Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management

Ismael Matino, Stefano Dettori, Valentina Colla, Valentine Weber and Sahar Salame

Applied Energy, 2019, vol. 253, issue C, -

Abstract: The efficient use of resources is a relevant research topic for integrated steelworks. Process off-gases, such as the ones produced during blast furnace operation, are valid substitutes of natural gas, as they are sources of a considerable amount of energy. Currently they are recovered, for instance, by using in hot blast stoves but sometimes part of such gas is flared due to non-optimal management of such resource. In order to exploit the off-gases produced in an integrated steelworks, the interactions between gas producers and users in the whole gas network need to be considered. The paper describes two models exploited by a Decision Support Tool that is under development within a European project. Such models forecast, respectively, the blast furnace gas amount and its heating power by obtaining an error between 1.6 and 6.9% in a time horizon of 2 h and the blast furnace gas demand by hot blast stoves by giving a prediction error between 5.0 and 12.1% in the same time horizon. The forecasted values of blast furnace gas production and main demand allow a continuous optimal planning of the blast furnace gas usage according to its availability and to the needs in the steelworks, by avoiding losses of a valuable secondary resource and related emissions.

Keywords: Blast furnace; Hot blast stoves; Gas production forecasting; Gas demand forecasting; Off-gas management; Echo state neural network (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919312528
Full text for ScienceDirect subscribers only

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:eee:appene:v:253:y:2019:i:c:90

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2019.113578

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().