Simulation

If you're involved with simulation in your plant, you may be familiar with Mynah's Martin Berutti and his blog. In one of his posts, he shares his thoughts on the business benefits, requirements, and steps for building a Virtual DeltaV system with a virtual plant and I/O.

I thought I'd share some of his thoughts from the presentation, which are not specific to the automation system. You may find yourself needing to capture your plant's experienced operators and operations personnel through the use of simulation, before they all retire to warmer, sunnier locales.

Martin shared some U.S. demographics that the average age of an energy industry worker is over 50 and that half the current work force (more than 500,000 workers) will retire in the next 5-10 years. Some of these retirements were accelerated by the global economic slump and ironically may accelerate again when the equity markets recover for those whose retirement funds dwindled.

As shared in a post last week, regulations and government oversight continues to grow, increasing the load on the plant's operations team. These regulations combined with global supply pressures on financial margins add to the operations burden.

The paradox that the era of plant automation has ushered in is that operator error is the highest cause of loss, followed by design error, process upset, and mechanical failure. The first two are directly related to experience and skill level. This regulatory environment has changed over the years where now operations management can be held liable for their actions or inactions on operations issues. Martin observes, "They didn't tell us in engineering school we could go to jail for something we did or didn't do!"

If you're considering simulation as a way to capture the operations team's expertise for the next wave of operators, maintenance techs, and plant engineers, Martin suggests four simulation approaches to avoid:

• The first is not to use process design models since they do not have the real-time performance or range of operating conditions of the dynamic simulations required for operator training systems.
• The second is not to emulate the automation system. If you're going to build skills and gain experience on the operator graphics, alarms, and controls, these items should be identical to the real system--not an approximation.
• Third, Martin councils to avoid adding simulation to the control system configuration. This increases opportunity for errors, adds complexity, and ups the risk of design errors. Adding simulation to the control system configuration also makes the process of keeping the operator training system consistent with the on-line control system difficult if not impossible.
• The final caution is to avoid starting the simulation development too late in the project cycle. These efforts are usually rushed and don't provide the depth of training that operators and other operations personnel need to acquire the skills and confidence to operate the process after it is commissioned.

The proper approach is to have a virtual control system, which is an exact replica of the plant automation system. The operator graphics, alarms and controls are identical to the running system. Also, the virtual system can be the testing grounds for new and modified control strategies.

Connected with the virtual control system is a virtual process/dynamic simulation. The fidelity of this model can range from simple I/O signal modeling and device tiebacks, to mass and heat balance models, all the way to complete mass balance, rigorous heat balance, reaction kinetics and associated thermodynamic properties. The level of model complexity depends on the initial business objectives, amount of knowledge capture, and skill level sought.

The virtual control system combined with the virtual process forms, in the words of ModelingAndControl.com's Greg McMillan, the virtual plant.

Knowledge transfer requires explicit learning--what the operating procedure says, implicit learning--how things really work, and tacit learning--how decisions made affect the whole process. Properly done, simulations provide the hands-on training for these three types of learning.

Defining your objectives clearly up front and following some of the guidance shared by Martin, can help reduce the errors and associated liabilities/risk, reduce operating costs through less unscheduled downtime, improve product quality, and increase time to market by reducing startup time.

MP3 | iTunes

At the upcoming June 10-12, 2009 Automatic Control Conference, Emerson's Greg McMillan and Terry Blevins will be presenting, Bridging the Gap between Academia and Industry. Describing this session, Greg wrote on the ModelingAndControl.com blog:

The first semester I taught the Chemical Engineering course "Introduction to Process Dynamics and Control" at Washington University in Saint Louis as an adjunct professor, the students could not relate to my attempt to introduce practical plant applications and considerations in the normal course of Laplace transforms and bode plots. The second semester I added a virtual plant that consisted of a DeltaV DCS running in the Simulate mode integrated with HYSYS dynamic process simulations for each student. I later configured most of the process simulations directly in control studio. I was amazed how fast the students learned how to work in the graphical configuration environment and operator interface. All they needed was a few screen prints on navigation to get them started. Several of the students subsequently got intern or permanent positions doing configuration at the local DCS industry center. I had these students with experience in the automation industry come back to speak to the next class. The result was a dramatic turnaround in appreciation and understanding of what they would face in industry. The students decided on their own to go online to find and buy tee-shirts with Duncan, the DCS mascot, windsurfing. I ended up buying tee-shirts too and we all posed for a group photo by one of the students.

