End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA

Gao, Chuang; Shen, Minggang; Li, Xiaoping; Wang, Lidong; Chen, Ming

doi:10.1007/s12666-018-1479-5

Cited by 25 publications

(18 citation statements)

References 26 publications

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“…Improving on the works of Wang et al, Liu et al (2018) used a least squares SVM method with a hybrid kernel to solve the dynamic nature of problems in the steelmaking process [20]. More recently in 2018, Gao et al used an improved twin support vector regression (TWSVR) algorithm for end-point prediction of BOF steelmaking, receiving results of 96% and 94% accuracy for carbon content and temperature, respectively [21]. Though the applications of these models have not been explored specifically in the dephosphorization process, these models do indicate that ML-based algorithm has the potential for dealing with non-linear patterns in data associated with BOF steelmaking processes.…”

Section: Introductionmentioning

confidence: 99%

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Phull

Egas

Barui

et al. 2019

Metals

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Ensuring the high quality of end product steel by removing phosphorus content in Basic Oxygen Furnace (BOF) is essential and otherwise leads to cold shortness. This article aims at understanding the dephosphorization process through end-point P-content in BOF steelmaking based on data-mining techniques. Dephosphorization is often quantified through the partition ratio ( l p ) which is the ratio of wt% P in slag to wt% P in steel. Instead of predicting the values of l p , the present study focuses on the classification of final steel based on slag chemistry and tapping temperature. This classification signifies different degrees (‘High’, ‘Moderate’, ‘Low’, and ‘Very Low’) to which phosphorus is removed in the BOF. Data of slag chemistry and tapping temperature collected from approximately 16,000 heats from two steel plants (Plant I and II) were assigned to four categories based on unsupervised K-means clustering method. An efficient decision tree-based twin support vector machines (TWSVM) algorithm was implemented for category classification. Decision trees were constructed using the concepts: Gaussian mixture model (GMM), mean shift (MS) and affinity propagation (AP) algorithm. The accuracy of the predicted classification was assessed using the classification rate (CR). Model validation was carried out with a five-fold cross validation technique. The fitted model was compared in terms of CR with a decision tree-based support vector machines (SVM) algorithm applied to the same data. The highest accuracy (≥97%) was observed for the GMM-TWSVM model, implying that by manipulating the slag components appropriately using the structure of the model, a greater degree of P-partition can be achieved in BOF.

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Section: Introductionmentioning

confidence: 99%

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Phull

Egas

Barui

et al. 2019

Metals

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“…However, the study lacks detail so their work cannot be replicated. Gao et al [13] used an improved twin support vector regression approach for T and pct C prediction, using 300 selected samples. They achieved hit rates of 96 and 94 pct within the error bound ± 15°C and ± 0.005 pct for temperature and carbon content, respectively.…”

Section: Related Work In Machine-learn-ing-based Prediction Modelsmentioning

confidence: 99%

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

Bae

Ståhl

et al. 2020

Metall Mater Trans B

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The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset.

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“…Park et al performed a sensitivity analysis to eliminate irrelevant input parameters and established models based on an artificial neural network and least-squares support vector machine for endpoint temperature prediction [3]. Gao et al proposed an improved twin support vector regression model for endpoint prediction [4,5]. Li et al established an endpoint prediction model based on a backpropagation neural network and the improved particle swarm optimisation algorithm [6].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

Wang

et al. 2021

Processes

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The endpoint carbon content is an important target of converters. The precise prediction of carbon content is the key to endpoint control in converter steelmaking. In this study, a real-time dynamic prediction of the carbon content model for the second-blowing stage of the converter steelmaking process was proposed. First, a case-based reasoning (CBR) algorithm was used to retrieve similar historical cases and their corresponding process parameters in the second blowing stage, based on the process parameters of the new case in the main blowing stage. Next, a long short-term memory (LSTM) model was trained by using process parameters of similar cases from the previous moment as the input and the carbon content for the next moment as the output. Finally, the process parameters of the new case were input into the trained LSTM model to produce a real-time dynamic prediction of the carbon content in the second blowing stage. Actual production data were used for the verification, and the results showed that the prediction errors of the proposed model within the ranges of (−0.005, 0.005), (−0.010, 0.010), (−0.015, 0.015) and (−0.020, 0.020) were 25%, 54%, 71%, and 91% respectively, which were higher than the prediction accuracies of the traditional carbon integral model, cubic model, and exponential model.

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End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA

Cited by 25 publications

References 26 publications

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System

Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

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