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.

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

In this final part of this series, we look at what model predictive control (MPC) can do for the following applications:

(1) Extend rangeability
(2) Improve resolution
(3) Enable preferential use of flows based on cost
(4) Send flows to multiple destinations possibly based on priorities
(5) Provide counteracting effects

MPC is the more powerful solution for an optimization problem (applications 3 and 4). MPC also offers the simultaneous manipulation of multiple flows, objective oriented tuning knobs, and manipulated variable costs that make the optimization more a science than an art. An experienced regulatory control person can make a PID do almost anything but many plants don't have that experience base. The MPC offers a solution that a person with some basic knowledge of the process and dynamics (process gain, time constant, and dead time) can understand. In my experience in teaching process control to chemical engineers at Washington University in Saint Louis, it was easier for students to understand and use an MPC because it was process oriented. On the other hand, the PID had dozens of parameters with a hundred different opinions on how to set them. If you don't believe me, check out the 484 page documentary of setting 3 of the 20+ PID parameters in the Handbook of PI and PID Controller Tuning Rules, which doesn't get into structure, options, and windup. Just think about trying to teach the PID nuances and heuristic rules well enough to turn a new employee loose on an optimization problem. I think you have a much better chance of success if the neophyte is armed with an MPC. With the disappearance of mentors and in-house technical courses all but the basics of PID control may well be lost. The manager of a process control group at a large refinery told me that he starts up with a PID but quickly moves every loop to an MPC because he doesn't have a Shinskey in his group.

We will see that MPC can also be used for applications 1 and 2 and thus cover the range of opportunities we discussed last week for valve position control (VPC). The principle drawback of the VPC solution is the lack of tuning guidance, no embedded economics, and no move suppression in conventional PID controllers to address multiple objectives (tight control of the critical process variable and the minimization of costs and variability from unnecessary movement of the expensive and large flow).

I first explored an MPC solution for application 3 for the classic case of the manipulation of fast but high cost flow and a slow but low cost flow. The solution as outlined in AdvancedApplicationNote1.pdf involves setting up one of the flows as an optimization variable. Normally one would pick minimization of the high cost flow but this made the fast flow less available for tight control in my tests because the optimization routine had a tighter than expected grip on this flow even when the penalty on error (PE) for the optimization variable was greatly reduced. The high cost and fast flow tended to ride its low limit. I achieved better load rejection performance by setting up the MPC for maximization of the low cost but slow flow. For this setup, the maximization of the low cost flow took a back seat to the tight control of the critical process variable when I reduced the PE for this optimization variable.

If the MPC allows the user to write to the relative costs manipulated variables based on the priorities of each manipulated flow, MPC offers a solution for application 4 without the addition of an optimization variable.

I next explored an MPC solution for the manipulation of a big (coarse) valve and a small (fine) valve. The solution as outlined in AdvancedApplicationNote2.pdf involves setting up the small flow as a second controlled variable. I was able to get good load rejection and set point response while minimizing the use of the big valve. I reduced the PE of controlling the small valve at its optimum position. The stick-slip limit cycle from the big valve can be broken by writing a zero to the move size limit for the big valve when the small valve is within an acceptable throttle range. This MPC solution can be extended to applications 3 and 4 by writing a set point (target) for the small valve based on costs or priorities. If it is the simple of case of trying to minimize the small flow because it is expensive, the optimum set point corresponds to a minimum throttle position that doesn't have excessive seating or sealing friction and hence stick-slip. Is the 1st or 2nd MPC solution better? The more I think about it, I think the solution outlined in the second application note offers more flexibility and is easier to set up but maybe that is because I am an old VPC guy and this MPC is a smarter way of doing VPC.

An MPC could be set up for application 5 but I am not sure whether the advantage of the built-in knowledge of the dynamics of the valves outweighs the disadvantage of the MPC inherent approach for simultaneous movement of the manipulated variables. Application 5 really demands sequential manipulation of the flows so that you are not wasting energy or raw materials. To force sequential manipulation, it appears to me you would have to have extremely high penalties for both valves being open and be able to deal with the discontinuity of the split range point with an MPC that is expecting a linear model.

