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IoT in the application of intelligent waste management | Thermal waste treatment & emissions


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IoT Use Case Podcast Episode 130 - b.telligent + MARTIN GmbH

Podcast episode 130 is all about the use of IoT in intelligent waste management in the context of thermal waste treatment and emission control. The guests are Max Schönsteiner, Head of R&D, Head of Research and Development at MARTIN GmbH für Umwelt- und Energietechnik, and Florian Stein, Principal Consultant and Team Leader for IoT & Industry 4.0 at b.telligent.

Episode 130 at a glance (and click):

  • [12:30] Challenges, potentials and status quo – This is what the use case looks like in practice
  • [18:31] Solutions, offerings and services – A look at the technologies used

Podcast episode summary

The episode is about the joint project between b.telligent and MARTIN, more precisely about specific use cases in the service and aftersales area with the aim of increasing the availability of the plants. In this episode, challenges and solutions, such as the integration of IoT technologies for optimizing plant management and data analysis, will be discussed. The focus is on the benefits of these technologies for the management and maintenance of waste incineration plants, including data processing and visualization using modern tools such as MQTT and Power BI.

Two specific use cases from the field of IoT applications are discussed:

  1. Waste incineration plants – This use case, implemented in collaboration with MARTIN GmbH, focuses on the application of IoT technologies to monitor and optimize waste incineration processes. Approaches to data collection and analysis are discussed with the aim of improving the efficiency and environmental compatibility of the systems.
  2. Traceability Portal – Another use case discussed in the podcast is the Traceability Portal. This system is used for the traceability of components and processes, which is particularly important in industrial and production-oriented environments to ensure quality assurance and compliance.

Both use cases illustrate the practical application of IoT technologies in the industrial and environmental landscape and show how companies can use digital innovations to optimize their processes and make them more sustainable.

Podcast interview

Hello Florian, hello Max. Glad to have you with me today. Welcome to the IoT Use Case Podcast. I’m really looking forward to the episode. Florian, how are you today and where are you right now?


Thank you for the invitation. I’m actually in Munich today at our office. I’m fine. I’m actually in the office because I’m meeting Max in person later.

You could actually have recorded together. Does that mean you have a small lunch or meeting planned today?



Very nice. Max, are you already in Munich or are you driving over later?


I’m already in Munich. Thanks again for the invitation, it’s a pleasure to be here and to discuss with you. We’ll have a small retro later to celebrate our successes in the project.

Awesome. I think you’ve known each other for a while. Can we talk about how you and your companies got to know each other? Is there a personal story or was it through a sales channel?


We have been collecting data for a relatively long time, around 10-15 years. We did a lot ourselves and at some point realized that this was difficult for us as a manufacturer of plants and machines. We needed support and looked for opportunities regionally. That’s how we had our first contact with b.telligent, with Florian. We didn’t initially come together. We followed a different path until we met at the World of Data 2022 two years ago. There was a great presentation about IONOS Stackable Open Source Data Platforms. That was the decisive factor in our decision to continue together.

On the subject of IONOS Stackable, all the buzzwords you’ve mentioned now, we’ll talk about them in a minute. Florian, did you or someone from your team give the presentation?

Stackable is more or less a subsidiary of ours, and the co-founder of Stackable gave the presentation. I was there and listened. As Max mentioned, we exchanged ideas afterwards, and the proximity between MARTIN GmbH and b.telligent, about a 10-minute walk, was another good point.

I am very excited to find out more about you and the joint project. Maybe we’ll start with a brief introduction to b.telligent, Florian, and then we’ll go from there… Max, you’ve already mentioned that it’s about your plants, so we’ll talk about that in a moment. Briefly about b.telligent: Florian, I am trying to describe it, please correct me if I have misunderstood something. B.telligent is active in the field of services, especially consulting, employs 300 people, has more than ten locations and over 500 customers. You offer the main services in the area of IoT analytics and device management. You are independent, work with many different partners, including cloud hyperscalers, and also have something to do with the IONOS Cloud. You have a strong history in Microsoft Power BI and data warehousing. You mentioned that Stackable is a subsidiary of yours. This means that you have many different companies that are affiliated with you. Is that correct, or are there any additions?


I can add that we originally come from the classic BI business, as you mentioned, including reporting, Power BI and the like. We then increasingly focused on building data platforms and the topic of data analytics, which is now also very important to us. We were recently awarded first place by Brand Eins. IoT analytics is also an important area for us, especially when it comes to setting up use cases and infrastructures.

You are Principal Consultant and Team Lead, specialized in IoT and Industry 4.0 Systems at b.telligent. Can you tell us exactly what your department does and which clients you work with?


