Process control is so detailed, fragmented, and experience dependent, it is difficult to see the commonality of process control solutions. In Deminar #9 at 10:00 am CDT Wednesday Sept 8, I will detail 10 key concepts in a unified approach that will be useful for process control improvement in 90% or more of the applications. Demos will be offered of the more dynamic consequences. The deeper understanding gained should be useful in developing process control improvements, most of which can be demonstrated by free use of virtual plants on the process control lab website http://www.processcontrollab.com/ .

To attend the event, go to http://bit.ly/JC-LiveMeeting
Use the information below to connect (if you're not using the available computer audio):
• Toll-free: +1 (877) 771-7176
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To view the recording of Deminar #8, click on the above picture. If you want to just view the slides click on Deminar #8 - PID Control of Runaway Processes

Self-regulating processes are the easiest to control given similar dynamics (e.g. delays, lags, and gains), nonlinearities, and upsets. In manual, the process variable will eventually reach a steady state for a self-regulating process. Integrating processes are the next most difficult to control because in manual the process variable will always be ramping even if there are no disturbances. Runway processes are the most challenging and potentially the most dangerous because in manual the process variable is always moving and can accelerate in its divergence even if there are no disturbances. Runaway processes are termed "open loop unstable." The acceleration is characterized by a positive feedback time constant. Both integrating and runaway processes have a low gain limit that causes slow rolling oscillations and a divergence off-scale, respectively. Integrating processes are more sensitive to integral action and secondary lags than self-regulating processes and runaway processes are more sensitive to integral action and secondary lags than integrating processes. The most common problem with integrating and runway processes is too much integral action (too small of a reset time) and the omission of derivative action for secondary lags (rate time should be set equal to largest secondary lag). Some highly exothermic polymerization reactors have proportional plus derivative control to avoid the potentially unsafe situation of someone adding too much reset action. I have been in the control room when an exothermic reactor has reached a point of no return where the temperature acceleration was so high despite full cooling, the only thing the operators could do was prepare for the rupture discs to burst and the reactor contents blow over to the flare stack tank. Highly reactive chemicals lead to rapid and complete reactions but can also lead to an uncontrollable temperature rise since the reaction rate and hence heat release doubles for every 6 degree increase in temperature. Runaway processes can look like integrating processes unless the temperature controller is left in manual long enough for the temperature change to be large enough.

Deminar #8 shows the dramatic correction needed for the tuning settings. The factors used in the short cut tuning method for near-integrators in Deminar #6 and the classic Ziegler Nichols ultimate oscillation method are detailed and demoed. Equations are offered to predict the ultimate gain and ultimate period showing the dramatic effect of a secondary process or thermowell lag and loop deadtime. If a secondary lag or the loop deadtime approaches the positive feedback time constant, the window of allowable controller gains closes and the loop is unstable for all tuning settings. The virtual plant is where you want to learn about runaway processes. You can't experiment much or have the loop in manual for more than a few deadtimes with a true runaway process.

It is hard to imagine that in this day and age we are relying on "after the fact" process performance analysis on a gross scale. I wonder if we still use spreadsheets and costs sheets to judge a process unit and operator performance. We can see differences in setpoints at a shift change but have little idea as which shift's operating points provide the most production and least cost of goods (COGs). If pet food and ethanol manufacturers have online process metrics to compare shift and plant performance, why don't we see more of it in chemical plants? As COGs, energy conservation, and environmental emissions become of paramount importance, the benefits of online process metrics should be obvious. Since "seeing is believing" the use of wireless measurements moved from unit operation to unit operation can help develop and justify installations.

For example, if the utility flow and the inlet and outlet temperatures or pressures to coils, heat exchanger, and jacket are measured for each reactor, evaporator, and crystallizer, the energy use rate can be put online as an indicator of reaction, evaporation, or crystallization rate. The energy use per unit feed used or product produced can be monitored online for energy conservation. The overall heat transfer coefficient can be computed online to provide a performance indicator of fouling.

