 80% of the details presented are forgotten. The PowerPoint slides often don't tell the real story. In my days, professors used the chalk board with only passing references to a book so my only record of knowledge is in notes long gone. Maybe the best way to make courses have a greater long term value is by providing labs for hands-on learning and refresher exercises, key memorable concepts, and resources for reference and further investigation. Audio should be combined with the presentation as exemplified by the slidecast of my Boston ISA presentation Exceptional Process Control Opportunities.

Considering that people don't have time to read books maybe courses and seminars and the structure of books themselves could provide better direction to areas of specific interest to solve problems. This is an argument for electronic books with interactive queries and demos.

For process automation, the articles and books written by practitioners are our best way of capturing and advancing the technology. Unfortunately users are not given the time or priority to write and most companies are reluctant to disclose information that could be considered to provide a competitive advantage for manufacturing. Consequently, suppliers of automation systems and services write most of the magazine articles and books on the practical application of process control. University professors write most of the journal articles and technical conference papers on the theoretical advancements in process control. The two groups don't talk much to each other. The use of industrial control systems for labs is one glimmering area of hope for the meeting of minds from universities and industry (see my last entry on "Exceptional Opportunities in Process Control - Expertise Development" and the June 1, 2009 entry "What I have Learned? - Bridging the Gap between Universities and Industry").

For me writing books was a way of organizing and expanding knowledge gained on the job. I found it allowed me to put technologies to bed (at least temporarily) so I could clear my head for the next area of expertise. My serious technical books in order of oldest to most recent publication date are: Axial and Centrifugal Compressor Control, Biochemical Measurement and Control, Continuous Control Techniques for Continuous Control Systems, Tuning and Control Loop Performance, Advanced Temperature Measurement and Control, Process/Industrial Instruments and Controls Handbook, Good Tuning - A Pocket Guide, Advanced pH Measurement and Control, Advanced Control Unleashed, New Directions in Bioprocess Modeling and Control, and The Essentials of Modern Measurements and Final Elements. My favorite book, which is a mostly serious collection of case histories written in a humorous way, is A Funny Thing Happened on the Way to the Control Room. My mostly humorous books in order of oldest to most recent publication date are: How to Become an Instrument Engineer - The Making of a Prima Donna, Logical Thoughts at 4:00 am, How to Become an Instrument Engineer - Part 1.523, The Life and Times of an Automation Professional - an Illustrated Guide, and The Funnier Side of Retirement for Engineers and People of the Technical Persuasion. The last two books were written solely for comic relief.

While I had to largely write the books on my own time (except for the last serious one), the companies I worked for were supportive in terms of approval and recognition. In the end I expect books helped me along with my heroes Shinskey and Liptak to be the first group of inductees into Control magazine's Process Control Hall of Fame.

I think the following message titled "Why Books" from Ted Stillwell who is of the same vintage as me concisely offers "memories of the way we were."

Because I learned process control on the job books provided the only formal learning environment. Starting with the first treatment plant, with a control panel that would not fit through the door, I began my knowledge quest about instruments and process control. Chemical Engineering published Process Automation a 14-Part Series. My first book purchase was Liptaks' Instrument Engineers' Handbook that I read commuting back and forth to the office. The process control companies offered a great training ground for young engineers. Highly experienced application specialists at these companies wrote most of the articles and books on process control. I have five books by Shinskey, the most recent being Feedback Controllers for the Process Industries (McGraw-Hill 1994).

Exceptional Opportunities in Process Control - Expertise Development

Fri, 23 Oct 2009 16:58:41 -0600

Before my talk at the Boston ISA section meeting on Oct 20, I had the opportunity to interview Sarah Tremblay and Ted Stillwell, automation engineers for a company that designs water and wastewater treatment systems. Sarah has a degree in mechanical engineering and has been on the job for one month. Ted has over 40 years of experience in the process industry. Like me, Ted started out in construction so he got a lot of first hand experiences on what worked in the field. The interview was an informal discussion for an upcoming Control Talk column on "Expertise Development" probably with a more catchy title such as "The Future is Now."

When I started as an E&I design and construction engineer after graduating with a degree in engineering physics, I went to a 12 week instrument school. One of the attendees at the ISA talk says he knows a company that had a 9 month training program. Such on-the-clock courses and programs are rare. Are we missing the boat? Sarah effectively said "not really" because such an intensive and extended training would not mean much to a new engineer who has not developed a real feel for the job. Sarah is learning by being responsible for small parts of a project. She asks a lot of questions. She visits job sites and goes on panel checkouts with Ted to see how designs translate to actual installations. This is the time honored tradition of how expertise is developed on the job. In 5 to 10 years, you have a proficient engineer. In my case, my development was accelerated by being sent after instrumentation school to E&I field construction for 2 years for the building or renovation and startup of 5 production units. Since sending new engineers to E&I construction is not a widely viable option, what can be done to improve this process?

There are no easy answers. Courses in chemical, electrical, mechanical, and systems engineering should have more emphasis on process measurement and control as practiced in industry. Practitioners (especially recent graduates) should be invited to give guest lectures on case histories of process control improvements and the type of jobs in the process industry. It should be emphasized that regardless of whether the job is in engineering, research, or production, all engineers rely on the automation system to see, analyze, and interact with the process. You need to know how to understand the system's interface and functionality to take full advantage of the systems capability. Process control labs with industrial control systems should be an essential part of this learning experience. Many of the leading universities have taken this approach as described in the June 1, 2009 entry on this web site "What I have Learned? - Bridging the Gap between Universities and Industry."

Sarah made a good point that course labs can be too controlled. The script is fixed and the student doesn't have the opportunity to explore different scenarios and ideas, implying the falsehood that on-the-job situations are typically as uneventful. To help address this issue, I think these labs should be offered as a stand-alone course rather than in addition to a "hands on" experience to demonstrate points in a lecture course. I think the lab should consist of both a physical and a virtual plant for the same unit operation. The virtual plant would allow the student to take the operation and control system to places not practical to achieve because of time and equipment limitations.

This education process needs to ongoing. It should not stop with the new job. Since extended training programs may be too much too soon besides being impractical from a standpoint of cost and time in today's work place, periodic seminars and demonstrations with a virtual plant would seem to be the most effective approach. Case histories and updates on technological advances are essential. The seminars and labs can be conducted via the web if interaction between the presenter and attendee is not sacrificed. Companies need to provide the time and encouragement for ongoing education. The ISA Certification of Automation Professionals (CAP) should be part of the career plan. Participation in ISA should be part of growth process for both the individual and ISA. There should be a company library of the best books on process measurement and control (see next week's entry here for my short list). Users should be encouraged to publish to help solidify their experience and share it with the profession. I always learned something about my application in the process of having to describe the problem, considerations, concept, and solution. See my May 28, 2009 entry "What have I Learned - Writing" on what worked for me. Sarah with a minor in English is ideally situated for this endeavor.

Given that the education process takes years of on-the-job experience it is critical that companies hire new automation engineers now to insure the existing expertise is transferred before the expertise is gone. See my Control Talk Column series "Going, Going, Gone" Part 1 (August), Part 2 (September), and Part 3 (October) for a discussion with some key people from what is probably the best process control group in the USA.

Most of the experienced engineers here in the USA are members of AARP.

Exceptional Opportunities in Process Control - ISA Boston Presentation

Thu, 15 Oct 2009 17:31:22 -0600

I will be doing the presentation McMillanISABostonExceptionalOpportunities.pdf next week at the Boston ISA section meeting. I will be giving out 10 free copies of my book The Funnier Side of Retirement for Engineers and People of the Technical Persuasion to balance out the serious stuff.

When?
Tuesday, October 20, 2009
6:00 - 7:00 Reception and registration
7:00 - 8:00 Dinner
8:00 - 9:00 Presentation

Where?
Best Western, Waltham, MA
380 Winter Street, Waltham, Massachusetts, 02451-8700, US
Phone: 781/890-7800 Fax: 781/890-4937

Exceptional Opportunities in Process Control - Online Metrics

Fri, 09 Oct 2009 16:21:44 -0600

The opportunity afforded by online metrics is worth summarizing in this series even though it has been discussed in several entries on this website.

The need to cut costs has translated to an increased emphasis on process efficiency and the ability to justify software, hardware, and personnel. Increasingly these need to be hard benefits (e.g. reduction in raw material, downtime, and energy costs).

