By turning the furnace down just 1 percent, Holland Malt can save enough electricity to power a small village for a year. Data science and modeling help the malting company reduce its carbon footprint.
Without malt, there is no beer. This main ingredient is made from grain such as barley. When the grain arrives at the malting plant, it is first cleaned to remove dust and other foreign matter. Then it is soaked with water for a day to raise the moisture content to 40-45 percent. This soaking triggers germination in the grain, breaking down the cell walls and developing enzymes that convert the starch into the sugars needed to form the alcohol. After five days, the green malt is transferred to the kiln, where it is dried to reduce the moisture content to less than 5 percent. This stops germination and prepares the malt for brewing.
"An eest is a very large kiln," explains Martijn van Iersel, manager of quality and technology at Lieshout-based malting company Holland Malt. "The green malt is spread out on the floor, then large fans blow warm, dry air over it. The passing air cools and absorbs some of the moisture from the grains. For each batch, we have 20 to 22 hours to complete the drying process and get the malt ready in time for transport to the customer, the brewery."
One of Holland Malt's biggest customers is parent company Swinkels Family Brewers, also based in Lieshout and known for the Bavaria brand. "If you compare all the processes in a malting plant, kilning is the most energy consuming," says Pieter Meeuws, business intelligence & reporting analyst at Swinkels. "Since we value corporate social responsibility, we decided to see if we could build on our growing data science competence and reduce electricity bills using a data-driven approach. A 1 percent reduction would save enough energy to power a small village like Lieshout for a year."
"Because we value corporate social responsibility, we decided to see if we could build on our growing data science competence and reduce electricity bills using a data-driven approach. A 1 percent reduction would save enough energy to power a small village like Lieshout for a year."
Pieter Meeuws, business intelligence & reporting analyst at Swinkels
Building on their existing relationship, Swinkels turned to Bright Cape's data science consultants for help. "We did a project with them in Ethiopia," Meeuws gives an example of their previous collaboration. "After we set up a greenfield brewery there, they helped us run a successful data-driven marketing campaign to best promote our new beer. When we contacted them on behalf of Holland Malt, they again supported us with their data science expertise." For Bright Cape, based in Amsterdam and Eindhoven, the Holland Malt project aligns nicely with its areas of focus. "We develop advanced data science solutions, ranging from exploration to user experience," explains senior data scientist Daphne van Leeuwen. "We are particularly active in finance, logistics and manufacturing, with an increasing focus on sustainability." The project also aligns with the efficiency and sustainability goals of Brainport Digital Factory, which brings Bright Cape and other software specialists together to pool, promote and apply Dutch software strengths on a global stage.
The goal of the project was to kiln a batch of malt within the required time frame of 20 to 22 hours while delivering the same high quality with the lowest possible energy consumption. "Due to different grain sizes and shapes, different barley types have different moisture contents and therefore different drying times. Conditions outside the kiln also play an important role. With low humidity, for example in winter, some batches can be ready after only 15 hours."
Van Iersel: "Until recently, we used to set the fans at full power regardless of the conditions. However, if we were to turn them down slightly so that the same batch takes 20 hours to dry, we would still stay within the set limits and have used less energy."
"Holland Malt already had many impact variables readily available, such as the humidity and temperature in the kiln, the shape and size of the barley and the layer thickness of the green malt," Van Leeuwen hooks in. "We processed that data, combined it with external factors such as outdoor humidity and temperature, and put it all into a model that, given a set of boundary conditions, predicts as accurately as possible how long it will take to complete a batch. Holland Malt can use this prediction to turn the knobs on the kiln, giving them new data that allows them to improve the model and get an even better prediction."
Holland Malt is now conducting extensive trials. Van Iersel: "We are seeing some promising results, which confirm that the model can indeed predict drying time correctly. We are approaching the point where, under the right conditions, we can turn the heating down a notch and actually start saving energy. Right now, we are still manually tweaking. Eventually, of course, we will go for a self-correcting model."
The project began last February and ended this January. Holland Malt is now engaged in extensive trials. Van Iersel: "We are seeing some promising results, which confirm that the model can indeed predict drying time correctly. We are approaching the point where, under the right conditions, we can turn the heating down a notch and actually start saving energy. Right now, we are still manually tweaking. Eventually, of course, we will go for a self-correcting model."
"We are also looking at whether we can optimize multiple furnaces at the same time," Meeuws adds. "Holland Malt has four of them and we're looking into whether we can put them in tandem in such a way that we can reuse their waste heat more efficiently. It's all about how we can make the best use of the model, how we can make the most of it to have more control over the process."
The project has opened Van Iersel's eyes to the possibilities of data science and modeling. "It's truly remarkable what you can achieve," he believes. For Meeuws, it was confirmation that you can never have enough data: "Holland Malt was already recording a lot, but the project showed us that we still had some blind spots. So collect as much process data as possible, even if you don't yet know what you're going to need it for."
Brainport Digital Factory works every day with 23 active members (business and technical education) to realize the Digital Factory. We have a modern open systems manufacturing platform, on which we apply new Digital Factory technology such as AR, Low code machine models, (private) 5G, precise localization and AGVs.
By combining in-depth domain knowledge from our members, arrive at highly innovative business and engineering solutions that concretize the digital factory for both SME and large-scale manufacturing companies.
By combining in-depth domain knowledge from our members, arrive at highly innovative business and engineering solutions that concretize the digital factory for both SME and large-scale manufacturing companies.
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