If you increase the controller gain by the same factor that you increase reset time (e.g. double the gain and the reset time), how does it affect key performance indicators such as quality, yield, on-stream time, and environmental costs? If you make the valve and measurement faster, how does it affect these same KPI? If you want to improve a KPI, what is the priority of solutions?

The equations for the peak (Ex) and integrated error (Ei) in terms of controller settings, shown on slide 1 of EffectsLoopTuning&Dynamics-KPI.pdf, provide an answer to many of these questions if you embrace your inner geekness as advocated in the Control Talk Jan 2010 issue "The Future is Now"

Both equations were derived in Appendix A and B of Tuning and Control Loop Performance (scheduled to be back in print by Momentum Press, 2010). The derivation of the equation for the integrated error was included in Appendix C of New Directions in Bioprocess Measurement and Control (ISA, 2007) along with a unification of controller tuning rules. This unification, which showed how all the major tuning rules give basically the same result for a controller gain to minimize peak error, was personally satisfying but possibly not for people who are adamant about the relative merits of personal favorite tuning rules.

Since the integrated error is inversely proportional to the controller gain and proportional to the reset time, doubling the controller gain and reset time cancel each other out. However, doubling the controller gain halves the peak error since reset time doesn't appear in the equation of the peak error. Reset time has an effect on peak error but it is negligible unless the reset time is decreased to the point where it approaches the loop deadtime. This can happen for deadtime dominant systems, but the peak error here is basically the open loop (error with the controller in manual) as evident from the equations on slide 2 of EffectsLoopTuning&Dynamics-KPI.pdf.

Nearly all the process control literature focuses on integrated absolute error (IAE) as the measure of loop performance. The IAE is a good measure of product that is off-spec that can lead to reduce yield and the raw material or recycle processing to product cost ratio (euros per kg and dollars per lb). If the off-spec cannot be recycled or the feed rate cannot not be increased to compensate, there is also a loss in production rate. If the off-spec is not recoverable, there is an additional waste treatment cost.

What we usually don't take into account is the filtering effect of back mixed volumes as indicated by the equation on slide 3 of EffectsLoopTuning&Dynamics-KPI.pdf. For chemical and pharmaceutical plants and refineries, there are large volumes that provide significant attenuation of oscillations. However, in other process industries, various pathways of variability do not have significant filtering and culminate in the final product. These processes are also more vulnerable to interactions because there is no smoothing of effect of one loop's control valve movement on another loop's process variable. This changes the whole view on how you tune controllers. For systems with little back-mixing, controllers are tuned to limit the transfer of variability from the controlled variable (controller PV) to the manipulated variable (controller output) to prevent interactions and to provide a smooth response. The controllers are also tuned for coordination by enforcing a closed loop time constant (Lambda). For pulp and paper plants, nearly all of the variability expressed by the IAE ends up in the sheet since most of the processing is done in pipes and inline or unagitated equipment. Lambda tuning has been exceptionally successful in optimizing the transfer of variability and the coordination of loops. The same requirements could occur for plastics and textiles, since the IAE in the polymer lines and extruders shows up in the yarns and webs. However, these plants may have extensive blend tanks that average out the plus and minus fluctuations in product quality.

I ran into a process control improvement (PCI) study, where after an hour of discussion and investigation it became obvious a reduction in the considerable variability observed in each textile line had no value because the product coming out of the huge blend tank was always in spec and the variable speed pumps were maxed out. My decision to move on to better opportunities was not well received, so we stayed for 2 days to confirm there were no PCI opportunities (reducing the size or inventory in the existing tank or replacing the pumps were considered accounting or process design improvements).

When loops are oscillating across the split range point (common case due to valve stick-slip and installed valve characteristics), there can be a cross neutralization of acids and bases or a cross compensation of hot and cold heat transfer fluids that increases reagent and energy costs. Here the IAE is important but an integration of individual reagent and heat transfer fluids is a better indication.