Our department mainly deals with scalable and secure cloud and edge infrastructure that we build for our customers. We have specialists in Infrastructure as Code and GitOps to effectively roll out containerized apps, especially in the edge area. We also take care of connecting IoT devices and integrating them into our data platform, which is done in collaboration with other teams at our company.

Could you describe the different customer segments you work with, in particular your project with MARTIN GmbH?

As we come from a traditional BI background, we are very broadly positioned, particularly in the telecommunications, e-commerce and retail sectors. We have been increasingly involved in the manufacturing, mechanical and plant engineering sector for around five years. Here, we work with MARTIN GmbH, among others, to connect their devices or systems to the cloud.

What are the classic use cases that you implement, especially for manufacturers and in mechanical and plant engineering? Is it primarily about digital services and condition monitoring of the plants?


The spectrum is very broad. We are intensively involved in condition monitoring and are currently working on traceability in many projects to ensure the traceability of individual components. The topic of GenAI is currently very present, including the development of chatbots for worker support and customer support. We also have the topic of predictive maintenance, where we integrate our data analytics department.

To get back to you and your joint project: Max, you are a machine and plant manufacturer specializing in thermal waste recycling. You manufacture plants that thermally utilize waste that cannot be recycled in order to extract energy and certain substances. So you work according to the recycling process and treat materials that can no longer be recycled. You are the world’s leading suppliers of plants for thermal waste treatment. Ultimately, these are recycling plants. Can you put it that way? And who are your customers?


It is not entirely correct. We are the waste treatment. We build plants for thermal waste treatment. We are the ones who come after recycling. We get what nobody else can recycle. In political terms, it is called non-recyclable materials, where no further recycling is possible. We try to get as much out of it as possible. Our aim is to recover as much as possible from it by extracting energy and certain substances and carrying out inertization and hygienization. We also try to remove pollutants, similar to the kidney function in the circular economy.

Do you have an example of what you recycle and what kind of pollutants?


Our fuels consist mainly of typical municipal waste, i.e. the contents of the black garbage cans at home. These contain a large number of elements from the periodic table and others that are to be destroyed by thermal processes.

So there are many customers from the municipal sector as well as private customers?


Across the board. We serve municipal customers, PPS and private customers worldwide; each of our systems is unique.

These plants are probably very large and complex. Are they special machines? Can you tell us something about the size and complexity of your plants?


We manufacture certain components ourselves, such as the incineration plant and the incineration grates where the waste is converted and incinerated. There are many bought-in parts, such as blowers and other components, which we integrate. The size of a plant is best described by its waste throughput, around 100,000 tons per year. This is a typical size, but it can also be much larger. The systems have a modular design with different combustion lines that have different maintenance intervals.

Where are these plants located? You mentioned that they are in use worldwide. Where are they located?


It concerns power plants, including large power plants, ideally located in urban areas, to produce district heating. District heating supply is one of the biggest things we do with our plant or customers. In Munich, for example, the oil incineration plant makes a significant contribution to the district heating network and uses residual materials to generate heat, which is then used in households.

So, they are in power plants and not in basements or similar places?


Exactly, we’re talking about around 100 megawatts of thermal output, i.e. large power plants.

I think that helps a little bit in the classification, to understand it a little bit. Your support over the entire service life of the plant. It’s all about maintenance and services for your customers. What is your vision for digitalization here? You have been collecting data for a very long time, but what is your vision for the IoT?


The service business is also very important to us. The systems run for 30-40 years and longer. Of course, things have to be serviced, replaced, spare parts, modernizations. The system is virtually alive and continues to develop. And our aim is simply to become the point of contact for digital services in thermal waste treatment in addition to the traditional service business that we operate. That is our goal.

[12:30] Challenge, potential and status quo. This is what the use case looks like in practice.

Now you’ve mentioned that you offer a comprehensive service. Could you describe why you are going down this path, perhaps based on your customers’ problems? What exactly are the challenges that your customers experience?


Exactly, the plants are highly relevant for us because they have waste to dispose of, and we need disposal safety. These plants are therefore very important. One of the biggest KPIs is the availability of the plant. It requires the longest possible availability, i.e. low downtimes and few unplanned stoppages in order to keep availability high. Availability is a relevant point. Our plants also face future challenges such as a shortage of skilled workers. Many of our customers have very experienced employees who can accurately assess trends. Our aim is to combine this process knowledge with data and data analytics to offer operators smart solutions.

You talked about trends that your customers’ experts are aware of. Can you give some examples to talk about the data?


The system is fully equipped with measurements – temperature, volume flow, pressures – which are very important for assessing the condition of the system. Are we operating at good temperatures? Are we achieving the desired volume flows and oxygen values? Measurement data is crucial in order to assess which area we are in and to maintain safety standards. Typically, the heat produced is turned into steam, and overproduction of steam, which is critical in terms of safety, must not occur. As a result, there are thousands of measuring instruments on the plants, some of which are particularly important, while others may receive less attention even though they could provide important information. Our process knowledge, gained through the design and configuration of the systems, enables us to evaluate whether the plant is still operating at its design point and whether the operating condition corresponds to the calculations or if deviations exist.

