Hasopor AB reduces their energy usage by 10%


Where do we spend our energy?

Hasopor AB in Hammar is the only business in Sweden that produces foam glass. With the strategic location - partly in the middle of Sweden's geotechnically challenging area, partly in the country's demographic center and Sweden's absolute center when it comes to glass recycling - circularity and sustainability become central parts of Hasopor's work. Hasopor are heavy consumers when it comes to energy and were interested in understanding their energy use in more detail in order to be able to optimize it. DAZOQ's system felt like a perfect service for the challenge and goal of becoming more energy efficient. Daniel Ellison, CEO at Hasopor, was also interested in measuring and visualizing more to enable insights into how to plan the optimization of the business going forward.

   For me it was important to see where we spend the most energy. I know that we constantly use around 3.2 mW, so it's crucial for me to understand where we use that electricity to determine where to focus our efforts on energy efficiency. Now, thanks to DAZOQ, we have knowledge about our major energy consumers and can prioritize efforts to reduce or optimize around them first." Daniel Ellison.

Results & Insights

"We use DAZOQ preventively"

Hasopor uses DAZOQ's system for troubleshooting in the production ovens. With seven ovens and a total of 351 heating elements, replacements are frequently needed. Before implementing the system, specific persons manually measured these elements when an impact on the process was observed. Now, with DAZOQ's system, they can immediately identify the issue and address it faster, even proactively.

   Before installing DAZOQ, it took an average of 1–3 days to manually identify which element was faulty. With DAZOQ, we can now immediately pinpoint the issue, saving us a significant amount of time, as a malfunctioning element has a considerable impact on our output. Niklas Sörling, production manager within development at Hasopor.


Hasopor has a clear goal when it comes to energy savings. They aim to annually reduce their energy consumption per m3 by 5%. So far, they have managed to decrease energy consumption by over 10% per3A fantastic result. The specific measures leading to these significant savings are challenging to pinpoint, but by monitoring their production, they receive immediate feedback on any adjustments made. This provides them with the opportunity to experiment extensively, thanks to the installed measurements.

   Now we can see directly in the system what is happening, and that's what makes it so exciting. It gives us more room to experiment to find the perfect settings, such as volume. We can also go back historically and see what happened. 'Okay, here we lowered the belt speed to see if we could increase volume. What was the output, and what was the process like?' It provides us with a fact-based foundation to continue the journey forward – Niklas Sörling.

When the skyrocketing prices hit last year, Hasopor could use DAZOQ EI as a competent and transparent colleague to determine whether it was worth running all furnaces or if they should halt production on some. Thanks to real-time data, it turned out to be more profitable to let one furnace rest during the most expensive periods. Something that would have been challenging to know without monitoring their energy usage.


Facts and measurements is the answer for an efficient future

Hasopor looks brightly towards the future, emphasizing continued measurement and visualization to automate and digitize the industry. The next step for Hasopor is to install DAZOQ's temperature and humidity meters to observe their correlation, or lack thereof, with production output. Hasopor also aims to closely examine its power peaks to identify potential reductions. Currently, utilizing DAZOQ's measurement, Hasopor obtains factual data on their power peaks, firmly believing that substantial annual savings are possible with an effective reduction strategy. Daniel strongly believes in data-driven decision-making, envisioning a future where they can leverage machine learning and ultimately AI. To pave the way for this, measurement, visualization, knowledge, and sensors are crucial activities.

   The next step, which I personally find really interesting, is to explore how we can optimize our entire production process using machine learning and AI. The process itself is quite simple – two, three raw materials in, seven furnaces, a lot of energy, and we get a finished product. However, I am convinced that we can make this even more efficient and precise with the help of AI. To achieve this, we need sensors to learn more about what affects our production and how. With each step we take in this area, the more we learn and the more we know how to proceed. Then, I am confident that we can have a system that governs our production. We have seven furnaces, but of three different types, so the recipe is a compromise that should suit everyone. If each furnace were to be adjusted and set manually, it would be an overwhelming task for the operators. If you ask one operator, he will say it's best done one way; ask another operator, and he will suggest a different way, and so on. Everyone has different opinions on what they think is best, which is why measuring is so important to obtain accurate and data-driven answers to determine the right recipe. Daniel Ellison.

DAZOQ helps companies increase their energy efficiency and sustainability through real-time machine-level energy monitoring. Through effective communication of savings potential, we can together contribute to a more cost-effective, resource-efficient and energy-efficient future. Contact us and we'll gladly tell you more.

Scroll to Top