Prediction of coke CSR using time series model in Coke Plant

Satish Agarwal (), Ranjan Kumar Singh, Adity Ganguly, Abhishek Kumar, Shweta Shrivastava, Ramesh Kumar, Rajeev Ranjan and Vikas
Additional contact information
Satish Agarwal: Tata Steel Limited
Ranjan Kumar Singh: Tata Steel Limited
Adity Ganguly: Tata Steel Limited
Abhishek Kumar: Tata Steel Limited
Shweta Shrivastava: Tata Steel Limited
Ramesh Kumar: Tata Steel Limited
Rajeev Ranjan: Tata Steel Limited
Vikas: Tata Steel Limited

OPSEARCH, 2021, vol. 58, issue 4, No 20, 1238-1259

Abstract: Abstract Coke is raw material for blast furnace for production of hot metal. Good quality raw material produces low cost hot metal and one of the important quality parameter for blast furnace is Coke Strength after Reaction (CSR), as it refers to coke “hot” strength, generally a quality reference in a simulated reaction condition in an industrial blast furnace. In this research, the effects of coal properties and process parameters on the Coke CSR were studied by Time Series forecasting and artificial neural network models. In this method, historical data of last 3 years was used to estimate the CSR value. In this investigation, thirty-four input parameters such as moisture, volatile matter, ash, fluidity, battery temperature etc. were used. An Unobserved Component Model of Time Series was found to be optimum with nine parameters of coal properties and other process parameters with maximum accuracy of 76%, on the validation dataset respectively. The operating range of coal properties and controllable process parameters is derived from model developed to operate for consistent Coke CSR of 65.5% and above. The potential saving from this modelling initiative is INR 120 Million/USD 1.75 Million.

Keywords: Time series model; Coke CSR; Coal properties; Process parameters; Coke strength after reaction (CSR) (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s12597-020-00506-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:opsear:v:58:y:2021:i:4:d:10.1007_s12597-020-00506-0

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-020-00506-0

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().