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"

Simulation was such an integral part of my job it is difficult for me to visualize a process control career without models. I was asked to join Engineering Technology (ET) at Monsanto in 1976 because I had developed a dynamic compressor model as the lead Instrument and Electrical engineer for what was the largest Acrylonitrile plant in the world. I developed the model in order to understand more about the incredible surge phenomena where reversals of flow could occur in less than 0.01 seconds leading as a minimum to a loss in efficiency and in some cases to the damage of shafts and seals of large and expensive compressors from the extreme momentum swings and vibration. In most plants the ability to initiate and explore abnormal situations is severely limited or not allowed. A dynamic model allows you to readily and quickly try out "What if Scenarios" whose only limit is your imagination.

ET developed FLOWTRAN, a process simulator that was directed by the government to be sold to Aspen institute. Several key specialists left with the FLOWTRAN to develop the process modeling software that eventually was the state of the art process design modeling software by AspenTech. In the ET process control groups, we used FLOWTRAN to get the process gains and then used IBM's Continuous System Modeling Programs (CSMP) followed by Raytheon's Advanced Continuous Simulation Language (ACSL), and ultimately HYSYS Plant for dynamic simulations. After retirement from my career in ET, I focused on using the DCS as a Virtual Plant for simulation and control. The graphical configuration environment where function blocks are equipment and wires are streams (e.g. DeltaV Control Studio and MiMiC) allows the development of dynamic process models in the same familiar way as the configuration of control strategies.

My vision of a virtual plant has a simple first principle model that starts with one component (e.g. water and air) that is corrected by an experimental model automatically generated by a simple test that takes less 10 minutes to execute for most loops. The result is a plant wide simulator. As more information is available and desired, the process knowledge embedded in the model grows but the fundamental basis is the same. No re-write is required. The opportunities and associated fidelity needed are as follows:

1. Control system set point optimization - Fidelity 5

2. Control strategy analysis and R&D - Fidelity 4

3. Root cause analysis and data analytics R&D - Fidelity 4

4. Operator training for abnormal situation management - Fidelity 4

5. Controller tuning and PID structure and options analysis - Fidelity 3

6. Batch configuration checkout and operator training for system familiarization - Fidelity 2

7. Loop configuration checkout - Fidelity 1

Fidelity 1: loop process variables respond in the proper direction to their loop output

Fidelity 2: measurements respond in the proper direction when control and block valves open and close and prime movers (e.g. pumps, fans, and compressors) start and stop.

Fidelity 3: loop dynamics (e.g. process gain, time constant, and deadtime) are sufficiently accurate (e.g. 50%) to tune loops and see process interactions

Fidelity 4: measurement dynamics (response to valves, prime movers, and disturbances) are sufficiently accurate (e.g. 25%) to track down and analyze disturbances

Fidelity 5: process metrics (e.g. yield, raw material costs, energy costs, product quality, production rate, production revenue) are sufficiently accurate (e.g. 5%) to find optimums

In the ISA New Orleans section short course I am teaching on March 3 and 4 titled: "Exceptional Process Control Opportunities - An Interactive Exploration of Process Control Improvements", I will use a virtual plant suitable for process control research, development, and education. I will demonstrate how a user can perform a 10 minute test of a manipulated process flow to provide a fidelity level 3 and 4 model. The contact for the course is Robert Deeb (ISA New Orleans section education chairman).

In the InTech Jan-Feb 2010 Web Exclusive "Advances in Flow and Level Measurements Enable Dramatic Improvements in Process Knowledge and Control", the following perspective was offered on the importance of flows for many types of process models including the following:

• Projection to Latent Structure or Partial Least Squares (PLS)
• Model Predictive Control (MPC)
• PID Adaptive Controller Tuning
• Neural Network
• First Principle

Flows determine what is going on in a process. If you don't get the flows right, not much else matters. Because of valve backlash, stick-slip, nonlinearities, and variable pressure drop, all types of process models have suffered from the use of valve positions rather than flow measurements. PLS, MPC, and PID performance assumes dynamics that are linear and independent of direction and size, all bad assumptions when valve positions rather than flows are used as inputs. Additionally, the valve nonlinearity from the installed characteristic varies with pressures at the inlet and outlet of the valve.

