"I love it when a plan comes together," says Col. John "Hannibal" Smith. The cigar-chomping leader of "The A-Team" was talking about his group's cartoonish, live-action TV adventures in the 1980s. However, he might as well have been discussing simulation's multiple and merging roles in many process applications. The confidence and satisfaction is the same.
Slow and costly models applied to only big-ticket applications have long since been joined by faster and less expensive simulations in a wider variety of settings. However, constantly improving and higher fidelity models, new variables and parameters, better software, more powerful computers, 3D displays and other advances are also blurring the lines between simulation's usual categories.
Most notably, static simulations used for design and configuration are being linked to dynamic simulations for operations and training, and these have been enhanced by closer-to-real-time data, which allows them to optimize actual operations, performance and products. As a result, tying and unifying simulation into one multi-functional bundle is letting users pick the capabilities they need without having to implement several different solutions.
For example, Exxon Mobil estimates its process applications worldwide are saving about $750 million per year by using ROMeo optimization software, Pro/II simulation software and other solutions from Invensys Operations Management, according to Joe McMullen, product manager for SimSci-Esscor at Invensys. ROMeo secures and reconciles measurements from Exxon's components, simulates subsequent conditions and courses of action, and recommends which ones will optimize the process and make it most profitable. Likewise, he adds that Royal Dutch Shell is also rolling out ROMeo in several refineries, and so far gains about $1000 in optimization benefits for every $25 it spends on support.
"The ultimate goal is to use one calculation engine for many different purposes," says McMullen. "As long as training simulators and process control systems are on different platforms, keeping them synchronized will continue to be an issue. By contrast, our dynamic modeling tool lets users easily create a model for use in a training simulator by just pressing a button, and our EyeSim 3D, virtual reality training simulator is starting to gain traction too."
Propylene Process Makes Its Own Model
Similarly, the biggest oil company in Sweden, Preem, has integrated Hysys simulation models from Aspen Technology into its DMCplus controller to develop new advanced process controls (APCs) and cut product variation in half. Preem has two refineries including its Preemraff Lysekil refinery that processes 11.4 million tons of crude oil per year (Figure 1), and 500 retail gas stations. In fact, Preemraff was where the company recently addressed the challenge of controlling its propylene/propane (PP) splitter, which is used to separate C3 streams into 99.5% pure, chemical-grade propylene and 98.5% pure propane. Any deviations in quality could affect Preem's profitability because of pure propylene's high market value.
Preem reports it initially tried to use traditional model-predictive control (MPC) for the PP splitter to drive its process to optimum performance and profitability, while still respecting all equipment constraints. However, reliable plant step test data, usually used to create an MPC design, was very difficult to obtain because of excessive settling times and disturbances that prohibited the PP unit from reaching a true steady state. So, Preem's engineers decided to develop the MPC model from data generated by a dynamic simulator instead of the actual process.
Now, it's important to remember that whatever applications that dynamic simulations end up serving these days, their starting point must still be a sound steady-state simulation.
For the PP project, a steady-state Hysys model of the splitter, heat pump and ancillary equipment was reused from a previous study. The Hysys model's predictions of column temperature profile and other variables were validated against plant data. Next, a dynamic simulation was constructed using AspenTech's Hysys Dynamics software by specifying added engineering details, including pressure/flow relationships and equipment dimensions. All basic controllers also were built in the model, which was then checked for consistency and calibrated against process data.
The dynamic simulation was configured to automatically run a sequence of events and record all selected variables. Step tests were then run with Hysys Dynamics, and the data was exported to the DMCplus' model application. Once the step test data was imported into DMCplus, the task of dynamic model identification took on the appearance of any other DMCplus project. Historical process data was also imported into DMCplus to validate the dynamic models. Finally, a DMCplus controller was built and a connection to the DCS was established. Once online, this Hysys-enhanced controller helped reduce propylene variation by 50%, saving Preem more than $55,000 per year.
Because of the extensive simulation effort—and the fact that the underlying model of the PP splitter was sound—no added controller tuning was necessary during commissioning, according to Dr. Nicholas Alsop, Preem's APC manager. The design procedure using dynamic simulation as an alternative to models from plant tests was validated. In addition, the Hysys Dynamics model was next used as the engine for an operator training simulator (OTS), further increasing the value of using dynamic simulation for this study.
"When the Hysys-based DMCplus controller was put in prediction mode with the real plant, the results were so good that no live plant tests were needed," says Alsop. "As a result of this good experience, we're using Hysys as a tool in every DMCplus controller project."
