Virtual reality is getting more like real reality every day.
Historically, simulation was the realm of NASA, aerospace, big oil-and-gas applications and other deep-pocket processes. However, ever faster and cheaper data processing is bringing simulation to masses of new users and types of applications in a seemingly inevitable progression from design and configuration, onward to training for routine and exceptional events, and more recently to optimization.
But why stop there? By bringing in more real-time data to make simulations more true-to-life, it soon becomes obvious they also can point out ways to improve an existing application's performance in the same way earlier simulations identify bugs and other fixes needed at the configuration and testing stages. Finally, as data processing keeps accelerating, moving information between a physical operation and its simulation is becoming easy and fast enough for some simulations to closely assist—if not actually run—some real-time operations. Simulation's next stop? Real-time process control.
Still, even though simulation is moving into new realms, this doesn't mean it's forgetting its roots in design.
"Modeling and simulation have already transformed military, nuclear, aviation, automotive and durable goods manufacturing, and now they're starting to transform our consumer packaged goods industry for the same reasons—it costs too much and takes too long to build physical learning cycles, and so the products they produce aren't innovative," says Tom Lange, Procter & Gamble's (www.pg.com) modeling and simulation director, who presented "Virtual Prescience" earlier this year at ARC Advisory Group's (www.arcweb.com) annual forum event in Orlando, Fla.
"In our modeling world, we build the first prototypes, and they fit, work and make financial sense. We do stuff before it exists in the real world because the computers we have now are faster than the fastest computers in the world just 10 years ago. All this computing power is allowing us to replace physical cycles with virtual ones and pursue realism," explains Lange. "So instead of building a model, doing some calculations, getting close to the physical experiment and securing some guidance, realism means making a model that's indistinguishable from the physical experiment. This requires using computing power for much bigger and more complex problems—doing parametric studies instead of point estimates. We're no longer interested in what's going to happen with one simulation. We want to know about the 64 designs around it or doing 128 runs of all the experiments around one finite element."
For example, physically testing mixing of liquids used to involve dumping in materials and just seeing where they went, but this real-world method didn't scale up from small models to larger tanks. However, simulation that includes computational fluid dynamics (CPD) can be easily scaled up, according to Lange. "Likewise, if we're trying to mix some dense, viscous fluids, they may not mix in a tank. If we did this experiment in reality, we'd now have to clean the tank ourselves. So using a simulation is more preferable because it can help us decide when to use a tank or a static mixer (Figure 1)," explains Lange. "And if we go with the static mixer, the simulation also can show how long it has to run, what its pressure drop will be and how well the material will be mixed. These are all questions that are entirely reasonable to be answered computationally. In their heads, people don't want to believe that simulations can be this accurate, but there are many times when the simulation performs better than the experiment."
Reaching Out with OPC
To better understand simulation's recent data processing gains and migration into optimization, we must look at how it is improving communications with their real-world counterparts.
"In the past, we used a simulator that our customer used. It had a fancy HMI package for graphics and software objects, but the problem for a system integrator like us was that it required a whole extra application step that we had to design, program and test, and this meant a lot of added time and money," says Ryan Gerken, technical director at E-Technologies Group (www.etech-group.com), a system integrator in West Chester, Ohio, which serves process and batch application users in consumer goods and pharmaceutical manufacturing. "So when we began a re-control project to migrate this same user's old batch process system from Honeywell's TDC 3000 DCS to Rockwell Automation's ControlLogix, we also needed a simulator because this production system runs at 100% capacity to keep up with demand. In this case, simulation can help us reverse engineer and work out problems without having to take down critical production lines."
During its search, Gerken reports that E-Technologies ran across Mynah Technologies' (www.mynah.com) MiMiC software, which is built on Microsoft's .NET and uses OPC-based servers from the OPC Foundation (www.opcfoundation.org) to expose its simulation environment, which allows it to be controlled from another HMI. "Previously, a whole simulation would have to be programmed separately, but MiMiC doesn't have to do this because it uses OPC servers that can communicate directly into the simulation environment," explains Gerken. "This connection allows users to write automation to an equipment database for valves or pumps, and spits the MiMiC environment to the HMI and back. So, if you have an HMI with graphic displays for those valves and pumps, then your operations guys can click in individual devices, open and close them in the simulation via the OPC server, and then simulate and run unusual conditions or alarms."
Gerken adds that E-Technologies even develops its own software modules and tools and then uses MiMiC to import and export them as templates via XML. "We can extend the existing software with our tools and then just click to generate PLC code, HMI tags, historian tags and models," adds Gerken.
