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Hybrid event-, mechanism- and data-driven prediction of blast furnace gas generation

Wenqiang Sun, Zihao Wang and Qiang Wang

Energy, 2020, vol. 199, issue C

Abstract: Blast furnace gas (BFG) is a byproduct gas and a significant energy source in integrated steelworks. Precise BFG generation prediction plays a pivotal role in site energy scheduling and management. However, it is difficult to accurately predict fluctuations in BFG generation due to the variable operational statuses and complex chemical reactions that occur inside the blast furnace, hindering efficient energy utilization and accordingly causing BFG to flare and contribute to environmental pollution. To tackle this problem, a hybrid event-, mechanism- and data-driven prediction method is proposed in this work. In this novel approach, blast furnace operational events are considered when predicting BFG generation, thus making predictions more accurate by integrating a priori mechanism knowledge associated with the blast furnace ironmaking process; additionally, this approach ensures high accuracy by selecting the best available data-driven prediction model for different event-associated periods. To demonstrate the predictive performance of the proposed hybrid method, comparative experiments are conducted using practical data from integrated steelworks. The results highlight the excellent performance and accuracy of the proposed method when compared with the results of widely used moving average and artificial neural network models.

Keywords: Blast furnace gas (BFG); Event-driven prediction; Data-driven prediction; Regression model; Artificial neural network (ANN) (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:199:y:2020:i:c:s0360544220306046

DOI: 10.1016/j.energy.2020.117497

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