“Easy digital twin” tech could disrupt the chemical market – transcript
Thiago Ribeiro (offstage): Hello and welcome to the Industry Forward Podcast. My name is Thiago Ribeiro, and I am the Global Head of Energy, Chemicals and Infrastructure at Siemens Digital Industry Software.
Today we are discussing Latticept and its Boltzmann reactor. Upon delivery, this hardware is packaged with a tailor-made digital twin. This software includes all of the physics-based logic and laser-scanned geometry an operator needs to gain near-instant insights. Better yet, this twin can run on hardware as simple as a laptop or a cellphone.
With me today is Benjamin Turner, CEO and Principal Consultant at Latticept. Ben, welcome to the show.
Thiago Ribeiro: I’m trying to imagine buying one of your reactors right now.
And I’m a customer right now, and you delivered to me a reactor, a physical reactor, and a digital twin. How does it change how I operate on a daily basis?
I mean, probably there are not many companies over there delivering [physical] equipment with a digital twin. How does it change my first day of operating?
Benjamin Turner: Yeah, so hopefully your first day of operating goes a lot smoother because [of] all of your commissioning activities that you discovered … on a computer, and you’ve solved it before you figured out in real life.
But actually, most of my clients now, they didn’t come to me, “hey, I want a new reactor.” It starts out with “we have an existing reactor that doesn’t work.”
Thiago Ribeiro: Aah! In agreement.
Benjamin Turner: So, we already start with, “we’re already having trouble. We want to use your digital twin to understand what’s going on with our system so we can purchase one from you, so we don’t have the same problems in the future.”
That’s exactly what’s happening with one of my clients. They had, a chemical engineer [who] said, “well, you need,” no offense to you, of course, “you need this much area inside of your reactor for this coil.” Well, putting all that area in there blocked the circulation from the impeller, so now it’s not mixing correctly.
You know, we should be able to catch all that way before we ever send a reactor in the field. Because, and these reactors aren’t cheap. I mean, they start at [$300,000] or $400,000. That’s probably the cheapest you can get. But as far as the cost of running a chemical plant, that’s like a rounding error.
The real cost is when your plant shuts down, or it shuts down without you expecting it, or you have a product that you have to reject because that’s just material that you basically just have to throw away. It can be hundreds of [thousands], if not millions of dollars, right?
[If] it doesn’t work right, it’s just garbage. So, we’re trying to prevent all of that, basically garbage, from happening before you ever step foot in [the] plant.
Thiago Ribeiro: One hundred percent.
And your background is in EPC (engineering, procurement and construction), right?
Benjamin Turner: That’s right.
Thiago Ribeiro: And now you are an equipment manufacturer.
Benjamin Turner: That’s right.
Thiago Ribeiro: But in the end, what we care about is real operations, right?
Benjamin Turner: That’s right.
Thiago Ribeiro: The people who are running those chemical reactions or mixing tanks, … they are concerned about delivering throughputs and being fast and accurate and reliable.
And I’m stressing this out because this is really new, including for me. Because again, back in the days we were running those simulations, we were throwing out the files at the end because the reactor was ready.
And now we can use these reactors, and we can change the parameters. So, if I’m changing the conditions. And imagine the scenario that I’m producing a cosmetic, or I’m doing a chemical reaction, and I need to change one of the raw [materials], or the product has a different viscosity, or I need to change the boundary conditions like the temperatures.
It will take a lot of time to do a design experimentation to understand the impact that would have in the quality of my final product. What you’re telling me that with a single file from the early phase design, I can understand what’s happening in the real operation?
Benjamin Turner: That’s exactly right. Yeah.
And 10 years in the future, someone will be able to pull up the M-Star (M-Star CFD) file that you started with and say, “hey, you know,” like you said, “[This] product has a slightly different viscosity.”
We can type that into the digital twin. We can run the analysis in just a few minutes. And we can see the actual results of what’s going to happen, without having to go back and, “first, we got to find the drawing files. We got to input it in CFD. We got to find a CFD expert first that can even do all this.”
You know, we really are trying to make CFD as a tool all the way to the end of the process.
Another thing too, I don’t think a lot of people realize until you actually are in engineering how many trade-offs … we have to make. And you don’t really know the impact of that. And with this new way of doing things, you can. So let me give you an example.
