Stamitalks Podcast
We at Stamicarbon are pioneers in the licensing and design of fertilizer technology with more than 77 years of experience.
Here we share the latest technology insights into urea, green ammonia, fertilizer sustainability, and digital trends for fertilizer plants, and we also discuss the role the fertilizer industry can play in solving global challenges.
Happy to share our knowledge with you.
Stamitalks Podcast
Chemical Plant Digital Twin, Process Monitor, AI
How can real-time plant insights optimize efficiency, minimize downtime, and improve reliability in chemical plant operations?
In the latest episode of #Stamitalks, Ali El Sibai discusses Stamicarbon's Process Monitor—a digital tool that provides real-time calculations of key process parameters, equipment and instrument performance metrics, and emissions.
Gain insights into the integration of #AI in chemical plant digital twins, unlocking possibilities for predictive maintenance, process optimization, and smarter decision-making.
All right, welcome everybody to a new episode of Stamitalks. Today we have Ali El-Sibai as our guest. Welcome, Ali, Thank you.
Speaker 2:Mark. Hello, mark, sorry, thank you for having me here.
Speaker 1:Yeah, it's nice to be welcome in this podcast as well. Today we're going to talk about the digital process monitor, a digital tool to help produce or help more efficient production. Maybe, before we get into the topic, can you tell us a bit more about yourself and your background.
Speaker 2:Yeah, maybe, before we get into the topic, can you tell us a bit more about yourself and your background? Yeah, so, as you mentioned, my name is Ali El-Sibai. I originally come from Lebanon and since I was in high school I had a lot of passion for chemistry. So I did my bachelor degree in chemistry, but then I discovered that it's not as challenging as I expected, so I switched to chemical and process engineering. So I did my master in this field in Germany and then I did my PhD in process systems engineering, also in Germany, and I worked as a research co-worker at Max Planck Institute.
Speaker 1:Okay, it's an interesting step up. So then it's still modeling chemical processes, or what did you do at Max Planck?
Speaker 2:Yeah, so at Max Planck I was involved in designing and the model-based design and optimization of CO2 methanation process for the storage of renewable energy. So basically at that time it was a really hot topic to explore how you can store renewable energy that is coming from solar or wind, and the idea is to store this electrical energy in the form of gas, and one of the candidates for that was synthetic natural gas okay, and so then, how did you end up at Stamik Arbent?
Speaker 2:yes, after I finished my research at Max Bank or I got my PhD as well I went back to Lebanon for one year where.
Speaker 2:I worked as a professor at one of the universities and then after one year, yeah, I came across one job advertisement from Stemmic Carbon and at that time they were looking for a modeling engineer to work on the digital twin principle for UEA processes. And for me at that time, yeah, I was fond with modeling, but also for me at that time I was fond with modeling, but also for me the digital twin concept was quite new at the time.
Speaker 1:Okay, what is a digital twin?
Speaker 2:So basically, what you do is you try to. What you do basically is you develop or you model a virtual replica of an existing chemical process. So in this regard, it is like a UER process and you use actually this virtual replica in order to better understand what is really happening in your plant. This, of course, requires that you are feeding this virtual replica or model by plant data in real time.
Speaker 1:Okay, so then, how do you get that data? Basically the client should send this data to us.
Speaker 2:So at the moment, yeah, the idea is the idea of this digital twin as it is now offered by Stemicarbon. Yeah is to, as I said, to monitor your plant.
Speaker 2:So, we think this is a more revolutionary sort of monitoring tool where operators do not only rely anymore on process key variables that are measured by instruments like temperature or pressure, but they also monitor key performance indicators or key process variables which are specific to certain equipments or important units or critical units in your plant, and these key variables will provide you with a deeper insight into what is really happening in your plant, in your plant operations, and accordingly, the operators can then take measures or actions in order to better improve the performance of the plant.
Speaker 1:Okay, yeah, so it's linked to basically the sensors that already are. Basically, data from that is gathered in the DCS. Yes, and then you link that up. So, then the data doesn't stay at the client. The data is sent to A cloud. To the cloud, yes.
Speaker 2:So basically, yeah, we leverage the advancement that happened in the past years in big data and also in storing and processing this data in clouds. So basically, what the client needs to do is to send us plant data in real time. He sends it to the cloud cloud. You have a model that is running that is a real, precise replica of the current plant of the client, and then you send this data. This data is fed after being processed and we make sure that this quality of the data is high. Then it is fed to the model and then the model runs and then calculates certain key performance indicators, as I mentioned earlier, and key process variables.