With all of the advanced algorithms and smart instrumentation available today, we can sometime lose sight of what are the real limits to loop performance. While it doesn't take an Einstein to figure this out, as a former physicist, I found an interesting analogy.

Einstein's reasoning that nothing can travel faster than the speed of light lead to incredible insights and revolutionary equations. For example if you substitute the speed of light for velocity into the equation for kinetic energy, you now have the famous equation that relates mass and energy (energy is equal to mass multiplied by speed of light squared). You also end up with a unification of space and time and warping by gravitational fields.

The absolute limit to feedback control system performance is the total dead time in the loop, which is the summation of all the final element, process, measurement, I/O, and controller execution time delays. A feedback control system cannot correct for something it hasn't seen yet and hasn't been able to change yet in the process (see "Funny you should Ask a Process Control Engineer" in the Funny Thing E-book). http://www.modelingandcontrol.com/FunnyThing/page-123.asp

The fastest closed loop time constant (Lambda) possible is the deadtime. If you substitute deadtime for Lambda into the controller gain equations for Lambda tuning, you end up with the Simplified Internal Model Control and factored Ziegler Nichols equations for the highest controller gain with a relatively smooth response. This unification of equations for controller gain was documented in Appendix C of New Directions in Bioprocess Modeling and Control. This Appendix also provides the derivation that the performance achieved in terms of integrated absolute error (IAE) for an unmeasured load upset is proportional to reset time and inversely proportional to controller gain. BioprocessModelingControlBookAppendixC

The implications of this for sample delays in terms of there being an additional implied dead time for detuned controllers is explored in Advanced Application Note 5.
http://www.modelingandcontrol.com/repository/AdvancedApplicationNote005.pdf

The hype of some advanced process control (APC) algorithms may lead one to believe this limit can be violated. Many of the early APC algorithms significantly increased the loop deadtime (See "Advanced Control Algorithms- Beware of False Prophecies in the Funny Thing E-book). While model predictive control (MPC) can potentially help dead time dominant systems, the original execution time (e.g. 1 minute) of separate MPC software packages was so large their applicability was restricted to slow processes. With the advent of the MPC embedded in the DCS, the execution time can be as fast as 1 second which means MPC can be applicable to all but the fastest processes (e.g. liquid pressure control and furnace pressure loops).
http://www.modelingandcontrol.com/FunnyThing/
http://www.modelingandcontrol.com/2008/08/tipsntechniques_tnt_tuning_fur_1.html

Deadtime compensators such as the Smith Predictor can make the PID algorithm think there is no deadtime in the loop. You can get fooled as well if the PID faceplate shows the compensated PV that has the deadtime removed from the consequences of its own actions instead of the original PV. Deadtime compensators allow the user to increase the controller gain. If the deadtime compensation is perfect, the increase in controller gain can be huge. However, many sources of deadtime are variable and unknown.
http://www.modelingandcontrol.com/2007/06/deadtimes_secret_identity_part_1.html
http://www.modelingandcontrol.com/2007/06/deadtimes_secret_identity_part_2.html

For PID controllers an underestimate of deadtime can lead to instability if one goes for the gusto of ultimate performance and pushes the limit beyond the original unfactored Ziegler Nichols equation for controller gain. For deadtime compensators and model predictive control, you can also get into some oscillations for overestimates of deadtime.
http://www.modelingandcontrol.com/repository/AdvancedApplicationNote003.pdf

Finally, there is another limit to control loop performance, signal resolution. You can't control to a degree finer than the resolution of the final element, measurement, or I/O. The resolution limit of digital devices today is nearly negligible (e.g. 16 bit A/D) but some older DCS (e.g. 12 bit A/D) could cause noticeable stair-steps in the temperature response from wide range thermocouple input cards and the standard input card. It is strange to me that the standard input card for many variable speed drives still uses an 8 bit A/D that significantly restricts the resolution of the final element. Today most of the resolution limit seen in control loops is from control valves. The principal cause is stick-slip and is usually lost in the smoothing afforded by process volumes and the compression of data (except for pH and other high gain processes) unless you have on-off or isolation valves posing as control valves or choose to save money by not buying digital positioners.
www.ControlDesign.com/articles/2003/164.html
http://www.ChemicalProcessing.com/articles/2007/200.html
http://www.controlglobal.com/articles/2008/063.html