So, a chemical engineer would say, “hey, I need 20 coils inside of this reactor.” Okay? So, I put 20 coils in there. “Hey, we need a jacket on the outside.” Okay. Now you’re starting to feel [that] the heat transfer is not right.
Well, why?
Well, [we discover] that the coil that we put in there is blocking the heat transfer from the wall. Okay, well, “we couldn’t have known that until we actually ran it.” But now with CFD, you can see the flow. Okay, “well, actually, had we made that coil slightly smaller or the pitch slightly larger, you know, we would have good flow.”
And that’s something that’s not going to show up in a correlation, right? That’s just not what they’re designed for, you know? And now we can do all those, we can do all that conglomerated engineering, for lack of a better term, unified engineering, all at once.
We can see how the chemical parameters change, mechanical parameters and vice versa, and try to get it right from the beginning instead of having to go back and fix stuff after the fact.
Thiago Ribeiro: So, if you come to think about it. The real innovation here is not designing chemical reactors, right? You built a process of designing those chemical reactors.
Benjamin Turner: That’s exactly right.
Thiago Ribeiro: And you’re using digital technologies, digital twins, simulation, GPU-enabled simulation, CFD, from the start and getting ahead of [the] competition.
And this is very interesting because This methodology perhaps could be applied to other [equipment] as well. What do you think about this?
Benjamin Turner: Yeah, absolutely.
So yeah, shell and tube heat exchangers [are] definitely a big one that’s coming up.
But yeah, to go back to what you said, yeah, the point being it’s a Boltzmann reactor system. It’s a system of chemical production. Not only is it not just a reactor, it’s not just a digital twin, it’s also a reliability platform in the future because everything about our reactor can also be inspectable.
So, from the beginning, literally from the basic engineering, with this little web app, all the way to some maintenance guy in the future, all of this has been streamlined through Siemens digital threads basically.
You know, we even do the finite element analysis (FEA) in (Simcenter™ STAR-CCM+™ software), the thermal stress. So, we use M-Star for the inside, that’s the main digital twin. But then all of this information is passed through the whole Siemens digital thread and stored later in (Opcenter™ software).
So, all that information is basically kept for whatever you might need in the future. You know, that’s the good part about data. [You] don’t know what’s important until you need it, but we’re just going to save all of it and figure it out later.
Thiago Ribeiro: Exactly it does make a lot of sense.
This is interesting because in the end, when I’m talking with CIOs and CTOs, the biggest concerns, of course, is revenue and operation. But we’ve decided many times [that] “data is the new oil,” but people manage their data in a very unstructured way.
And they only realize that they didn’t have the better strategy when problems happen, right?
Benjamin Turner: Yup, you’re exactly right.
Thiago Ribeiro: And you’re telling me that, thinking about it on early phase design to commissioning to operation, thinking in the beginning how to leverage that data is quite important. Am I understanding this correctly?
Benjamin Turner: You’re exactly right. Yeah.
So, imagine this happens all the time. You have some failure in the field on the 4th tube or what have you. “Well, why? We did the analysis and we can’t figure [it] out. There’s no immediate answer to that. Well, maybe it’s something that happened in fabrication.”
So, without [literally having] the videotaping of what happened during fabrication, you don’t know what happened. So that’s actually what we do. We videotape the entire fabrication, all of the welding. We laser scan before and after we weld so we have the actual weld bead shape before and after. We’re trying to collect as much data on the entire reactor process so they can be post-processed in the future.
It’s like a time capsule for someone later that can do discovery, right? We don’t know what we’re going to need, so we’re just going to save all of it so that someone in the future has it already.
And yeah, the data collection is… Working with the brownfield plant, sometimes it’s hard enough to just find a drawing. You’re never going to find an existing digital twin of the reactor you purchased 20 years ago. I mean, it just doesn’t exist, right?
So, we’re trying to completely change how chemical production is done.
Thiago Ribeiro: Absolutely.
So, you’re telling me that, “now we have a third option, which is M-Star.” Because when problems happen in operation, you [have] 2 paths. At least, you used to have those two paths.