When I worked in process control improvement (PCI) in the technology department of a large chemical company, we had to show new benefits each year that were at least twice our salary to justify our job. By the end of the five year process control improvement effort we had 75 million dollars per year in savings documented. The PCI core group had 5 modeling and control specialists working with 20 or more process control engineers at key plants. The benefits reported depended upon the skills of particularly one person Glenn Mertz) who was extremely proficient in cost sheet analysis and working with operations and process technology.

Some companies are fortunate enough to have PCI as part of their culture as seen in the Control Talk Columns "Going, Going, Gone - Part 2" (September) and Part 3 (October) for examples. For many companies, benefits need to be reported in order for PCI and our profession to move forward or even exist. See the December 1 and 5, 2008 entries on this website "Past, Present, and Future of Automation - Part 5 (Benchmarking and Opportunity Assessment)" and Part 6 (Operator Interface) and the December 28, 2007 entry "Biggest Opportunities in Process Control Improvement - The Operator (Online Metrics) for more discussion of the aspects and importance of identifying and showing PCI benefits.

There are a lot of initiatives in the plant to improve plant operation by better operating procedures, equipment, and maintenance. All of these people take great pride in their work and are naturally eager to attribute better process operation to their efforts. Process technology often has the last say. The best way for PCI to get credit for improvement in plant operation is for the improvement and change to be visible in the data historian. A visible change in capacity, efficiency, or quality after a change in the process control system provides the documentation needed. If the PCI could be turned on and off, the correlation would be irrefutable but this is usually not practical. If no other events occurred when the PCI went online, a beginning of improved plant operation coinciding with the completion of the PCI, and a good explanation of cause and effect, will normally suffice for PCI to get credit. To help guide management and operations, comments should be entered in the historian and event makers for PCI provided.

PCI metrics for continuous process capacity are generally available from product flow measurements, downtime due to trips, and the time to startup or make a product grade transitions. PCI metrics for batch process capability can be generated from batch size, end point concentration, batch cycle time, and time in between batches. Quick and dramatic improvements in batch capacity have been achieved be the elimination of operator attention requests, manual actions, trips, and wait times for resource allocation (e.g. utility or charge systems), lab results, and reaction completion. Model predictive control and override control applications have been very successful for fed-batch processes. Reductions of 25% or more in batch cycle time are common for PCI. For a summary of some of the many possible batch control opportunities see BatchCycleTimeReduction.pdf from my PCI days.

PCI metrics for process efficiency are best expressed as a ratio of kilogram (pound) of input used (e.g. feed, fuel, reagent, and utility) per kilogram (pound) of product produced. For fuels, the numerator in the ratio may be expressed in thermal units, such as kilojoules (BTUs). For batch processes, the totalized input flow is divided by the batch size multiplied by the fractional product end point concentration. For continuous processes the instantaneous input flows are divided by intermediate or final product flow multiplied by the fractional product concentration. Synchronization of input flows to output flows can be done by the addition of a time constant equal to residence time and a time delay equal to the transportation delay. The flows can be totalized to compare shifts and periods of operation. Online process efficiency measurements require online or at-line analyzers or inferential measurements from first principle, neural network, polynomial, or statistical (e.g. PLS) models. These models in turn require flow measurements because nonlinear valve characteristics, backlash, and stick-slip make the use of controller outputs directly as model inputs ineffective and misleading. While reactant and fuel flows are typically measured, utility and reagent flows are often not. This short sightedness by plant projects (figuratively and literally), severely limits the ability to make improvements in the efficiency of use of these process inputs. I would wager a 10% reduction in the use of these inputs would more than pay for the flow meters. The old saying, you cannot control what you don't measure holds true for process efficiency. If I was a project manager, I would have a flowmeter on any input flow whose usage cost per year exceeded twice the installed cost of the flowmeter. I would at least provide the process connections for inserting a mobile wireless flowmeter. Where energy heat transfer rate calculations (e.g. heat removal rate as an inference of reaction rate) would be useful, I would install wireless RTD temperature transmitters on the streams entering and exiting the coils, exchangers, and jackets. Wireless transmitters allow the user to find during actual process operation the applications with the maximum benefit.

Exceptional Opportunities in Process Control - Live Process Flow Diagrams

Fri, 25 Sep 2009 17:05:30 -0600

The first document you have on a project is typically a process flow diagram (PFD). The PFD defines the process. It is the ultimate source of information and sets the plant performance and design. It is interesting to me that we really don't know how well existing operations match the PFD. When I have posed the question of what is really the mass flow, pressure, temperature, and density of a plant stream to university students and even seasoned engineers in industry, the usual reply is that the stream conditions are what is shown in the PFD. As humans we are naturally optimistic and want to think everything is as believed and stated. As engineers we are accustomed to numbers being accurate to several significant figures. Alas, if you had the knowledge of what is really going in the process there would be a rude awakening. While uncomfortable, the awareness leads to better process control improvements.

In the PFD and in chemical engineering courses, the plant is assumed to be at steady state. Of course this does not work for batch processes. Less obvious is that it doesn't work well for continuous processes with merging and diverging trains of equipment and recycle streams. Even if a plant was at steady state, I doubt it would be within 10% of the PFD design conditions on all of the PFD process variables due to non ideal and unknown effects in the process calculations or simulations that generated the PFD. Maybe things have changed a lot, but in my days working at a large chemical company, the process engineers manually updated personalized spreadsheets that attempted to close the material and energy balances (unless we are talking about nuclear reactions, energy and mass are conserved - neither created or destroyed).

What if a plant had a live online PFD? What if we had live online material and energy balances? What if we had temperature, pressure, mass flow, and inferential measurements of the composition in every important process stream?

Coriolis flowmeters offer a true mass flow measurement that does not depend upon composition, density, velocity profile, Reynolds number, or viscosity. The physics of the measurement afford a rangeability and accuracy that is unexcelled (for an excellent perspective see the article by Peter Ginn "Tt's the Physics!", InTech, Feb 1996). Coriolis also provides a direct density measurement, a tube temperature measurement, and when coupled with an accurate differential pressure transmitter (DP) for viscous fluids, an inferential viscosity measurement. In the last couple of years, major improvements have been made in Coriolis technology. For example, Coriolis meters can measure two phase flow and can infer void fraction. Meter sizes can be as small as 2 millimeters making them ideal for labs and pilot plants. For slurries and clingy sticky fluids, straight tubes and higher velocities can be used to prevent coatings and accumulation of material. Coriolis meters can potentially provide more accurate batch charges than weigh tanks because Coriolis meters retain a better long term installed accuracy than load cells since Coriolis does not suffer from drift or installation effects. For more information on Coriolis see the EssentialBookCoriolisExcerpt.pdf.pdf from the new ISA book Essentials of Modern Measurements and Final Elements

When a Coriolis meter is put on a stream, the only process variable missing for a live online PFD is pressure, which could be easily added via a wireless pressure transmitter. For streams with acids and bases, wireless conductivity and pH transmitters could provide additional information on stream composition. For example, in absorbers for CO2 capture, wireless pH and conductivity measurements in concert with a Coriolis density and temperature measurement can provide inferential measurements of solvent concentration and CO2 loading important for optimizing absorber flow distribution.

There is a lot of talk about online process metrics but as far as I can see, what is done is loop metrics principally on process variability. A live PFD would enable online process efficiency metrics (e.g. yield) for each unit operation besides tighter mass balances. The stream variables would also lead to better data analytics and prediction of product quality.

Exceptional Opportunities in Process Control - New Sensor and Valve Technologies

Tue, 15 Sep 2009 16:28:51 -0600

I spent the first 7 years of my career in instrument design and construction. After being responsible for the calibration, installation, and commissioning of instruments for a half dozen plants in the 1970s, I became painfully aware that the actual performance of the measurements and control valves was largely unknown. These were the days before the advent of smart instrumentation. We didn't know the effect of stiction and backlash on valve position or the effect of impulse line, process and ambient conditions on sensors. We didn't know what was the installed accuracy of measurement or if a valve or measurement had a timely and sensitive response. We shifted set points and just shook our heads when the material and energy balances did not close. Since we were mostly interested in capacity we just pushed on to make more product. Operating efficiency and turndown were not as much an issue, which was fortunate because we didn't have the spectrum and accuracy of instruments for knowing process performance. The time I spent in the 1980s working on pH, furnace pressure, and compressor surge loops were the ultimate test of sensor and valve sensitivity and speed. My perspective on the importance of the field devices was solidified in the 1990s, when I was part of a corporate wide process control improvement program, most of the opportunities involved tuning loops and adding feedforward control and loops for fed-batch operation. A lot of great ideas went by the "way side" because of missing or imprecise measurements and unresponsive valves.