Not well recognized is that for PID control of integrating processes, valve deadband causes an implied resolution limit in the PV for a reversal of direction that is the deadband multiplied by the integrating process gain and size of the correction needed in the controller output to balance out the disturbance. Real control valves with digital positioners have a deadband that is less than twice the resolution limit.

Line "D" of a pet food plant never operates as well as the other lines. Line "A" has the best performance. The operators for line "D" say that line "D" is different and it can't do better. When a line "D" operator gets sick, a line "A" operator fills in on Line "D". Line "D" begins to do as well as line "A".

A builder and operator of ethanol plants puts process metrics on the operator screens for each plant that are viewable by operations at all of the plants. The competitive nature of people kicks in and all of the plants start to do better.

The energy cost for a lime kiln is displayed online. Model predictive control (MPC) is installed and the energy costs drop by 10%. Projects are started to install MPC on all of the lime kilns.

Online process metrics can blow away war stories, motivate operators, increase the on-stream time of advanced controls, justify process control improvements, and develop correlations between key performance indicators and operating conditions. For example, processes may show daily and seasonal performance variations because of the change in feed and cooling water temperatures. Also, process may run better or worse at night and or weekends and holidays depending upon whether automation, maintenance, and process engineers are supporting or distracting and interfering with operations.

However, the implementation is not necessarily straightforward. Process metrics can show us something essential but we may not always like what we see. The president of an MPC company years ago was unequivocally against online process metrics because they may initially take a dive when the MPC is turned on.

I installed online metrics of base reagent cost to show the advantage of adaptive pH control for neutralization of an acidic waste stream. The tighter control increased the reagent costs for disturbances that drove the pH below set point or for increases in the pH set point because the addition of caustic was larger and sooner driving the pH to the set point faster. For disturbances and set point changes in the opposite direction the tighter control decreased the reagent costs. So is tight control right or wrong and are process metrics in this case not useful? If there is a penalty for being below set point, it should be added to the online cost metrics. If not, the controller should be tuned with a lower gain when the pH is below the set point.

Consider a batch operation where the process must be heated up before a reaction occurs. A control system that gets temperature to set point fast will increase the steam use per batch by overdriving the control valve past its resting position. The question is whether the reduction in batch cycle time is worth more than the increase in steam per batch.

You cannot control what you can't measure. To control plant profitability we need to have the automation system and computations to put process metrics online. Undoubtedly, improvements will be needed to the metrics and to the automation systems that affect them. Filtering and averaging will be needed to screen out noise and delays added to make process inputs coincide with process outputs. New measurements and valves will be needed. Throttling valves with better deadband and resolution can reduce limit cycles. Coriolis meters can provide accurate flow measurements and inferential measurements of stream compositions important for yield, quality, and production rate calculations. Ambient, piping and equipment wall, feed, and coolant temperatures can help provide indications of previously unknown adverse effects.

I see a future where the cost and revenue per production rate, batch, shift, day, night, week, month, and season besides yield and on-stream time are displayed for each production line. Data analytics will be used to develop correlations for projections to latent structures or partial least squares (PLS) to provide predictions of process metrics online and to provide a drill down to contributions most affecting the metrics for better process understanding. The trends and future predictions of these metrics immediately translate to improvements and eventually "closing the loop" for plant profitability. I expect an MPC will be developed to use process metrics as controlled variables and the principal components as manipulated variables.