This got me thinking how technology has impacted the way we learn. For most of us mid-career and later, learning involved the journey Greg attempted in his first round with the class. We'd patiently learn parts and pieces with the hope that it would all be tied together in the end and become understandable.

He realized that this method was not effective in the world where answers to anything seem but a Google search away. The students he was teaching also had used near life-like video games for the better part of their teenage years. The effective way was to show with near life-like process simulations the big picture first and start the hands on process early. In the presentation, the authors describe this learning process: Explore ∗ Discover ∗ Prototype ∗ Demo ∗ Improve ∗ Deploy ∗ Educate

In the paper that accompanies their presentation, Greg and Terry describe this virtual plant:

The "virtual DCS" is not an emulation or translation but is a virtual replication of a complete DCS with all of the standard and optional advanced tools. Control system trend charts, displays, configurations are exchanged between a "hardware DCS" and "virtual DCS" by standard copy, import, export, and download functions. The incorporation of process models in a "virtual DCS" creates a "virtual plant", which offers an opportunity to integrate and build process knowledge with the "state of the art" advanced tools for Process Analytical Technology (PAT) and Advanced Process Control (APC)...

The entire lineup of Bridging the Gap presentations looks quite impressive and includes professors from the University of Texas at Austin, Rose-Hulman Institute of Technology, Purdue University, and Washington University in St. Louis. Last month, I covered the Rose-Hulman unit operations presentation.

If you're responsible or involved in the process of educating tomorrow's process manufacturing engineers, this might be a conference for you.

MP3 | iTunes

Wow, what a fast and furious week... I'll close the week by highlighting a recent article by ModelingAndControl.com's Greg McMillan. He coauthors the article, Virtual Plant Provides Real Insights, which appears in the January 2009 edition of Chemical Processing magazine.

Greg's a published author on the topic of pH control and widely known his expertise. Working with Monsanto engineers, they sought a better way to control the pH of a wastewater pit. Maintaining pH between a permissible range of 6 and 9 was a labor-intensive activity. It also required veteran operators. Inexperienced operators, typically working night shifts, would call the plant engineers with a nearly full pit with an out-of-range pH and an imminent pumpout about to happen. Plant engineers typically don't enjoy being awakened to hear this.

The initial solution was to replace the pit with two 40,000-gallon tanks. The issue with this solution was high capital costs and limited plant real estate for the tanks. Plant engineers worked with Greg to see if a better solution could be modeled and developed. The goals were to minimize capital costs, provide reliable operations, and be easy enough to operate, even for less experienced operators.

Using much of the wisdom he freely shares at ModelingAndControl.com, Greg developed a virtual plant running a DeltaV system with embedded simulation on his notebook computer. Virtual plant means it runs both the simulation of the plant and the control system logic. Greg describes the setup:

The virtual plant included a dynamic model of the process with material and charge balances as well as mixing and injection delays, and a dynamic model of the control valves with deadband and resolution limitations. The models were configured and embedded in a distributed control system (DCS) along with the control strategies. The integrated nature of the virtual plant eliminated the need for separate programs, interfaces and emulations. We could develop and test the actual control modules and displays used in the plant.

Working with the lab data history, the team developed titration curve tabular data. They next matched the titration curve of the process model with the laboratory titration curve. They ran the demineralization unit batch sequence for different equipment, injection and automation system designs. The model showed where the biggest causes of upsets to pH level occurred.

They could also do what-if analysis to see if fast inline pH control could catch the disturbances and smooth them out. The result of the modeling and control analysis was that the pH could be controlled with 10,000-gallon tanks instead of the original 40,000-gallon tanks for project capital savings of more than 50%.

The article gives the design details of what process designs, process instrumentation, and control strategies were required to achieve the initial objectives sought.

If you've got a retrofit project ahead, you might consider a modeling and control analysis to see if large capital savings are possible. In today's global economic environment, this could make you a hero.

I also wanted to pass along that Greg was conducting a pH survey for a revision to his pH book:

Help Greg McMillan fine-tune his focus on pH issues by answering a few questions online. Taking part will give you a chance to win a copy of "Advanced pH Measurement and Control" as well as other prizes. When taking the survey, if you don't know the answer to a particular question, just select the 0-1% choice.

MP3 | iTunes

I won't spoil the press release in the works about the donation of a DeltaV system to a major university for use by a consortium of universities, but I will share that Emerson's Terry Blevins was at the university last week. He was there to provide an introductory process control and DeltaV system overview.

