Easier-to-use features and simplified integration lead to new applications for simulation software
Simulation software and its capabilities have come a long way in recent years. The latest versions include easier-to-use and more advanced features, increased computing speeds and simplified integration with other simulation programs, as well as data analytics and Industry 4.0 technologies. These modern features allow today’s simulation tools to be employed in a variety of applications throughout the lifecycle of a plant. As a result, chemical processors are using simulation not only for design and optimization tasks, but also for other challenges, such as increasing safety and avoiding operational risk, achieving sustainability goals and training employees.
While simulation has become the de facto method for designing and optimizing processes in the chemical process industries (CPI), for many years, users didn’t apply the technology to other types of analysis, such as overall profitability, safety issues or smaller engineering problems, because it took too long to get an answer or because the simulators were too difficult to set up and use. As a result, some software providers have built solutions with lower-fidelity models that are easier to build and use. Meanwhile, other providers have taken steps to increase speed of calculations and simplify the use of rigorous process simulators.
“Democratization of simulation software is becoming more important,” says Steve Brown, CEO of Chemstations (Houston; www.chemstations.com). “Everyone in the organization should be familiar with and use simulation to address challenges, but we find there’s resistance to doing that, so the expertise tends to get siloed into a few experts. But to get maximum value from the simulator and the answers it can provide, you need to provide more people with the ability to use the simulator and its data, and the way to do that is to make it easier to use.”
As a result, simulation providers have focused on making the latest versions more user friendly. “We have reduced the number of mouse clicks needed to get to where you need to go, increased visibility of the most used functions and provided really good graphical interfaces with better reporting capabilities. This allows engineers to more easily use the simulator to solve a problem and take the data out of the simulator to present the results to the people who need to use it to make decisions.”
Another change Chemstations has made is to increase the computing speed of its rigorous process simulator by taking advantage of parallel processing, which uses all available computing cores. “This means that instead of using just one core of the user’s computer, we can spread the workload across as many cores as are available, which will speed the process considerably,” explains Brown. While the initial intent of the improved calculation time was to allow faster execution of large optimization projects, the increased speed opens the door for simulation of smaller-scale projects and “what-if” studies.
Because newer simulation programs are easier to use, says Brown, it allows more collaboration between departments. “In the past there were silos of information where one group used a set of tools and generated data, then passed it to the next group,” he says. “But it makes more sense for these separate groups, who are really all working to achieve the same goals, to use the same simulation model and data. Sharing the model means all the data is consistent, so we are working toward making sure the data from the simulator can easily export into the data standards that exist within other engineering tools.”
For example, an engineering team may build a model and then wrap an Excel interface around it or send the data to the supervisory control and data acquisition (SCADA) system so that operations and maintenance staff can see what’s going on and perform their own engineering studies without having to understand the underlying engineering of simulation or be trained on the simulator. “The way this type of integration works is that the engineer builds a model, connects it to the OPC [open platform communications], and the operations and maintenance staff can see the values coming from the simulator and apply them to performance monitoring and other tasks,” explains Brown.
Scott Lehmann, vice president of operational risk with Sphera (Chicago, Ill.; www.sphera.com) agrees that simulation and the plant’s digital twin can be used to pull all the disparate systems together for a better view. “There are a lot of different systems managing separate things, so facilities often have disparate data, systems and business processes, which means there are a lot of separate decisions being made and, often, they are being made out of context,” says Lehmann. “The digital twin starts to pull it together with dynamic visualization so users can see the geography view — here’s the status of the plant, here are the various impairments, here are the activities that are happening. By pulling all this together, it provides a richer view, making it easier to make informed decisions.”
And, as we enter the realm of Industry 4.0, integration becomes more important than ever, says Ahmad Haidari, global industry director, process, energy and power with Ansys (Canonsburg, Pa.; www.ansys.com). “When we talk about plant and asset performance, we often think of data analytics, which includes collecting data from sensors and historical behavior and placing it into the data analytical platform to be reviewed, so now the question becomes, ‘What role will simulation play in Industry 4.0?’” he says. “The answer lies in the digital twin. Users can model an existing asset, merge it with sensor data, operational data and historical data and effectively marry engineering and operations with a virtual replica of the plant that can be employed to solve real-life issues.”
