Modeling and Control

Training an operator on a new control system often includes hands on experience with a training system that supports dynamic process and control system simulation. The hardware for such a training system may be constructed using spare parts from the new control system. A simple simulation of the process can often be implemented using the control system tools provided to configure calculations and logic. The plant control strategies and operator interface should be used without change when create the training system. However, one of the barriers in doing this is the fact that the IO configuration used for measurement, calculations, and control strategy may need to be modified to work with a process simulation. As the Foundation Fieldbus team worked on the function block specifications, one of our objectives was to provide an easy means of integrating process simulation into measurement and control applications. Also, the ability to override IO values was something we felt that an instrument technician or control engineer would find helpful in checkout of a control strategy or a display configuration.

After some investigation, the function block team proposed that a SIMULATE parameter
be included in all IO blocks. This parameter was defined to have the following attributes:

 Simulate Enable/Disable
 Simulate Value
 Simulate Status
 Field Value
 Field Status

The actual measurement value and status of the IO block are reflected in the Field Value and Status. When the Simulate Enable/Disable attribute is changed to Enable, then the IO function block uses the Simulate Value and Status in place of the Field Value and Status. Thus, an instrument technician that is checking out a control strategy before startup can simply Enable simulate and then write to the Simulate value and status attribute. In the IO blocks, the simulated value and status are processed the same as the field element signal. Thus, when a process simulation writes calculated measurement values and status to the Simulate Value and Status attributes of IO blocks then values based on these simulated measurements will appears in the control strategy and operator screen.

When the function block team initially presented the Simulate parameter to the Fieldbus Foundation Technical Advisory team, there was much discussion about whether we should including this capability in field devices. The concern was that, in an on-line system, the operator would not know if the measurement he sees at his interface station is simulated or the true measurement value. To address this concern, the function block team added to the specification the requirement that all Foundation fieldbus devices support a physical jumper that can be used to disable the simulation capability in an on-line system. Also, an explicit alarm was added to BLK_ERR that indicates when simulation is enabled. By taking these steps, the function block team was able to include SIMULATE as a standard parameter in Fieldbus Foundation IO blocks. This capability has proven to be very valuable in system check and in enabling the development of operator training systems.

My favorite “Far Side� cartoon has Einstein at a chalk board full of derived equations ending up with the ultimate equation “time=money.� In my mind, the negative free time of the process control engineer places some doubt as to whether this endangered species still exists. There have been sightings but the uncertainty principal says we can only ascertain their location or function but not both.

Experimental models do a good job of minimizing the time and expertise required of process control engineers by not relying upon process knowledge. Since these models are identified from test data, they are consistent with the ultimate goal of matching reality even if process understanding lags behind. Each technique excels at addressing a particular aspect. For example, Neural Networks (NN), Projections to Latent Structure (PLS), and Model Predictive Control (MPC), excel at identifying the nonlinear, interdependent, and dynamic, respectively, nature of process inputs. The strong point of one method is often the weak point of others and in the end somebody with some sort of process understanding should check to make sure the models make physical sense. There are several watch outs. For example, avoid extrapolation by a NN outside of its training data range because nonlinear relationships can take off exponentially. Since PLS and MPC assume linearity, you have to be careful about deviating too far from an operating point to the point where turndown and startup may require the identification and switching of different models. NN and PLS don’t try to model the process time constant or integrating process gain, so there is a model mismatch for well mixed volumes where the residence time translates to a process time constant or a “near� or “real� integrating process gain. Also, NN and PLS are often sold based on just throwing existing historical data at them ignoring the transfer of variability by closed control loops and not perturbing process inputs. The richness of the dynamics, the rangeability, and the identification of cause and effect suffers. What has been so important to the success of MPC, seems to have been lost

What about all the other types of models?

Tiebacks are very attractive because they initially require hardly any effort. They can be automatically generated from the configuration. These are great for control system familiarization and interface improvements (e.g. operator training and critiquing of graphics) and I/O checkout. They can be used to mimic the process response by the heuristic customization of ramp rates triggered by piping path logic to test out the configuration, particularly important for complex continuous and batch control systems.

