Water crisis 2030 and IoT: Digital solutions to scarce resources and infrastructure

Hello, Ricardo. Hello, Antoine. Nice to have you with me today. I’m super excited and welcome to the IoT Use Case Podcast. Ricardo, how are you and where are you right now? Are you working from home or are you in the office today?

Ricardo

Hello Madeleine and hello dear community. I am in beautiful Bad Pyrmont today. I am now more or less within striking distance of the Bad Pyrmont wastewater treatment plant, which is a small preview of our topic today.

Very cool. Wastewater treatment plant is the right keyword. Very briefly for classification. Bad Pyrmont is a municipality in Lower Saxony, right?

Ricardo

Exactly, we belong to the district of Hameln-Pyrmont, a city with about 40,000 to 50,000 inhabitants.

Greetings to Northern Germany, but many from Bavaria are also listening, I believe it’s always worth a trip. A very nice town, but first also hello to you Antoine. Glad you joined us today and took the time. How are you doing? You’re probably not in Bad Pyrmont, where are you?

Antoine

I am in the far south. Hi Madeline, thanks for the invitation. I don’t live in Germany, but my arm could be in Germany. I’m on the border right now, on the French side of Basel, so in the border triangle. I’m here for a few more days and then I’m going much further south because I’m going to Argentina and Chile. So it’s good that we can still do that before this big trip.

Very exciting. I have to ask you something. You have this catchphrase on LinkedIn, “Rockstar (well… Pianist.).” You’re also a musician, and for the listeners who are with us now, both Ricardo and Antoine, you’re well equipped with podcast equipment. What is it about being a rock star with you?

Antoine

I always tell my daughter I’m 27 because all rock stars die at 27. Since not everyone is excited that I’m already a rock star, it may just be that I’m not 27 yet. That’s the only reason for me, my music is so perfect that it can’t be that there’s another reason that I’m not a rock star. But yes, I am unfortunately only a pianist.

Okay, yeah, very cool. I also play music, I used to have a band, so I always think it’s cool to talk to some musicians here. Ricardo, you also have a podcast mic, you’ve been in the podcast business a little bit longer now, right?

Ricardo

Yes, I think since 2020. We can all remember, the time when everyone was at home and this whole podcast medium was hyped, I got well equipped at some point. Here today, tomorrow we’re also in another recording, not for a podcast, but for a YouTube channel. One could say that you are simply engaged in multimedia activities.

Let’s start into the topic. A short introduction for those who do not know Aerzen or Aerzen Digital Systems yet. You are the digitalization specialist In the Aerzen Group, one could say. The parent company comes from the mechanical engineering sector, i.e. Aerzener Maschinenfabrik. You build rotary lobe blowers and are an expert in compressor technology. Aerzen Digital Systems is however neutral and independent of this on the market and you develop solutions for a worldwide possible machine park management of different devices and machines. It is about increasing energy efficiency, but also about increasing the availability and reliability of these machines, also with artificial intelligence. You are the managing director of Aerzen Digital Systems GmbH by profession. What exactly does Aerzen do in the area of wastewater and what overlaps are there here with Aerzen Digital?

Ricardo

Exactly, Aerzener Maschinenfabrik is known worldwide for its rotary lobe blowers, screw compressors, which are used, among others, in the field of water/waste water for the generation of the so-called process air for the sand trap, but also for the biological treatment. It is important to know that we as Aerzen Digital Systems are the specialist around the topic of digitalization and AI, but we actually only use these techniques. We are actually more customer-centric and say, this is basically our root to some extent, what problems do operators have in operation with our rotary lobe blowers and screw compressors today? Leverage these technologies, AI, such as in cloud computing, Software as a Service, and Reinforced models to improve operations around our machines. I always like to go back a bit further and say that we are ultimately a bit of a technical assistant to our customers in the operation of our blowers. We do this with a team of now almost 30 employees. In the meantime, we have also built up a large number of references worldwide, as we have already mentioned here with Bad Pyrmont.

Very nice. Now you’ve brought Antoine with you. Antoine, I just said it, you’re in business development as a senior business developer at GF Piping Systems. How did you two actually meet? Is there a personal story?

Ricardo

Antoine is quasi host of the podcast “(don’t) Waste Water” and is for me one of the influencers in the field of water/waste water and in this regard I became aware of him. I said, we as Aerzen and worldwide manufacturer for blowers in the water/wastewater sector have to get together with the Influencer. So, I had simply messaged him casually, and from that, a certain relationship or communication essentially developed.