An important point is that if you don't have the capability of determining actual capability and benefits of the automation system, projects will seek the lowest cost alternatives. A classic example of capital cost superseding performance was the proliferation of rotary piping valves that were posed as throttling valves by the addition of spool type positioners to modulate a piston actuator, linkages, and stem connections fundamentally designed for on-off service. The leakage specs and price were attractive. Deadband and resolution limit were not considered. Since the valve specification didn't require the valve actually move in response to the small changes in signal commonly incurred in a control loop and there was no position feedback measurement either locally or remotely, the user did not know the real price paid. Aggravated by noisy flow measurements with poor turndown, increased process variability was attributed to mysterious sources. Without online loop metrics, there was little recognition of the deterioration in loop performance. Since the normal practice of testing whether a valve worked was to make 25% or larger changes in signal, instrument technicians and engineers where unaware that the valve did not respond to the small changes in controller output each scan. Putting a smart positioner on a piping valve with the feedback measurement of actuator shaft rather than ball or disk stem position in a rotary piping valve only added to the confusion. The actuator shaft would move in response to the positioner but the ball or disk did not due to extensive seal friction, ball or disk shaft windup, and backlash in the connection and linkages. It was only after actual tests in the flow labs of control valve manufactures was the true cost of these valve recognized. The publication of the lab test results and the subsequent ISA standards developed on valve step testing, the availability of position feedback as a secondary process variable on digital signals, and the analysis of resolution (e.g. stick-slip) and deadband (e.g. backlash) lead to an increased awareness and hence dramatic improvement in valve dynamics.

Today we have smart transmitters and control valves with a rangeability, resolution, and sensitivity that is an order of magnitude better than the typical fare of the last century. A combination of embedded intelligence and new sensor, transmitter, valve, and positioner technology have resulted in dramatic improvements. Combined with the ability to have additional process variables, diagnostics, and alerts reported to the control room by digital signals and the mobility afforded by wireless communication, we can increase the spectrum and flexibility of the field automation system including finding the optimum locations for process analysis and control. Doors will open for online data analytics, process performance metrics (e.g. energy, quality, and yield) and increased opportunities for basic and advanced control improvements to address the increasing needs of process efficiency, flexibility, and rangeability. My recent Control Talk column "Downturn Turndown" digs into the increased importance of sensor and valve performance with of course a top ten list to cap it off.

Recently it was realized that research and development could greatly benefit from the advanced performance, intelligence, and historization of smart industrial automations systems. The future is best exemplified by the lab optimized industrial distributed control system with industrial pH, dissolved oxygen, pressure, temperature, and mass flow measurements for bench top and pilot plant bioreactors that was pioneered by Broadley-James Corporation. The portability and reduced installation cost of wireless instrumentation increase the already significant advantages of moving advanced industrial automation system capability upstream in the commercialization process.

The foundation of a process automation system is the measurements and final elements. If you don't get these right not much else matters. Measurements provide the only window into the process and final elements provide the only means of affecting the process. The height of the pyramid consisting of increasingly more advanced layers of process analysis and control depends upon the integrity and breadth of the foundation. The goal of the book I just finished is to create a foundation where the sky is the limit for automation. The book royalties go to the Center for Energy and Environmental Resources at the University of Texas where tests are being conducted on the use of wireless conductivity, flow, pH, pressure, and temperature measurements for carbon dioxide capture research.

The new book titled Essentials of Modern Measurements and Final Elements makes no assumptions other than the reader has some technical background. In Chapter 1 Modern Measurement Fundamentals, special care has been taken to explain technical terms and concepts on the use and performance of measurements in the process industry. There is a special emphasis on the advances in wireless instrumentation and communication. Chapters 2 through 6 focuses on the details needed for the best implementation of specific types of measurements that would be used on automation upgrade and new plant projects today in the process industry. Chapter 7 on Final Element Fundamentals follows an approach similar to Chapter 1 in assuming no industrial experience so the material on control valves, dampers, guide vanes, and variable speed drives is beneficial to students and new employees. Chapter 8 gets into the details on the types of control valves that are used in 95% of the applications in the chemical and petrochemical industry. The book concludes with the latest details on WirelessHART automation systems in Chapter 9. The questions at the end of each chapter are designed to stimulate the thought process involved for a successful application.

Exceptional Opportunities in Process Control - Sample Time

Wed, 09 Sep 2009 14:03:32 -0600

I hesitated at first to include sample time as one of the exceptional opportunities in process control because in most loops it is not issue. Then I realized I should give my perspective on the effect of sample time for the following reasons:

(1) Since we live in a digital world, sampled data is the norm. Just from the volume of applications, the opportunity is large

(2) There are no clear guidelines for various types of process control applications

(3) In some applications conventional sample times can cause severe safety and performance issues

(4) In most cases the tuning of the controller dictates that sample times could be significantly slower. If DCS module execution times and wireless communication time intervals could be increased, controller loading is reduced and wireless battery life is prolonged, respectively

(5) If we want more at-line analyzers to provide measurements of stream compositions that tell us what is really going on in the process and offer the opportunity for a more advanced level of control, we need to understand and address sample processing and analyzer cycle times

(6) If we want to move to more wireless measurement that give us the flexibility and intelligence for process control improvement, we need to understand and address wireless communication intervals

I am considering sample time as the time between updates in sampled data in the broadest sense. The following discussion should be useful for determining whether DCS scan or module execution times, wireless communication time intervals, model predictive control execution time, and at-line analyzer cycle time will affect control system performance.

If you are pressed for time you can skip the discussion below and just check out ProcessControlSampleTimes.pdf

There is considerable confusion as to when sample times affect the ability of a control system to compensate for unmeasured disturbances. The following is my quick attempt to provide some concepts to sort out fact from fiction and provide some guidance.

The performance of a control loop depends upon the tuning. Specifically, the peak and integrated errors are inversely proportional to the controller gain. The peak error is not affected much by the integral time setting. However the integrated error is proportional to the integral time. Thus, a loop with good dynamics can be made to perform as poorly as a process with bad dynamics by sluggish tuning. The effect of slow sample times is hidden by large integral times or small controller gains. Thus, it is critical for any comparison, that tuning criteria be specified. In fact there is an implied deadtime as a result of the tuning of the loop as derived and discussed in Advanced Application Note 5. The tuning of the controller puts a practical limit on how fast the sample time must be for the effect to be negligible.

If a controller is tuned for maximum performance, the peak error is proportional to the loop deadtime to process time constant ratio. The integrated error is proportional to the deadtime squared. These statements are strictly true only when the process time constant is large compared to the loop deadtime. The loop dead is the sum of final element deadtime (e.g. valve pre-stroke time delay, deadband, and sticktion), process deadtime (e.g. mixing, thermal, and transportation), automation deadtime (e.g. sensor lag, transmitter damping, and sample times), and small process time constants. All of the time constants smaller than the largest time constant become effectively deadtime in the first order plus deadtime approximation used in industry. Process and automation system dynamics places an ultimate limit on loop performance. There is a corresponding ultimate limit on the sample time.

The relationships between process dynamics (e.g. total loop deadtime), controller tuning, and loop performance is detailed in the Theory section in Chapter 2 of Advanced Control Unleashed, and Appendix C in New Directions in Bioprocess Modeling and Control. All of my books and many of my articles take advantage of the fundamental understanding gained from these relationships.

The effect of sample times can be accessed in terms of practical and ultimate limits on performance. Critical loops where peak errors can cause destruction or environmental releases such as compressor surge control, furnace pressure control, exothermic reactor temperature control, and RCRA pH control, the tuning is necessarily aggressive. As a result the practical limit is much closer to the ultimate limit. For a discussion of cases where exceptionally fast sample times are needed, checkout the April 2, 2007 entry "Analog Control Holdouts."

