Increase intelligence in your operations, at scale

Sentians Industrial AI solution for the process industry is tailored for the process industry, to give you a competitive edge. We have the experience and the AI technology to lower cost and increase revenues and at the same time reduce CO2 emissions for energy heavy processes.

• Easy to integrate

  • Full functionality

• End to end solution

With our solution we can increase efficient, production output and stability and improve the quality of production outputs while reducing the amount of scrap for e.g. paper mills, enrichment plants, chemical plants, metals production, food and beverage and other large production plants.

You obtain quantifiable improvements in a matter of months, with minimal deployment efforts.

Over time as the solution becomes tried and tested, more and more of the production can be made autonomous.

Predictive Maintenance & Anomaly Detection

Less down time more production

They key to improved availability is to get early warnings. By discovering anomalies in your data you are given an early warning and can avoid downtime and improve your performance. With predictions you can prevent failures and plan your downtime. Our leading edge AI finds the patterns in your data that has been previously hard to find using both unsupervised and supervised AI methods.

Intelligent control and automation

Optimized production

By training AI models on the real-world historical data from a plant, we add an additional intelligent layer to your operations improving and automating decision-making in real time. Our solution optimize the processes, such as throughput, energy or raw material use and also combine it with external factors where relevant.

We connect to control system data, subsystems and also to external data sources where relevant.

Many process models have been created when there was less data available, These models are also often hand crafted by experts. With more data and machine learning it is often possible to improve the 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.

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