Antoine

At GF Piping Systems, we build Lego bricks. Everyone is interested in the house that you can build with Lego. Nobody is interested in the Lego bricks. It rarely happens that we get into discussions and get to add and or support something. Aerzen is a big Lego brick, but throughout the water and wastewater industry, it’s also just one Lego brick. These Lego bricks must work together at the end of the day. Therefore, the topic of IoT use cases is extremely interesting to address here.

Yes, very nice. Can you tell a little bit about what GF Piping Systems does exactly? You guys are in the piping systems business, so can you briefly tell us who you are, what you do, what your core business is?

Antoine

So GF Piping System is part of the Georg Fischer Corporation. The Group is divided into three parts. GF Casting Solutions supplies all major car manufacturers. GF Machining Solutions builds the machines that can produce all of this worldwide. My division, GF Piping Systems, deals with piping systems. These are pipeline filters, valves, sensors. This is perhaps the unknown that we are supplying. We have been in the digitalization business for about ten years. We learned some things there, we did some things wrong and hopefully we are doing a lot of things right now.

Pipeline system already more or less includes the word water. So can you say what the issues are that you are concerned about now here in the water/wastewater area? You host the podcast “(don’t) Waste Water.” That means you are the expert around these topics. What’s driving you and your company here in the water/wastewater industry?

Antoine

Quite right, as you put it, we direct flows. So it can be water, can be gas. We can move anything that is liquid or gaseous from point A to point B. The question is, how efficiently do you do that? We are in a position today where water scarcity, water quality, and water loss are issues. The issue we’re heavily involved with is that it’s complex to run a system like that. It pushes the limits of the human brain. The question is, how can we as supermen run our system a little better, not just the piping, but the whole system. The pipeline is part of the equation, and that’s why we think digitalization can greatly help us as water professionals. That depends on measuring the right parameters, defining the right interfaces, building a good strategy to make it all work better. It really hinges on making the whole thing work better. Not because we like it or want to, but because we have to. Water scarcity, we are 40 percent short of the water we need by 2030. Energy losses, we have a whole system, which is carbon-intensive, which then also needs much more energy than many think. We think it has many many potentials for improvement.

Yes, absolutely. Maybe we can discuss this big topic of sustainability a bit within these ESG criteria. As you say, there are not only environmental issues at play, but also social aspects and also governance issues to some extent. Now let’s take a look at the big picture and what the relevance of the topic is above all. Many are familiar with the topic, but haven’t fully grasped the details of what it actually entails in terms of benefits and, most importantly, the various aspects that can impact the business. Ricardo can you explain a little bit of this big “why” behind it?

Ricardo

Yes, very much so. So I think we’ve already just touched on it. It is not about machine technology and not about piping systems, but about the overriding question of how we can sustainably provide enough water for mankind. In the end, the fresh water issue is also always a wastewater issue. 70 percent of all freshwater supplies are used in the year for agricultural technology or for the agricultural business unit to irrigate fields. But this could also happen through purified and filtered water, in order to make more fresh water available to all people in this world. We in Germany or in Europe are really very privileged when it comes to water/wastewater. We are now seeing in the hot days that there are regions, even in Europe, that are now experiencing water shortages. This is just a snapshot for us. But there are many other countries in Asia, the Middle East and Africa where this is the daily business. In Germany, almost 95-99 percent of us all have a connection to a central sewer system with our nearly 10,000 wastewater treatment plants here in Germany. However, if you just look at Europe alone, this is not the case everywhere. In Italy, for example, 20 percent of all houses do not yet have such connections, and only 55 percent of all wastewater is actually treated properly according to European Union standards. This also shows the disparity that we already have within Europe, and I’m not even talking about worldwide. This is a point that at some point leads to why, for example, people from other regions try to flee. They are not only perhaps looking for a new life because they are politically persecuted, but because it is no longer possible for them to live there in the Sahara like that. If you simply imagine that almost half a million children die worldwide every year due to inadequately treated wastewater. We are not even talking about a lack of fresh water, but simply diseases such as diphtheria or similar, which break out there due to the fact that it is not being treated. So maybe you understand this compulsion of people to go somewhere else.

Yes, this is an insanely far-reaching topic, also with corresponding consequences. Antoine, I would ask you now from an operator’s point of view. We have various use cases in the network where digitalization can help a great deal, be it efficiency improvements, but also sustainability issues, environmental protection issues and improved product quality. There are various approaches, also based on data, to leverage and counteract these added values. How do you see the topic of potential savings? What kind of deficit is there in practice today?

