Optimizing the return rate of a steam network at Total with the creation of an optimized flow propagation model and real-time identification of anomalies.
The petroleum refining process involves a power grid that powers the industrial asset and heats the water to steam.
Any loss in the steam network has a direct impact on the energy efficiency of the installation, and on the costs of production.
Its optimization is therefore crucial for the profitability of Total's refining activities.
After creating a digital double of the steam network, DCbrain analyzed the plant's building management system data.
The analysis showed that to optimize the steam system as a whole, it was necessary to optimize each unit of steam production separately.
DCbrain has calculated, through Machine Learning, the efficiency level of each unit before re-aggregating these data into streams.
Our ability to propagate the output of each unit in the global network explains the accuracy of our results.