Over the years, process manufacturers have used a variety of tools to optimize industrial processes for specific economic goals. Multivariable process control (MPC), or better known as advanced process control (APC), is the most popular tool for generating and increasing profits. However, as computing technology continues to advance, manufacturers are moving to simplify systems and reduce reliance on complex and time-consuming modeling techniques. As such, one of the most pressing challenges is leveraging knowledge and capable resources to efficiently deploy and maintain assets.
Thanks to a robust and secure cloud service that leverages artificial intelligence (AI) and statistical techniques, it is easy to acquire time-series data common to the manufacturing floor. In recent years, AI has proven to be a valuable offline analytical tool for process engineers. Still, the question has always been when industrial users will be able to leverage AI to close the loop and remove humans from the decision-making process.
At a recent briefing with Intelecy, we learned about use cases where process makers have successfully embraced AI. 
A solution that performs closed-loop optimization in real time. What was once a complex solution for data scientists, custom integrations, and data lakes, according to Intelecy, is now delivered via cloud services, Software-as-a-Service (SaaS), and additional benefits of MLOps I’m moving to a simpler solution. (Machine learning operations) care the model.
Headquartered in Norway, Intelecy was founded with a simple yet powerful vision: to harness the vast amount of data generated and harness its full potential to enhance sustainable industrial production. it was done. Intelecy’s mission is to enable industry organizations to improve processes and reduce waste, emissions, energy consumption and costs. The company boasts a no-code industrial AI solution that can create machine learning models in minutes for real-time predictions that drive optimization for efficiency, quality and sustainability.
We learned an interesting use case for Intelecy. Norway-based food and beverage company TINE Jaren began production in 2014 at a facility that continuously streams his 40,000 tags representing more than 250 million lines of data daily. The company wanted to optimize protein powder production yields, reduce variability and keep quality on target. They already have his NIR sensor that measures proteins continuously, and process changes were done manually. A process engineer at TINE Jaren used Intelecy AI tools to build a machine learning model that predicts how protein content will evolve one hour ahead of him. Intelecy’s gateway is streaming his OPC-UA data bidirectionally in real time. (AI/ML) Predictions from the predictive model were sent back to the factory SCADA that controlled the process. As a result of this project, operators were able to predictably auto-tune their filtering loops, resulting in less variability and higher yields.