Pioneering advances in dynamic modeling by Alex Muravyev offer a next generation of pressure-flow solvers that will be robust and flexible enough to provide flows from valve positions. The solver is expected to handle complex piping networks and the discontinuities from batch and startup sequences (AdvancedSimulationPressureFlowSolver.pdf). The ability to consistently and comprehensively compute flows for all streams will enable dynamic models to reach the highest levels of fidelity required for research, development, and design of automation systems for nearly all applications. Presently, models can only move up in fidelity when flow control loops are installed on the key streams so that feedback action removes the nonlinearity and unknowns of the valve and piping system. New pressure-flow solvers can eliminate this precondition. A side benefit will be the demonstration by these models of the improvement in process performance that can be gained from cascade, feedforward, and ratio control. The quantifiable benefits from demonstrable test cases can justify new flow devices to provide missing flow measurements or improve the accuracy of existing flow measurements.

Before my talk at the Boston ISA section meeting on Oct 20, I had the opportunity to interview Sarah Tremblay and Ted Stillwell, automation engineers for a company that designs water and wastewater treatment systems. Sarah has a degree in mechanical engineering and has been on the job for one month. Ted has over 40 years of experience in the process industry. Like me, Ted started out in construction so he got a lot of first hand experiences on what worked in the field. The interview was an informal discussion for an upcoming Control Talk column on "Expertise Development" probably with a more catchy title such as "The Future is Now."

When I started as an E&I design and construction engineer after graduating with a degree in engineering physics, I went to a 12 week instrument school. One of the attendees at the ISA talk says he knows a company that had a 9 month training program. Such on-the-clock courses and programs are rare. Are we missing the boat? Sarah effectively said "not really" because such an intensive and extended training would not mean much to a new engineer who has not developed a real feel for the job. Sarah is learning by being responsible for small parts of a project. She asks a lot of questions. She visits job sites and goes on panel checkouts with Ted to see how designs translate to actual installations. This is the time honored tradition of how expertise is developed on the job. In 5 to 10 years, you have a proficient engineer. In my case, my development was accelerated by being sent after instrumentation school to E&I field construction for 2 years for the building or renovation and startup of 5 production units. Since sending new engineers to E&I construction is not a widely viable option, what can be done to improve this process?

There are no easy answers. Courses in chemical, electrical, mechanical, and systems engineering should have more emphasis on process measurement and control as practiced in industry. Practitioners (especially recent graduates) should be invited to give guest lectures on case histories of process control improvements and the type of jobs in the process industry. It should be emphasized that regardless of whether the job is in engineering, research, or production, all engineers rely on the automation system to see, analyze, and interact with the process. You need to know how to understand the system's interface and functionality to take full advantage of the systems capability. Process control labs with industrial control systems should be an essential part of this learning experience. Many of the leading universities have taken this approach as described in the June 1, 2009 entry on this web site "What I have Learned? - Bridging the Gap between Universities and Industry."

Sarah made a good point that course labs can be too controlled. The script is fixed and the student doesn't have the opportunity to explore different scenarios and ideas, implying the falsehood that on-the-job situations are typically as uneventful. To help address this issue, I think these labs should be offered as a stand-alone course rather than in addition to a "hands on" experience to demonstrate points in a lecture course. I think the lab should consist of both a physical and a virtual plant for the same unit operation. The virtual plant would allow the student to take the operation and control system to places not practical to achieve because of time and equipment limitations.

This education process needs to ongoing. It should not stop with the new job. Since extended training programs may be too much too soon besides being impractical from a standpoint of cost and time in today's work place, periodic seminars and demonstrations with a virtual plant would seem to be the most effective approach. Case histories and updates on technological advances are essential. The seminars and labs can be conducted via the web if interaction between the presenter and attendee is not sacrificed. Companies need to provide the time and encouragement for ongoing education. The ISA Certification of Automation Professionals (CAP) should be part of the career plan. Participation in ISA should be part of growth process for both the individual and ISA. There should be a company library of the best books on process measurement and control (see next week's entry here for my short list). Users should be encouraged to publish to help solidify their experience and share it with the profession. I always learned something about my application in the process of having to describe the problem, considerations, concept, and solution. See my May 28, 2009 entry "What have I Learned - Writing" on what worked for me. Sarah with a minor in English is ideally situated for this endeavor.

Given that the education process takes years of on-the-job experience it is critical that companies hire new automation engineers now to insure the existing expertise is transferred before the expertise is gone. See my Control Talk Column series "Going, Going, Gone" Part 1 (August), Part 2 (September), and Part 3 (October) for a discussion with some key people from what is probably the best process control group in the USA.

Most of the experienced engineers here in the USA are members of AARP.

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 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.

All plants have oscillations. Process control improvement can reduce or eliminate these oscillations. In these days of tight budgets and resources, how do you justify cost and effort to fix the problem?