Finally, there are the models based on chemistry and physics (not necessarily popular subjects). Very sophisticated software has been developed to provide a graphical flow sheet simulation of processes. Unfortunately, these generally require a sophisticated budget and user. Most of the big players focus on continuous steady state operation, the traditional realm of chemical engineering programs. Separate special purpose packages are typically required for batch. My experience with "state of the art " process modeling software is that they do a good job of process design but are not as good as you might expect in showing the process dynamics especially considering they carry the label “high fidelity�. The process gain is off because the installed characteristic of the control valve and measurement scale are not included, the process dead time is too small because transportation and mixing delays are missing, and the process time constant is too small because thermal lags and jackets/coils are missing. To top it off, the trends are way too smooth because there is no mixing or sensor noise and no limit cycles from control valve stick-slip or backlash. For more enlightenment on the issues with dynamic process simulators see the Control magazine August 2005 article titled "The Light at the End of the Tunnel is a Train (Virtual Plant Reality)".

When you sit back (something I am getting better at being partly retired) and look at the whole picture, it seems fractured.

Why aren’t there basic generic first principal models that focus on the process dynamics without getting bogged down in the complexity needed for process design? Why aren’t there hybrid models that take advantage of the best of what each method has to offer? What would we call these models that provide the type of fidelity needed for process control? Are we stuck in a rut because each expert thinks their particular method is best? Are there people with broad enough skills and attitude to pull it off?

The virtual plant offers a break through in training and knowledge discovery but its potential depends upon the ability to develop dynamic simulations that capture the process relationships and response important for process understanding and control.

The best practice of practical real time simulation could easily fill a book but I need to wind this up and move on to other opportunities so here are a few ideas on how to make a process model more flexible in terms of cost and performance and maintainability. It is important to realize the art of simulation is simplification to what is essential.

A significant portion of the time is spent trying to decipher the intricacies of a plant’s DCS configuration and displays. If there is an accurate P&D with the relative location of every pump, fan, valve, and measurement noted along with the complete DCS tag name, and there is browser access to each tag name to assign DCS outputs as process inputs and process outputs as DCS inputs in the model, the need to dig into the configuration is vastly reduced. Note that special DCS I/O such as pulse counts must still be identified and separately addressed.

The computational requirements, numerical hazards, and data requirements on the piping system and fluid flow of a pressure-flow solver are considerable. If there are flow loops for every throttle valve, then the complexity and cost of a pressure-flow solver may be avoided. Of course, this simplification will not identify improperly sized pumps, valves, and pipes. I propose it would be better to add imbedded flow loops in the process simulation rather then venturing into a pressure-flow solver. This simplified approach uses a combination of flow loops and a pathway methodology where the 1 or 0 status of on-off valves and pumps determine an open piping path. The total flow coming out of a piping tee can be written back as the flow going into the tee. The use of flow loops reduces but does not eliminate the need to simulate valve backlash and stick-slip. If a pressure-flow solver is deemed valuable, than I suggest a sequential modular method to avoid ill conditioned matrices and numerical problems during batch operations and the startup and shutdown of equipment.

If the model starts out with initialized but settable molecular weights, densities, and heat capacities, then levels, temperatures, blending, and temperature can be simulated. If the dissociation constants for bases and pH are added, then pH can be added. For the modeling of vaporizers and evaporators, it may be sufficient to add vapor pressures and boiling points of selected components as a function of composition. For reactors, the standard form of Arrhenius and Michaelis-Menten kinetics may be sufficient. Neural networks may be able identify kinetic rates to provide a simpler and higher fidelity hybrid model. The complexity of a full blown physical property package could be reserved for more complex vapor equilibrium problems such as distillation.

Finally, it is most important to get the dynamics right. The process models from on-demand and on-line tuning packages such as DeltaV Insight and model predictive controllers such as DeltaV Predict can be used to supplement or replace first principle models for specific parts of the process.

For my virtual plant experience and top ten list check out
http://www.controlglobal.com/articles/2007/385.html
http://www.controlglobal.com/articles/2007/359.html
and the “Education� and “Process Simulation� categories on this website.

I left the blogsphere to do a beta test of pH modeling and control. I return on July 2 to ask the question "Is This the Time"?

Is this the time for process and automation system designs to minimize dead time and interactions? Is this the time for university graduates to understand controller modes and parameters and the power of the DCS environment? Is it possible for these graduates to know how to tune a controller with non perfect mixing, measurements, and valves? Is this the time for users to know how to improve control system performance? Is this the time for new engineers to have a virtual mentor? Is this the time for suppliers to be able to demo the value of better measurements, valves, tuning, and advanced control?