Inlet and outlet pressure measurements provide an indication of fouling (a heat exchanger specialist in my days in Monsanto's Engineering Technology said the pressure drop through the tubes of a heat exchanger is the best indication of fouling). Pressure measurements also show when a key unit operation is being starved for cooling supply especially since cooling tower and refrigeration systems often lagged beyond production increases in the 1990s. For example, a sold out fed-batch reaction at the top of a structure was periodically starved for coolant water by less profitable batch operations. Override control cut back the feeds to prevent a temperature trip but wireless measurements could have led to a more profitable scheduling of unit operations and improvement in coolant system design. Piping system and pump problems also extend to the process side.

An example that had all of the above problems big time is the stressed out production unit in Figure 3-6 in Advanced Control Unleashed The multiple lines of reactors, evaporators, and crystallizers converging and diverging were going up and down like Yo-Yos due to frosting (formation of crystals on surfaces) and subsequent defrosting (heating to melt the crystals). The plant was operating at double its original design capacity. Controller outputs would go to their high limit due to pipelines and pumps that were too small. Bigger control valves did not help much because the available pressure drop was insufficient. De-bottlenecking projects actually reduced capacity due to lack of measurements and the process knowledge they provide.

Thermowells on columns can find the most sensitive tray location by making a simple manual change in the flow manipulated by the temperature control (distillate flow). The best tray is the one with the largest and most symmetrical change in temperature for changes in the distillate to feed ratio (greatest and most linear process sensitivity or gain) as shown in the Figure 7-6 of the ISA book Advanced Temperature Measurement and Control Figure 7-7 shows the consequence of selecting the best tray and not so good tray for closed loop temperature control (Advanced-Temperature-Distillation-Column-Control-Excerpt.pdf). Three dimensional (3-D) profiles can provide a dramatic visualization of the change in the process gain with time as feed composition changes.

3-D plots generated of cross direction and machine direction sheet thickness profiles from a meter traversing the sheet provided the knowledge for optimizing the mixer, extruder, and sheet line as studied in the ISA 2001 conference paper "Constrained Multivariable Control of Plastic Sheets"

3-D plots are a natural for batch operations to show composition (e.g. cell density), pH, and temperature batch profiles for a series of batches.

For more information do a search for "metrics" on this website and you will find a variety of opportunities discussed for online process metrics and plots to help operations, process technology, and process control improvement initiatives. Wireless measurements and metrics can fill in the missing information for process simulations and vice versa.

In the old days we installed thermowells, pressure connections, and orifice flanges so we could move temperature and pressure gauges around to provide coarse local indication of equipment and piping system performance. Today portable wireless measurements can provide accurate indication in the control room offering opportunities that could be subject of a book or two let alone the blogs on this website.

Since efficiencies and most of the process gains for temperature and pressure are function of flow ratios (reactant/product, fuel/product, fuel/steam, steam/product, reagent/product, and coolant/product) flow is a key measurement. While a favor coriolis mass meters for process stream since they provide an accurate mass flow and density useful for inferential measurements of composition, which ultimately what you want to know, averaging annubars and thermowells can enable the use of wireless DP and temperature measurements for computing mass flow. Just knowing the material balance would be a big step forward as discussed in the Jan-Feb 2010 InTech article "Advances in Flow and Level Measurements Enhance Process Knowledge, Control"

Wireless pH and conductivity offers the ability to develop inferential measurements and prove the best electrode technology as revealed in the Jan-Feb 2010 InTech WEB Exclusive article "Opportunities for Smart Wireless pH, Conductivity Measurements"

Whenever I see real control valves with digital positioners and diaphragm actuators, I get a bit giddy with excitement. If on the other hand I see on-off valves installed to perform the role of process control, I just shake my head in dismay. If flows are turned on or off, there is very little process control opportunity. Flows, whether process or utility, are the levers for the process. If we can only jerk the levers around, we will have a jerky process. The Feb-Mar 2010 InTech article "Key Design Components for Final Control Elements" details this perspective as well as the essential design features needed. If you have throttled flows not only do you have a means of affecting but also a way of optimizing the process. It would be a rare coincidence if the flows were exactly at their best value at the right time. There is almost assuredly an opportunity to increase capacity or yield or decrease energy use by changing the flow to reduce variability and/or moving a measurement closer to it optimum operating point. Sure there are options to sequence the turning of flows on and off but such pre-programmed actions lack the feedback correction needed to deal with disturbances, non-idealities, and unknowns in industrial processes. Unfortunately, graduates from chemical or biochemical engineering programs may mistakenly be thinking they can set the flows per the process flow diagram and process design simulation program. Sure they probably had a course on control theory, but maybe all they got was a mathematical view of process control isolated rather than integrated with process research, development, and design.