While simulators are famous for training operators, the story doesn't end there. Once trained, users naturally and almost reflexively want to use these virtual tools to help improve and maintain their applications. This is drawing simulation into a host of new applications and closer to operations too.
For example, RWE npower's (www.rwenpower.com) two-unit, 1,000-megawatt Fawley Power Station provides electricity to 1 million people in Hampshire, U.K., so this peaking plant must start up quickly and efficiently and synchronize to the grid to cost-effectively respond to seasonal spikes in demand. Consequently, the 40-year-old, oil-fired plant has used Emerson Process Management's (www.emersonprocess.com) Scenario simulator since January 2008 to mirror its actual Ovation expert control system, which monitors and controls the boiler, turbine and other critical plant processes and systems. The station's high-fidelity Scenario simulator has virtual controller technology, in which up to five virtual controllers reside in one PC, and this enables greater affordability and scalability with a reduced footprint. Fawley has 4100 simulated I/O points that exist in 12 Ovation virtual controllers (Figure 2).
Fawley reports that Scenario's training abilities are especially valuable for peaking plant operators because their work is based on fluctuating demands. In this environment, the simulator can train new operators while keeping current operators sharp and familiarizing them with new control strategies, even when the plant is not running. As a result, when extra megawatts are needed, the plant can synchronize to the grid more quickly and within the necessary parameters to avoid equipment damage.
"With the simulator, new operators can come up to the standards of more experienced operators much sooner than would be possible if they were interacting with the control system only when the unit was operating," explains David Marmot, RWE npower's electrical and instrumentation leader. "By training operators how to start the unit faster to meet peak demand, we have the opportunity to not only enhance the plant's operational performance, but our financial performance, too."
Likewise, because well-trained operators who are prepared to handle abnormal operating situations can reduce costly plant trips, Fawley also had 20 malfunctions, including critical ones, pre-programmed into the simulator to further train its operators how to respond to emergency situations. Scenario uses tie-back logic, algorithmic models and first-principle models to provide training and engineering simulations that can be tailored to meet each facility's operational needs.
"Ongoing operator training is important to power generators preparing for the retirement of experienced operators. However, beyond training operators, our customers are achieving added benefits by using our simulation technology for engineering analyses and validating new control logic," adds Bob Yeager, president of Emerson's Power and Water Solutions division.
More Data + Better Math = More Reality
So what's at the root of all simulations and what's driving them and their software's increasingly sophisticated capabilities? You guessed it—a huge and growing pile of mathematical calculations.
For instance, GenCorp's Aerojet (www.aerojet.com) division in Sacramento, Calif., is using the MathWorks' (www.mathworks.com) MATLAB and Simulink software to build a control system to deliver constant pressure in the fuel tanks of Kistler Aerospace's K-1 space launch vehicle. When finished, K-1 will be the first commercial, reusable launch vehicle with low-cost access to space for low-earth-orbiting satellites. K-1's modified Russian rocket engine from Aerojet burns liquid oxygen (LOX) and kerosene, but as LOX levels in the tank drop, more pressure is needed to force it to the combustion chamber. To restore pressure, LOX consumed during firing is replaced with helium from external storage tanks. However, helium creates a new control engineering problem, because LOX requires steady pressure, while the helium requires constantly increasing pressure.
As a result, Perry Stout, K-1's controls engineer, developed a solution in which flow between the high-pressure helium storage tanks and the low-pressure LOX tanks is controlled by a series of flow-regulating solenoid valves and an orifice that can vary in size with ambient conditions, and used MATLAB and Simulink to design a control system that regulates valve operations and orifice size. First, he derived and wrote out the physical equations, moved these core equations into Simulink, developed and tested a model and graphically added heat-transfer equations and closed-loop control laws without writing added code. Next, he used MATLAB to analyze the design and modified it in search of optimal conditions, a task that would have taken several months using a conventional engineering process.
"If this was a Fortran project, the control system design would have involved an entire team," says Stout. "A manager would have been required to divide the modeling, simulation and control tasks among several people, closely monitor and coordinate all activity and summarize the results."
Jason Ghidella, Simulink process marketing manager for MathWorks, adds that, "Simulators are becoming more dynamic and non-linear because high-end users want better performance and safety. They want to see if their control strategy is in the ballpark, but they also want to throw all kinds of unusual conditions at it too," says Ghidella "Non-linear simulations are based on partial differential equations (PDEs) and differential algebraic equations (DAEs), and these require a lot of calculating that can't be done in traditional ways. So simulator developers must find other ways to understand and solve these dynamic problems and then generate a response."
