These days operators run machines based mostly on their experience of the best running parameters and setpoints, and utilizing historical data to understand the root causes of anomalies. A lot of real-time data is available from the automation system, but the use of real-time measurements is mostly limited to controlling the process.
In addition to typical production planning and control, data-driven applications incorporating Artificial Intelligence (AI) can process a large amount of data to provide a more holistic view of the entire production chain – something we humans could never do by ourselves.
While these Industrial Internet applications can be used to instantly optimize processes, they also allow us to predict what’s around the corner: Thanks to the more predictive nature of the available information, it’s possible to gain better control of the daily work in a mill or plant by pre-planning activities and running the process with fewer resources.
In practice, this means, thanks to AI-based applications and machine learning, it will be possible to predict upcoming variations and incidents, make decisions based on predicted events and act proactively. In other words, the benefit of advanced analytics is that it can recognize repeating patterns or a chain of events in a large volume of data leading to a certain incident in the future. By recognizing these patterns, it can predict when the same incident is likely to reoccur and alert the user in advance.
Based on prescriptive analytics, the system can indicate how to run or modify the machine to ensure it is safe to extend the operations until the next planned maintenance break. This allows a move from scheduled to outcome-based equipment maintenance.
The successful companies of tomorrow will base their decision making increasingly on insights from predictive applications, allowing them to take the optimization of their production and business to a new level.
In future, Artificial Intelligence will most likely take wider responsibility for mill operations by automatically entering the best setpoints into the system, and recovering from detected problems autonomously by changing its own settings to adapt to the predicted problem. These kind of more autonomous mills of the future are already on the horizon!
The advantages of mill-wide optimization
Jun 4, 2020
The development of the Industrial Internet in the direction of Artificial Intelligence (AI) and autonomous mills will enable mill-wide optimization of processes. Good examples of this and good results can already be seen in the energy industry, which has been taking concrete steps toward this target with centralized remote control rooms and daily heat network optimization.
Towards new AI-based business models
Jun 19, 2019
High-quality data will be a key source of business value in the future. While machines and equipment are one part of a wider production system that creates value, software will have an increasing role in transforming data into digital services and solutions.
Predicting the future using data
Dec 12, 2019
Instead of typical production planning and control, data-driven applications incorporating artificial intelligence (AI) help us take a more holistic view of the entire chain of production by utilizing data from several sources. While Industrial Internet applications can be used to instantly optimize processes, they also allow us to predict what’s around the corner.