Knowledge- and data-driven prediction of blast furnace gas generation and consumption in iron and steel sites

Shuhan Liu and Wenqiang Sun

Applied Energy, 2025, vol. 390, issue C, No S0306261925005495

Abstract: Accurately predicting the blast furnace gas (BFG) generation and consumption is a big challenge for steel sites due to the low quality of industrial data and the variability of equipment operation states. To address this issue, a knowledge- and data-driven (KD) prediction method for BFG generation and consumption is proposed. This method employs dynamic time warping to describe the relationship between the historical database and the training set for BFG data streams with the same operation state, enabling the prediction model to learn the characteristics of BFG generation and consumption across all operation states. The method also considers the fluctuation characteristics of BFG generation and consumption, applying the 3σ criterion, mean substitution method, and Kalman filtering model to improve data quality. Additionally, by incorporating process knowledge, a rectified linear unit is proposed to refine the prediction results. The results demonstrate that the KD prediction model outperforms traditional data-driven models. Specifically, the KD–genetic algorithm (GA)–extreme gradient boosting (XGBoost) model delivers the best performance for BFG generation prediction across all operation states, with a mean absolute error (MAE) of 193.01 m3/min, symmetric mean absolute percentage error (SMAPE) of 0.22 %, mean absolute percentage error (MAPE) of 0.44 %, and R2 of 0.9957. For BFG consumption prediction in the hot blast stove group, the KD–back propagation neural network (BPNN) model demonstrates superior performance, achieving MAE of 69.03 m3/min, SMAPE of 0.24 %, MAPE of 0.22 %, and R2 of 0.9996.

Keywords: Knowledge-driven; Data-driven; Steel sites; BFG prediction; Characteristic data stream (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925005495
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:390:y:2025:i:c:s0306261925005495

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.2025.125819

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 ().