If there are appreciable back mixed volumes whose residence time is much larger than the control loop period, the integrated error (Ei) where the plus and minus errors cancel out for a disturbance can be a better indication of the effect on product quality. Taking into account that the integrated error is also the IAE for an over-damped or critically damped response, we realize the simplification of the relationship of off-spec to an integrated error offers considerable understanding as to the effect of tuning settings.

This topic will roam on for 4 parts. In part 2, I discuss the effect of the peak error on onstream time and environmental costs. In part 3, I cover how measurement and valve dynamics impacts both types of errors and hence KPI. In part 4, I conclude with some rules of thumb on the priority of PCI solutions for various scenarios.

Knowledge of the flows and the accumulation of material in a unit operation are fundamental to the understanding and analysis of process and equipment performance. Flows are the primary way of affecting the process. Root cause analysis requires sensitive and repeatable flow measurements. I have seen costly expert systems fail to deliver benefits because of missing or inaccurate flows ("Drowning in Data, Starving for Information - 1").

The process gains of the more important process variables (e.g. composition, pH, and temperature) are best quantified and visualized in a plot versus a ratio of flows (e.g. coolant/feed, reactant A/reactant B, reagent/feed, reflux/feed, and steam/feed). If you are still into differential equations, you can checkout my Advanced Application Note 4 to see how process gains are dependent upon the ratios of flows.

The importance of flow ratios for affecting the process is seen in the prevalence of flow ratio control as detailed in my entries "What Have I Learned? - Flow Ratio Control" on this website.

The amount of time material spends in a unit operation is critical for crystallization and reaction. For continuous operation of well mixed volumes, the amount of time is the residence that is the fluid volume divided by the total throughput flow. Conversion is maximized by increasing volume or decreasing feed flows. For batch processes, the amount of time is the cycle time. Conversion is maximized by charging the feeds as fast as possible (increasing feed flows), to leave more of the batch cycle time for conversion.

In the direct material balance control scheme where the distillate flow is manipulated for overhead receiver level control, the sensitivity of the temperature and hence the composition control requires an exceptionally sensitive level measurement, low noise, and a high controller gain. Changes in distillate flow do not affect the column until there is a corresponding change in the reflux flow that maintains the material balance.

Then of course, there is the need to minimize the amount of storage of materials in the process. Ideally, storage tanks would be almost empty with just enough raw materials and intermediates to continually meet the flow demand of downstream operations and just enough products to continually meet the flow demand of customers.

For more information on how advances in flow and level measurements can improve material balance control, residence time control, inventory control, and process analysis and modeling, checkout "Advances in Flow and Level Measurements Enhance Process Knowledge, Control"

As I look back over my experience with pH and conductivity measurements, the following opportunities stand out.

(1) Selecting the best sensor technology for a wide range of process conditions
(2) Eliminating measurement noise
(3) Predicting sensor demise
(4) Developing process temperature compensation
(5) Developing inferential measurements of process concentrations
(6) Finding the optimum sensor location

You really can't ship most chemicals to the electrode manufacturer and electrodes sent back after a problem often don't tell the whole story including handling, maintenance, and process conditions. The manufacturer's application support people are often at a loss as to what was really the problem. Then there are the insidious spikes that come and go with no sense of the source or the fix.

The biggest source of continual pH noise is fluctuations in acid and base concentration at the electrode. Operating points on the relatively steep portion of the titration curve require a degree of mixing that goes way beyond the norm. Electrodes are moved to a location that is the best compromise between noise and measurement delay and lag.

Users can install a test setup in the plant to compare the performance of various electrode technologies but this is time consuming and does not allow experimentation. Tests in the lab usually involve "dumb" lab meters with the data ending up in a spreadsheet oblivious to the historian and the tools in the DCS for neural networks and data analytics.