It seems to me online process metrics are the key for a manufacturer, process control group, and automation company to thrive in a competitive worldwide economy. Loop tuning and performance is just the beginning. As automation engineers we tend to think of the loop as the "end all." We need to get outside of the box that is the loop to prevent islands of automation. We need to think in terms of unit operation control and how these units interact to affect the process as a whole. We need "oneness" guided by process metrics as introduced in my control talk column. This is the moment.

http://www.controlglobal.com/articles/2008/287.html

For a list of some items we need, see slide 57 in my presentation.

http://www.emersonprocessxperts.com/archives/2008/11/assessing_oppor.html

One of my favorite cartoons is Einstein at a blackboard with his equations for the "General Theory of Relativity" all leading up to one concluding equation "Time = Money." In this same vein, the great movie line comes to mind: "show me the money" . More than ever, if you want to do neat things in process control, you need monetarily decisive benefits.

What if a group of 5 modeling and control specialists working with the best automation engineers in the plants were given the freedom to find and improve the process control in 100 control rooms with Distributed Control Systems in a major chemical company without having to justify each venture for 2 years? Once upon a time this actually happened. The result was ongoing benefits of 75 million dollars a year. How did this start and finish and what does it say about the future?

The initiative began with a benchmark study of companies with the best track records in process control. The top three companies achieved a 8% reduction in the cost of good by a balanced application of 9 technologies in basic and advanced control and the use of data. The largest benefits came from better PID control (e.g. better tuning and control strategies), unit operation control (e.g. automated sequences), and advanced regulatory control (e.g. feedforward and override control). The next biggest source of benefits came from putting process metrics online via real time optimization and data analytics. The benchmarking study and other stuff supporting this discussion can be seen in a presentation I recently made to big chemical company viewable at the site:

http://www.emersonprocessxperts.com/archives/2008/11/assessing_oppor.html

We found in the benchmarking that each layer of technology was built on a solid foundation of supporting technologies to form a pyramid. The performance of regulatory control depended heavily upon the scope and sensitivity of the measurements and control valves and the tuning of the loops. In turn the success of model predictive control relied upon the performance of the regulatory loops. It is noteworthy that model predictive projects often report bigger benefits than those shown in the report because the improvements made in the regulatory control are attributed to the MPC project, which in a way is justifiable because the improvements to the loops probably would never have been made without the systematic approach used in well managed MPC projects. Finally it was obvious in the study that real time optimization worked best if was integrated into the predictive handling of constraints and interactions provided by an MPC.

The benchmarking study lead to 2 year process control improvement program developed by Vernon Trevathan, a recent inductee into the Control Magazine Automation Hall of Fame. You can read about the highlights of his career and his role in the program in my interview of him in my "Best of the Best - Part 3" series in Control Talk.

http://www.controlglobal.com/articles/2007/061.html

In the beginning of this initiative I conceived, trialed at an agricultural chemical plant, and documented in a internal report an opportunity sizing and opportunity assessment process, as noted in my Control Talk Column "Up for the Ashes."

http://www.controlglobal.com/articles/2008/092.html

While I give myself credit for the original concept, the real success of this methodology and consequently the whole initiative was the result of Glenn Mertz who had a unique combination of process control and accounting skills and extensive plant experience applying the latest technologies. For each control room studied, Glenn went through the cost sheets of the production unit, found the best periods of operation, and quantified the demonstrable gaps between actual and practical process metrics such as production rate, raw material and energy cost, and rework. Glenn also dug out research reports and process simulation results to provide define theoretical goals and gaps. All of this could have been just an exercise if it wasn't for the fact Glenn was able to get agreement from key process engineers in the plant to the goals and gaps to form an opportunity sizing that was then the basis of an opportunity assessment conducted for 3 days at the plant. Ultimately Glenn reported the benefits by again scouring the cost sheets and working extremely well with plant engineers in operations and process technology.

It would be nice if we could have a Glenn on each our teams, but given this is improbable what can we do? Can we get Glenn to give up his days on the golf courses in Door County Wisconsin in the summer and Fort Meyers Florida the rest of the year and travel on crowded flights in coach to not so gorgeous places? Can we create a virtual Glenn? Not likely. What is possible? For my answer and the conclusion to this series, see my entry on Dec 5.

