The ability to monitor performance and detect anomalies is an important weapon in a cold rolling mill (CRM) operator’s arsenal, helping to meet the challenges of the current market. But that is just the beginning of what advanced data analytics can offer aluminium producers, as Felix Lenders, Nuo Li, Dennis Janka and Andrew Cohen, ABB, explain.
Cold rolling mills play an important part in aluminium production, but with producers increasingly faced with challenges to stay competitive, meet customer demand for increased product quality, and cope with a widening variety of materials, how can they ensure that at the practical level, acceptable thickness and flatness tolerances decrease, while improving throughput and yield?
One response to these business and technical challenges is through greater use of digital technologies, such as advanced data analytics. The idea is simple: if CRM operators were able to observe the status of equipment and processes in near real time, they would gain crucial insight into performance – and particularly into deviations in product quality or problems with the process. This insight would allow operators to actively manage production to better meet the various demands on them.
But achieving this is more easily said than done. Despite the conceptual simplicity of the rolling process, in reality it is a complicated operation that depends on a multitude of factors. To fully exploit the full production potential of the CRM in today’s market therefore requires a connected and integrated approach to automation optimization, control, and decision-support tools.
The good news is that CRMs are often already equipped with modern control systems that include multiple sensors to monitor and record vast amounts of data. And with sampling times in the range of milliseconds, hundreds of sensor values are recorded – more than 3GB worth a day, during mill operation. This includes measurements of flatness, tension, speed, and strip thickness.
This data is routinely exploited by service engineers for commissioning and maintenance of rolling mill devices. But this valuable resource is rarely analyzed for performance purposes due to the difficulties of manual analysis and data pattern screening – a tedious and costly endeavour by any measure.
