Forecasting blast furnace gas production and demand through echo state neural network-based models: Pave the way to off-gas optimized management. (1st November 2019)

• Record Type: Journal Article
• Authors: Matino, Ismael; Dettori, Stefano; Colla, Valentina; Weber, Valentine; Salame, Sahar
• Is Part Of: Applied energy. Volume 253(2019)
• Publication Date: 2019-11-01
• Subjects: Blast furnace -- Hot blast stoves -- Gas production forecasting -- Gas demand forecasting -- Off-gas management -- Echo state neural network; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113578
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Forecasting blast furnace gas volume flowrate and heating power by ESNs. Prediction of blast furnace gas demand by hot blast stoves through ESNs. Low prediction errors obtained during the test of the models with real data. Inputs related to the scheduling of the process are fundamental for the models. Low computational burden is required by the models during training stage. 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.