Okay, so you’ve already mentioned that availability is particularly important to you. You have these values available digitally to support the customer. What happens if the values are not as desired, for example, if the oxygen levels do not meet the desired limits? What would be the worst case scenario if this data is not recorded?


In the worst case, if certain values are not met, we have strict monitoring of the emission values. We use a sophisticated gas purification system in our plants to separate pollutants from the waste that pass into the gas phase. The worst-case scenario would be that we exceed emissions and the exhaust air contains pollutants that are harmful to the environment. If this is measured, the plant is switched off to clarify what has gone wrong. Our aim is not to get into this situation, but to recognize at an early stage that we are moving in this direction and to act accordingly, such as adjusting the air distribution or changing volume flows. Loads could also be reduced and certain things waited for. The biggest challenge for us is actually the waste fuel, because what is thrown into the black bin, we are unfamiliar with. The biggest challenge for us is actually not knowing what is thrown into the black garbage can. That’s why the measured values are so important, because we can only react to them.

And your goal is to act proactively, right? So, if trends emerge that are outside the target value, you give your customers appropriate recommendations for action. There is also a strict obligation to provide evidence when it comes to emissions, isn’t there?


Exactly, the obligation to provide evidence is very strictly regulated. That’s why we’ve stayed out of it so far, but it’s technically possible, there’s no question about that. It is simply more important for us to really identify the added value for customers with the data. And our strategy is to work very closely with our customers. We work with the customer to develop KPIs, specific topics and certain recommendations that we give them in order to really provide the customer with what they need in a focused manner.

[18:31] Solutions, offerings and services – A look at the technologies used

You have now opted for b.telligent. We’ve just heard a bit about how you came together. What were the technological requirements for the solution here? Your machines are somehow located all over the world. You probably have certain technological requirements that are important to you. Which were they?


The first step of moving away from on-premises, i.e. no longer hosting the data locally, was particularly important. This was not feasible for us in terms of capacity and would not have brought any added value in terms of building up expertise. We have also revised the requirements that must be met. Our plants are typically KRITIS, which means they are very important and must comply with strict cybersecurity regulations. We needed a platform that was very secure and transparent, but we also wanted to integrate innovative solutions. The path to open source and the collaboration with Stackable, IONOS and b.telligent resulted from this.

You say you wanted to move away from on-premises. Why exactly did you want to take this step?


The problem with on-premises was that we had to maintain and operate the entire infrastructure ourselves. However, our core business is the construction of systems and the use of data, not the operation of IT infrastructure. We considered whether we should do it ourselves or bring in experts and invest in a cloud infrastructure that enables faster scaling. The decision was made that if we can guarantee security in the cloud, then that is a feasible path for us.

And then you opted for b.telligent?


Yes, we have commissioned Florian and his team.


Exactly, as Max already mentioned, scalability is very important, especially due to the amount of data and the many plants with separate lines. Scaling simply works better with the cloud.

What exactly can your solution do now? What does this involve and what makes the solution so special?


We have installed an edge device on our systems that collects the data and sends it to the cloud. In the cloud, we have various pipelines to achieve the desired data quality. We have also provided calculations and evaluations. There is also a visualization where we can display trends, similar to the control system of a power plant. We also use Power BI to create KPI reports and give our customers access to them.

Florian, can you add what comes from b.telligent and Stackable and what MARTIN GmbH does itself? Where exactly do you come into play?


The Stackable data platform is hosted on the IONOS Cloud. What we do is that the MARTIN employees install the edge device on the systems, and the data is then forwarded by OPC UA via a broker using MQTT. In our Stackable data platform, we record the data on the MQTT broker. We use NiFi as a rule engine to distribute the data into the various paths for real-time and batch analysis.

So you use OPC UA for data collection and the data is then forwarded via MQTT. We are talking here about raw data such as temperature, volume flow, oxygen values and steam values. You don’t supply any hardware, but record the data and link it to Stackable, right?


Exactly, you can think of Stackable as a Kubernetes-based data platform. This is hosted in the IONOS Cloud. We record the raw data and save it first. Each system has different tag names in OPC UA, which makes data harmonization necessary. After standardizing the data in the next layer, we carry out calculations and prepare them for visualization in a “trusted layer”. In the real-time part, we use Kafka to harmonize and standardize the data on the fly, store it in a time series database, which enables real-time monitoring with Grafana.