Glenn Dissinger, Ph.D., product manager for AspenTech's Hysys product family, adds that, "Preem's simulation of its PP splitter is really about putting APC on top of operations. Previously, users performed step tests by putting in disturbances, and then waiting days for results when the process returned to a steady state. Now, their dynamic models allows them to get results in minutes, and calibrate them with actual operations data. As a result, more users are aware of the need to look at dynamic simulations." In fact, to further accelerate simulation efforts, AspenTech has introduced AspenSearch, which allows users to quickly locate existing models and other plant information to reduce the time it takes to accurately model an asset's operations.
Training, Optimization is a Two-Way Street
Thanks to recent gains in data processing speed and capabilities, it seems that process simulations that start out in one realm can quickly flow into other areas as needed. For instance, Fertilizantes Nitrogenados de Venezuela (Fertinitro) is a urea and ammonia producer located in the José Antonio Anzoátegui Petrochemical Complex in eastern Venezuela, and it synthesizes 1.3 million tons per year of ammonia, and synthesizes and granulates 1.5 million tons per year of urea for nitrogen fertilizer. To improve its operations, Fertinitro recently developed simulation models and deployed operator training simulators (OTSs) in both its ammonia and urea processes. The project cost about $3.5 million and implemented Honeywell Process Solutions' UniSim R300 in the ammonia process and UniSim Operations R320 in the urea process.
Natalia D'Ambrosio, process superintendent at Fertinitro, reports that, "OTSs were needed because traditional training didn't focus on plant operations, and we needed to improve the operators' skills to optimize conditions, familiarize new operators with the plant and reduce plant shutdowns caused by operational errors. The operators also needed to learn how to solve critical situations in a safe way."
Consequently, points configured for the ammonia plant's simulation included 164 process streams, 336 field-operated devices, 163 controllers, four anti-surge controllers, 20 instructor variables and 19 malfunctions.
"The major challenges for the ammonia OTS were to simulate turn-ons for the burners and reach the correct heating rate in the reformer furnace; achieve the right temperatures in the convective duct coils in the reformer; and find the correct CO2 leakage in the removal section," explains D'Ambrosio.
Likewise, points configured for the urea and granulation plant's simulation included 70 process streams, 112 control valves, 150 field-operated devices, 10 instructor variables and 12 malfunctions.
"The major challenges for the urea OTS were to manage thermodynamic packages; reach the correct conversion levels in the urea reaction and the reactor top temperature; produce the correct quantity of low-pressure steam in the high-pressure recovery section according to the plant load; simulate the CO2 compressor's anti-surge control; and stabilize the pump that sends recycle solution to the high-pressure system," says D'Ambrosio. "In the granulator's simulation, several equations were developed to simulate its behavior. These granulometry equations were studied using MathWorks' MatLab software, and then a Unisim interface was prepared for them." (Figure 2)
The models developed for the ammonia and urea OTSs represented conditions in the plants with an average deviation of 5%, according to D'Ambrosio. As a result, plant shutdown hours decreased from more than 120 in 2009 to less than 10 in 2010 and almost zero in 2011.
"So far, 96 operators have trained on the ammonia and urea OTSs, including about 35% newcomers," adds D'Ambrosio. "Some process conditions have been improved, letting us increase the steam generation and reduce natural gas use. New control logics and operating philosophies also are proven in the simulator to validate their effectiveness and operational security before they're implemented. Our next challenge is to integrate both of these simulations to allow even more realistic and complex training and even better oprimization."
Martin Ross, product manager for Honeywell's UniSim solutions, reports that simulations were previously constrained by available computing power, but those limits have been removed, and the beneficiaries are users like Fertinitro. "The cost of calculations is way down. The equivalent of 9 gigabytes of memory used to cost $25,000 to $30,000, and now it's basically free," explains Ross. "This is enabling simulation across PCs, allowing users to build larger models and solve more complex problems. Now it's easier to look at a steady-state simulation's recommendations, and then push it through a dynamic simulation for quick assessments, adjustments and redesigns. These days, both design engineers and engineering procurement contractors (EPCs) can use simulation to optimize their processes. Meanwhile, everyone is using simulation for training, but they're also using it to address asset management and lifecycle issues too."
In fact, Honeywell has combined its UniSim Design Suite for process modeling and UniSim Operations Suite for training since 2005, and recently refreshed its HMI to be more intuitive and Windows-based because users want fewer lessons to sit through and more Xbox-style interfaces. "If a user has one simulation for detailed engineering, then they want to use the same platform later for operations and other jobs," adds Ross. "We say that once you've got a good model, then you can also use it in a bigger framework for tasks like training. Process simulation is simply becoming more accessible to all kinds of users, who want to initialize training with real plant data or gain other competency tools."