If the fixed flow mindset results in the use of on-off valves and missing feedback measurements, the opportunities are difficult to identify and may require years and a bunch of money not only for the field instruments and valves but also for the piping and equipment modifications. Just think if you want to install a thermowell and there is no nozzle on the vessel or column in the right location? Also, on-off flows create the step disturbances you would hope would be relegated to control theory textbooks.

Dynamic simulations can show the way but a large expensive automation project can be a hard sell without an installed example. If on the other hand there are sensitive throttling valves and process measurements, opportunities can be trialed and implemented by taking advantage of the ever increasing incredible capability being built into the modern DCS. The key characteristic is sensitivity, which is the smallest change in the controller output or process variable that the valve and sensor, respectively will consistently respond to. Once the sensitivity threshold is reached the output will change by the full amount whereas the output will only change by a quantized amount that is a resolution limit, the other major component of precision. Often the term "resolution" is mistakenly used instead of sensitivity. Resolution, which has a stair-case response, was mostly an issue with rack and pinion actuators and older A/D converters with wide signal ranges (e.g. 1980s generation DCS thermocouple input cards). The resolution today of digital I/O far exceeds the sensitivity capability. The consistent precise response to change is more important than an exact match between input and output for valves. For example, valve span or bias errors (offsets) are clearly not much of an issue because the feedback loop will correct for them provided there is a full range of control possible. Measurement span and bias errors can also be corrected by upper loops or operating procedures, but accurate besides precise measurements are important for closing material balances for process analysis, diagnostics, and optimization as discussed in the Jan-Feb 2010 InTech article "Advances in Flow and Level Measurements Enhance Process Knowledge, Control"

Wireless measurements offer the opportunity to move the transmitters to find opportunities and the optimum location if the process and equipment design engineers had the understanding to provide the connection options. Wireless pH offers the ability to develop inferential measurements and prove the best electrode technology as revealed in the Jan-Feb 2010 InTech WEB Exclusive article "Opportunities for Smart Wireless pH, Conductivity Measurements"

Let's pull together this series on errors and conclude with a check list. The idea was prompted by perusing a popular book written on just the value of check lists. I didn't think you could write a book on just one concept but the result of saving lives for surgical procedures is impressive. I know as I have gotten older, check lists are essential to just remember what I am suppose to be doing. I have found checklists to be helpful for me from both a practical and psychological viewpoint when rushed or overwhelmed with details, tasks, and objectives.

In the following list, increases in on-stream time can increase efficiency besides capacity by eliminating the time and off-spec and waste associated with abnormal operations, startup, and shutdown. An increase in yield or decrease in recycle can be taken as a decrease in raw material costs (same production rate for lower feed rate) or an increase in production rate (higher production rate for the same feed rate). The order of the list is in order of things to check and somewhat in the order of priorities.

Check List to Improve Process On-stream Time, Production Rate, and Efficiency
(composition measurements include conductivity, dissolved oxygen, pH, and turbidity)

1. Use smart transmitters with the best sensor technology and integration of process and ambient conditions compensation.

a. Avoid older technologies particularly ones with mechanical elements

b. Seek sensor and transmitter with the best sensitivity and repeatability

2. Pick sensor location and installation method to provide the most representative measurement in process with no stagnation, best velocity, fastest response, and least noise.

a. For DP and pressure transmitters, avoid impulse lines (sensing lines) by direct mounting transmitters or using diaphragm seals and filled systems

b. For DP and vortex flow meters insure uniform velocity profile

c. For thermowells and electrodes increase velocity to reduce response time and coatings but not so high to cause abrasion, static charge, or vibration

d. For thermowells and electrodes pick location with good mixing, minimal transportation delay, and least bubbles, slime, and solids