This could be the time if modeling is embedded in the DCS and becomes a common tool in universities and industry. It started to happen about 5 years ago. Terry Tolliver and Robert Heider at Washington University use an industrial DCS as a virtual plant and in a computer control lab to teach process modeling and control as part of their chemical and system engineering programs. Atanas Serbezov at Rose-Hulman Institute of Technology uses a DCS in a lab to teach process control to Chemical Engineers. A DCS system has just been installed at Purdue University as part of the Engineering Research Center with Rutgers and the New Jersey Institute of Technology. If you doubt the value, talk to the students. In industry, Broadley-James, Lilly, Lubrizol, Monsanto, and Solutia have started using embedded modeling in a DCS for process design and control.

Modeling was such an integral part of my career, it is difficult for me to imagine how I would have learned and accomplished anywhere near as much without it. Modeling was a key part of my job even in the old days when you had to key punch cards for the IBM Continuous Simulation Modeling Program (CSMP) and submit them for an overnight run in a room full of main frame computers. When I got terminal server access to a computer with the Advanced Continuous Simulation Language Program (ACSL), I thought I was in heaven even though ACSL was designed for the aerospace industry. When graphical flow sheet simulators on a PC came along that we could interface to a DCS, I was blown away even though the interface was slow and cumbersome and the model speedup was rather minimal and inconsistent. I gave this all up to retire in 2002 and set up the virtual plant at Washington University. This wasn’t quite enough so I ended up in Austin in the fall of 2004 to see if I could help the future of modeling by making it more accessible. While only 50% of my 75% part time venture is actually doing modeling, I am at a juncture to see how well it can be used.

Eventually the automation world will evolve to where modeling is an integral tool for learning and 4D processes (development, design, deployment, and diagnostics). While my physics and process modeling background leads me to focus on models based on first principles, data driven models such as neural networks, linear dynamic estimators (e.g. MPC models), and multivariate statistical process control such as projection to latent structures (PLS) have great relevance because they have make no assumptions and detect the inevitable unknowns. I envision a future of hybrid models embedded in the DCS that use the best that each of these modeling technologies offers. Is this the time?

I remember my very first time …. for simulation ….. for processes .… for chemical not biological. I was working as lead instrument and electrical engineer on what was then the largest acrylonitrile and hydrogen cyanide plant in the world and I decided maybe the huge compressor that provided the air for reaction with propylene and ammonia was worth a closer look. If the compressor tripped a whole day’s production was lost plus startup was the most dangerous mode of operation. Then there were the consequences of surge. The amount of energy from flow reversals was enormous. Surge cycles reduced the compressor efficiency and could damage the rotor from excessive vibration. Even though it wasn’t in my job description, I decided as an extracurricular activity to write a dynamic model of the compressor. I learned that surge can occur in seconds after crossing the surge set point and that once the compressor got into surge you could not rely upon feedback control could not get it out. The nearly full scale flow reversals every couple of seconds was just too much for a flow controller. I learned the importance of a feed forward signal from reactor feed valve position and an open loop backup. After a year in field construction for installation and startup, I was invited either to do an encore for the next plant in England or based on my simulation program venture take a job in engineering technology. Even though the overseas assignment was tempting, the chance to work with some of the brightest minds in process modeling and control (Henry Chien, Larry McCune, Bob Otto, Terry Tolliver, and Vernon Trevathan plus the legacy of Joel Hougen and Ted Williams) was too much to pass up. Dynamic simulation became the most important tool for me for more than 30 years.

I have used models of compressors many times in some pretty exciting situations. No other disturbance is faster. The precipitous drop in flow that precedes surge occurs in 0.05 seconds. To simulate these dynamics you need a momentum balance besides a material and energy balance as shown in the ACSL program on page 121 of the E-book: http://www.easydeltav.com/controlinsights/compressorcontrolstudent/. The phenomenon was worse than falling off a cliff with a bungee cord with a perpetual rebound.

For a summary of some my compressor startup experiences check out “Compressor Surge Control – Traveling in the Fast Lane� on page 19 of the E-book:
http://www.easydeltav.com/controlinsights/FunnyThing/default.asp

The process of creating a model is in itself a knowledge building activity because it makes you think through first principal and dynamic relationships. Often insight into the problem is gained before the model is completed but then again there are the surprises from the test runs of the models due to the interactions and multivariable nature of most processes. Last week I gave my first model as an example. Here I jump forward 30 years to my most recent model, which explores glucose control of a mammalian cell culture.