For excellent final elements, clean sensors, and transmitter damping settings of 0.2 sec, we can suggest practical and ultimate sample times for different types of processes with typical dynamics. The ultimate limit is set to be less than 1/10th of the sum of the minimum loop deadtime and process time constant with some consideration as to maximum practical controller gains to reduce valve cycling and noise amplification. For any loop with a control valve, the minimum loop deadtime is about 1 second for an unmeasured disturbance so the ultimate limit on sample time is about 0.1 second. The practical limit reflects current tuning practices. For integrating processes, the process time constant shown is the inverse of the integrating process gain (denoted by single exclamation point). The double exclamation point denotes a runaway (positive feedback) process time constant. Consultants says it is impossible to generalize but I think some guidance is helpful to the user with the realization there are always exceptions and the actual process dynamic and tuning should be identified by automated online tuners and adaptive controllers (e.g. DeltaV Insight). I didn't consider ultimate sample times slower than 60 sec. Note that slower sample times will affect the deadtime identified. A Rough Guide to DCS and Measurement (e.g. Wireless) Sample Times is offered in ProcessControlSampleTimes.pdf

For many digital devices the update is available near the beginning of the sample time (latency is negligible), which means the average deadtime from the sample time is about half the sample period. For at-line analyzers (field analyzers with automated sample systems), the result is not available until the end of the sample processing and analyzer cycle time, which translates to an average effective deadtime that is about 1.5 times the time interval between updates in the analyzer output signal.

The detrimental effect of sample time is greater than deadtime in that for continuous sources of dead time such as process transportation and mixing time delays and small process time constants, there is a continuous train of updates. For sampled data there are no intervening values. Consequently, the effects can be worse. For example, there is aliasing of oscillations where the indicated amplitude is smaller and the period is larger than actual. There can be jitter due to variations in latency and lack of synchronization of digital data that introduce variable time delays and noise for rapidly changing signals.

The PIDPLUS modification of the traditional PID developed for wireless applications helps the PID deal with the sample time from digital devices and communication, and at-line analyzers. The improvement is most dramatic for self-regulating processes but is also significant for integrating processes as seen in the tests documented in ControlStudiesPIDPLUS1.pdf. The PID-Plus algorithm also breaks the limit cycle from the resolution limit from the deadband setting for exception reporting of wireless devices because integral action is only done when there is a measurement update.

Exceptional Opportunities in Process Control - Integrating Process Tuning and Performance

Wed, 02 Sep 2009 16:29:25 -0600

Unlike self-regulating processes that will line at a steady state after disturbances have died out, integrating processes will ramp until a physical limit is hit. The ramping response is caused by the lack of negative feedback (e.g. self-regulation) in the process as defined in Advanced Application Note 4. In other words an increase in the process variable does not increase a counteracting effect to make the response bend over and reach a equilibrium.

The most common integrating process is level. Since the discharge flow is not appreciably affected by level (except for the rare case of gravity flow), any difference between the feed and discharge flows causes the level to ramp. The low limit is the vessel running dry and the high limit is the vessel spilling over or flooding a vent system.

Other common examples are

(1) Gas pressure control of columns, furnaces, and vessels when changes in operating pressure does not appreciably affect the vent flow rate

(2) Batch temperature control when changes in vessel temperature does not appreciably change the heat transfer rate

(3) Batch pH control when there is no reagent reaction or consumption or reagent concentration does not appreciably change reagent reaction or consumption rate

(4) Batch dissolved oxygen control when the change in oxygen absorbed does not appreciably change the oxygen transfer rate

(5) Batch product composition control when a change in product concentration does not appreciably affect side reaction or degradation rate

(6) Vessel solids concentration control when changes in solids concentration does not affect the evaporation or precipitation rate

(7) Bioreactor biomass or cell density control before the stationary and death phases

Many processes due to a long process time constant or large process gain, will appear to ramp because the steady state is beyond the time range or control region, respectively. What the user sees on the trend charts and what the controller sees as a response from the process variable is a ramp. These processes called "near-integrating" or "pseudo-integrating" processes are better analyzed and tuned as if they were integrating rather than self-regulating processes. Temperature control of any continuous process with a large residence time (volume/flow) can be treated as a "near-integrating" process.

Most of the more important loops have an integrating or "near-integrating" response. Furthermore the ramp rate (%/sec) for a % change in controller output (integrating process gain) is often incredibly slow. These slow ramp rates require exceptionally high controller gains and large integral times.

The test results for a single use bioreactor (SUB) with what would appear to be a small volume (100 liters), revealed an integrating gain of 0.000008 %/sec/%, that was 30 time slower than a bench top bioreactor. The SUB volume was about 30 times larger than the bench top bioreactor volume. The relative size of the volumes is a strong factor but the relative size of other parts such as heat transfer area play a role. This was the first time temperature control was tried on a SUB in this lab. Fortunately an adaptive controller was in service that identified the unexpectedly slower integrating process gain. The best response was achieved with a controller gain of 80 and an integral time of about 10,000 seconds. A Lambda factor of 0.05 was needed. The test results are shown in "BioreactorTemperatureTuningTestResults.pdf."

The principle opportunity for integrating processes is realizing and using higher controller gains and larger integral times. We tend to use too much integral action (too small of an integral or reset time) because we are impatient and integral action provides a continual driving action to eliminate error. We don't normally think of using higher gains because the problem of instability from high gains is drilled into us in all our courses and books on process control, our older measurement systems often gave flaky signals, and before we had structure and set point filter options, high controller gains caused the controller output to peg on a set point change. Properly installed smart transmitters with integral sensors and primary elements have a noise level that is low enough and a sensor sensitivity and repeatability high enough so that the amplification of small changes provides corrective actions rather than amplification of noise or extraneous actions. The proper use of the many PID parameter, control options, and structure today allows the user to minimize the disruption to the operator and other loops.

Most people don't realize there is a window of allowable controller gains. As I mentioned we all know too high of a gain causes instability. For many integrating processes, this controller gain is way above our comfort level (e.g. gain > 100). More often we run into the low limit for controller gain (e.g. gain < 10). Too low of a controller gain causes overshoot and slow rolling oscillations. The correction is non intuitive. You need to increase the controller gain. Even with a high gain and integral time and rate action, it is difficult to prevent overshoot with an integrating process unless you take a very slow approach by using a PID structure that provides no step change in the controller output on a set point (e.g. proportional and derivative action on PV and integral action on error). The overshoot and speed of approach problem was the primary motivation for the simple control strategy for making a temperature go as fast as possible and then stop right at set point as discussed in the article "Full Throttle Batch and Startup Response"

The Lambda tuning equations for integrating processes automatically makes the controller gain large enough to stay above the low limit in the window of allowable controller gains. This is accomplished by keeping the product of controller gain and integral time to larger than 4 divided by the integrating process gain as seen the last slide of "LambdaTuningEquations.pdf." However to get an acceptably fast enough response, Lambda factors much lower than the user is accustomed to must be used. Not shown is the fact that derivative action is helpful. The rate time should be set to the next largest time constant for a self-regulating process and the largest time constant in an integrating process. These rules are consistent for a "near-integrating" since the integrating process gain is the process gain divided by the largest process time constant leaving the next largest time constant as the one used to set the rate time.

Temperature control of exothermic reactors where the reaction rate increases with temperature and particle or crystal size control where the formation rate increases with particle or crystal size can have an integrating followed by a runaway (positive feedback) response where is it is critical to maximize the controller gain and integral time.

Post Retirement Key Points - Part 4 (2009 Articles)

Tue, 18 Aug 2009 16:19:44 -0600

My articles in 2009 are focused on pH and wireless measurement and control. Not listed below is an article planned for later this year on the use of wireless pH for inferential measurement of solvent concentration at the University Texas Research Campus pilot plant for carbon dioxide capture.

"Virtual Plant Provides Real Insights", Chemical Processing, Jan, 2009
"ImprovingpHSystemDesignandPerformance.pdf"

(1) Modeling and control in a virtual plant showed that the size of the neutralization vessels could be reduced from 40,000 to 10,000 gallons reducing the project capital costs by more than $500K for a strong acid and base system. The virtual plant was also able to detail mixing, reagent injection, and valve requirements

(2) Translation of the controlled variable from pH to percent reagent demand (X axis of the titration curve), provided faster recovery from upsets.

(3) It was expected that the resolution of the reagent valves needed to be exceptional. It was surprising how important resolution was for the feed valves. What would be normally considered a good resolution for the feed valves caused excessive deviations in the vessel pH. Stick-slip in the feed valves showed up as short term deviation rather than a limit cycle in the pH because of the feedback correction by the pH loop

(4) Innovative Methods of continuous and semi-batch mode offered maximum operational flexibility.

