| Issue |
Metall. Res. Technol.
Volume 112, Number 6, 2015
|
|
|---|---|---|
| Article Number | 603 | |
| Number of page(s) | 15 | |
| DOI | https://doi.org/10.1051/metal/2015040 | |
| Published online | 26 October 2015 | |
Data mining – new perspectives on predicting coke quality in recovery stamp charged coke making process
1 Principal Researcher, Coal and Coke
Making Research, R&D, Tata Steel Ltd., 831001 Jamshedpur, Jharkhand, India
e-mail: hp.tiwari@tatasteel.com
2 Chief Coke Plants, Tata Steel Ltd.,
831001 Jamshedpur,
Jharkhand,
India
3 Head, By-product Plant, Coke Plants,
Tata Steel Ltd., 831001 Jamshedpur, Jharkhand, India
4 Sr. Manager, Information technology
services, Tata Steel Ltd., 831001 Jamshedpur, Jharkhand, India
5 Chief Coke Ovens, Coke Plant, Tata
Steel Ltd., 831001 Jamshedpur, Jharkhand, India
Received:
28
April
2015
Accepted:
24
September
2015
Data mining technique has been used to develop the coke quality prediction model for stamp charge coke making. The developed model is being used to optimize the coke strength after reaction based on its coal blend properties and operating parameters. The study shows the actual plant coke CSR and predicted coke CSR of same coal blend were much closer proximity. Actual plant data have been used for developing and validation of the model. The technique was successfully used to produce superior coke from the same coke oven batteries without damaging the oven health.
Key words: Data mining / properties of blend / process parameters / coke quality / stamp charged coke making
© EDP Sciences, 2015
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.