Even if you are not into bioreactors, you might want to read on because there are insights here for concentration control of batch reactors in general. Just substitute your favorite reactant for glucose and reaction rate for consumption rate.

Most bioreactors to date have a glucose feed rate scheduled as part of batch sequence. The feed rate changes are usually developed during research and development and fixed for the commercial process for the industrial plant. The chances that the glucose feed rate exactly matches the glucose consumption rate is next to none.

The advent of analyzers to measure glucose at-line and NIR probes to measure glucose online opens the opportunity for glucose concentration control and consequently finding and maintaining the optimum glucose concentration for cell growth and product formation as discussed in the article titled “Unlocking the Secret Profiles of Batch Reactors� http://www.controlglobal.com/articles/2008/230.html

For a step change in glucose feed rate, there should be an integrating process response. However, test results from a bioreactor model show that for step increases in glucose feed rate the response started to ramp but then leveled off and decreased for steps on day 2 and flat lined for a +5% step and accelerated upscale for a +10% step on day 8. This odd behavior is the result of a glucose consumption rate that parallels the exponential, stationary, and death phases of the batch. These phases can be seen in the dissolved oxygen controller output. In Glucose Test Results, slide 1 shows a batch with automatic glucose control and slides 2 and 3 show batches with the glucose controller in manual with steps at day 2 and 8 of +5% and +10%, respectively in the controller output. Not shown is the ramp down and eventual depletion of glucose for decreases in feed via steps of -5% and -10%. In each case, the glucose feed and consumption rate were in balance because the controller was in automatic prior to the first step. This may not be the case for new or improperly tuned controllers.

Slide 4 shows the glucose control test results described in the aforementioned article for an online probe (no delay) and an at-line analyzer (11 hr sample delay). These test results show that determining the integrator gain and arrest time is essential and that the use of a feedforward signal can provide remarkable improvement especially for at-line analyzers.

Obviously the size and duration of the steps and their time in the batch determines whether you see a self-regulating, integrating, or runaway response and even an eventual reversal of the process gain. So how does one tune such an animal? The short cut method as described on pages 53-57 of the Good Tuning: A Pocket Guide 2005 second edition published by ISA, which uses the initial change in the ramp rates, may be your best bet. The method identifies a “pseudo integrator� (“near integrator�) gain and does not require the controller be in auto or the process be lined out at the start of the tests. The referenced pages are in the book excerpt Good Tuning Short Cut Method.

The relay oscillation auto tuner can provide successful results if the step size is large enough to overcome the changes in the consumption rate. However, for at-line analyzers, the ultimate period and consequently the test time may be too long. For the default setting of 3 cycles and assuming an average period of 4 deadtimes, the auto tuner would typically take 12 deadtimes. The user may find it adequate to use the results available after just one cycle (4 deadtimes). The short cut method can provide an estimate of the tuning settings in about 4 dead times assuming you make 2 steps and wait at least 2 deadtimes to see the change in ramp rate. Regardless of method, the tests for an at-line analyzer with a 4 hour or longer sample time will cover several shifts. New adaptive online tuning tools such as DeltaV Insight offer the opportunity to non-intrusively find better tuning settings from the initial response of the loop at the start of the batch and the response to normal set point changes during the course of the batch. However, the auto tuner and the short cut method might be still be useful for getting a new loop in the ball park to enable basic closed loop control from the get go.

The glucose consumption rate depends upon cell growth and to a lesser extent on product formation rates. The oxygen uptake rate can be estimated from the dissolved oxygen controller output (more specifically the secondary loop flows for air and/or oxygen sparge). If this inferred oxygen uptake rate is then corrected for maintenance and yield factors, it is a good candidate for a feedforward signal if the dissolved oxygen control is fast and tight so that changes in process are rapidly transferred to the controller output and the mass balance of dissolved oxygen in the broth is maintained.

This week I completed a model with the help of Roger Reedy that allowed me to confirm some concepts besides detail how the design of the control system can cut a project cost almost in half by the use of 10,000 gallon instead of 40,000 gallon neutralization tanks. It wasn’t an easy pH control application but not many of them are. The titration curve slope and the hence process gain changed by a factor of 1,000:1 from the extremes of the pH scale range to the neutral point. The influent pH could swing from 12 to 2 pH during the regeneration of a demineralization system or an area pump out. The disturbances could be fast because of plug flow, batch sequences, manual operations, and the stick-slip action of control valves. If pH control is not your thing and it is “High Time We Went� per the Joe Cocker song I am listening to, here is the escape clause.