Antoine

We supply pipes for the networks. Today, if you look at the water cycle, 75 percent of the energy is used on the grid, you might even say wasted. If we didn’t have digitalization, everything would have to work centrally with large wastewater treatment plants. For this, you need a very large network. Perhaps not everyone realizes that the water travels for kilometers to the sewage treatment plant after a toilet flush. When you open the tap, the water comes through several kilometers of piping. That was a must before digitalization. Today, this water could be treated in much closer proximity. This means you have to have many smaller plants with much shorter distances between the user and the place where the water is processed. This is much more efficient because you save 75 percent of the energy on the network. But you would need an engineer or an operator in every small plant. Of course, that can’t work. This is absolutely not feasible. That’s where IoT, machine learning, whatever buzzword you want to insert here, can help enable this new paradigm. Der zweite Teil der Geschichte ist natürlich, die bestehenden Anlagen wird vieles genauso gemacht, wie man es immer gemacht hat. The wastewater treatment plants as we know them today were first conceived in 1914 and not so much has changed since then. There was once a researcher who did a study on wastewater treatment plant improvement and his paper is half an A4 page. He just made a graph: in the 30s, 60s, 80s and 90s, the line is very, very flat. There has been no change in the process. It all worked out that way because you had the luxury: lots of water, lots of energy, and everything was okay. In today’s world, that is no longer feasible. As you just said, Ricardo, these water refugees, they are ten times the refugees from World War II. Every 20 seconds, a child dies due to a water problem. It is extremely important that we use these new tools well, so that we can solve the problem over time.

If I want to leverage this potential and go down this path, what are the requirements that need to be met by such a digital blower in order to be able to leverage all this added value?

Ricardo

We need to consider what kind of savings potential we are actually talking about here and what leverage there is for this. We have about 10,000 wastewater treatment plants in Germany and they consume 4,400 gigawatt hours per year. To put this in perspective, I’m sitting here in Bad Pyrmont right now and we have the former Grohnde nuclear power plant 10 km away. That produced about 10,000 gigawatt hours in its last year. This means that half of a nuclear power plant is running in principle only to ultimately supply the blowers of Aerzen with electricity, among other things. They’re one of the biggest consumers of electricity at a wastewater treatment plant at about 60 to 70 percent. What can be done there? We have an extremely wide range of options for further developing and optimizing our products, the rotary lobe blower and also the screw compressor, at the physical-thermodynamic level, which we have also done very successfully over the last ten years. We had potential savings of 10 to 15 percent. Only, as in so many things, the energy efficiencies there are finite. At some point, you can no longer compress air, and if it gets colder in the process, you would have created a perpetual motion machine in principle. This means that we have already reached a more or less technical limit, where it is simply no longer possible to develop this machine technology further. Now it is a question of using them more efficiently and it has come to our attention that during the operation of our rotary lobe blowers, various influencing factors cause them to become more inefficient in terms of energy. The suction-side air filter cartridge, which prevents foreign substances from entering the machine and, as a result, the water, can become clogged over the machine’s runtime, sometimes within several weeks or months. If you just look at that for a machine that has something like an average 55 kW power input, that’s about a 4% energy efficiency loss. That means 2.2 kW, and if I extrapolate that over the year, that’s about 10 2-person households that are wasted because this air filter is changed at the wrong time or not optimally. The process continues to function, but extremely inefficiently, and the same applies, for example, to increased intake temperatures. We often come to wastewater treatment plants where, for example, the ventilation or the exhaust air of the machines inside the machine room is not optimal, and if the intake temperature there is higher than it should be, for example, which is not uncommon, the consumption is increased. In the Bad Pyrmont wastewater treatment plant, for example, it may be 20 degrees outside and 35 degrees in this machine room. This corresponds to approximately 2.4 kW. Extrapolated, that’s almost 4,000 euros per year. If I now multiply just these two aspects, and these are really the smallest, but the most striking for all our listeners: we have an average of three machines per wastewater treatment plant, times 10,000 wastewater treatment plants, then we are talking here about potential savings of about 180 million euros per year. I don’t even want to start talking about CO2 emissions. You can just see what needs to be solved. But we can’t deploy an engineer everywhere now, because that’s not economical and we have a shortage of skilled workers. This means that we have to use the technological possibilities of digitalization and IoT in order to be able to support the operator in the best possible way and ultimately provide him with a technical assistant.