3. Use real throttle valves with smart positioners.

a. Avoid on-off and isolation valves posing as throttling valves. Go to a control valve manufacturer instead of a piping valve manufacturer

b. Seek actuator, positioner, and valve type with best sensitivity of installed flow characteristic and signal response with least stick-slip and backlash

c. Verify positioner feedback measurement is representative of internal closure member (e.g. ball, disk, or plug) and not just actuator position

4. Tune control loop with on-demand auto tuner or adaptive controller to meet loop objectives. Tuning speed is chosen to:

a. Insure an exceptionally smooth PV and output response by decreasing transfer of variability from PV to output (increasing Lambda) for:

i. level loops on surge tanks to minimize feed upsets
ii. deadtime dominant loops (deadtime >> process time constant)
iii. interacting loops (e.g. headers)
iv. loops on piping or equipment with no back mixing (e.g. blenders, heat exchangers, extruders, static mixers, sheets, webs, and yarns)

b. Provide good load rejection of moderately fast disturbances by increasing transfer of variability from PV to output (decreasing Lambda) for:

i. Fed-batch and continuous agitated vessel and column composition, level, pressure, and temperature loops

c. Provide good load rejection of extremely fast disturbances by setting the gain and reset as a factor of deadtime rather than the time constant for:

i. Continuous agitated vessel and column composition, pressure, level, and temperature loops

d. Provide minimal overshoot of setpoints of slow lag dominant loops (process time constant >> loop deadtime and slower than 10 minutes) by tuning the loops as near-integrating processes for:

i. Fed-batch and continuous agitated vessels and column composition, pressure, and temperature loops (setpoint changes occur at startup or for changes in batch phase and product grade)

e. Provide minimal peak error by maximizing controller gain even if it requires increasing reset time to maintain robustness for:

i. Prevention of SIS activation
ii. Prevention of pressure relief
iii. Prevention of environmental violation
iv. Prevention of equipment damage

5. Add DCS signal filter or damping adjustment to keep loop output fluctuations from noise less than the valve deadband to prevent excessive valve packing wear and inflicting disturbances on loop. For wireless transmitters use damping adjustment to reduce keep transmitter output fluctuations from noise less than wireless deadband to eliminate unnecessary communication and extend battery life.

6. Eliminate on-off actions

a. Replace on-off control by switches with loops.

b. Eliminate manual actions by adding loops, keeping loops in highest design mode, adding feedforward, and automating and tuning loops to handle startup and abnormal operating conditions

c. Replace pure batch with fed-batch automation by replacing discrete sequential actions (e.g. stepping feeds) with loops (e.g. throttling feeds)

7. Tune loops that create feed disturbances (e.g. surge level loops) to provide a smooth slow transition in feed rate.

8. Add cascade control to compensate for nonlinearities and pressure disturbances (e.g. secondary flow loop and secondary coolant temperature loop).

9. Add feedforward control of measurable fast disturbances not compensated by secondary loop.

10. Optimize setpoints by operating closer to constraints for production rate or product quality spec.

a. Eliminate operating margin imposed by shift's perceived sweet spot or operating margin caused by process variability from not doing check list items 1-9

b. Find more efficient operating points based on R&D reports and virtual plant exploration - confirm with process tests

b. Add model predictive control to optimize setpoints as process conditions and market requirements change.

At my recent presentation to the ISA Saint Louis section meeting on "pH measurement", I had several people around my age say how nice it was to see me still involved in advancing our profession. Maybe it was the beer and the top ten lists but just maybe it was also that I represent a generation of expertise rapidly disappearing via retirement. The ability to still learn and share keeps me going but I realize time is running out so I intend to take this blog to the next level by coupling it with a web lab series to provide an interactive self-learning experience for exploring process control improvements (PCI). I intend to start the web lab series on April 7. Recordings of the PCI topics and demos along with instructions on using the associated labs will be viewable anywhere anytime.

In the meantime, we need to finish up this series so let's see what we can do as automation engineers to minimize loop errors.