I think the future is advanced process control (APC). My definition of APC is any technology that puts process knowledge on the line online. Feedforward control is APC when the feedforward gain and dynamic compensation are based on process knowledge. On-demand and adaptive auto tuners, such as DeltaV Insight, are APC tools because these tuners identify the process dynamics that are useful for process diagnostics and training besides model based tuning. For example, the process deadtime can be monitored as an indicator of heat transfer surface fouling in temperature loops and the dynamics can be inserted in simulations for operator training and scenario testing and prototyping of PID enhancements (e.g. set point filtering and structure) or Model Predictive Control (MPC). There are many higher level technologies. In a recent presentation I made to a major chemical company I showed these technologies, the results from a benchmarking study of the top ten companies in the use of process control, and practical tips on how to conduct an opportunity assessment. The presentation can be seen at:

http://www.emersonprocessxperts.com/archives/2008/11/assessing_oppor.html

Slide 8 shows the pyramid of technologies that includes process performance monitoring (data analytics and process metrics), abnormal situation prevention, property estimators (inferential composition or quality measurements), model predictive control (MPC), rampers and pushers to maximize or minimize a controlled variable (e.g. feed rate), linear programs (LP) for optimization given defined constraints and economics, and real time optimization (RTO) for variable constraints and economics. The importance of process knowledge in all of these technologies is obvious. Slide 9 gives a straightforward "easy to remember" relationship between controller tuning for loop performance. The equation indicates before, during, and after APC implementation, the controllers should be tuned.

The amount of effort and the performance of the upper level technologies rest upon the strength, breadth, and integrity of the foundation of basic control. As you improve the number, type, and sensitivity of the measurements and control valves, the performance of these systems improve by reducing the number of unidentifiable disturbances and enabling more first principle calculations and inferential measurements, such as frosting rate, fouling rate, crystallization rate, and reaction rate important for diagnostics and batch profile control as discussed in a recent article in Control magazine.

http://www.controlglobal.com/articles/2008/230.html

Decades ago, field pressure and temperature gages were installed. These were not very accurate. prone to be broken, and obviously were not visible in the control room or historized. With wireless, we can afford to get many more measurements into the control system. Wireless measurements offer the opportunity to provide many of these missing measurements at a reasonable cost. However, the choice of measurements for data analytics (principal component analysis and projection to latent structures) must be judicious. Randy Reiss, the developer of online data analytic algorithms for Emerson, says "more measurements for analytics means more correlations. However, it introduces the possibility of dominate correlations that do not relate to product quality. That would skew the model for the worse. So there is a double edge sword there."

For portable bioreactors, laboratory analyzers, and sterilization systems, wireless adds flexibility and utility. Wireless access to process and loop performance monitoring systems in the field makes troubleshooting much smarter. Wireless access anywhere to virtual plants with process performance scores for university courses on process control makes learning almost like a video game. There are many more applications for wireless than the monitoring of remote tanks and pipelines. The following Control Talk column slated for the December issue of Control magazine discusses the role of wireless in APC.

WirelessControlTalkColumn

Randy Reiss's list of the "Top Ten Reasons You Will Go Wireless Next Year" in the above column provides a reality check in case we are thinking of making everything wireless. This list has the insight, bite, and humor typical of the lists Randy has contributed to my column in recent months. Upon reading the draft of the column, Randy said "it's the best argument I have heard for wireless." Randy agreed to the post of this quote after checking with his PR agent.

Scott Broadley, the president of Broadley-James, is participating in a beta test with Emerson on the use of wireless transmitters on portable single use bioreactors (SUB) whose size is steadily growing from pilot plant (100 liter) to production (1000 liters) scale. Scott is also looking forward to the elimination of ground loops and noise by wireless pH transmitters particularly where the solution ground is not used or where AC noise gets through the power supply. Scott says, tongue-in-check, "we could hook the pH and DO transmitter up wirelessly to a Twitter account so your cell phone is getting constant text updates on how your bioreactor is "feeling" . Scott offers the following additions to the top ten list for going wireless.....(11) Each bioreactor can have its own Face book page where operators from different shifts can leave their comments......(12) Each transmitter can be on Twitter and send you instant text messages on your phone when it is moody..."