Haidari provides an example: If you’re looking at the performance of a mixing tank and the sensors show the material being mixed is too thin, you may not know why, but the digital twin of the same mixing tank shows the material is too thin because the shear rate is too high, so the solution is to back off the impeller. “Tight integration between data analytics and simulation allows simulation-based digital twins to help companies see the reason why something is happening with the performance of equipment and solve the problem in a more informed manner, allowing them to better control the equipment and process, prevent unwanted downtime, reduce waste and predict when a process may go out of control.”
Smaller, but important challenges
Thanks to easier-to-use simulators, smaller challenges can now be addressed, and one of the newest applications for the technology falls under increasing safety and mitigating operational risks. When the simulation program has the ability to take data generated from the assets, the historian, operator rounds or the maintenance management system, it can be used to monitor assets in real time through a dynamic visualization. This allows users to simulate “what-if” scenarios without impacting the live plant, which can be extremely helpful when it comes to looking at the risk profile of certain tasks or actions and finding ways to offset or mitigate that risk.
Reducing risks. “Using simulation to see the operational risks and how they come together to impact the cumulative risk allows users to examine risk data in real time, to play with different scenarios so they understand the risks of various scenarios and the trade-offs of each action and risk and then take the path that best suits their needs,” Sphera’s Lehmann says. “It provides actual insight that allows them to make informed decisions about whether they need to execute changes, when and where they need to execute them and which actions will result in the least amount of risk,” (Figure 1).
Lehmann continues: “The value of the digital twin in this type of risk management is that it makes the hazards and risks visible and available in real time so users can connect process safety management to operations and balance the risks against productivity.”
An example would be an operator who is about to open a vessel, but instead of going in uninformed, he may use integrated information from the simulator studies to better understand the current state of the plant, the current state of that vessel and how those factors may impact his safety and the job he’s about to do, as well as how opening that vessel may impact productivity of the vessel itself and the entire plant. “The concept behind using simulation for operational risk management is that it provides transparency and allows processors to balance risk against productivity,” says Lehmann. “Is this the right time to open the vessel? Will it stop production and cause a problem with productivity or safety? Is it better to wait and do it later or will waiting create a failure situation where productivity may be more drastically impacted down the road? It’s not a case of doing nothing, it’s a case of getting that balance right.”
Avoiding safety risks is another benefit of simulation tools, says Ansys’s Haidari. “Industry is pushing operational boundaries in order to increase efficiency, but this means processes are working to extremes and operating conditions now involve higher pressures and higher temperatures, which can lead to accidents,” he says. “But, via simulation models, processors can explore and determine under what conditions a reactor may veer into thermal runaway or face equipment failure due to corrosion or erosion. The models can tell us how to safely and efficiently run the reaction without unwanted byproducts, thermal runaways or hazardous leaks” (Figure 2).
Sustainability. Simulation is also being applied toward improving sustainability throughout the plant, according to the experts. “Simulators have always been very good at modeling the steps necessary to clean up the process, allowing users to model carbon capture, scrubbing of contaminants, wastewater clean up and other necessary processes, but companies didn’t use it,” says Paige Marie Morse, industry marketing director, chemicals, with AspenTech (Bedford, Mass.: www.aspentech.com). “But now that sustainability is becoming a goal, people are considering things like reducing energy use and greenhouse gas emissions as important metrics to watch and simulation can help them examine different ways to significantly reduce energy use and greenhouse gas emissions and how doing so will affect productivity and profitability both for new processes and existing ones” (Figure 3).