"Is Wireless Process Control Ready for Prime Time", Control, May, 2009

My time in spent building and starting up chemical plants, working in process labs, and dealing with pH measurement noise gave me a greater appreciation for the significance of being able to eliminate instrument wiring. This article offers my take on the value wireless and shows incredibly tight wireless bioreactor pH control. Some biopharmaceutical processes require control within 0.02 pH of set point for optimum operation. The pH control demonstrated in this wireless pH test on a bioreactor with a disposable liner (single-use-bioreactor) was an order of magnitude better than required, the tightest pH control I have ever seen. Most of the credit goes to new wireless PID algorithm and the exceptional capability of the pH electrode and wireless pH transmitter. Finally, the wireless measurement did not have the spikes exhibited by the wired pH transmitter from ground noise, showing that wireless can eliminate a significant source of noise.

"The Essentials of pH Measurement Design, Installation, Maintenance, and Improvement", ISA 55th International Instrumentation Symposium, League City, 2009

This paper is a chapter out of "The Essential Book" scheduled to be published in time for ISA Expo 2009 in Houston.

Post Retirement Key Points - Part 3 (2007 - 2008 Articles)

Mon, 10 Aug 2009 14:19:27 -0600

I am back from vacation. I am still feeling fine from a nice break from the heat of a book deadline and Austin's record temperatures. I was up north in Minnesota and Wisconsin where it was 25 degrees cooler. I happened across an exhibit of Cray computers in the Museum of Science and Technology in Chippewa Falls, the home of Cray Research, Inc. Samuel Cray attributed part of the company's success to a motto of "taking our jobs seriously but not taking ourselves seriously." Hopefully my Control Talk column is an example of this motto by combining a humorous look at ourselves with technical straight talk. A compilation of the column's comics was featured in the July issue of Control magazine in the online section "Out of Control Cartoons".

Then there are the outbursts of craziness designed to loosen us up such as The Funnier Side of Retirement for Engineers and People of the Technical Persuasion, which just won the ISA Raymond D Molloy Award as the best selling book in 2008. Since humor is derived from exaggeration of commonly recognizable traits, please don't buy this book if you want a detailed analytical realistic treatise. For this you can get any one of a dozen or more guides to retirement. If you like bizarre humor, this book may offer some laughs.

The following list of articles and associated papers in 2007 - 2008 are totally serious except for an occasional top ten list.

"Improve Control Loop Performance", Chemical Processing, Oct, 2007

(1) Nearly all control loops eventually affect the process by the manipulation of a flow via a control valve. Control loop performance depends upon valve performance.

(2) Valve specifications do not require a valve actually move in response to a change in signal. When valve performance has been considered, response time and rangeability are frequently the criteria. The real issues are valve resolution (sticktion) and deadband (backlash). If a properly selected and sized valve-actuator assembly has good resolution and sticktion, the valve will generally have good rangeability and response.

(3) Using a "state of the art" digital positioner can eliminate the positioner sensitivity problems prevalent in positioners for the last 50+ years but the positioner can be lying about valve performance if the feedback measurement is actuator shaft rather than ball or disk position in a rotary valve. Putting a digital positioner on a valve designed for on-off service and tight shutoff by a piping manufacturer is like putting makeup on a pig. On the other hand, putting a digital positioner on a valve designed by throttling service by a control valve manufacturer may be the best thing you can do for your loop.

(4) For pH control, the resolution of the control valve can determine the number of stages of neutralization needed.

"Virtual Control of Real pH", Control, Nov, 2007

"Advances in pH Modeling and Control", ISA 54th International Instrumentation Symposium, Pensacola, May, 2008

An online virtual plant can be adapted to match the actual plant by the simple innovative use of an integrated model predictive control (MPC). In this neutralization system, the influent acid concentration was quickly adapted to match the ratio of reagent to influent flow in the virtual plant to the actual plant. The virtual plant demonstrated of ability of model predictive control to replace fuzzy logic control for reagent optimization. An improvement in the kicker algorithm provided immediate savings of more than $100K per year in reagent cost.

"PAT Tools for Accelerated Process Development and Design", Bioprocess International, Process Design Supplement, Mar, 2008.

"Bioprocess Control: What the next 15 Years will Bring Part 2 - Process Modeling",
Pharmaceutical Manufacturing, June, 2008

Most process and control system improvements in bioreactors are set by biochemists and biochemical engineers in the research. A virtual plant running 500 times real time can complete a bioreactor batch in 15 minutes that would take several weeks in the lab or pilot plant. Virtual experimentation can accelerate process development and design. The integration of advanced control tools in the virtual plant can demonstrate the effectiveness of substrate and batch profile control. The results can justify additional online analytical measurements. The fast playback of virtual and actual plant batches in a minute or two offers incredible opportunities for online analysis via integrated data analytics and adaptive control tools. The potential benefits are faster commercialization, higher yields, and real time release.

"Unlocking the Secret Profiles of Batch Reactors", Control, July, 2008

The purpose of a batch reactor is to manufacture a product of a particular composition. The progression of the batch to the desired end point (the batch composition profile) is the most important indicator of batch performance. However, batch reactors rarely have any measurement of this profile. For chemical reactors, the main measurements indicative of the hidden profile of real interest are pressure, temperature, and feed flows. Multivariate statistical techniques such as Projection to Latent Structures (PLS) may be able to predict end points but the composition profile still remains a secret. If actual or inferential measurements of the profile are available, model predictive control can maximize the slope of the profile and hence the progression of the batch. The result is a faster batch for a given end point or a higher end point for a given cycle time. Also, the variability in batch profiles is transferred to feeds resulting in more repeatable batch profiles.

There is a misconception that biological processes are not as highly automated as chemical processes. Bioreactors generally have more control loops than a typical chemical reactor. Cell cultures have temperature, pressure, air flow, oxygen flow, inert flow, carbon dioxide flow, sodium bicarbonate flow, substrate flow, nutrient flow, pH, and dissolved oxygen control. Major advances in at-line composition measurements, such as the Nova Bioprofile Flex Analyzer combined with an auto sampler can provide measurements of substrates, nutrients, byproducts and cells every 4 to 12 hours depending upon the application. The Fogal Dielectric Spectroscopy probe can provide a measurement of the integrity of the cell membrane (cell viability). When combined with a turbidity measurement of cell density, the Fogale probe offers an online indication of live and dead cell concentrations.

One of the obstacles of online composition control is the time delay from the sample cycle time. The time in between samples for at-line analyzers can vary from an hour to a day. Fortunately, an unexpected side benefit of the enhanced wireless PID (developed to handle the larger and more variable time delays of wireless measurements) is exceptional control using measurements from at-line analyzers. The wireless enhanced PID has been shown to provide tight and stable control using at-line analyzers in specific studies for glucose control and in generic studies for continuous and batch processes. The results are documented in slides 29-34 of Interphex2009_Advances_In_Bioreactor_Modeling_and_Control.pdf. See the May 11, 2009 entry "What have I Learned - Cost and Source of Oscillations (Part 4)" for more details.

The new control algorithms (max slope MPC setting the enhanced wireless PID) coupled with new at-line and online analytical measurements will make bioreactor profile control common place leaving chemical reactor control even further behind. Are we going to let this happen?

Next week we conclude with the 2009 articles that include results of wireless control in a bioreactor with a disposable liner called a "Single Use Bioreactor" (SUB).

Post Retirement Key Points - Part 2 (2005 - 2006 Articles)

Fri, 24 Jul 2009 13:58:37 -0600

My publications are notorious as "no-fluff" zones. My articles "Life's Batch" and "Maximizing PAT Benefits from Bioprocess Modeling and Control" should have been a 5 part series. After 120 blogs, 84 Control Talk columns, and 14 articles since I retired from my full time job, you might think I might be running out of ideas. I wonder myself when I sit down to write but once I feel a flow with the music, the main constraint is time. There is always something to say even if it is just shedding more light on an old subject. It is kind of surreal since I am a quiet guy. As I get older I am going to have to make sure I don't repeat myself, repeat myself, repeat myself.

