Data-driven Multivariate Analysis of Basic Oxygen Furnace Used in Steel Industry

Sorsa, Aki; Ruuska, Jari; Lilja, Jarmo; Leiviskä, Kauko

doi:10.1016/j.ifacol.2015.10.099

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…The purpose of the blast furnace in the process is the reduction of iron ore in the hot metal [28]. Sulfur is also removed in a desulphurization process before the material moves to the basic oxygen processing (BOF) converter [29]. In the BOF converter, oxygen is blown through the top lance and inert gas is purged from the bottom to refine the hot metal into crude steel.…”

Section: A Process Overviewmentioning

confidence: 99%

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

et al. 2022

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The steelmaking industry is one of the most energy-intensive industries and is responsible for 4% of the world's total greenhouse gas emissions. Solutions to improve operational efficiency can therefore bring major improvements to the overall environmental performance of the entire industry. This article proposes a novel steel quality prediction system based on gradient boosting trees that can be used to predict the quality of steel products during manufacturing. The prediction system enables the detection of possible surface defects in the early phase of the manufacturing process, thus avoiding costly and time-consuming manufacturing efforts to address defective products. In this study, we trained a prediction model with data collected from an SSAB Europe steelmaking plant in Raahe, Finland. From the 296 process parameters measured in the liquid steel stage of steelmaking, we selected 89 input features to train and test the prediction model. The model was then integrated into a quality monitoring tool (QMT) to utilize real-time manufacturing data in its predictions. The validation process showed that the prediction model can find more than 50% of defective steel products by marking only about 10% of the steel products as potentially at risk of surface defects in plate rolling. This can potentially save time in the quality control phase and improve process efficiency. To gain more insights into the model predictions, we used SHAP (SHapley Additive exPlanations) to find a potential connection between the process input parameters and surface defects.

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Section: A Process Overviewmentioning

confidence: 99%

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

et al. 2022

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“…The authors report SVMs as the best predictor. A different study [14] employs multivariate polynomial regression to predict the endpoint content of Fe, Mn, P and S. The authors trained the proposed model on a dataset collected over three years. Having used 56 static variables as the input to the model, the reported results show Fe as the most difficult target to predict, while Mn, P and S have a high correlation but with a significant variance.…”

Section: On the Steel Industrymentioning

confidence: 99%

Hybrid static-sensory data modeling for prediction tasks in basic oxygen furnace process

et al. 2022

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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 key information 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.

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A Gibbs Energy Minimization Approach for Modeling of Chemical Reactions in a Basic Oxygen Furnace

Kruskopf

Visuri

2017

Metall Mater Trans B

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Data-driven Multivariate Analysis of Basic Oxygen Furnace Used in Steel Industry

Cited by 5 publications

References 11 publications

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

Explainable Steel Quality Prediction System Based on Gradient Boosting Decision Trees

Hybrid static-sensory data modeling for prediction tasks in basic oxygen furnace process

A Gibbs Energy Minimization Approach for Modeling of Chemical Reactions in a Basic Oxygen Furnace

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