Smart Energy Management

Reducing operational costs at Wienerberger

‘Applying AI’ on the sensor data that was gathered at their manufacturing facilities allowed Wienerberger to better understand and control the highly energy consumption of their production processes.

By doing so, Wienerberger has become capable to perform smart energy management at their plants, which resulted in a significant reduction of the operational costs and carbon footprint.
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About Wienerberger

Wienerberger is a leading manufacturer of building materials with its headquarters based in Austria. Since producing roof tiles and bricks is part of their core business, they have their own manufacturing facilities that are responsible for this, including 11 brick factories in Belgium. Next to this, three of the most important pillars of Wienerberger’s strategy for 2023 are ESG, innovation, and operational excellence.  In order to achieve the sustainable growth they aim for, Wienerberger realized they can’t ignore the high energy consumption of the production processes in their plants. As a consequence, the key question they asked was: how can we leverage smart energy management to reduce operational costs and the carbon footprint of our production processes?


Challenges

For most manufacturing firms like Wienerberger, fully understanding the energy consumption of the production processes is already a challenge, controlling, predicting and optimizing it is an even bigger hurdle to tackle. With regard to this, there are multiple challenges that Wienerberger faced when finding out what drives the high energy consumption of their production processes.

First of all, producing roof tiles and bricks is a slow, continuous process that involves different types of materials. These are just a few specific characteristics of their complex production environment that explain why it is hard for Wienerberger to achieve a clear understanding of the energy consumption drivers.

Second, in order to measure the process variables that could have an impact on the energy consumption of the production processes, Wienerberger employs over 100 different sensors that gather data on these variables. Although collecting sensor data is a necessary first step in gaining insight into the energy consumption, preprocessing these raw data is a crucial next step to really achieve this objective. Therefore, an extensive cleaning of the data, parsing for multiple formats, and selecting the sensor data with the best predictive power are some of the required preprocessing tasks.

Third, Wienerberger faced the challenge that not all properties of the oven state could be measured directly. In order to fully control and reduce the energy consumption of their production processes, they could not ignore these variables just because they can’t be measured directly. After all, these process variables could have an influence on the energy consumption and so they should be taken into account as well. As a consequence, digital twins simulations that act as virtual sensors should be created to measure these process variables.


Solution

Since Faktion has already helped different kinds of manufacturing firms in understanding and optimizing complex industrial processes, we were able to identify the key parameters and features that influence the outcome. Using time series analysis and different types of regression models, our team provided a quantification of all influences on gas consumption in the baking and drying processes. This gave the process engineers at Wienerberger a reliable model that takes into account the material properties (amount, density and chemical composition), process settings (speed of kilns, target temperature, …), and environment characteristics (outdoor temperature and humidity) and allows them to understand, simulate and optimize the energy consumption of the production processes.


Results

Combining and analyzing the vast amount of sensor data that was gathered in the manufacturing facilities enabled Wienerberger to perform smart energy management, which brought them several benefits. Overall, the process engineers at Wienerberger have gained an increased understanding in the energy consumption drivers. As a result, they now have the capability to reduce the energy consumption in their plants without compromising on product quality.

Not only did this led to a significant reduction of the carbon footprint of the production processes, it also helped Wienerberger to decrease the operational costs as the high energy and gas prices had turned the energy consumption into an even larger cost. Another related benefit is an improved forecasting of the production costs of the baking and drying processes at their manufacturing facilities. Finally, the Faktion team has leveraged the full potential of the sensor data, so we also provided outlier detection and root cause analysis to Wienerberger. By doing so, they are now able to detect anomalous events in the production systems, prevent down-time, and learn what factors really cause deviations in the production processes.

Jeroen_Boeye-IMG_6370
Jeroen Boeye, PhD
Head of Machine Learning
About the author

Jeroen is leading the Machine Learning team on a mission to bring value to our customers using data. The team uses Computer Vision, Reinforcement Learning, and Natural Language Processing to reach that goal.

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