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A hybrid model: unlock new realms of knowledge
While some knowledge in the baking process needs to be captured, other knowledge needs to be first unmasked. Take, for instance, how to reduce gas consumption and CO2 emissions without compromising quality.
Traditionally, the baking industry has relied on empirical and theoretical models to study oven behavior in its ideal state (the physics-based model). We enhanced this with real-time data and self-learning algorithms to mirror the current state of the oven (the data-driven model). We believe a hybrid approach taking the best of both worlds – a good fundamental structure proposed by the physics-based model enriched and enhanced by a vast trove of real-time data provided by the data-driven model – will achieve the most accurate representation of reality and yield the best results when navigating new knowledge.
This hybrid model – or digital twin – integrates real-time sensor data, physics-based models, and oven experts’ domain knowledge to optimize energy consumption. The recommendations of the model help reduce gas usage by 10-20% and hence, drive a more sustainable baking process.
The first step is doing so in an “operator-assisted” manner: a dashboard shows the operator a recommendation that can either be accepted or not. The recommendation is then executed manually by the operator by changing settings via the HMI, and the model gets feedback that the recommendation was a good one. The more trust the operator gets in the recommendations, the more hands-off those recommendations can be implemented. The next step would be to automatically execute the recommendation upon acceptance. The final step would be to remove the manual acceptance and have the machine decide which recommendations to execute.
A hands-off, lights-out process of baking buns with considerably lower energy consumption.
Find out more about the Sustainable Oven Service in the video below:
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