Olivier Baudouin, process manager with ProSim (Labege, France; www.prosim.net) agrees. “Different simulation tools can be used to address a variety of sustainability issues, including energy use and water treatment,” he says. “For example, our Pinch Analysis can be used to optimize the thermal power consumption and, coupled with our Exergy Analysis, improve the energy efficiency of a process. And, our Water Pinch analysis module can be integrated into simulation software to reduce water consumption and wastewater generation, linked with useful unit operation models for water treatment, such as ultra-filtration, nano-filtration and reverse osmosis. Of course, at the end, the profitability of the plant will lead to the selection of the technological choices, thus a powerful economic evaluation module has to be available in simulation software, allowing users to find the balance between sustainability and profitability,” he says (Figure 4).
Maintenance. Maintenance is another area that can now take advantage of simulation and its ability to directly connect to the process, says Ed Fontes, chief technical officer with Comsol (Stolkholm, Sweden; www.comsol.com). “Once connected to the process, the model is not generic, rather it’s a detailed replica of the specific process, making it possible to monitor and plan maintenance of a reactor, process or plant,” he says. “Following equipment, such as a reactor, with a connected simulator will allow you to see what is happening with that reactor in a more useful way than just using measurements or a digital twin alone. Integration of the two has led to the practice of running large processes with very few engineers because everything is optimized and controlled using models and sensors instead of having employees looking at individual displays. Everything is centrally connected to the computer systems, which are connected to the model allowing users to see what all that data means for the process and maintenance of the reactor” (Figure 5).
He continues: “The digital twin of the plant or of the different units within the plant can even be run in advance of the actual process so that the process can be watched in detail and users can estimate when the equipment will be subjected to fatigue or failure and take maintenance action before that happens. Or, you can know in advance when there will be deactivation of the catalyst and know how much time you have before you have to change or regenerate it. Connected simulation allows you to do these maintenance tasks with greater accuracy by continuously monitoring the equipment, process and plant with simulation and sensor measurements to provide a detailed view of what will happen and prevent shutdowns before they occur.”
Training. Simulations of the control systems can also be helpful for training operators. “In the past, the digital twin was used to train panel operators and provide that plant interface with the offline control system environment, but now, using pre-developed 3D CAD [computer-aided design] drawings and software tools that automatically generate the 3D environment, it provides a very high-resolution, high-quality virtual-reality view of the plant, allowing users to bring field operators into the same environment,” says Mart Berutti, vice president of process simulation with Emerson (Austin, Tex.; www.emerson.com). “This means that without even having the plant built, users can have a virtual control room with a control system simulator with a panel operator in there interacting with the control system in a high-fidelity simulation of the process. Field operators can be working in the same virtual environment doing simulated field routes. The cost basis of this solution has come down drastically and the time to build it has also been reduced, so it’s a cost- and time-effective way to get operators trained on a new asset before it is even built and operating” (Figure 6).
Amanda Thompson, SIMIT product marketing manager, USA, with Siemens (Plano, Tex.; www.siemens.com) agrees that control-system simulators are advancing training. “It provides the opportunity for extremely realistic operator training because the operator training system is based on the same control system they will use in the real plant,” she says. “It shows the same operator screens so they learn how to navigate through them. They can address alarms and work different operator procedures all in the same environment they would use in the real plant, but without the risk of performing training in a real plant situation where a training error could result in negative impacts to the process or safety. Simulation allows them to redo the operating procedure over and over until they learn how to do it correctly.”
And, experts say that due to the easier-to-use features and ability to integrate simulation with other programs, the list of useful applications will continue to grow in the future. “In reality, everything can’t be measured and everything can’t be simulated, so the real crux of the issue is how can I use what can be measured and what can be simulated in a hybrid fashion,” asks Ravindra Aglave, director, energy and process/computational fluid and particle dynamics, simulation and test services, with Siemens. “The problems processors face are complex and can’t be solved via one single software platform. Instead, it is a portfolio containing many different softwares, each intended for a specific engineering aspect of the chemical process. These softwares have all existed for decades, but with today’s outlook on digitalization and having a digital thread throughout the lifecycle of the plant, more and more software platforms are becoming integrated and compatible, allowing processors to work in a co-simulation environment which can plug results from one study into the next and so on for all kinds of improvement opportunities in the future.”
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