The "before" and "after" distribution and location of process variability depicted in slide 1 in ProcessControlBenefits.pdf is the classic presentation on how tighter control can result in benefits. If you can reduce the standard deviation (sigma), you can move the set point closer to the constraint without increasing the number of violations of the constraint. What I have added to the slide is the practical situation where operations give themselves a cushion or margin, particularly if there is no online monitoring system with data analytics that can provide the process knowledge and confidence needed to operate at edge of the product range to gain a competitive edge. In my experience the margin almost always exists, it is just a matter of how much. The margin is perhaps easiest to visualize in plastic sheet manufacturing. The greatest variability in sheet thickness and optical clarity occurs near the edge. An extra margin of sheet is trimmed off to make sure there are no off-spec sheets. Without doing anything to provide tighter thickness control, the trim width could be change if there was enough process knowledge and confidence. The benefit from less scrap can be taken as a decrease in raw material and utility cost to obtain the existing capacity or as an increase in capacity for the existing raw material and utility use as noted in the categorization of possible benefits on slide 2.

The key idea here is that most benefits are not achieved until we change a set point. We can find the existing margin by the intelligent use of an online data analytics system and we can create a new margin by tighter process control. Once we know the margin, we need to move the set point to eliminate the margin. A "good" process control engineer can draw straight lines. A "great" process control engineer can move the straight lines.

Often we are not so lucky to have an online measurement and closed loop control of the product quality or concentration that is the ultimate process output as implied by slide 1. What we have is lot of intermediate unit operations in a plant each with a multitude of process inputs and process outputs that can be oscillating. As a minimum many chemical and biochemical plants have a reaction unit operation followed by separation, purification, and formulation unit operations. For solid products, there is often additional equipment for crystallization, centrifuging, drying, and blending. Each of these unit operations has process inputs and outputs with a degree of variability.

So we have short term or long term oscillations at various points in the process and can reduce or eliminate these oscillations. How do we justify the cost and quantify the benefits of better process control?

In order to estimate what we can gain from process control improvement, we need to know process gains. A process gain is the change in a process output divided by the change in a process input. There is a steady state process gain which is the final change after all transients have dies out and the process has reached a new steady state. Steady state simulations can provide these process gains and through virtual experimentation quantify the changes in the product composition or quality for changes in an upstream process variable. For oscillations there is also a dynamic effect where the oscillations of a process variable are attenuated by downstream volumes. The attenuation is proportional to the period and inversely proportional to the residence time of volumes with back mixing from turbulence, recirculation, and agitation. The follow equation can be used to estimate the amplitude of oscillations in a process output (Ao) for oscillations in a process input (Ai), a steady state process gain (Kp) between the process output and input, a period of oscillation (Po), and for a residence time of a back mixed volume (Tm). The residence time is the volume divided by the total flow rate through the volume.

Ao = Ai * Kp * [Po / (6.28*Tm)]

We can compute the steady process gain from first principle equations as shown in Advanced Application Note 4 posted on March 25 or get it from a steady state simulation as long as we avoid a valve position as the process input. The installed characteristic of teh valve and hence the slope of this curve's contribution to the process gain is typically not simulated correctly. Dynamic simulations that have a flow-pressure solver should be able to predict the oscillation amplitude but in practice the results are poor because these simulations do not sufficiently model process and automation system dead times, valve backlash and sticktion, and control loop tuning that determines the period of the oscillations.

The best way to estimate the relationship is the find the process variable furthest upstream with the same dominant period of oscillations that are in the product. The ratio of the amplitudes (Ao/Ai) is the dynamic process gain. For a given reduction in the amplitude Ai, you can estimate the corresponding reduction in amplitude Ao. A power spectrum analysis of the process variables can be used to find the variables with the corresponding dominant frequencies. We then need to follow through and see how much of a margin we can create by a reduction in the product oscillation amplitude.

Once we have the margin, we need to work backwards (upstream) to get at what is the corresponding reduction in utility flow or feed flow. How do we do this? Again we need to use process gains. We divide the product margin by the process gain to get the change to be made in a key upstream loop set point once we have reduced the oscillations in the product. Consider the case where the key loop is a reaction or distillation temperature loop. We then divide the change in reactor or column temperature set point by the steady state process gain for the required change in coolant temperature and reflux flow, respectively. Next we divide this change in coolant temperature or reflux flow by the steady state process gain for the required change in coolant flow and steam flow, respectively. Finally, we multiply the required changes in utility flow by their cost per unit flow to get at the cost savings. Knowledge of the process and the gains are in the process gains and the periods of oscillation. Online data analytics can find the margins, power spectrum analyzers can find periods, and online controller tuning can find the process gains.

The leading cause of oscillations is a level loop with overly aggressive tuning and in some cases excessively sluggish tuning. Several very sophisticated process studies have come to down to this simple fix. Next week we will look in more detail at this culprit and explore the other causes of oscillations.