Abstract
As one of the world’s leading steel producers, Tata Steel generates and records substantial quantities of data on a daily basis, much of it linked to its manufacturing process. This research seeks to utilise the techniques developed under the general heading of Data Science to enable the company to derive maximum benefit from the data it records. In particular, the research centres on the data collected during the production of steel slabs from liquid iron at the company’s Port Talbot operation. The key outcome of the research being to provide improved insight on the performance and maintenance of its continuous casters.At present, there are a number of existing organisational challenges, including poor communication and data sharing that inhibit best use of the collected data. Initial investigations focused on understanding existing data and identifying key data sources that could be exploited to explain events and activities surrounding caster performance. Initial findings revealed that, maintenance events data and real-time sensor data to be effective in identifying key performance indicators.
The data sources considered contain rich information to explain both ‘normal’ and ‘anomalous’ casting behaviour patterns associated with an unexpected caster performance failure. A Data Science Methodology provided by Rollins (2016), includes business understanding, data understanding, data preparation and modelling, which has been employed to approach this problem to develop a failure prediction model. The knowledge gained from learning the pre-failure conditions from historical data has enabled a predictive model to be developed. This model has the ability enable Tata Steel to make informed decisions about when to take a caster out of production for maintenance. Ultimately, this research promotes better utilisation of current data to help optimise the scheduling of caster maintenance. The findings of this research present Tata Steel with the opportunity to minimise the occurrence of unnecessary caster maintenance; delivering productivity increase and cost reduction.
| Date of Award | 2023 |
|---|---|
| Original language | English |
| Sponsors | KESSII & Tata Steel |
| Supervisor | Andrew Ware (Supervisor), CK Tan (Supervisor) & Penny Holborn (Supervisor) |