Antoine

Even if we were to deploy an engineer everywhere, he needs the right tools. When you are driving a car and you have absolutely no view of the road and now you see a wall in front of you. What do you do? You hit the brakes and hopefully can stop before you hit the wall. Now, if the road is all open and you still don’t have a clue, what do you do? I drive really, really, really badly. That’s what we’re seeing with wastewater treatment plants. One usually has little info about the water because it is measured only from the treatment plant. Before that, the treatment plant engineer has no overview of the water coming in. What does that mean? If immediately the water rises, the COD or whatever the relevant parameter might be, the only choice they have, they go to their Aerzen machine and do everything on turbo mode. That’s the only thing they can do because they couldn’t see anything building up on the network through time. Then, when everything returns to good, they go back to zero. So they hit the brakes. You need a lot more info, the more info you have, the better your plant will run.

With Aerzen, you have created a solution that makes it possible to use this digital data. I think you call that AERprogress. Can you briefly explain how this works and how this adds value?

Ricardo

At the end of the day, it is precisely a matter of supporting the operator at this point, because we are dealing with a very highly complex process and there are also many dead times in this control loop. So if I now have a dirt load that arrives at the wastewater treatment plant, then I may not even see the influence in my aeration tank, where I now need this oxygen to keep bacteria alive or to motivate them to clean or purify this wastewater. What is AERprogress doing here? At the end of the day, AERprogress is nothing more than a technical assistant for the operator, which makes it possible to analyze the entire machinery and also, in part, this biological treatment process from a holistic perspective. It provides our application knowledge, our know-how as an OEM, in the form of algorithms or even model pipelines that we have developed explicitly for our machines in order to identify such excess consumption here. Replacing wear parts in advance, how do I actually achieve the process air requirement that the process has right now? If I do all this with one large machine, if I do it with several smaller ones, I can optimize this control cycle even further, so that I don’t just go: oh, I have a lot of dirt load, I press the turbo button, oh, now I have less again, then I turn the machine off completely again. We try to optimize this entire control circuit with the help of frequency converters, for example, and to reduce this oscillation of the circuit, so to speak. This allows us to save another 10 to 15 percent for the wastewater treatment plant and, above all, we can ensure that the wastewater treatment plant operator no longer has to take care of our machines as a preventive measure. A wastewater treatment plant operator usually has several things to do in a day. Some they have to do, some they should do, and some they could do. But you just have less and less money to clean up wastewater. I think that is clear to everyone. There’s not just a little bit more money every year that we’re allowed to put in, but usually it’s more money or more dirt load for the same amount of money. This means that the wastewater treatment plant operator must actually also only focus on what he really has to do. We can support them in this very well, so that they then really only have to deal with these machines reactively when our platform contacts them. Otherwise, they can focus on the treatment result.

It’s often the case that you have different data silos. For example, I have your rotary blower, my piping systems, and many other components. Now I first have to consolidate this data in order to generate the added value from it at the end. Antoine, could you please explain how I can break down these data silos and ultimately realize the added value?

Antoine

The important thing here is to avoid DRIP, which is Data-Reach-Information-Poor. It’s extremely easy to collect a lot of data these days and then still not have any info. In France, a few years ago, there was a regulation where we had to measure all the micropollutant data in every wastewater treatment plant. And that was done.

What was that? Micropollutants?

Antoine

Micropollutants, trace substances, pharmaceuticals, things like that. 60 percent of it is removed by the wastewater treatment plant, while the remaining 40 percent flows through, and we would need to measure what could pass through. This was measured every month of every wastewater treatment plant of a certain size in France and that costs a lot of money. The analytics are quite expensive. This was then all moved into the database. No one then used this database. It was stored there in a huge Excel spreadsheet and that was it. Nothing has been done with that. After three years of this regulation, it was simply decided that you no longer measure it. This is exactly what you want to avoid. The right way is to take a holistic picture of the different data and just get all the data sources. It can be a valve, it can be a compressor, it can be so many things. You just have to record everything once to understand where the data is coming from. Then you can connect them. If I measure the pH value, then I can also calculate the other parameters. Maybe I don’t need to measure the other parameters anymore, but I refer to this proxy. For trace substances, for example, I can measure COD and I don’t have to measure my trace substances anymore because they are proportional to COD. That’s maybe five to ten parameters, but then those are my guiding parameters that I use in operations. Everything else is then connected to these parameters. That means you look at the plant in order to select the right parameters. Then the plant can safely continue to run for decades, based on these parameters. This basic work, choosing the right parameter and then classifying it correctly, that is important. This is the core hint that I can share today. Take the time to choose the right parameter and then build the whole strategy logically on this basis. This is now the first stage. Second stage is: How do I properly incorporate these parameters? That’s the data silo issue you brought up. I mentioned at the beginning of the discussion how we have been digitalizing as a GF for ten years. At the beginning of this, we thought we had to go digital and that had to be an addition to our product. That means, everything must be closed, if you buy a valve from us, then you also get the data from us, which you can only use with us. We at GF Piping Systems felt that this was extremely clever, because it was a very good argument for selling more valves.

