Carbon Re brings together experts in machine learning and cement to build models that help cement producers gain greater oversight of their plants, lowering their carbon emissions while maintaining the cement quality they require. However, the journey doesn’t end when our models are deployed to our customers’ plants: it is crucial that the machine learning team at Carbon Re is able to continuously monitor the models’ performance and their effect on the plants’ operation. Our internal monitoring platform serves as the central nervous system of our models. It provides a real-time dashboard that tracks the accuracy and reliability of our models, as well as the state of the plant, flagging unusual behaviour. This allows the team to quickly analyse and react to any issues.
One of the key solutions offered by Carbon Re is our soft sensor modelling: we train models to make higher frequency predictions for measurements that are traditionally monitored at a lower frequency. For example, measurements of free lime (a key quality metric for cement) are typically only available every few hours. Our free lime soft sensors generate predictions every 15 minutes, effectively filling the gaps between real measurements and enabling the operators to have a more consistent view over the plant, ensuring better operational stability.
Our monitoring dashboard helps us quickly investigate issues with these models. For example, when the platform started flagging the predictions of one of our soft sensors as being outside the expected range, the team was able to quickly drill down on the issue. We established that the root cause was that the plant in question was operating at a significantly lower throughput than usual, as it was gradually being shut down for maintenance. Since our model was not trained to operate in this type of plant condition, the behaviour was expected.