The first thing is to make sure the measurement is fast and precise enough. So far as loop performance is concerned, precision is more critical than accuracy. The bias or offset in a measurement and control valve position can be corrected by feedback control. The offset in valve position is eliminated by the process loop. Similarly, the offset in a process loop is eliminated when the loop is in cascade or remote cascade modes. For loops operated in the auto mode, operations have often compensated for the measurement offset by tweaking the set point. This is not to say that measurement accuracy is not important.

Improving the loop's speed of response often comes down to keeping sensors clean (e.g. electrodes and thermowells), minimizing signal damping and filtering, selecting sensor locations that eliminate transportation and stagnation delays, using boosters for big valves, maximizing positioner sensitivity, minimizing deadband, and maximizing the controller gain (last week's blog).

Control loops have a difficult time dealing with the poor precision experienced as excessive stick-slip and backlash (deadband) in control valves and insufficient resolution, repeatability, and sensitivity in measurements (older measurements technologies, such as floats and rotameters can also exhibit stick-slip and backlash). Fortunately, an increase in A/D input card bits have greatly improved the resolution of transmitted signals so that sensitivity and repeatability is the remaining focus. This is unfortunately not the case for variable frequency drives manufacturers whose standard input cards have only 8 bits. A resolution limit is more degrading than a sensitivity limit. For example for a 1% resolution and 1% sensitivity and a change in the true process variable of 1.5%, the changes in measurement would be 1% and 1.5%, respectively. The deadband setting in wireless transmitters is really a sensitivity setting. When the change in a wireless transmitter measurement exceeds this setting regardless of the direction, the full change in the process variable is communicated.

Pages 12 through 15 of EffectsLoopTuning&Dynamics-KPI.pdf show the relative effect of measurement accuracy and resolution on variability. For control valves, process variability is introduced when excessive slip-stick and deadband causes an appreciable limit cycle in loops that have single and two or more integrators, respectively (pages 19 and 20).

The total loop deadtime can be approximated as the sum of all the delays and small lags in the loop whether they are in the DCS, valve, process, or measurement. For flow, pressure, level, and inline temperature and pH loops, most of the loop deadtime comes from the automation system. If you consider that the remaining loops that have significant process deadtime, such as vessel or column temperature, have seriously detuned controllers that create an effective deadtime per Advanced Application Note 5, you realize you have the opportunity as a process control engineer to make big reductions in loop deadtime that are also low cost and quick compared to changing process piping or equipment to reduce transportation or mixing delays.

Fast disturbance originate from manual operation, on-off actions, sequences, or setpoint changes. The elimination of operator actions, on-off control (e.g. level switches), and the use of set point rate of change limits and fed-batch rather than pure sequential batch, can dramatically slow down disturbances since throttling control by intention is smooth. If we keep all loops in their highest design mode and limit on-off valves to SIS actions and isolation, we could eliminate step disturbances. Page 22 shows how slowing down the disturbance dramatically reduces the peak and integrated errors for an integrating process. Not shown here is the fact that slowing down disturbances can also reduce interaction between loops. This phenomenon explains why it is difficult to get pharmaceutical companies excited about doing a better job of bioreactor control after reaching setpoint. The disturbances from cells are incredibly slow (e.g. process time constants of days).

Maybe we should not slow down disturbances because all of our control texts are based on step disturbances. Slowing down the upsets relegates us to improving the set point response in the startup of a continuous process or for changes in phase in a batch process. Whoops, even here we could use strategies such as "Full Throttle Batch and Setpoint Response" to eliminate most of the job of the loop.

There are always opportunities to make us more appreciated even when we are not improving loops. Since spouses were at the ISA Section Saint Louis Meeting, I interjected the following list. The spouses laughed although a second opinion was suggested for some of the items. See what your spouse or significant other thinks.

Top Ten Reasons Why an Automation Engineer Makes a Great Spouse or at Least a Wedding Gift

(10) Reliable from day one
(9) Always on the job
(8) Low maintenance - minimal grooming, clothing, and entertainment costs
(7) Many programmable features
(6) Stable
(5) Short settling time
(4) No frills or extraneous features
(3) Relies on feedback
(2) Good response to commands and amenable to real time optimization
(1) Readily tuned

How does controller tuning affect on-stream time and environmental costs?