Start-Up and Incidents Handling
Similarly, simulations are even being used to assist routine operations, as well as evaluate and respond to alerts and events. For instance, Rio Tinto Alcan's (www.riotintoalcan.com) Gove bauxite mine and alumina refinery is located at Nhulunbuy in Australia's Northern Territory, and it just completed a $3-billion expansion that will increase its alumina production from 2 million to 3.8 million tonnes per year and allow the refinery to run independent of its local bauxite reserves. As part of its expansion and to improve plant capacity, RTA Gove chose a new double-digestion technology, which uses low-temperature digestion for removal of trihydrate alumina, followed by high-temperature digestion for monohydrate alumina. Few double-digestion circuits are established yet, so RTA Gove needed a simulator to train its operators without adversely affecting plant operations, and to help its multi-DCS controls architecture avoid data mismatches.
Consequently, RTA Gove picked Honeywell Process Solutions' (www.honeywell.com) UniSim Operations simulator to do months of operator training prior to start-up on its control system that was developed six months earlier. UniSim Operations is a direct-connect, full-replica, dynamic process simulator, which allows a high-fidelity model of the process to run in real time and appear from the DCS console as though a real plant is being controlled. UniSim's software contains a library of modules that mathematically represent the behavior of process equipment, logic and control components under dynamic conditions. The modules include heat and material balances, operating equations, thermodynamics and physical property calculations. These modules are used as building blocks to create a realistic representation of a specific process, area or plant.
Using these tools, RTA Gove's double-digestion process model includes 135 tank modules, 85 pumps and 1037 control valves. There are 386 field-operated devices and 7370 control points simulated. Training features also include 1242 malfunctions. The process model takes about 0.2 cpu seconds to run, and the model runs every 2 sec, which is more than enough to realistically simulate the plant's process dynamics.
"One of the biggest benefits we've received from UniSim is improved operator effectiveness. Like most operating alumina refineries, our equipment is operated continuously, and many operators are not well-practiced in running under start-up, shutdown or emergency conditions," says Manoj Pandya, Rio Tinto Alcan's alumina projects manager. "Similarly, in new installations, operators may have even fewer skills in managing the process and the knowledge of the equipment limits, even under normal operating conditions. UniSim enabled us to train our operators in advance, so they could practice new skills without hindering the plant."
Immersed in the Future
Of course, one of the most powerful expressions of simulation are displays that go beyond copying flowcharts of processes to duplicating whole facilities on screen. However, pretty pictures aside, a useful simulator must first reproduce real-life processes and situations in great enough detail, with sophisticated enough mathematics, and with sufficient resulting dynamism to be useful to operators on the plant floor.
For example, Invensys Operations Management (www.invensys.com) recently introduced its EYEsim immersive, game-style simulator that merges first-principle simulation with augmented reality to help users see and safely interact with control room, field devices, processes and entire plants. Invensys says that control-room operators, field operators and maintenance technicians can use EYEsim to train in tandem and interactively solve problems under trained supervision (Figure 3). EYEsim is driven by Invensys' DYNSIM high-fidelity process simulator, FSIM Plus software, I/A Series control system emulation and other compatible programs.
"The increasing complexity of plants, combined with a changing workforce, demands next-generation tools that can safely and interactively train new operators and engineers without putting them, the community or the environment at risk," added Tobias Scheele, Invensys' advanced applications vice president. "This system provides a stable, realistic environment for learning routine operations and maintenance, as well as practicing rarely performed volatile tasks such as plant shutdowns. In addition, using computer models of real equipment allows endless experimentation without ever taking the equipment off-line, which also mitigates production risks."
Not Ready for Real-Time—Yet
Despite all the gains and assistance they can give to processes, simulations aren't being used to control operations or field devices directly. "Simulations use starting values to make their calculations, but we haven't reached the point where they're using real-time transmitter data," adds E-Technologies' Gerken.
Though still in the monitoring realm, one of the only substantive links between simulation and the real world is the OPC servers, which can access both real-time operations and simulators. "If you need to prove that the controls in a DCS will react properly in a given scenario, then you can have a substitute simulation engine go through OPC to the controls, look at OPC-based data, and see that the devices are responding appropriated based on what they've been told," says Kevin Wright, system consultant to ABB's Process Automation division (www.abb.com). "This is sort of like partial-stroke valve testing for software. Likewise, there have been a lot of efforts to automate and save on traditional manual testing of safety-instrumented systems, but the Catch-22 is still how to validate the validator? Eventually, it will likely be done in pieces by running an automated test procedure, recording results and then monitoring the final elements."