Here are the key points for my 2005 - 2006 articles

"Life's a Batch", Control, May, 2005
(Click "Download Now" button at end to get Equations and Figures)

1. The key to good batch temperature control is the secondary loop setup and tuning

2. An inlet or outlet secondary temperature loop linearizes the process gain of the primary batch temperature loop and makes the primary loop dynamics faster

3. An inlet jacket or coil temperature can correct for coolant disturbances before they appreciably affect the batch temperature

4. An outlet jacket or coil temperature can correct for heat transfer surface disturbances before they appreciably affect the batch temperature

5. The use of a heat exchanger in a recirculation loop instead of a jacket or coil creates a delayed integrating response in the secondary temperature loop that is problematic if much integral action is used (not discussed in this article)

6. The difference between an inlet and outlet jacket or coil temperature multiplied by coolant flow provides a measurement of heat release and hence reaction rate. The inlet temperature should be delayed by the transport time through the coils or jacket (Volume/flow) to match up the inlet time wise with the outlet temperature

7. If the jacket or coil flow rather than a makeup flow is throttled, the increase in the process gain and process delay of the secondary loop can causes oscillations

8. The secondary loop should be tuned with mostly gain action for a fast response otherwise disturbances start to affect the batch temperature and an exothermic reactor can develop a runaway response

9. Coolant valves should be judiciously sized sliding stem (globe) valves with digital positioners to reduce the limit cycles from stick-slip and deadband

10. Most batch temperatures will oscillate across the split range point because of the dramatic difference between the installed valve characteristic curves and the increase in sticktion near the closed position

11. Trim coolant valves should be considered to reduce oscillations around the split range point and provide fine adjustments (see the March 16 and March 24 entries on this site on the "Manipulation of Multiple Flows")

12. The integrating response of batch temperature will cause a limit cycle from deadband even if the secondary temperature loop has no integral action

13. A highly exothermic reactor can runaway if the secondary temperature measurement or heat transfer rate is too slow

14. To reduce the batch cycle time for to reach a batch temperature end point, the jacket and coil valve can be set wide open and a control strategy such as the following used where appropriate:

a. A temperature rate of change calculation multiplied by the deadtime triggers the shutoff or positioning of the coil or jacket valves. If the feeds are to continue or there is some residual heat generation, the batch temperature should be put in automatic (see 2006 article "Full Throttle Batch and Startup Response" for details)

b. A reactor temperature controller can throttle the reactant feed rates nut there may be an appreciable inverse response from the dilution and cooling effects of increasing a reactant feed rate

15. Model predictive control is more effective approach where there are multiple constraints for batch reactors being pushed beyond their nameplate capacity

16. Coriolis mass flow meters can correct of reactant concentration and provide a model of reaction product concentrations

17. Equations can estimate the ultimate gain of self-regulating, integrating, and runaway process for process gains, lags, and dead times and provide a deeper understanding of what affects performance and why batch reactor temperature loops require higher controller gains and lower integral times

18. The primary temperature controller integral time setting should be scheduled based on totalized feeds and the secondary temperature controller gain and integral time setting scheduled based on the position of split ranged valves

"What If? Virtual Plant Reality", Control, Aug, 2005
(Pages 3 and 4 of "How to Survive the Oncoming Train of Technology")

1. Process flow diagram (process design) simulations circa 2005 that are made dynamic

a. Can provide a reasonably accurate steady state process gain and the residence time based process lag time if the physical properties are well known

b. Generally do not model mixing lags, transportation delays, installed valve characteristics, valve backlash or sticktion, mixing or sensor noise, and sensor lags, or bubble or particle distribution and size

c. Have trouble simulating batch operations, startups, and shutdowns because equipment instantaneously go to equilibrium conditions and the program can develop numerical instabilities for extreme conditions and zero flows

d. Cannot possibly emulate all of the batch and loop control capability in a DCS and thus must relay upon being interfaced to a DCS which is problematic in terms of running faster than real time (synchronization and acceleration issues)

2. Dynamic simulations that focus on the dynamics of interest can focus on the details important for process control

"Model Predictive Control can Solve Valve Problem", Control, Nov, 2005

Advanced Application Note 002

I don't need to say anything here since it is covered in the application note and the March 16 and March 24 entries on this site on the "Manipulation of Multiple Flows." Dare I repeat myself?

"Maximizing PAT Benefits from Bioprocess Modeling and Control", Pharmaceutical Technology, IT Supplement, Nov, 2006

There are so many uses of a virtual plant it is mind boggling. Just search for Virtual Plant on this website. In particular, check out the Oct 8, 2008 entry "High Fidelity"

"Full Throttle Batch and Startup Response", Control, May 2006

This article shows a simple calculation when the reactor temperature will reach set point based on rate of change and deadtime can minimize the time to reach set point. The calculation is particularly appropriate for the integrating response encountered in a batch operation or in the startup of a continuous piece of equipment where the discharge flow has not started. It is important to remember for integrating processes, the controller output must be driven past the balance point (resting valve position) to make the process variable move. With self-regulating processes, you can go to the balance point directly but even here you get there faster if the output is initially drive past the balance point.

I really like blogging. The only reason the blogs are fewer these days is that my time is consumed with finishing up the "Essential Book" so it is available in time for ISA Expo. What free time I have is spent taking advantage of Austin being the "Live Music" capital.

Post Retirement Key Points - Part 1 (2003 - 2004 Articles)

Mon, 06 Jul 2009 12:59:49 -0600

As I reflected on my career, I reaffirmed that what drives me is gaining a deeper understanding and sharing what I have learned, hopefully with a few laughs along the way. Throughout my career I sought with an open mind the knowledge and insights of the leaders in process modeling and control. I then used simulations to rapidly explore process relationships and to prototype control improvements that incorporate process understanding. The knowledge prepared me to solve tough plant control problems.

During my career at Monsanto I wrote a bunch of articles in the 1980s for InTech on my time in the plants with some humor introduced to help make the material more accessible and memorable. These articles were compiled and published in the book A Funny Thing Happened on the Way to the Control Room available for viewing as an E-book in the April 3, 2009 list of my books on this website. This is my favorite book, I didn't write much in the way of articles or books in the 1990s. I was on the road most of the time.

When I retired from Monsanto-Solutia in 2001 (sans package), I taught at Washington University. The students were great but after the course and lab was developed, it became routine. Also, I felt isolated.

I tell people I flunked retirement. I moved to Austin in September 2004 and started a second career as a part time consultant at Emerson Process Management. This gave me a chance to keep up to date with the latest new tools besides continue my exploration of process control opportunities. Plus it felt like home since Monsanto and Fisher Controls were one for most of my career.

I have been blessed with access to the best minds. In Monsanto's Engineering Technology I got to work with the leaders in process modeling and control. Some went on to distinguished chairs at prestigious universities, several were inducted into the Process Control Hall of Fame, some served as presidents of ISA and AIChE, and others left to become the principal technical resources for leading simulation companies. Here in Austin in Applied Research I get to work with the brains behind DeltaV. Plus my second career is more balanced. Except for the spike in work this year, I take a total of 4 months off each year to travel to see relatives, friends, and neat places and to write books.

Key points of my articles written in my post retirement years provide a quick overview of what I have been doing. The entries on this website in July will focus on the dozen articles I have written since retiring from my full time job. Here are the articles from 2003-2004.

"Has Your Valve Responded Lately", Control, May, 2003
"What is Your Flow Control Valve Telling You", Control Design, May 2004

Putman publications decided to do an encore publication in a second magazine. Some nomenclature typos were corrected in the reissue of the article in Control Design.

1. Deadband originates from backlash in the linkage and connections between the actuator and the plug, disc, or ball. Stick-slip comes from friction in stem packing and seals around the sealing of the plug, disc, or ball for process isolation

2. Deadband from linkage and connection backlash and stick-slip from trim and packing friction create deadtime for slowly changing controller outputs

3. Deadband will create a limit cycle in any control system where there are two integrators in series, such as a PI controller on an integrating process (e.g. level)

4. For deadband, the limit cycle amplitude is the ratio of deadband to controller gain

5. For stick-slip, the limit cycle amplitude is the product of the open loop gain and the stick-slip

6. For both deadband and stick-slip, the limit cycle period is proportional to the controller integral time and inversely related to the controller gain

7. Large actuators can have a large stroking time for a large change in signal

8. The size of the changes signal typically used to checkout control valves will not reveal the deadband or stick-slip and make all but the largest valves look good

9. A volume booster can reduce the stroking time of big actuators but has a large deadband. The booster should be put on the positioner output to quickly drive through this deadband. The booster bypass must be opened enough to prevent fast cycling from the positioner output looking into the booster's small inlet volume

10. Unstable oscillations can break out for large disturbances when the integral action in process loop becomes faster than the valve response. The integral time must be greater than the product of the valve slewing rate, disturbance size, and controller gain. (Not mentioned in the article but frequently discussed on the this website is that position read back from digital positioners and the PID dynamic reset limit option can automatically prevent the controller output from outrunning the valve)

11. Limit cycles are attenuated (filtered or washed out) by vessels or columns. The ratio of the attenuated to original amplitude is proportional to the period of the oscillation and inversely proportional to the residence time (volume/flow)

12. The control valve with the best response is a sliding stem valve with a digital positioner. If one must use a rotary valve, avoid tight shutoff and high friction packing and use a diaphragm actuator with a short shaft and splined connections between the actuator shaft and the stem of ball, disc, or plug. Make sure the stem is cast with the ball, disc, or plug to avoid another connection with backlash

Postscript: Rotary valves designed by piping manufacturers have a lot of deadband and stick-slip as discussed in the July 2009 Control Talk column "Downturn Turndown" in Control magazine.