By the way, a note on Kafka at this point: we did a separate episode on this topic with Confluent. Feel free to listen to it, I’ll link it in the show notes. This is about data labeling, i.e. giving the data a common denominator and uniform comprehensibility in order to use and process the data quality that must be available.


Exactly, you put it right.

In terms of data transmission, do you use Wi-Fi or mobile communications in such a critical area as a power plant?


We always use the customer’s firewall. Data is transferred one way and always in consultation with the customer.

And Florian, if you have the data that is on the Stackable data platform and hosted in the IONOS Cloud, how is the visualization and data analysis done? How do you recognize whether values such as volume flows or oxygen values are not on target?


For these analyses, we need the expertise of Max and his team. We coordinate closely to define the target values and build reporting on them. We calculate new values or KPIs by building Spark applications that perform and store these calculations. We have an expert for visualization in Power BI who visualizes the target and actual values in close cooperation with Max’s team.

Florian, what makes b.telligent special compared to other providers on the market? Especially in terms of openness to hyperscalers, certain certifications and benefits, as well as data management and security?


What sets us apart is our many years of experience in the data business; we have been active in it for 20 years. We are experts in data structuring and in setting up data architectures with different layers – raw, standardized, curated – and can map these automatically. We have gained our expertise through numerous projects with customers, particularly in the manufacturing sector. This often requires specialist input, which we can quickly translate into data understanding and offer scalable solutions from start to finish. We also have ready-made modules for quickly building solutions, such as Infrastructure as Code, which we often implement with Terraform, or GitOps approaches with Argo CD, for which we have developed templates. These enable us to roll out data platforms quickly and then deliver added value to customers really quickly.

Exactly, also from practical experience. Max, you said that you were primarily looking for a provider that offers appropriate encryption and security, but also that you retain control over the data. What goes in and out, topics like that. What else do you think is special about b.telligent?


So it was important for us that they are independent of the platforms. You have AWS skills, Azure skills and experience with other platforms such as IONOS. We found that very interesting because while AWS or Azure have great applications where it makes sense to use certain features, we need to keep an open mind and find out where we can achieve the most success. That’s why it was important for us to have a cross-platform contact person to support us.

I’m just looking at the time because I still have a lot of questions that interest me, but we can sort that out later. Or if you say this is exciting, we have a similar topic, you are welcome to contact Max and Florian. I link their LinkedIn profiles in the show notes. The last question for today: Have you already gained experience in working together? Do you have anything you would like to share with the audience? What experiences can you share? Are there any pitfalls to watch out for? What do you want to emphasize?


I think close cooperation with the customer is very important. In our case, Max contributed a lot of specialist knowledge and we pushed ahead with the topic of data governance at the same time, which is also very important to address and establish at an early stage. Open communication is key. We had clear goals, but also scope to choose the most sensible path in an agile way. That made things a lot easier. If we were to start again, I would involve one of my employees more from the beginning. This is an exciting topic, and every software engineer finds it interesting. It is particularly fascinating in the context in which we work. We are in the process of taking over operations and it would have been tactically wise to get someone involved earlier. That didn’t happen, but if we had to start such a project again – fortunately we don’t have to – I would.

Yes, important learnings that you can take with you for follow-up projects. To summarize again at the end: Today we have a good understanding of what the business case is for MARTIN GmbH, what motivates customers and why they are going down this path. The focus is on creating added value from the data, carrying out analyses and passing on machine knowledge in order to ensure availability and also comply with issues such as emissions regulations – all based on data. Florian, thank you very much for the explanations. How does that work exactly? How do you go from edge devices to the cloud, to your data platform? Thank you very much for your time. I leave the last word to you. Many thanks from my side for the exciting insights into your project.


Thank you, Madeleine. It was a pleasure to be here. If you would like to find out more, on 6.6. is the World of Data in Munich at Nockherberg. Max will give a presentation there. Otherwise, I’m looking forward to the next time.

Small hint: 6.6.2024, World of Data in Munich. Florian, can you briefly explain what kind of trade fair this is?


The World of Data is a trade fair for data, IoT and data analytics enthusiasts. There will be presentations by customers and external experts on their data projects. Briefly summarized.

Very nice, I’ll link that in the show notes. If you listen in afterwards, no problem, just connect with Florian and then you can contact him again. Sorry, Max, but the last word really goes to you.


Thank you. Great, it was a lot of fun. I think we have now created a great basis for tackling the next use cases. AI is another topic where we now actually have a great basis for continuing and coming up with new ideas. We already have lots and lots of ideas about what we could do and are of course happy to talk to people and tell them a bit about how we do what, why we do what, at any time.

Thank you very much and have a nice rest of the week. Take care. Ciao.

Please do not hesitate to contact me if you have any questions.

Questions? Contact Madeleine Mickeleit

Ing. Madeleine Mickeleit

Host & General Manager
IoT Use Case Podcast