TY - JOUR
T1 - Hybrid static-sensory data modeling for prediction tasks in basic oxygen furnace process
AU - Sala, Davi Alberto
AU - Van Yperen-De Deyne, Andy
AU - Mannens, Erik
AU - Jalalvand, Azarakhsh
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/6
Y1 - 2023/6
N2 - In this paper, we propose a novel data-driven prediction system for Multivariate Time Series (MTS) in an industrial context, where classic relational data contain keyinformation in order to properly interpret the MTS. Particularly we focus on the accurate endpoint prediction of temperature and chemical composition at the basic oxygen furnace, which is a step in the steel production pipeline where liquid iron is refined to steel. The precise prediction of temperature is important for proper process control while reaching the target chemical composition is essential for quality control. Our deep learning methodology employs two modules followed by an aggregation block; a Convolutional Neural Network (CNN) handles the MTS, while in parallel, the static data is processed by a Fully Connected Network (FCN). We enhance the CNN performance by adding two Squeeze-and-excitation (SE) blocks, which act like an attention module over the different channels. By taking the MTS data into account we improve the prediction by up to 10% relative over the models which only consider the static data. The hybrid FCN-CNN-SE architecture slightly improves the state-of-the-art MTS approaches by 2%, with less outliers on the prediction of final temperature and phosphorus concentration, while being easier to implement and more scalable to larger datasets and input space than current solutions.
AB - In this paper, we propose a novel data-driven prediction system for Multivariate Time Series (MTS) in an industrial context, where classic relational data contain keyinformation in order to properly interpret the MTS. Particularly we focus on the accurate endpoint prediction of temperature and chemical composition at the basic oxygen furnace, which is a step in the steel production pipeline where liquid iron is refined to steel. The precise prediction of temperature is important for proper process control while reaching the target chemical composition is essential for quality control. Our deep learning methodology employs two modules followed by an aggregation block; a Convolutional Neural Network (CNN) handles the MTS, while in parallel, the static data is processed by a Fully Connected Network (FCN). We enhance the CNN performance by adding two Squeeze-and-excitation (SE) blocks, which act like an attention module over the different channels. By taking the MTS data into account we improve the prediction by up to 10% relative over the models which only consider the static data. The hybrid FCN-CNN-SE architecture slightly improves the state-of-the-art MTS approaches by 2%, with less outliers on the prediction of final temperature and phosphorus concentration, while being easier to implement and more scalable to larger datasets and input space than current solutions.
KW - Basic oxygen furnace
KW - Data-driven prediction models
KW - Multivariate time series
KW - Neural networks
KW - Steel production
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U2 - 10.1007/s10489-022-04293-7
DO - 10.1007/s10489-022-04293-7
M3 - Article
AN - SCOPUS:85141751168
SN - 0924-669X
VL - 53
SP - 15163
EP - 15173
JO - Applied Intelligence
JF - Applied Intelligence
IS - 12
ER -