"The Next Generation - Adaptive Control Takes a leap Forward", Chemical Processing, September, 2004

1. Nearly all controllers are detuned (backed off from maximum performance) to some degree to provide a smooth response and to deal with the inevitable changes in the process dynamics

2. Older technology adaptive controllers had these undesirable features
a. The process had to be disturbed or oscillated (e.g. patter recognition)
b. The dynamics were embedded in tuning settings
c. No real insight as to where the process has been or where it is going
d. Tuning method was fixed
e. Always playing catch up even if same situation was seen a thousand times

3. The next generation adaptive controller can
a. Normal changes in a controller's set point or manual output are used
b. The process dynamics are displayed and historized
c. From changes in the process dynamics, plant problems can be diagnosed
d. Several tuning methods are available
e. Tuning settings identified can be scheduled for preemptive action

4. "The information on changes in the process model may be directly used to monitor loop performance and to provide more intelligent diagnostics. The models can provide the dynamics for simulations and identify candidates for feedforward control and advanced control techniques. For example, loops dominated by a dead time or exhibiting disturbance models for multiple variables, are prime candidates for model predictive control. The dynamic process models in general can be used to create or adapt real time simulations for prototyping new control strategies, exploring "what if" scenarios, and training operators. Process gains that decrease or time constants that increase with feed totals are ripe for real time optimization of the run time between defrosting or cleaning and catalyst reactivation or replacement. The beauty of this route is the models and tuning settings are available from the adaptive controller for a higher level of control by a better knowledge of the topology"

"Advanced Control Smorgasbord - A Lot of Tasty Choices", Control, May, 2004

The online version is missing the following introductory sentences at the beginning of the first paragraph.

"By the time I was assigned to my first electronic control room project, some very smart engineers had already developed most of the techniques to exploit PID controllers.
Relative gain arrays and simple decoupling of the controller output were used to analyze and deal with interaction on a steady state gain basis. The outputs from PID controllers, whose process variable was a constraint variable, were sent to a signal selector to form an override control scheme to maximize or minimize a manipulated variable."

1. Previously, advanced process control (APC) required software packages at $100K a clip, separate computers, special interfaces, and consultants to do the studies and implementation. The total bill could easily approach or exceed a million dollars for a medium project, the biggest chunk being the consultant's time charges. Even a greater consideration was that the process knowledge to exploit or to just maintain the system disappeared when the consultants left the site

2. At the turn of the century, APC technologies were integrated into the basic process control system. License fees were minimal and whole cost of implementation decreased by a factor of twenty or more by the automation of the configuration, displays, testing, simulation, and tuning

3. In the time it takes to read this article, a model predictive controller or neural network could have been configured

4. Perhaps the biggest opportunity for driving the application of APC is the development of online process performance indicators

5. The key variable for process performance monitoring is the ratio of the manipulated flow to the feed flow

6. The controlled variable is best expressed and plotted as a function of the flow ratio (e.g. pH versus reagent to feed ratio, column temperature versus reflux to feed ratio, exchanger temperature versus coolant to feed ratio, and stack oxygen is versus air to fuel ratio)

7. The process efficiency is seen in difference between the actual and optimum ratio rather than in the gap between the actual and optimum controlled variable

8. A novel method has been developed to use model predictive control (MPC) to simultaneously adapt multiple first principle process model parameters

9. For closed loop process control, consider
a. PID for tight control of integrating or runaway processes
b. MPC for multivariable control, interactions, and optimization

10. For online property estimators for continuous processes, consider
a. ANN for highly nonlinear predictions with uncorrelated inputs
b. LDE for lag dominated linear predictions with uncorrelated inputs
c. PLS for steady state predictions from large number of correlated inputs

ANN is an artificial neural network, LDE is a linear dynamic estimator, and PLS is a projection to latent structures or partial least squares prediction discussed in Chapter 8 of Advanced Control Unleashed

What Have I Learned? - Bridging the Gap between Universities and Industry

Mon, 01 Jun 2009 13:12:04 -0600

Sometimes it seems universities and industry reside on planets that are light years apart. Too bad we don't have Star Ships with warp drive. Universities have leading edge research. Industry has "state of the art implementation."

Why are universities and industry "worlds apart?"

Engineers in industry don't seem to understand how to apply the research from universities. Professors don't appear to really know what is needed in industry. The tools are quite different. Engineers in chemical, pharmaceutical, and pulp & paper plants configure their control strategies in a distributed control system (DCS). Professors typically have their graduate students program their algorithms and test cases in Matlab.

One way to get industry and universities on the same page is to provide a DCS to the university with all the tools needed for research, such as a Matlab interface. In many cases the Matlab code can end up being configured in the DCS as part of the maturation of the innovation. The use of the DCS minimizes the reinvention of the wheel, such as the PID algorithm with all of its evolutionary enhancements. The setup facilitates the transfer of knowledge between the universities and industry. Being able to explore, prototype, and demo university innovations in a DCS makes it more real to industry and leads to rapid deployment and sharing of actual plant results.

If there is a unit operations lab, process control lab, or pilot plant, the DCS can be used to control the equipment used in the experiments. Students gain valuable experience in learning how to work with a toolset that is designed to meet industrial standards. Just learning the nomenclature and working with a DCS gives the student practical skills and confidence when as a new employee the student enters the control room. The window to see and affect the process is the DCS. Whether the student is going into automation or process design & technology, the student needs to be able to understand how to access and review modes, limits, options, and variables that determine how well a process runs. For example, the student gets to work in a university DCS on PID features commonly used in industry:

(1) PID limits (e.g. output, set point, and anti-reset windup limits)
(2) PID options (e.g. set point tracking of the process variable in manual, dynamic reset limiting, and nonlinear gain modification)
(3) PID form (series and standard)
(4) PID structure to determine whether each PID mode (proportional, integral and derivative) works on the process variable or the error (difference between the set point and the process variable)
.
The first semester I taught the Chemical Engineering course "Introduction to Process Dynamics and Control" at Washington University in Saint Louis as an adjunct professor, the students could not relate to my attempt to introduce practical plant applications and considerations in the normal course of Laplace transforms and bode plots. The second semester I added a virtual plant that consisted of a DeltaV DCS running in the Simulate mode integrated with HYSYS dynamic process simulations for each student. I later configured most of the process simulations directly in control studio. I was amazed how fast the students learned how to work in the graphical configuration environment and operator interface. All they needed was a few screen prints on navigation to get them started. Several of the students subsequently got intern or permanent positions doing configuration at the local DCS industry center. I had these students with experience in the automation industry come back to speak to the next class. The result was a dramatic turnaround in appreciation and understanding of what they would face in industry. The students decided on their own to go online to find and buy tee-shirts with Duncan, the DCS mascot, windsurfing. I ended up buying tee-shirts too and we all posed for a group photo by one of the students.

The main obstacle to the use of the DCS in the university is the initial installation and training. This is addressed by the support of industries with the same DCS who have a working relationship with the university and the local business partners of the DCS supplier. This method has enabled over 100 DeltaV DCS installations at educational institutions.