Since a consortium of universities is involved, a neat things done for this installation was to setup VPN access and Windows Remote Desktop access into the system to configure, test, and run the DeltaV control logic and calculations created using MatLab. In preparation for teaching the course, Terry used VPN to connect to the university's intranet. He then opened a remote desktop connection into the DeltaV system to prepare the models and simulations he was going to use to teach the course. The other universities' graduate students will use this same method as they collaboratively advance their research.

I thought I share some of these basics in case you are a college student or new to our world of process automation. Fair warning--for those experienced automation professionals, quickly hit the "back" button to avoid going any further into this post.

Terry begins his introductory presentation with organization and layout of a process manufacturing plant with the caveat that there is no "typical". Plants are divided into process areas and these areas are defined based on the equipment or process grouping. Examples are tank farms, boiler houses etc.

Terry gave a field device and wiring overview, showing examples of two-wire, four-wire, HART and Foundation fieldbus devices and how they connect into an automation system's I/O.

Next, he covered documentation of the plant control and instrumentation. Typical documentation includes a plot plan, which is a physical layout of the plant. Process flow diagrams show the major pieces of equipment in a process area and their design operating conditions. A P&ID (Process & Instrumentation Diagram) shows the piping and instrumentation installed. Loop sheets show the details of instrumentation and field wiring. Terry referenced the ISA S5.1 tag number convention standard that helps identify I/O as pressure, flow, temperature etc. and its readout and output function.

Terry showed the change in technology in distributed control systems over time from a hardware and field wiring perspective from individual wiring per device, to bus-based I/O. To familiarize the students with the hardware they might see in plants, he showed pictures of controller and I/O cabinets, marshalling panels, junction boxes, and panels with connections to other intelligent devices.

He then got specific with the hardware components and software applications in the DeltaV system and showed how the students could set up virtual plants with simulations of a running process against their control strategies.

After I passed a draft Terry's way for review, he pointed me to one of his earlier ModelingAndControl.com posts, Control Basics and Terminology that covered these basics plus more including:

• Introduction Historic Perspective
• Field Devices and Wiring
• Documentation of Plant Control and Instrumentation
• Characterizing the Process, Terminology
• Control System Objectives
• Manual and Feedback Control
• Feedforward Control
• Cascade Control
• Override Control
• Split Range Valve Position and Ratio Control

He even posted a test for those of you really ambitious new learners out there. If you're new to the world of process control, take a look at these links when you have some bandwidth and see if you find them valuable.

Update: A colleague from our DeltaV Twitter community points out that my hyperlink to Characterizing the Process, Terminology was not linked correctly. I've now updated it. Thanks for keeping me on my toes!

Update 2: Another reader found my incorrect use of "are" instead of "is" in the second paragraph. Specifically:

Consortium is a collective noun and therefore singular, not plural. The same applies to nouns such as group, herd and flock. The predicate (are/is) relates back to the singular subject (consortium), not to the plural object (universities).

As regular readers can attest, I need all the help I can get when it comes to grammar!

Interphex2008, the Pharmaceutical and Biotech manufacturing conference is going on this week in Philadelphia. Before Emerson's Terry Blevins and Mike Boudreau left, they passed along the presentation they are giving on Thursday, March 27. It's entitled, Application of PAT in Product Development. They are joined by University of Texas at Austin PhD graduate student, Yang Zhang and Broadley-James' Trish Benton. Here's an excerpt from the abstract:

The Process Analytical Technology, PAT, initiative encourages innovation in pharmaceutical development, manufacturing, and quality assurance to enhance understanding and control of the manufacturing process. The challenge for many manufactures is to identify how best to address the opportunities that PAT offers. Broadley James, Emerson Process Management, and the University of Texas are working together to examine and quantify the potential to reduce cycle time and out-of-spec product through the use of high fidelity, dynamic simulation and multivariate analytics. The objective of this work is to show that the impact of PAT can be maximized through the integration of these tools during product development (PD).

In the presentation, Terry begins by discussing the U.S. Food and Drug Administrations' PAT initiative, which has a framework that identifies some of the tools they discuss in the presentation. These include:

• Multivariate data acquisition and analysis tools
• Process and endpoint monitoring and control tools
• Continuous improvement and knowledge management tools

Terry describes on-line process analytics including fault detection and quality parameter prediction. Tools for detection of abnormal operations vary for measured and unmeasured disturbances. For measured disturbances, principal component analysis (PCA) captures contributions that can be associated with process measurements. Deviations may be quantified using Hotelling's T-square statistic.

