TIM used to predict production temperature deviations on the Edge.
Ensuring product quality and reducing energy consumption in raw material production processes starts with precise temperature control. Manufacturers of cement, lime and other raw materials aim to monitor and manage process temperatures in the most efficient way and they therefore now also look at predictive analytics. But process temperatures are often impacted by a large number of variables and with conventional predictive models it is almost impossible to detect all upcoming parameters. This makes it difficult to implement a system to accurately monitor the temperature at every stage of the production process.
To tackle this challenge, the industrial process AI specialists of Wizata joined forces with the time-series machine learning experts of Tangent Works. Together, we provide a dynamic and scalable AI solution for advanced time series forecasting and anomaly detection that is fully integrated in the production process to automatically predict temperature deviations on the Edge.
Wizata allows to combine industrial data and key process expertise, including important time lags that are often involved in temperature control use cases, while TIM adds the automated and scalable machine learning capabilities needed to build autonomous and accurate temperature forecasts. This powerful combination allows industry leaders in raw material transformation to automatically analyze upcoming temperature deviations and act to guarantee process stability.
The Wizata industrial AI platform and TIM InstantML toolbox proved to be very complementary and the partners have now expressed the ambition to further standardize and roll-out this valuable solution as well as to develop other powerful applications such as an intuitive anomaly detection wizard which will be released in the coming weeks.
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