“Besides embedded process models saving projects a chunk of money, improving plant performance, and justifying better controls and valves by studying the dynamics and integrated functionality of the process and automation system design, you can learn neat stuff like:�

(1) Speed besides size is important
(2) Feedforward signals can do more harm than good
(3) Feedforward head starts based on deltas can help
(4) Linearization of the process variable can be robust and useful
(5) Valve stick-slip can be the upset that keeps on giving

If you are caught within the gravitational pull of this study, I can’t guarantee it is not a black hole that sucks you into another dimension.

A process model constructed and embedded in the DCS was used to study a conventional pH and a reagent demand control system with and without feedforward control. In all cases the control loop was in the recirculation line of a vessel to provide a fast feedback correction of abrupt and large disturbances. The feed and reagents were injected at the inlet of a static mixer just before the recirculation stream reentered the vessel. Middle signal selection of 3 pH electrodes was used to inherently ignore a single sensor failure of any type, reduce measurement noise, ignore spikes and slow sensors, and facilitate online diagnostics and calibration. The inline control loop was extremely fast. The transportation delay was only about 2 seconds. The largest potential source of deadtime was injection delay associated with opening and closing of the reagent control valves but this was minimized by coordinated action of close coupled isolation valves at the injection point. Insuring model fidelity for a pH system simply came down to matching the slopes of the model’s titration curve with the slopes of the plant’s lab titration curve. The following file shows the model and lab titration curves on slides 1 and 2 and the control system on slide 3. Not readable is the slope of 0.015 at 2 and 12 pH.
pH System02 Study Results

First you need to get good lab curves by taking samples of the influent at key times such as steps in a batch sequence when acids or bases are used or during unusual operations such as the pump out of containment areas. The samples should be at the process temperature and titrated with the same reagents used in the automation system. The sample time, temperature, and volume and reagent type and strength must be noted and reagent addition volumes and pH must be tabularized. The typical graphical plots of titration curves showing a vertical line between 3 and 11 pH are next to useless.

The feedforward signal and linearized process variable for reagent demand control were created by use of the same signal characterizer block where the input array was pH values and the output array were corresponding X-axis values per the titration curve. The X-axis was scaled 0 to 100% for the Y-axis and the pH measurement scale of 2 to 12 pH. The first input to the signal characterizer for feedforward control was influent pH. The first input to the signal characterizer for reagent demand (feedback control) was static mixer outlet pH. The second input to both signal characterizers was the pH set point.

Since influent pH measurement errors as small as 0.04 at 2 and 12 pH can cause feedforward errors of 20% or more per the titration curve, it was decided that continuous adjustment by means of a pH feedforward signal could be making large incorrect changes in the reagent flow. It was reasoned that large changes computed in feedforward signal due to large changes in influent flow or pH could be useful as a delta head start to pre-position the valves for the start of a large upset and then let the feedback controller do its thing. This proved to be the case although the feedforward was complicated by the blend of the recirculation stream with the influent at the inlet to the static mixer. Unfortunately, the accuracy of the feedforward curve depended on the accuracy of the titration curve.

Reagent demand control does not deteriorate significantly for changes in the titration curve because only relative changes in the slope are important for linearization and any information is usually better than no information about the shape of the curve. Reagent demand control uses the X-axis of the titration curve scaled as a 0-100% process variable and set point. This control ignores the pH fluctuations near neutrality because these correspond to very small changes in reagent demand due to the steep slope. Reagent demand control also recognizes the true distance of the influent from the set point, which is important for startup and well as disturbances.

Results of the auto tuner showed that the pH controller gain needed to be very low (e.g. 0.02) because of the high process gain from the steep slope of the titration curve at the 7 pH set point. The reagent demand controller gain could be 10 times larger (e.g. 0.25) – see slides 5 and 6 for screen prints of auto tuner results.