However, it didn’t play out that way. For the customer, this makes absolutely no sense, because their devices have to communicate together. You can’t have a closed system like that. You learn that the hard way. Now we are a total open ecosystem. I think a lot more suppliers do that these days, that you have everything open. How do you create the link between the different systems? The operator still works only eight hours a day. He’s not going to look at 15 different systems and then choose, this one tells me this, that one tells me that. No, it all has to come centrally and fit best in their smart phone or on their light computer and that means the different systems have to work together. If the interface does not exist, then simply talk to the supplier. The needs may not always be as clear yet in an industry like water. But suppliers are always open to a conversation and say: your thing is completely closed, you have no future with it. Let’s work together there, that we open it up, that we link everything together then. That’s the next step Once we have found the main parameters, to incorporate them properly, that they speak together. Then, in the long run, continue to make sure that it works properly. The best option for that is the next level, that not many people have reached yet. That’s a Digital Twin. You just have another system that’s not in production, but that’s totally a simulation or totally machine learning, AI. In the system you can then test everything, because nothing happens. Only the best scenario is then rolled into the real system. It’s step by step and you can’t jump right in at the highest level. It just doesn’t work. You have to do this basic work of data cleaning, correct parameters etc.

You will work together with Aerzen Digital Systems in the future or already today. Your piping system and correspondingly such a rotary lobe blower would now be exactly this interface that is needed to bring this data together. Is this the path you want to take? So you want to bring that kind of data from your piping systems together with Aerzen’s system where you can just see air filter cartridge data or maybe plant data where the temperature is rising. Is that the path you want to take in the future? Collaborate and break down those data silos? Or is it still too early for that?

Antoine

It’s absolutely not too early for that. We need to work together. But I think if only the piping system and the air compressor work together, it’s a very small team. It takes many more participants in this Global Village to resolve the issue. But it has to start somewhere. At the end of the day, all systems must work together.

At this point, perhaps a corresponding call to action. So now when you are faced with the issue and you hear that, and you realize you have similar cases where we say we actually want to build partnerships. Antoine and Ricardo are open for questions and also for discussions. I’m linking your LinkedIn profiles in the show notes so that people can just get in touch and also expand that partnership to really drive this movement forward. It is now also the case that the whole thing is also regulated to a certain extent by the state. The data will have to be and will be made available. Of course, manufacturers and machine builders are now faced with the challenge of using this data together with the operators. There’s a lot happening right now. Ricardo, can you talk a little bit about what impact you or you as Aerzen Digital Systems see there and what impact it can have on the entire industry?

Ricardo

I think, in the end, it’s exactly the point that we’ve just already discussed with Antoine. We are now the industrial masters when it comes to connecting our own machines, but we have not yet become masters in networking plants with each other. The networking of the devices, with other partners on the construction site or in the trade, is simply not there at the moment. That’s exactly the problem with these data silos. At the end of the day, it has come to the attention of the European Commission that there are now manufacturers who record data, prepare it, but then in principle do not make it available. They store the silos for themselves and sometime later they make a business model out of it. This is what the European Data Act addresses, and I see it as both an opportunity and a risk. The EU Commission simply wants to supply the digital ecosystem with its data to a greater extent, so that data is really made freely available to everyone. Therefore, opportunities and risks for all equally. This is very difficult for providers if they do not make their data available today but try to build business models on it. They’ll have to hurry. When this silo is broken, the data is available to others. But I think the advantage lies with the operator. At the end of the day, Aerzen and Georg Fischer are also operators in our own production, where we also see today that manufacturers of production machines also do not offer such open interfaces for all data and information. In my view, this data monopoly should be opened up so that smaller companies or specialists can also take a look at a holistic process from the data point of view and then offer added value for the customer in the thought of an overall social good. I just find this making data available to everyone and breaking down these data silos is somewhat comparable to making information available to a broad mass, as Johannes Gutenberg did with the book. There was a certain privilege of information there and only individuals could use it, through the printing of books this has been made available to all. So for me, that is the best comparison to the European Data Act. I also see it as a certain risk, but I also see it as a great opportunity for Europe to deal more with these issues of data, data preparation and added value.

