By training our models on real-world historical plant data, we add an additional intelligent layer to your operations, improving and automating decision-making in real time. Our solution optimizes processes such as throughput, energy or raw material use and combine it with relevant external factors.
We connect to control system data, subsystems and to relevant external data sources.
Many process models have been created when less data was available, often hand crafted by experts. More data and machine learning can often improve these models, e.g. improve energy distribution in district heating networks through improved distribution models. Recipes can also be more efficiently controlled compared to e.g. step models. This can all be done through our Sentian ICA.