At the Automatic Control Conference in Saint Louis on June 11, I am co-chairing a session with Professor Tom Edgar from the University of Texas on "Bridging the Gap between Universities and Industry." The presentations are:

(1) "Bridging the Gap Between Universities and Industry"
(2) "Digital Process Control Lab at Washington University"
(3) "The Bioprocess Laboratory at Washington University"
(4) "Rose-Hulman Institute of Technology Unit Operations Laboratory"
(5) "Engineering Research Center for Structured Organic Particulate Synthesis (Rutgers, Purdue, New Jersey Institute of Technology, University of Puerto Rico at Mayaguez)"
(6) "Using a Distributed Control System (DCS) for Distillation Column Control in an Undergraduate Unit Operations Laboratory (University of Texas)"

My next blog will be June 22. In the mean time enjoy summertime.

What Have I Learned? - Writing

Thu, 28 May 2009 12:21:36 -0600

I haven't had any special courses or training in writing and some may say it shows. I wouldn't advocate anyone following my style, especially if you trying to promote your products or ideas. I tend to lead the reader on path of discovery by laying out the situation and the problems and then some interesting ideas. Maybe it is the user in me (33 years at Monsanto and Solutia), the scientist in me (Physics), or my approach to writing that wants to leave it up to the reader to make the judgments and assessments.

However, people today want to know upfront the bottom line. They may not have time or the background to come to useful conclusions. Plus my emphasis on detailing problems can be formatted with a more positive approach of presenting opportunities and solutions. Next month I will offer a short introductory overview of my recent articles that emphasizes the scenario, essence, and value of the ideas developed.

The main point of this blog like all of my writing is to share what I have learned. My goal for next year is to help prevent significant expertise and knowledge in process automation from being lost forever. I would guess 100 or more automation professionals are retiring each year who have published at best an infinitesimally small portion of their expertise for posterity. Also, new engineers are facing special challenges. My sense is the new kid in the control room doesn't have the mentors or the internal technical training programs I took for granted. They may be thrown into the midst of a difficult problem with no guidance.

Beginning this Fall I will be making presentations at Local ISA sections and interviewing young and seasoned automation professionals recommended by the sections to get a better idea of new and lost process control expertise. The first stop may be the ISA Boston section. I lived in Cambridge when I was overseeing a project at Badger. I am looking forward to returning to Harvard Square and Legal Seafood Market. I can dream of returning to Fenway Park. The interviews will be published in my monthly Control Talk column in Control magazine. I enjoyed doing the 3-part series in Control Talk on "The Secret Life of pH Electrodes" based on interviews at Broadley-James and Rosemount Analytical in Irvine California (nice place to visit).

In the mean time here is what I have learned about writing in no particular order except what pops into my brain (kind of the way I do my first draft).

(1) An outline is a good idea but it is just a starting point. I don't truly know where the article or book will take me at the beginning. The value I get out of writing is the discovery process. I find concepts and ideas along the way. I gain knowledge besides sharing knowledge. In my next book, I realized after about 5 pages into the first chapter that an important message is the dramatic change in the performance of modern measurements. The installed accuracy of key measurements has improved by one to two orders of magnitude compared to my days in E&I design and construction. A smart closed coupled coplanar DP with static pressure and temperature compensation has 0.02% installed accuracy. A radar level gauge can detect changes as small as 0.04 inches in level. A Coriolis liquid flow meter can have an installed accuracy of 0.05% with a rangeability of 200:1. The bench top and installed accuracy of pressure, level, and flow measurements in 1970s and 1980s was typically 0.5% and 2%, respectively. I have an article from that era that quantifies the deterioration from uncompensated process and ambient operating conditions. Then there was the noise introduced by wiring problems (see May 19 entry). Smart wireless instrumentation offer a whole new ball game if you don't screw it up with a bad installation. The limit to control loop performance is not the measurement but more than ever is the control strategy, the final element, and how well you tune the controller.

(2) The most difficult thing is writing the first sentence. The second most difficult thing is writing the second sentence. The third most difficult thing is writing the first paragraph. The fourth most difficult thing is writing the first page. The lesson here is to just get started. Writing is an iterative process. My problem is that I get bored rehashing ideas I have unleashed and want to move on to the next page, article, column, or book. I don't iterate enough. Here is where a person who knows the subject can help by reading and commenting on your drafts. Beware of technical writers or copy editors who want to rewrite the whole thing because it often results in a loss or change in meaning and intent.

(3) Once you get going, don't stop. A flow is important. After about 10 pages, the thoughts start to flow fast and free. At this point, music (particularly the best of Concrete Blonde, The Goo Goo Dolls, Josh Groban, Don Henley, Meat Loaf, Matchbox 20, Bruce Springsteen, and U2) adds inspiration and makes writing more fun for me. I think this was the only way I was able to write a dozen serious technical books, a half dozen funny technical books, fifty articles and papers, and 8 years of Control Talk column. It also helps to have an understanding management and spouse. Can you envision getting approval of humorous books, columns, and "top ten lists" through the official channels of a big corporation? Can you imagine your spouse letting you spend 8 hours writing on a weekend? Lastly, I do the diagram and figures last. This is mind numbing work for me that would sap my creative energy and interrupt the flow.

(4) Use a lot of sub headings and bullet lists. This gets the reader interested and helps to cherry pick what is of greatest importance. My article "Maximizing PAT Benefits from Bioprocess Modeling and Control" is a good example of missing lists and sub headings. In my defense, the article was a last minute deal. I had just a couple of days and was just doing a core dump of what I thought was important.

(5) Go back and improve the introductory paragraph to each section and the introductory sentence to each paragraph. This is a good idea. I am going to try it when I have time.

(6) Develop the solution, include the assumptions, and detail the verification. I tend to do the first two parts of this suggestion and provide evidence of the last part. However, it would help the readers to make suggestions on how to prove out the idea for their application. No solution is universally true. There are always exceptions. I have benefited from the best minds in process control but I have noticed that the bigger the mind the bigger the ego. A blind spot is developed that makes experts unwilling to acknowledge when their solutions don't work in a particular application. Maybe it is my science background or an ego deficiency but I am always looking for exceptions and I never think any of my ideas as perfect. You learn more from things that don't work.

(7) Don't make statements that are to be accepted as fact. I am particularly sensitive to statements made to be accepted as true that are mostly untrue. For example, "Thermocouples (TCs) are faster than RTDs." This statement is generally accepted as true but is it useful and is it in fact misleading? What if someone chooses a TC instead of a more accurate RTD because he or she thinks the TC is faster? If you had a bare element there might be a difference of a couple of seconds in the response but the uncertainty in the time lags of most temperature systems are an order of magnitude larger. Once you put the element in a thermowell, the construction and fit and length of the thermowell determines a time lag for the assembly that is an order of magnitude larger as well (no pun intended). Also most controllers are tuned so slowly, you don't see the effect of small changes in time lags. The bottom line is that you will probably never see the difference in speed between a TC and an RTD. Having said that I can envision exceptions, such as the temperature control of inline mixing of streams with an aggressively tuned controller. I have just never seen this application in practice.

(8) Use short clear sentences and paragraphs that build on each other. Providing qualifications can result in long sentences. Also, one thought for me quickly leads to another and to another and to another, which leads to run-on sentences. I need to constantly go back and split my thoughts into separate sentences with a building block approach. I am currently trying in my first draft to break thoughts up. So far it doesn't seem to interrupt the flow, which was my original concern.

(9) Don't get hung up on perfect grammar or a perfect piece. If you don't give copy editors something to do, they will start some serious messing with the sentences. Everyone wants to feel like they are contributing and doing their job. Also, what copy editors are looking for in terms of commas and hyphens may change. Finally, obsession with sentence structure can lock up your mind. I talked to a copy editor who said he needs to find another job so he can write a book. As a copy editor, he thinks too much about the technical details of writing.

(10) Think of writing as if you were having a conversation with a good friend. This makes the whole writing process less intimidating and lets you be more frank and less formal.

(11) Use plenty of examples and illustrations. It takes time but sure drives the point home. I have seen a lot of misinterpretations of an idea or its intent. The fault is really my own and not the reader. For example, in my article "Is Wireless Process Control Ready for Prime Time" a reader thought there was a concern being expressed on noise from variable speed drives on wireless transmitters when what I meant was the opposite. Wireless transmitters should eliminate these and other noise problems associated with wiring. I no longer assume any concept is obvious.

(12) Use free association and both sides of your brain. This allows you to take creative leaps you probably didn't even realize before you started writing. In my case it also enables me to add humor such as "Top Ten Lists" and the cartoon descriptions for my illustrious friend Ted Williams.