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

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

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

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

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

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

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

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

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

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

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.

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

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

Other common examples are

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Here are the key points for my 2005 - 2006 articles

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Advanced Application Note 002

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

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

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

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

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

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

The loops with the most severe oscillations listed in order from biggest amplitude to smallest amplitude are pH loops, level loops, flow loops, pressure loops, batch temperature loops, heat exchanger temperature loops, and column temperature loops.

The following is a list of the sources of product quality oscillations in the approximate descending order of frequency of occurrence based on my experience. I have even offered my best guess in parentheses as to the percentage of applications that can be tracked to these root causes for chemical and biochemical products. You may wonder why pH loops didn't make the top of the list since it has the most severe oscillations. The main reason pH loops are down the list is that most pH loops are in waste treatment (WT). Also, the pH loops in reactors and bioreactors tend to have much lower process gains than WT pH loops and some process regulation from reagent consumption. Interacting temperature loops on furnaces, reformers, and reactors are severe problems but are near the bottom of the list for applications for specialty chemicals and biochemical products because multi-zone or profile temperature control are more prevalent in the petroleum, petrochemical, and bulk chemical industries. The following list is for normal operation of loops with good valves and does not consider oscillations that originate from the startup and shutdown and failure of equipment. Next week we will see the implications of "not so good" valves.

(1) Too much reset action in level loops on surge and feed tanks (40%)
(2) Discontinuities at split range point for pH, pressure, and temperature loops (20%)
(3) Interacting pressure and flow loops on headers (10%)
(4) Too much reset action in overhead pressure loops on columns and vessels (10%)
(5) Set point response of batch temperature loops (5%)
(6) Interacting temperature loops for 2 point composition control of columns (5%)
(7) Interacting temperature loops on furnaces and reactors (5%)
(8) Set point response of batch pH loops (5%)