The residual space that is not captured by the principal component score space reflects changes in unmeasured disturbances that can impact operations. These deviations can be measured with the Q statistic, squared prediction error (SPE).

For the quality parameter estimation, detection of deviations is addressed using projection to latent structures (PLS).

Armed with these statistical tools, Mike shows how the basis for bioreactor process modeling. In the book Mike coauthored with Greg McMillan, New Directions in Bioprocess Modeling and Control, they present a first principal bacterial model that was developed for fungal, bacterial, and mammalian cell processes. The intent of the process model is to more quickly evaluate input step techniques and control strategies in the PD stage.

The BioProcess International magazine article, PAT Tools for Accelerated Process Development and Improvement describes this collaborative effort between Emerson and Broadley-James technologists and University of Texas researchers along with how these tools can accelerate life science manufacturers' PD phase.

In the upcoming March issue of BioProcess International magazine, there is a great article by Emerson's Greg McMillan and Michael Boudreau, Broadley-James' Trish Benton, and the University of Texas at Austin's Yang Zang. The article, PAT Tools for Accelerated Process Development and Improvement, describes the collaborative effort between Emerson, Broadley-James, and UT, "...to examine and quantify the potential for faster optimization of batch operating points, process design, and cycle times." The specific objective of this collaboration:

...is to show that the impact of PAT can be maximized through the integration of dynamic simulation and multivariate analytics in a laboratory-optimized control system during product development.

Greg and Michael are putting many of the ideas they described in their book, New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits, into practice.

The authors outline the challenge for the 400 biotechnology medicines currently in development, which require overlapping and iterative stages for process development and commercialization. These stages include:

...cell line selection and development, media optimization, process conditions optimization and verification, scale-up, project definition, and plant design.

This team is working on beta tests using this new dynamic model and on-line data analytics and wants to make the results fully public to promote wide use and to advance these concepts and methodologies.

If you're like me and not in the biotechnology field, much of the article may get a little deep. I did glean a few tidbits you might find useful. By creating a dynamic model, one of the big benefits to the team is the ability to speed up the model by up to 1000 times real-time. Whether you're simulating the growth of mammalian cell lines or have another slow process, this can really help reduce trial and error time.

Another key is that the model, configuration, and tools can run in the "virtual plant" PC environment or can be downloaded to the automation system. With proper scale up factors:

...the embedded tools go readily from bench-top bioreactors to pilot plants and eventually industrial-scale bioreactors.

With the recognition by the FDA that quality cannot be tested into products, which led to the creation of the Process Analytical Technology (PAT) initiative, the authors discuss the role of analytics in their efforts.

Principal component analysis (PCA) and projection to latent structures (PLS) are two multivariate analysis techniques that can help analyze continuous and batch process operations. The authors' beta test is focusing on the on-line use of these analytical techniques where PLS detects deviations in quality parameters and PCA detects abnormal operations from measured and unmeasured disturbances.

Given the importance of new product development for pharmaceutical and biotechnology manufacturers, anything to reduce the overall development time and build in quality monitoring as prescribed in PAT should be a welcome addition.

In an earlier post about a Mynah Simulation Consultant joining the automation blogging community, I sent a note to Jason Covington who manages their forums and blog expressing my wish that these were RSS-enabled. This meant that I could receive updates as posts were made, instead of having to go visit the site and check for new things.

Today, I received a comment back from Jason sharing the good news that their site is now RSS-enabled. I'll share Jason's comment here in its entirety:

We have added an RSS Feed for all of our MYNAH Forums content:
http://www.mynah.com/forum/RSS_topic_feed.asp

This link allows you to pick a Reader if you don't already have one.

If you already have an RSS News Reader installed on your computer, use:

feed://www.mynah.com/forum/RSS_topic_feed.asp

The MYNAH Forums allow you to pick specific levels for your RSS Feed. For example, if you are only interested in getting a feed of the latest posts from the MiMiC Users' Forum, you can click the RSS Feed button in that specific Forum and receive MiMiC-specific feeds only:

http://www.mynah.com/forum/RSS_topic_feed.asp?FID=7

Enjoy!

Jason Covington
MYNAH

Now I can quickly scan the conversations going on around simulation, OPC, and other connectivity solutions and see where experts from Emerson can join in.