Rough Locations, Unusual Settings
Naturally, as simulations merge their static and dynamic sides and pick up speed and mobility, they're also starting to show up in some unusual applications and some very harsh environments. For example, energy and mining consultant Norwest Corp. of Golden, Colo., and an independent oil company client recently needed a full-field simulation to optimize high-pressure, air-injection and horizontal-infill drilling for a tight reservoir in the Williston Basin—located in Montana, the Dakotas and Saskatchewan—but they couldn't afford a costly simulator. So, they researched and chose Tempest software from Roxar, which is part of Emerson Process Management.
Using nine years of available production data from a similar field nearby to calibrate their simulation model, Norwest and its client report they achieved an excellent history match; used the model to optimize the timing and sequence of infill drilling; converted existing wells to air injection; and saw oil production spike when new wells were drilled. At the end of the first year, production and injection forecasts matched actual performance, even during transient operations. The partners more than doubled estimated recovery and applied the lessons learned to older fields nearby. Estimated primary recovery was only 8-10% of original oil in place, but the client recently predicted recovery of 24%. Besides high-pressure air injection, they're investigating a hybrid air-and-water injection method to further improve recovery and reduce operating expenses.
Norwest presently uses Tempest for black oil, compositional and thermal simulation in projects ranging from multi-component coalbed methane (CBM) to tight gas reservoirs and enhanced recovery operations. Reservoir simulation ought to be done routinely on many more oil and gas fields, according to John Campanella, Norwest's senior reservoir engineer.
"I think reservoir simulation should be brought down to every engineer's desktop," says Campanella. "We need to push simulation out of the back room and into the mainstream where people can use it on a daily basis. Besides big 3D projects, there are a lot of existing fields where simulation could be applied, but too often it gets skipped, and one big reason is cost. Most simulator licenses are priced too high for everyone to access when they need it, and cost is a big issue for smaller oil companies and consulting firms like us. It's hard to justify a package that costs more than $200,000 like several we evaluated. While other simulators may have more bells and whistles, Tempest does the job efficiently and cost-effectively on almost anything from small, conceptual models to full-field CBM models. Some of our clients don't have the expertise to do CBM modeling or large-scale simulation. Others have the ability, but their people are spread too thin, and so they come to us to help get things moving."
For example, Campanella adds that Norwest was contracted to reevaluate another oil field with more than 50 years of production. After building a simple conceptual model, history matching and running a simulation with Tempest, Norwest demonstrated that the water-oil contact was about 140 feet lower than previously believed, and identified a deeper target capable of producing clean oil in a section thought to be completely wet. "After the simulation, that well was deepened, completed, and produced 100% oil for almost six months."
Similarly, in an earlier project, Fluor, CCA-Wesco and Siemens Industry joined to clean up 8900 cubic yards of heavy metals, organic material and radioactive waste in two silos at a former uranium refinery covering 1050 acres in Fernald, Ohio. From the 1950s until the plant closed in 1989, the silos had become the world's largest source of radon at 13 to 16 million picocuries per liter (pCi/L) each, so controlling and mitigating the gas was a crucial part of processing and removing the waste.
Consequently, the partners in the Fernald Closure Project selected Siemens PCS 7 DCS to automate Fernald's clean-up process and radon control system (RCS) because it could work via Profibus and WinCC software with the 12 other control systems employed during the project. In addition, PCS 7 and the RCS helped train operators running the plant, and also provided validation and verification required by the U.S. Dept. of Energy (DoE).
"PCS 7's process object viewer let us audit all the alarm messages and priorities, which was an absolutely vital part of this project," says CCA-Wesco's engineering manager, Shirley Jeffreys. "This helped us make sure interlocks and all of the alarms were in place. Also, a simulation station in the control room played an important role because the application's second step was to verify that the automation functionality was in place and working appropriately. We couldn't have proven the software prior to completion of construction and installation without the process simulation. We wrote a full process simulation for every system, and any changes were tested prior to implementation." (Figure 3)
Frank Showalter, the Fernald project's facility operations manager, adds that, "The simulation was vital because it helped speed the development process. For example, the team was able to verify the control strategy and the ability to transfer and process hazardous material during the code development before construction was complete. The process could be immediately evaluated in the control room and during interactive meetings via the Internet, allowing personnel in multiple locations to easily collaborate on the project."