Speaker 1:Okay, and what type of key performance indicators and process variables can you think of?
Speaker 2:Yeah, so for a UEA process, what we are talking about, what can be interesting for the client as a key performance indicator, is actually the specific steam consumption. What also can be interesting is the ammonia emission, maybe the load, and when we talk about key process variables, we talk about, for instance, the efficiency of the stripper, because it is actually the unit which consumes the highest amount of steam.
Speaker 2:So, by making sure that you are operating your stripper in an efficient way, you are actually reducing steam, for example. Okay, so yeah, efficiency of the stripper tube load in the stripper we talk about, maybe, water content in the low pressure carbamate condenser we talk about, and C ratio at the outlet of the reactor. So all these variables are actually variables that are either not measured online you don't have real hardware to measure them or they are difficult to measure online.
Speaker 1:What do you mean by measuring online?
Speaker 2:Yeah that you have a tool that is measuring it in real time Like a sensor. Yeah, like a temperature, for example, you can measure it online easily.
Speaker 2:But when you talk, for instance, about the NC ratio, yeah, the current situation is that you need to take samples. For example, you have NC meter, which is actually a hardware that you use to measure the NC, as the name implies. So the thing with the NC meter, for instance, is that you need to take a sample from the outlet of your reactor and then you need to take the sample to the lab and then you do analysis and the result of the analysis will come after three hours or four hours. Then you can know, for example, what is the urea content at the outlet of the reactor, which gives you an indication if your reactor is performing well or not.
Speaker 2:Now, for example, when we talk about this type of measurement, there are two issues, two limitations. One is the time. So it takes to take the sample and then run it in the lab. We are talking about three hours, four hours and, yeah, this is also troublesome because you are taking a sample from a high pressure synthesis loop and then also it is prone to errors. So either in your lab, for example, you can have systematic errors where you don't get really, you may not get really accurate results, for instance, it could happen and it takes, as I said, a lot of time four hours minimum, or three hours, and this is normally it is done in a urea plant in every shift, so every eight hours. So you can imagine, for example, that you need to wait eight hours to know if your reactor is performing well or not. Okay, and then let's say, if your reactor was not performing well, you are losing like eight hours before you take actions, which means that you are losing like eight hours before you take actions, which means that you are losing money, for example.
Speaker 2:Another example is the ammonia emission. Currently, you don't have a real online measurement device for ammonia emission, so you don't know if you are emitting too too much ammonia or not, and this is also currently. You have very strict regulations on ammonia emission, so it's not only about losing money in terms of ammonia, but also maybe you will be fined or you have you have regulations from from the countries nowadays that you need to abide by.
Speaker 2:The risk of losing your license to operate, or a fine, I don't know depending, but this also shows the importance of also being able to measure these type of emissions online. These variables or key performance indicators cannot really be measured by instruments, and this is where the process monitor can help a lot.
Speaker 1:Okay, so then you have the thermokinetics models of atomic carbon. Are those models then in the cloud, or how does that work?
Speaker 2:Yeah, so, as I said, normally you are creating a virtual replica of your plant, so a mathematical model of your plant, and currently what we use are first principle models, and this means, of course, that you have kinetic and thermodynamic models embedded within the process model. And, yeah, these models are running in the cloud and they are being fed with plant data in the cloud and they are calculating in the cloud. Okay, in the cloud, okay. And then, yeah, the key variables and the key performance indicators are then displayed on a web page, basically. Yeah.
Speaker 1:So it's online accessible. So I'm assuming that's all protected because I can imagine if you have so you sent the data feed from your DCS to Stamina Carbon in the cloud, data feed from your DCS to Stamina Carbon in the cloud. We use Stamina Carbon kinetic and thermodynamic models to calculate all of those values and then publish that on a webpage that's only accessible for the client.
Speaker 2:Yes, so basically the client can have access to these dashboards, so to say these dashboards, so to say these dashboards, and all. All he needs is basically a screen and internet connection. Okay, yeah, so he can. He can display this on a cell phone or a tablet or on a big screen in the DCS room.
Speaker 1:I could use my watch. Yeah, okay, but I think a big concern that clients would have is I'm going to give all my data to Stemmic Carbon? What about data security? What's?