The basic process control system (BPCS) forms the inner protective layer for safety instrumentation systems (SIS) as shown on page 5 of EffectsLoopTuning&Dynamics-KPI.pdf. The performance of the BPCS loops must limit excursions to be well within the operating limits that correspond to the trip points of the SIS. Specifically, the peak error for the largest and fastest disturbance should not cause a trip. The SIS should only be activated for failures or extremely abnormal conditions. The trip of a process unit not only causes downtime but can cause off-spec and additional waste during the shutdown and startup of the unit. The start-up of the process unit is often the most operator intensive and hazardous time. The importance of minimizing peak errors to prevent shutdowns can involve all types of loops (e.g. flow, level, pressure, and temperature). We normally think this is important only for continuous loops but I have been able to increase a fed-batch reactor capacity by 25% by eliminating level, pressure, and temperature trips by a series of override controllers tuned to minimize peak errors.

The peak error from the closure of a downstream valves (e.g. trip of reactor feed valves) on the discharge of a compressor controller must not cause an excursion of the operating point of the compressor to the left of the surge curve. If the operating point reaches the negative slope of the characteristic curve, it is like the compressor is falling off a cliff. The operating point jumps to a negative flow operating point in 0.03 seconds. This precipitous drop rivals water hammer in disturbance speed (both phenomena involve momentum balances that are orders of magnitude faster than material balances). Once a compressor gets into surge, the feedback controller is helpless and needs an open loop back-up (e.g. kicker) to get out of trouble as detailed on pages 6 - 8. Surge cycles can cause a decrease in compressor efficiency and damage by excessive vibration. I have also seen where surge caused a runaway speed response.

RCRA environmental regulations may classify a pond as hazardous waste if the pH of an effluent stream going into the volume momentarily ventures outside the permissible 2 to 12 pH range. Even though a short term excursion can not possibly change the pH in the volume and is effectively filtered by the volume where the change in pH is not detectable, the volume may still be classified as hazardous. For these systems, peak errors are incredibly important and kickers are used as shown on page 10 to prevent RCRA violations that not only can cause excessive fines but necessitate the process unit to apply for a new permit that might not be approved. A violation could result in the permanent shutdown of a unit because operation is no longer economically feasible or even allowed under new permit requirements.

Many process units have relief devises (e.g. relief valves and rupture discs) to prevent the over pressurization of piping and equipment. Often, pressure letdown and vent loops are the first line of defense. The peak error for the largest and fastest disturbance should be sufficiently away from the relief device setting to prevent fatigue and activation of the relief device taking into account setting tolerances and fatigue that cause a premature relief. The activation of a relief device is hazardous and causes downtime and waste burned in a flare stack or at best in a waste heat boiler.

So how do we minimize peak error? Given a set of dynamics and disturbances, the solution is to maximize controller gain even if it means increasing the reset time. This is seen in the first equation on page 1 but also intuitively from the realization that gain provides an immediate response whereas reset provides a gradual response. In the fed-batch reactor example cited above, the override controllers were proportional-only with their gains set high enough to cut back the reactant feeds immediately when the reactor pressure and level from the gas released as a byproduct or the temperature from the exothermic reaction approached settings that would cause a trip and the associated delay and disruptive restart of the feeds.

We can reduce the peak error per the first equation on page 2 by increasing the process time constant and decreasing the loop deadtime which increases the maximum allowable controller gain. We can also decrease the open loop error in the time frame of the controller's response by increasing the disturbance time constant. The fastest possible tuning should be able to stop the excursion from a disturbance after the loop deadtime. Thus, slowing down the disturbance slows down the excursion and reduces the peak reached in one loop deadtime (more on this next week). The process time constant is typically set by process equipment size and design, but we as automation engineers can greatly affect the disturbance time constant and the loop deadtime and sensitivity. We can iimprove the degree of automation, interaction, speed, reliability, and precision in automation systems. The opportunity may be larger than we realize. Up to 50% of downtime is attributable to instrumentation problems as noted in the March 2010 Control magazine article "Look to Valves for More Uptime"

Next week we will look at how the dynamics and precision of measurements, valves, and disturbances affect peak and integrated errors. We conclude this series with a check list for improving loop performance (Part 4).