Fernald's simulator also provided realistic, hands-on training opportunities. Operators practiced work evolutions before equipment was installed to develop their skills and validate operating procedures. Their view of the process was identical to normal process operation, and they could experience the impact of their inputs. Also, engineers could simulate process upsets, so that operators could learn to recognize abnormal conditions before emergencies developed. Fluor reported that the clean-up project was finished in 2006.
Mark O'Rosky, Emerson's simulation and training manager, confirms that traditional static simulations for design and dynamic simulations using process changes for high-fidelity control system modeling have been joined by longer-term lifecycle simulation models, which are also known as multi-purpose design simulations (MPDS). These MPDSs take dynamic data and use it for many tasks, such as evaluating and optimizing formerly fixed designs, or running simulations at five or six times normal operating speed to get results faster. "Users are already manipulating real-time data to get more accurate responses from their simulations. So, in the future, I think we're going to see vendors including simulations for real-time adjustments to controls. A simulation will watch a process and give feedback during operations."
Pictures vs. Reality
One of the most exciting sides of simulation lately is its recent push into 3D displays and immersive environments. It seems everybody wants the Xbox experience in their displays and simulations, perhaps so younger operators will be drawn to process control. However, despite their hypnotic graphics, 3D and immersive displays still haven't really come into mainstream use yet.
Similarly, even fans of simulation's emergence in 3D, agree that crisp and colorful graphics are useful only if their "realism" truly reflects what's going in their process application. "For example, process applications in the North Sea are now legally required to have dynamic simulations, and use them for operator training," adds AspenTech's Dissinger. "However, the question remains: how realistic does a simulation need to be? The nuclear power industry may need exact replicas, while chemical and refining processes may need different levels of realism, up to and including buying a complete, duplicate set of DCS hardware. Just as any simulation must begin with a good steady-state model that's calibrated and tuned with current data and converted to a dynamic operation, it must continue to represent true operations and show operators how to recognize and respond to them."
Besides making sure your APC model is accurate, Honeywell's Martin adds it's also vital to make sure you don't implement more simulation than you really need or are going to use. "The Holy Grail of simulation is dynamic optimization, but the other question is: do you really need it and can you maintain it?" says Martin. "This isn't a technical question, but it's a deployment problem. Can you build a model that's good enough to give you a worthwhile benefit?"
In addition, Martin adds that 3D displays will soon serve as engineering platforms where—not just simulation—but a host of control and automation tasks can be carried out. In fact, Honeywell is integrating its UniSim process simulators with Virthualis' MindSafe 3D simulation to develop a 3D simulation solution to enhance operator training and plant safety. The partners are integrating the two to provide a holistic virtual environment that can be used to efficiently design, analyze and verify plant operations, as well as prepare operation teams for safe, reliable and efficient operations (Figure 4).
Figure 4. Honeywell's UniSim process simulator has integrated with Virthualis' MindSafe 3-D simulator to provide a holistic, virtual environment that can respond to changing conditions, such as heat from fires that can influence pressures, and other conditions in pipes and equipment.
Photo courtesy of Honeywell and Virthualis
While UniSim models what happens inside the pipe work and process equipment, MindSafe does the same for the external environment. This creates realistic, interactive scenarios that respond to changing conditions. For example, heat from fires can influence pressures and other conditions in pipes and equipment, which, in turn, can affect possible leaks. This system can be used to perform both engineering studies and emergency response training.
Into the Cloud Too
Because one of the main forces allowing simulation to get into more varied, smaller-ticket applications is less costly and more powerful data processing, it's no surprise that a few simulations are being performed in virtual and cloud-based computing environments, and being displayed on smart phones and other mobile devices.
For instance, Mynah Technologies just launched a Virtual Dynamic Simulator service for designing and implementing simulations via private cloud computing and VMWare's virtualization technology. Mart Berutti, Mynah's president and COO, reports its benefits include:
- Reduced training and development system hardware needed;
- Protection of dynamic simulation to control system simulator communications by using virtual LANs;
- One-button start-up of training or development systems;
- More security and availability for the training and development system;
- Flexibility to handle multiple control system implementations and dynamic models in the same system;
- Flexibility to allow training or development systems from any networked PC or thin client; and
- Ability to handle system expansion and upgrades with minimal hardware.
"Simulation in the cloud takes regular dynamic simulation and applies virtual computing technology, so it can be easily used for training and other purposes," explains Berutti. "However, you still have to start with a good model."
Using the same technology as the public cloud, the private cloud allows flexible access and agility, but does it behind the security of the end user's firewall. "We have our own private cloud for business, and we're building internal private clouds for users," adds Berutti.