Mass-balance Based Multivariate Modelling of Basic Oxygen Furnace Used in Steel Industry

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

doi:10.1016/j.ifacol.2017.08.2065

Cited by 9 publications

(9 citation statements)

References 17 publications

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“…The LSSVM model can then be defined as 1 ( ) ( , ) . (6) The Gaussian radial basis function (RBF) has good mapping ability [36], and is a widely used kernel function in LSSVM. This Gaussian kernel function is defined as (7) where σ is the width parameter of the kernel function.…”

Section: A Static Modeling Methodsmentioning

confidence: 99%

“…Thus, basic oxygen furnace (BOF) steelmaking is a critical bottleneck process in the entire production chain [5]. To guarantee safe production and improve product performance, many scientists and engineers pay a great deal of attention to quality predictions in the BOF steelmaking process [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the literature on quality predictions in the BOF steelmaking process focuses on static models [6]. Wang et al [7] presented an input-weighted SVM for endpoint prediction in the BOF steelmaking process.…”

Section: Introductionmentioning

confidence: 99%

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A Dynamic Analytics Method Based on Multistage Modeling for a BOF Steelmaking Process

Chang

Tang

Liu

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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This paper proposes a dynamic analytics method based on a least squares support vector machine with a hybrid kernel to address real-time prediction problems in the converter steelmaking process. The hybrid kernel function is used to enhance the performance of the existing kernels. To improve the model's accuracy, the internal parameters are optimized by a differential evolution algorithm. In light of the complex mechanisms of the converter steelmaking process, a multistage modeling strategy is designed instead of the traditional single-stage modeling method. Owing to the dynamic nature of the practical production process, great effort has been made to construct a dynamic model that uses the prediction error information based on the static model. The validity of the proposed method is verified through experiments on real-world data collected from a BOF steelmaking process. The results indicate that the proposed method can successfully solve dynamic prediction problems and outperforms other state-of-the-art methods in terms of prediction accuracy.Note to Practitioners-With the development of cyber-physical systems, abundant real-time data have been collected from the converter steelmaking process. These data provide an opportunity to solve product quality prediction problems using data-driven models. This paper proposes a dynamic analytics method based on a least squares support vector machine with a hybrid kernel to address this challenging issue. To improve the model's performance, a differential evolution (DE) algorithm is used to optimize its parameters. Because of the fierce physicochemical reaction in the converter furnace, it is difficult for a single-stage model to achieve accurate predictions. Thus, a multistage modeling strategy is proposed to address this difficulty, and a dynamic model based on feedback error is developed to realize real-time prediction. We verify the effectiveness of the proposed method using real data from a BOF steelmaking process. The computational results reveal that the proposed method has a higher prediction accuracy than other methods, making it helpful in guaranteeing the specified product quality and in maintaining stable BOF operation.Index Terms-Dynamic analytics, multistage modeling, least squares support vector machine, hybrid kernel function, basic oxygen furnace.

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Section: A Static Modeling Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dynamic Analytics Method Based on Multistage Modeling for a BOF Steelmaking Process

Chang

Tang

Liu

et al. 2019

IEEE Trans. Automat. Sci. Eng.

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“…A multivariate solution is presented in [3], where a search algorithm is used to select relevant static features (from a total of 52 features) for predicting the endpoint concentrations of Fe, Mn, P and S within the BOF process. The authors trained a distinct model for each of the seven possible steel mixes and endpoint target.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Time Series for Data-Driven Endpoint Prediction in the Basic Oxygen Furnace

Sala¹,

Jalalvand

Deyne³

et al. 2018

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Industrial processes are heavily instrumented by employing a large number of sensors, generating huge amounts of data. One goal of the Industry 4.0 era is to apply datadriven approaches to optimize such processes. At the basic oxygen furnace (BOF), molten iron is transformed into steel by lowering its carbon content and achieving a certain chemical endpoint. In this work, we propose a data-driven approach to predict the endpoint temperature and chemical concentration of phosphorus, manganese, sulfur and carbon at the basic oxygen furnace. The prediction is based on two distinct datasets. First, a collection of static features is used which represent a more classic data-driven solution. The second approach includes time-series data that provide a better estimate of the final endpoint and enable further tuning of the process parameters, if necessary. For both approaches, model-based feature selection is used to filter the most relevant information. Results obtained by both models are compared in order to estimate the added value of including the time series data analysis on the performance of the BOF process. Results show that a simple feature extraction approach can enhance the prediction for phosphorus, manganese and temperature.

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“…Steel production is a complex process that requires the use of a complex of technological, energy and transport equipment, each of which requires appropriate automation. Today, carbon steel is mainly produced using blast furnace route where basic oxygen furnace (BOF) basic oxygen furnace has an important role [8].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Application in the Energy Saving Technology of Basic Oxygen Furnace

Stepanets

Mariiash

2020

IAPGOS

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The fulfilment of the condition for the simultaneous achievement of the desired chemical composition and temperature of the metal is ensured by controlling the oxygen consumption and the position of the oxygen impeller lance. The method for solving Model Predictive Control with quadratic functionality in the presence of constraints is given. Implementation of the described solutions will contribute to increasing the proportion of scrap and reducing the melting period without changing of technological process.

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Mass-balance Based Multivariate Modelling of Basic Oxygen Furnace Used in Steel Industry

Cited by 9 publications

References 17 publications

A Dynamic Analytics Method Based on Multistage Modeling for a BOF Steelmaking Process

A Dynamic Analytics Method Based on Multistage Modeling for a BOF Steelmaking Process

Multivariate Time Series for Data-Driven Endpoint Prediction in the Basic Oxygen Furnace

Model Predictive Control Application in the Energy Saving Technology of Basic Oxygen Furnace

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