Speaker 2:this is actually true. Yeah, since I joined Stemmic Carbon three and a half years ago, when, when you talk to a client and tell him like yeah, please, you need to send us the data, you see that a lot of resistance, although they see the value in the product itself. But data security was always a concern and, of course, because we see a lot of potential in this tool, we also tried in STEMI Carbon to mitigate the concerns of the clients and a number of actions were taken. So, for instance, we make sure that data goes in one direction, so it goes only from the plant data to the cloud and we do not send anything back to the DCS of the plant. So transfer of data is unidirectional. The plant, yeah, so transfer of data is unidirectional. The second thing, what we did is that we partnered with Microsoft Azure, so we use it as a cloud computational platform for us, okay, and, of course, microsoft is one of the most reliable cloud service providers and they have the ISO 27001.
Speaker 1:Microsoft does or Stemmic.
Speaker 2:Carbon. Well, I think it is Azure if I'm not wrong. Okay, and what we also do at Stemmic Carbon this is something we do actually additionally is that we have a software department in our company and we, every year, we do some sort of not some sort of we do a penetration and security test, okay, in order to make sure that, yeah, it is very tough or it's maybe extremely hard to to to to breach into our clouds or data.
Speaker 1:So penetration test is paying somebody to try to hack us.
Speaker 2:Exactly, and so far they always failed. So that's a good sign.
Speaker 1:That's a good thing to add there. Okay, so then the data is sort of secured, the data security is less of an issue. At least then the operator of the plant can see the results on their watch, tablet or just on their screen, and then they respond to the values that they see.
Speaker 2:Yes.
Speaker 1:Correct. Do we give some advice on whatever they say, whatever is a high water content or low water content, whatever there's something not efficient in your plant? Do we give a suggestion on what to do next or what's the next step?
Speaker 2:Yeah, okay. I'll answer that. But back to the security thing. I just want to point out also that we are quite confident in the cybersecurity measures that we take, that we even have our own proprietary knowledge running in the cloud itself Because, as I said, we have the process model, which contains thermodynamic models, and this is really quite valuable knowledge for us, and still we deploy it in the cloud. So we really trust in the security.
Speaker 1:Put your money where your mouth is.
Speaker 2:Yes, so, yeah, now back to your question do we suggest or recommend actions to the operators? The answer would be no, because what we currently do is we provide, as I said, like the DCS, operators with dashboards that display information, but it is up to the operators to analyze this information and also to take the necessary or the measures that would help in bringing these values into optimal values, or these variables into optimal values. But, of course, we always try to bring most value to the client, to, um, yeah, bring most value to the client. Yeah, so we provide training to the operators in order to help them better understand how to interpret or or analyze the information, and also we provide a context help, or is it help? Context, yeah, yeah, uh, my time, yeah, and in that, for example, let's say, if we say, if the water content is high in the LPCC, for example, the low pressure carbonate condenser, what you can do is this or this or this, for example. But, of course, it's up to the operators to act and decide what to do and decide what to do.
Speaker 2:What we also actually and this is also quite valuable thing that comes with the process monitor is that we provide consultancy sessions, and this is the frequency of these consultancy sessions depend on how the agreement, the initial agreement with the client during the signing of the contract, but it could happen maybe every three months or something like that, and during these sessions we talk to the client, we discuss with them how they are operating, because we also have access to the process, monitor ourselves and we keep track of what is happening in the plant more or less, and we analyze what is happening and then during the consultancy sessions we of course we can bring recommendations to the, to the operators or or the engineers in the plant. We can also do help them with certain troubleshooting, if or or if they have any questions, or so we can explain things to them or provide help as well, and support.
Speaker 1:So then, what types of let's say improvements are you generally suggesting? Is this just for? I think you've many? You mentioned energy reduction, but how does that? How much value does it bring? Is it like 1% or 10%? Or what types of benefits can a client get? Of course, depending on how a plant the plant before deploying a process monitor.
Speaker 2:And, of course, that depends. However, we have a user case from our experience. We had the situation where we managed to reduce specific steam consumption by almost 3% and also increase productivity by 3%. In addition to that, because of the consultancy sessions, we also managed to help the operators of the plant to be aware of a mechanical failure that would have happened to a stripper. And, yeah, based on the data that we were receiving, our senior engineer quite experienced one he could analyze the data and he could realize that there is something wrong in the stripper. And indeed they did an inspection and it turned out that there will have been almost like a mechanical failure in the stripper, and it's an equipment that costs millions of dollars. In addition, you would have needed to shut down the plant. So they could save a lot of money and they were very, very happy with this. So, in addition to just the process monitor itself, the consultancy to them proved to be very, very valuable. I can imagine.