A comparison of the conventional pH and reagent demand control is shown on slide 7. The spikes in the static mixer pH are caused by 0.4% stick-slip of the water valves. If the resolution of the water valves was improved from 0.4% to 0.1%, the spikes went away. If the resolution of the acid and base valves upstream or at the static mixer deteriorated from the specified 0.1% to 0.4%, there were many more spikes from the limit cycles of these valves. Normally, a 0.5% resolution control is consider good. This is not so for high process gains. Neutralization systems with pH set points near neutrality are excellent indicators of actual valve resolution and a perpetual stick-slip limit cycle. If you want to know more, check out “Improving pH System Design and Performance� at the Emerson Global Users Exchange this September and the Chemical Processing article on control valves last October.
http://www.chemicalprocessing.com/articles/2007/200.html

I was at a “Guess Who� concert at the “One World Theatre� here in Austin the “Live Music Capital� last night thinking wow the lead singer who obviously wasn’t born when the band had their greatest hits sounded the same but better than the original recordings. The tone and inflections were not only right-on but enhanced. While the singer I heard at the Austin “Bat Fest� who sounded more like Meatloaf than Meatloaf was impressive, this concert blew me away. I moved to the other side of my brain to the dynamic world of process control and pondered if “high fidelity� or in this case “hyper fidelity� is possible in dynamic models.

For the last 30 years I have been creating and using models of medium fidelity. When I got into bioreactor modeling, I moved into the realm of “high fidelity� as a result of necessity and opportunity.

For pharmaceutical processes, the process and control system design is set in the process research and development phase, often by the biochemist. By the process design and commercialization phase, the set points and control strategy or lack thereof is set in stone. If my modeling was going to be used for improving the product concentration and quality at the end of the batch or reduce the batch cycle time, I needed to move my model upstream from design into development. Also, standing with my bioreactor model demo next to the Broadley-James booth at Interphex 2007, Scott Broadley and I saw a synergist opportunity. Scott as a leading supplier of bench top systems completely automated with a full capability DCS and the latest technology in probes envisioned he could enhance the knowledge and system capability offered with a dynamic model of the process that could explore “what if� scenarios with a virtual batch running 1000x real time. I could see besides getting the needed process test data and characterization of the model, Broadley-James would be as interested as me in making the details public knowledge whereas pharmaceutical companies who expressed interested in participating in the model development would keep the results and conditions as closely guarded secrets. PATtools

Looking toward the future Emerson and Broadley-James (principally Trish Benton and Michael Boudreau) have ventured into the world of high fidelity by the parameterization of a bioreactor model in DeltaV Simulate Pro Control Studio based on cell culture runs to create a virtual plant.

So what if you are not into bioreactor or high fidelity modeling? There are plenty of uses and reasons for models of various levels of fidelity that can get you a virtual plant.

Top Ten Reasons for a Virtual Plant

10. You can’t freeze, restore, and replay an actual plant batch
9. No software to learn, install, interface, and support
8. No waiting on lab analysis
7. No raw materials
6. No environmental waste
5. Virtual instead of actual problems
4. Batches are done in minutes instead of hours or days
3. Plant can be operated on a tropical beach
2. Last time I checked our wallet we didn’t have $1,000K for a plant to test
1. Actual plant doesn’t fit in my suitcase

For my own edification and possibly yours, I did the following core dump of uses and my assessment of the level of fidelity required on a scale of 1 to 10 where 1 is for tieback simulations where feedback by discrete values (e.g. valve limit switches and motor run contacts) go to the right status and analog values move in the right direction (e.g. loop process variables respond in the right direction to changes in controller output).

Typical Uses of Models and Levels of Fidelity Required

Process Development
Media or reactant optimization and identification of kinetics on the bench top - 10
Optimization of process conditions in pilot plant - 9
Agitation and mass transfer rates - 8*
Process scale-up - 8
* - assumes computational fluid dynamics (CFD) program provides necessary inputs

Process Design
Innovative reactor designs or single use bioreactors (SUB) - 7
Vessel, feed, and jacket system size and performance - 6

Automation Design
Real Time Optimization (RTO) - 7
Model Predictive Control (MPC) - 6
Controller tuning (PID) - 5
Control strategy development and prototyping - 4
Batch sequence (e.g. timing of feed schedules and set point shifts) - 3

Online Diagnostics
Root cause analysis - 5
Data analytics development and prototyping - 4

Operator Training Systems
Developing and maintaining troubleshooting skills - 4
Understanding process relationships - 3
Gaining familiarity with interface and functionality of automation system - 2

Configuration Checkout
Verifying configuration meets functional specification - 2
Verifying configuration has no incorrect or missing I/O, loops, or devices - 1

My world has been automation system design with some ventures in into process design for neutralization systems where pH controllability is so highly dependent on equipment and reagent injection dynamics but I am looking forward to the high fidelity experience.