Higher value added products are generally produced by batch operations. Often these products are sold out and extra batches translate to significant increases in revenue. Prime opportunities are specialty chemicals and drugs, especially new biopharmaceuticals where optimization took a backseat to time to market in the initial plant and automation system design.

I looked over my past experience with Monsanto, Solutia, and Emerson and have come up with myriad of methods to reduce batch cycle time. I have divided them up into opportunities to help feedback loops to get to setpoint faster that are important for Fed-Batch operations and for startups and transitions of continuous operations (Part 1) and opportunities to shorten phases and holds that are important for Pure-Batch operations (Part 2). These techniques like all new configurations and strategies should be thoroughly tested by simulation and closely monitored and adjusted for safe and efficient operation. Today's blog is a preview of Deminar #7 on July 14.

Fed-Batch Opportunities

1) PID on Error Structure - This structure maximizes the kick of the controller output for a setpoint change. The overdrive (driving of output past resting point) is essential for getting slow loops, such as temperature, to the optimum setpoint as fast as possible.

2) SP Track PV - With this control option the setpoint is changed to its optimum with the controller in automatic providing the kick from the PID structure (1). For batch operations this option is commonly used. For continuous operations with few setpoint changes (no grade transitions) and extremely long run time (e.g. years), the setpoint is held at its last value. However, even here loops with slow reset action (large reset times), such as level, the use of the SP Track PV option can prove useful when putting these loops back in service after maintenance.

3) SP Feedforward - For low controller gains (controller gain less than inverse of process gain), a setpoint feedforward is useful. The setpoint feedforward gain is the inverse of the dimensionless process gain minus the controller gain on a percent basis. If the setpoint and controller output are in engineering units the feedforward gain must be adjusted accordingly. The feedforward action is the process action, which is the opposite of the control action, taking into account valve action. In other words for a reverse control action, the feedforward action is direct provided the valve action is inc-open or the analog output block, I/P, or positioner reverses the signal for a inc-close (fail open) valve.

4) Output Lead-Lag - A lead-lag on the controller output or in the digital positioner can kick the signal though the valve deadband and sticktion, get past split range points, and make faster transitions from heating to cooling and vice versa. When combined with the enhanced PID algorithm described in Deminar #1, the lead-lag can potentially provide faster control when online analyzers are used for closed loop control of the integrating response associated with batch operations.

5) Deadtime Compensation - The simple addition of a delay block with the deadtime set equal to the total loop deadtime to the external reset signal for the positive feedback implementation of integral action (see Deminar #3). The controller reset time can be significantly reduced and the controller gain increased if the delay block deadtime is equal or slightly less than the process deadtime (see Advanced Application Note 3 entry March 25, 2009 on this website).

6) Full Throttle Batch - The controller output is put at its output limit to maximize the rate of approach to setpoint. When the projected PV equals the setpoint less a bias, the controller output is repositioned to the final resting value captured from the last batch. The output is held at the resting value for one deadtime. For more details, check out the Control magazine article "Full Throttle Batch and Startup Response."

7) Feed Maximization - Valve position control, Model Predictive Control (see Advanced Application Notes 1 and 2 entries March 25, 2009 on this website), or override control is used to maximize feeds to limits of operating constraints (e.g. maximum vent, overhead condenser, or jacket valve position with sufficient sensitivity). Alternatively, the limiting valve can be set wide open and the feeds throttled for temperature or pressure control. For pressure control of gaseous reactants, this strategy can be quite effective. For temperature control of liquid reactants, the user needs to confirm that the inverse response from the addition of cold reactants to an exothermic reactor and the lag from the concentration response does not cause temperature control problems. All of these methods require tuning and may not be particularly adept at dealing with fast disturbances unless some feedforward is added. Fortunately the prevalent disturbance is a feed concentration change that is often slow enough due to raw material storage volume to be corrected by feedback control.

8) Profile Control - If you have a have batch measurement that should increase to a maximum at the batch end point (e.g. maximum reaction temperature or product concentration), the slope of the batch profile of this measurement can be maximized to reduce batch cycle time. For application examples checkout "Direct Temperature Rate of Change Control Improves Reactor Yield" in a Funny Thing Happened on the Way to the Control Room E-book April 3, 2009 entry on this website and the Control magazine article "Unlocking the Secret Profiles of Batch Reactors"

This blog was kind of fun to write "With A Little Help from My Friends" (beer and music). By the way, the album "Joe Cocker" was apparently only produced on vinyl and 8-track tape. I fondly remember riding in my roadster with the top down listening to "Saint James Infirmary Blues" on the way to Monsanto's New Orleans plant. There are great songs on this album that carried me through startups that never made it onto a CD.

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

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.

MPC Implementation Methods for The Optimization of a Slow MV with Good Load Rejection by a Fast MV, September 22, 2005 (970KB PDF)

MPC Implementation Methods for the Optimization of the Response of Control Valves to Reduce Variability, January 3, 2007 (463KB PDF)

Compensation of Dead Time in PID Controllers, December 6, 2006 (4,074KB PDF)

First Principle Process Gains, Dead Times, and Time Constants, December 28, 2006 (97KB PDF)

Effect of Sample Delay on Standard PID Tuning and Loop Performance, August 28, 2008 (1,919KB PDF)