Speaker 2:So it's basically energy reduction, it's improved productivity and we could help them, let's say, with the maintenance, to avoid failure in one of their most important equipment, avoid on-plan shutdowns, and there's also something related to sustainability.
Speaker 1:That's an improvement of this process Stability.
Speaker 2:Sustainability. Sustainability yes, as we said, a process monitor can help you also with reducing ammonia emission, for example. It could help you in case of reduction of specific steam consumption. You are basically also reducing energy, so you are operating your plant in a more sustainable way. Yeah, because you will need less steam. Less esteem means means less burning of of co2 or natural gas sorry, yeah. And and then less release of CO2. What we also have because also currently you also have the plants in certain parts, especially from the EU, there are certain regulations that the plants need to abide by in terms of emission of CO2, etc. So we try also to add value in this aspect by developing. We are currently working on developing a CO2 footprint that would help the operators to keep track of the CO2 footprint and, in this case, of course, help them operate their plant in a more sustainable way.
Speaker 1:Okay, and if a client would, let's say, revamp their plant or maybe change a bigger piece of equipment, then I'm assuming that the plant model changes and also the process monitor needs to be adjusted.
Speaker 2:Yeah, this is definitely true Because, as I said, you are monitoring the plant, your plant. You want to know what is really happening at the moment in your plant, so for that it is always important that the model really is a very, very accurate replica of your current situation in the plant. And whenever you change the design or the sizing of the equipment, this needs to be included and taken into consideration and then modify or update your process model.
Speaker 1:Okay, and one last topic I wanted to discuss with you is, let's say the AI. I think the artificial intelligence is something that everybody has gotten more experience in in the last years. Why not just hook some AI model to the data that you have and come up with a model that predicts your plant performance?
Speaker 2:Yeah, well, of course you can. We are also considering and we are developing certain tools that we like to embed within the process monitor and that are also based on AI or sort of machine learning models. But it is always important to remember that AI models or machine learning models are trained or developed based on data, so they are data-driven models. They are not governed by any physical or chemical laws. So the quality of the data is quite important and if you want to have a quite efficient or quite reliable and precise AI model, it is important that the data you are using, that it is of high quality and also probably big size as well. Now the situation is that in a plant, when you are running a urea plant, most of the time you want it in a stable manner or close to a stable way.
Speaker 1:Yeah, so so you don't have a lot of variability.
Speaker 2:You don't have a lot of variability, however, in at stemmy carbon. We have the benefit of having simulators. Yeah, so we also have training, or OTS, operator training simulators. Yes, and these are. These simulators are actually based on first principle models and our reliable thermodynamic and kinetic models, and we use this to generate large amount of data with very high variability, and that also, in addition to the planned data we collect, allows us to really develop AI models. So we are currently working in this direction. We are working on developing AI models by combining plant data and also what we call synthetic data. A step ahead. What you can also try to do is to combine first principle models with data-driven models and you come up with what we call a hybrid model, and these are actually more reliable and more precise than just using AI models.
Speaker 1:Okay, Because I'm just thinking, now that you explained this, that usually in the, let's say, AI image generators I think everybody has played around with those online you end up with a hand with six fingers.
Speaker 2:It could happen.
Speaker 1:yes, you don't want to have a urea plant with something that doesn't exist or that's not a real model I think that's the risk that you're describing.
Speaker 2:Yes, exactly One word can be easily a risky thing. Also, our clients in the urea business are quite conservative, so it's very, very hard to convince them to just have to monitor the whole plant just based on AI models alone. I believe that you can have certain tools that are based on AI and you embed them, but, as I said, you need to derive these models or develop these models using high-quality data with high variability, data that covered a large or wide range of operation, considering different scenarios, which actually we are privileged to have as well at Stemmic Carbon.
Speaker 1:Okay, that's all very interesting. Have you had a because I'm also looking a bit at our time is there anything regarding the process monitor that we, that we're missing, that we need to discuss?
Speaker 2:I think not. You pretty much covered everything. Well, you did most of the talking. I just asked a few questions.
Speaker 1:But again, it's really nice to have a deeper understanding of how the SOAR works and what the context of the process monitor is. So, ali, thank you so much for giving us this insight, and also a big thank you to our listeners for tuning in to Stummy Talks. Thank you.
Speaker 2:Thank you very much. Stummy Talks.