If you've never used RSS to see the value of having information come to you instead of you going to find it, I recommend spending a few minutes with one of the browser-based RSS readers like Google Reader or Bloglines. There's no software to install with these.

And while you're there, add the Emerson Process Experts RSS feed and perhaps other automation and process industry ones from my blogroll so you can share your expertise.

The folks at Mynah Technologies with their Mimic simulation software, virtual I/O network gateways, PLC I/O interfaces and host of drivers for the DeltaV system continue to build conversations through their forums and experts blogs within the forum. Recently Dr. Aleksandr Muravyev a simulation consultant created a Mimic Distillation Modeling package to simplify software acceptance testing and operator training around distillation columns.

Dr. Muravyev joins the automation blogging fray with this post about this distillation modeling package. If you have experiences with this product (or even if you don't) feel free to join in the conversation. It's a forum so you'll need to register first.

I did ask the Mynah folks if the forum software they are using supports RSS feeds so I could get these updates coming to me instead of going out and seeking it. It does and they will soon be adding this functionality.

That's a great thing as some RSS readers like Google Reader support both regular and mobile viewing. It means I can keep up with the automation and technology bloggers whenever a have some spare moments. From playing with the various RSS readers, I seem to be gravitating to using Outlook 2007's embedded RSS reader for my Emerson intranet-based RSS feeds and Google Reader for external RSS feeds. How about you?

Pharmaceutical Technology Europe has a recent article entitled, Artificial intelligence the key to process understanding. It discusses the opportunity to enhance the FDA's Process Analytical Technologies (PAT) initiative using artificial intelligence based tools like neural networks, fuzzy logic and genetic algorithms. I shared this article with Greg McMillan who has been quite immersed with advanced control as it applies to bioprocesses.

I received this response which I'll share in total (I've inserted some context-sensitive hyperlinks to his work on Process Control Insights):

There are opportunities to improve plant performance in the front end of the process where most of the product qualities are set by the use of online process models, batch analytics, and Model Predictive Control (MPC). Online process models based on first principals offer a significant source of knowledge discovery for both the process and the control system. The models are part of a virtual plant that enables virtual experimentation for the exploration of "what if scenarios".

This is important for the next steps of implementing online batch analytics and MPC. Since fermentation batches take days to weeks to complete and the cost of wasted batches is considerable, the virtual plant can provide data on various degrees of adverse operating conditions that would be infeasible to obtain from the actual plant in terms of time and cost.

The virtual plant facilitates the development of techniques for the proper unfolding and alignment of batch data and more advanced analysis techniques such as super model based Principal Component Analysis. Neural networks can be employed to provide reaction rates when information on the kinetics is insufficient.

Fuzzy logic rules can be formularized and tested for a wide variety of scenarios. Inferential measurements can be developed for viable mass growth rates and product formation rates to fill in the blanks between lab measurements for MPC applications to improve batch consistency and yield and to reduce batch cycle time.

In summary, the virtual plant offers a synergistic environment for the application of online batch analytics, artificial intelligence, and advanced control. These opportunities and others are discussed in the book New Directions in Bioprocess Modeling and Control published and in the lectures on the Process Control Insights website.

As much as this pains me to say it, people usually prefer from hearing from other people with experiences to share than from marketing folks. This a big reason for the growth and popularity of supplier user group meetings including our Emerson Exchange.

A personal example of this is when I was shopping for an HDTV and was Googling around for information on the various technologies and products. I found the best source of information in forums (especially avsforum.com) from users that had experiences with a particular technologies and manufacturers. I found these experiences to be more valuable than the product review sites or manufacturer sites.

This is probably why more shopping sites include user comments in addition to product review articles and specs.

I point all this out because there are some very vibrant forums and email lists that have been going on in the automation world. Good examples include Control.com, Fieldbus Online, ISA Technical Discussion and Industry lists to name a few. These are in addition to the many blogs appearing which I try to capture in my right-side blogroll.

Joining this trend of increased knowledge sharing is Mynah Technologies, whose Mimic simulation and integration products are part of many DeltaV system installations. They have rolled out a Mynah Forum area for users of these products and Mynah folks to share ideas, tips, tricks, feedback, etc.

The forums include a blog, MYNAH Experts' Blog, led by Martin Berutti, whom I've known for many years. Martin kicks off his inaugural post, Simulation Objections Answered, discussing common objections to the consideration of simulation as part of process automation projects. He summarizes four objections with his reasons for reconsideration.

Give it a read and feel free to add your thoughts.