To see if the opportunities are more than a dream and if the problems can become just a bad memory, check out the InTech web exclusive article "Opportunities for Smart Wireless pH, Conductivity Measurements"

It is well known that measurement noise reduces or eliminates the use of derivative action. Since rate is not popular (another story), the exclusion of rate is not seen as a significant disadvantage even though temperature loops could benefit from rate since it can compensate for thermowell and heat transfer surface lags and reduce overshoot. In the 1980s and 1990s many temperature loops suffered from the prevalent use of 12 bit I/O and wide range thermocouple input cards that caused a resolution error of 0.25 degrees in a signal whose true rate of change of temperature was usually much slower than 0.25 per minute. The result was a poor signal to noise ratio. We tried to filter the heck out of the signal so we could use rate but this added another lag. Fortunately, today we have 16 bit I/O systems and smart transmitters so that signal resolution is better than the sensitivity of the sensor - just one of the many reasons to get your automation system into the 21st century.

A wider consequence of measurement noise not so readily recognized is the reduction in permissible controller gain. For loops with a true integrating or "near integrating" response where the process variable ramps when the controller is put in manual, the high limit for controller gain is way above the normal range of consideration. For example, level and batch temperature loops normally have a ramp rate so slow (0.000001 %/sec), that the controller gain could be higher than 50 if there was no measurement noise and the reset time was not too small (a big "if"). Since the peak and integrated errors are inversely proportional to the controller gain, these and other loops could significantly benefit from a smoother signal and better tuning.

What is measurement noise and where does it come from? In my book, measurement noise is any fluctuation in the measurement signal that should be ignored by the controller. If the controller reacts to a fluctuation it really cannot correct, the loop inflicts a disturbance upon itself. If resolution problems are behind us, the biggest sources of measurement noise are inadequate axial (back) mixing, bubbles and foam in liquids, liquid droplets in steam or gas, inconsistent profiles, lqiuid and pressure waves, and insufficient measurement rangeability. Measurement noise is amplified by high process gains (e.g. steep titration curve for pH control) and sensitive measurement ranges (e.g. - 0.25 to 0.25 inches of water column for draft pressure control). The Table in MeasurementNoiseSourcesControlBandAmplitude.pdf provides a summary of my assessment of noise sources, control bands (allowable control error), and noise amplitude (peak to peak) for common loops. The noise amplitude should be less than ¼ the allowable control band for fast disturbances. A reduction in noise amplitude is ideally achieved by eliminating the source of the problem. If the correction is not practical or is not yet implemented, a signal filter is often used to attenuate the noise. The ratio of the amplitude of the filtered signal to raw signal is roughly proportional to the ratio of the period to the filter time when the filter time is greater than the period (simplification of the Bode plot attenuation equation). The filter time becomes effectively additional deadtime in a loop when it is less than the process time constant. If the filter time is considerably greater than the process time constant, the measured process variable amplitude may look better but the real amplitude is worse because you are seeing a very attenuated version of the real world. I have seen where an ISA conference speaker said he almost did not get permission to give his presentation because the improvement was so great it was considered proprietary. He had increased the measurement filter so much he was drawing a straight line no matter what was happening in the process. I have seen where a biochemist withdrew a temperature sensor halfway in its thermowell and proudly said this was the way to run the bioreactor because the temperature reading was so much smoother. Then there were the cases of sand in thermowells and the mounting of extruder temperature sensors in massive blocks of metal giving the illusion of smooth temperature. These are all old stories but I am sure people are being fooled today especially since one can so easily add a filter via the damping setting in the transmitter, the analog input block, and the PID block. Provided the filter setting is not so large it eliminates any recognition of process variability, the key symptom of too large of a filter setting is a long control loop period or recovery time if the controller gain is not so detuned you can't see the effect of more loop dead time (see Advanced Application Note 5 for estimation of how the detuning of a controller is equivalent to additional deadtime in the loop). To prevent the loop from inflicting disturbances upon itself by reacting to noise, the filter time should be set just large enough to keep the fluctuations in the controller output smaller than the resolution (stick-slip) of the final control element (e.g. control valve). A less desirable but widely used way of keeping the fluctuations in the controller output small enough is to reduce the controller gain.

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 "It'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.

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.