One is you get a very experienced person using intuition. And based on his experience or her experience of what happened in the past. So, they would identify a problem, “say, hey, I’ve seen this five, ten years ago, and I think the problem is that.” And they would use heuristics and shortcuts to try to find a problem and solve them. This is sometimes great because we’re leveraging experience, but it’s based on intuition. So, it’s prone to errors.
Another path would [be], “okay, so I’ll use traditional simulation and digital twins, But the problem is it’s going to consume a lot of time and a lot of resources,” and definitely we cannot use a cell phone to do that. Probably I need a cluster, I need a supercomputer, and I need PhDs in chemical engineering to do all of those simulations. It would take forever to extract the insights that I need.
Now we have this third path, which is, by the way, GPU enabled. I can use a computer. I can use a web mobile. I can use a laptop. And I can simulate in almost in real time like much, much faster, orders of magnitude faster and extract value from it.
Is this right?
Benjamin Turner: That’s right. Yeah, most users of M-Star are not PhDs.
And that’s exactly [it], you want to take all of this wisdom of this maintenance engineer, this operations guy who has this feel for how this process should work. “Okay, but he needs [answers]. He needs numerical answers he can show his manager something.”
He can literally sit down in front of M-Star, which [has] a simple interface, change a few buttons, or change a few numbers. [He can] rerun the simulation and get actionable insights, [which] are physics-based, that he can take to his managers or his stakeholders or whoever he needs to. And [he can] give [them] real physics-based results, not just based on intuition.
And he can do it the same day now, because he will literally be provided [with] the digital twin of the asset that’s in his facility. So, he doesn’t have to call anyone. He doesn’t have to go out to a consulting firm to get a PhD. He doesn’t need to know what Omega Epsilon, you know, all these different turbulence models. He doesn’t even know any of that, right? All he needs to know is.
Thiago Ribeiro: You’re scaring our audience right now.
Benjamin Turner: Exactly.
It should be as easy [as] a spreadsheet now. I mean, engineers usually build spreadsheets that non-experts can use, right? You type in numbers and you get numbers out that you can, those actionable insights, right?
Now we’re trying to take that to the next level. Instead of just an Excel sheet where we’ve baked in some physics, we have a really, like a 3D volumetric analysis tool that bakes in all these complicated multi-phase physics.
But you don’t need to know how really the back end of how all these interact together because we’ve already validated all that, right? You just need to know, “hey, if we change the density, or we change the viscosity, or we change the RPM.” We [then] change those fields in M-Star. We run the simulations, the report comes out, “okay, it’s good or it’s not good” and we can move on.
And like you said, a maintenance guy, an operations guy, anyone can do that.
Thiago Ribeiro: This is awesome. And I would go as far as to say that you are disrupting the market. This is a new business model, and I believe it’s going to be very profitable.
Thiago Ribeiro (offstage): And that brings us to the end of another fascinating episode of the Industry Forward Podcast! Ben, it was an absolute pleasure having you on the show.
Today, we delved into how Latticept is revolutionizing the process industry by pairing physical reactors with ready-to-use digital twins. This is making complex simulations accessible to everyone from engineers to maintenance staff. This innovative approach helps prevent costly errors, optimize operations and truly leverage data throughout the entire lifecycle of a plant.
Thank you, Ben, for sharing your incredible insights and expertise with us. And to our wonderful audience, thank you for tuning in and joining us on this journey. We look forward to having you with us next time!
To learn more about digital twins, click here.
Thiago Ribeiro – Global Head of Energy, Chemicals and Infrastructure at Siemens Digital Industries Software
As Global Head of Energy, Chemicals and Infrastructure at Siemens, Thiago defines strategy, investment priorities and go-to-market guidance that drive profitable growth and long-term customer value.
Thiago has almost two decades of experience in chemical engineering and the energy industry. He has led large-scale digital transformation for companies worldwide, such as in the United States, Canada, Europe, the Middle East, South America and Asia.
Connect with Thiago on LinkedIn
Benjamin Turner – CEO and Principal Consultant at Latticept
As CEO and Principal Consultant at Latticept, Ben runs the Siemens M-Star CFD reseller. The company also acts as an equipment supplier that pairs its physical products with out-of-the-box, complete digital twins.
Ben has almost two decades of experience in mechanical and chemical engineering. He has worked in all tiers of industry including the development of pharmaceutical cell therapies, polymerization reactors and thermal oxidizers.