Yes, at this point the hint, when this episode goes online, the EU Data Act podcast episode is also already online, so feel free to listen in there. There, I’m talking to a lawyer and a partner of ours on that very subject. We have now talked a lot about silo thinking, but we have also talked very clearly about net added value and also the relevance of the topic. What was brought up is this issue, you can’t even map it all out as a human being anymore. As an operator, when I look into the future, will the compressor be smarter than me? Are machines becoming more and more intelligent? Will the compressor be smarter than me as the operator? What do I have to control? Am I becoming more of a helper to the machine? Ricardo, how do you see it? What will come in the future?

Ricardo

I don’t think it’s going to get to the point where we’re going to have the super-intelligent machine that threatens humanity, as you sometimes get the feeling with iRobot or something, like right now in the question’s lead-in. I also don’t think the machine will be any smarter than the operator. I think that is the wrong way to put it, because then the operator’s experience is also underestimated to some extent. I just think that the staff at the wastewater treatment plants should be supported in the best possible way. However, it must be enriched with data and information or these must be provided and appropriate scenarios must be proposed to them. So that means, as Antoine just rightly said, buzzword, digital twin. The compressor simply has to be part of an intelligent system, which can optimize itself. This is not in the productive system, but ultimately in a kind of parallel view, where several algorithms heuristically try to optimize this entire wastewater treatment plant or the biological treatment process. At the end of the day, the operator of the wastewater treatment plant must be offered scenarios where they can decide: I can continue to operate my plant as it is now, or I can, for example, make better use of my CHP unit or make better use of renewable energies and thus save x% of electricity and y% of CO2. The daily job of a wastewater treatment plant operator will really change in the sense that they will also become or evolve into a manager who can then decide on the basis of predefined scenarios – take scenario A, B or C. They will no longer be as much involved in the really day-to-day operational business, but will simply make decisions and manage resources in order to drive holistic optimization and process improvement at the wastewater treatment plant.

Antoine

I see it as five stages. Stage one: I obtain data. Stage two: I get information from the data. Stage three: the machine gives me an indication of what I should do as an operator. Stage four: the machine does it and tells me, hey, wait, I did this, just for your information. Stage five: the machine does it all by itself and I, as the operator, don’t have to do anything at all. I wonder if one day we’ll go all the way to level five? Whether we want it is also unclear to me. But for us to go to level 4 or at least level 3, that should be a goal for us because that means we would run the whole system much more efficiently. The next question that comes then is security. How secure is a system if the machine can then run the whole system? A pirate can go in and then do something wrong. My answer for this is always the same. When we used Artificial Intelligence at the wastewater treatment plant in the UK to simply measure how often untreated wastewater was being discharged into the rivers, it was found that it was happening hundreds of times and sometimes several times a day. This means that man is not perfect. The machine is not perfect either, but the machine may not be worse than the human. Probably the combination of man and machine creates a good result.

That’s a nice closing word for today as well. And I think I have many more questions that I could ask about that. But thank you very much for this podcast. Thank you so much for sharing these insights. I think the path we want to take is clear. The relevance of the topic is clear, and so is the net added value, which we have also shown here in euro amounts. Of course, the issue of sustainability also plays a crucial role, especially in the context of ESG perspectives. In addition, we should not disregard the issue of silo thinking, as it is a technological barrier that must be overcome. Ricardo and Antoine, I want to thank you very much for being with us today. Your insights have been extremely valuable. We may have the opportunity for a follow up as I have many more questions. But first, I want to thank you both very much for your participation. I’ll leave the closing word to you. Thank you for being a part of this.

Antoine

Thank you!

Ricardo

I can only agree. Best thanks in any case again to the great moderation. Thanks to you too, Antoine. I think this was a great outside look from you. Now, of course, I’ll leave the closing word to our charming Frenchman.

Antoine

So thank you both for having me, it was a very interesting discussion for me and I look forward to the next conversation.

Thank you very much and have a nice rest of the week. Take care, ciao!