Molten steel temperature prediction model based on bootstrap Feature Subsets Ensemble Regression Trees

Wang, Xiaojun; Ping, Yuan; Mao, Zhizhong; You, Mingshuang

doi:10.1016/j.knosys.2016.02.018

Cited by 22 publications

(9 citation statements)

References 24 publications

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“…For example, in [6,7,44], both subsampling-based technique and subspace-based technique are used in the ensemble generation process. In this paper, we also use a hybrid method that combines these two types of techniques.…”

Section: B Ensemble Generationmentioning

confidence: 99%

“…As discussed in [6], there are two ways of aggregation when using two ensemble generation methods in one framework. One aggregation rule is the one that used in FIGURE 2, i.e.…”

Section: B Ensemble Generationmentioning

confidence: 99%

“…In order to put the result of our method into context, we will compare it with several potential competitors. They are TSE-GRNN proposed in [8], BFSE-RTs proposed in [6], TLEGP in [7], and modified AdaBoost.RT (M-AB.RT) proposed in [9]. Note that all these predictors are developed based on ensemble learning.…”

Section: B Baselines and Criteriamentioning

confidence: 99%

“…The second type of data models utilize the ensemble technique in machine learning and are referred to as ensemble models. For example, Bagging technique has been applied in several ensemble predictors for molten steel temperature [4][5][6][7]. Moreover, subspace-based ensemble techniques have also been applied [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Molten Steel Temperature Prediction in Ladle Furnace Using a Dynamic Ensemble for Regression

Qiao

Wang

2021

IEEE Access

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The accurate prediction of molten steel temperature is of great significance to the control of tapping temperature in ladle furnace. The more accurate the prediction is the better performance the controller will attain. In order to further improve the accuracy of existing data-driven predictive models, we propose a dynamic ensemble for regression to predict molten steel temperature. In contrast to existing ensemble models, we only select one base model with the highest competence from the pool for each test pattern, rather than fusing all base models. We can thus alleviate the effect of weak base models on the ensemble. Specifically, the operation of dynamic selection can be implemented by estimating the competence of all base models in the region of competence. In addition, the proposed ensemble generation method has taken into account the diversity and accuracy, which are crucial factors of deriving better ensembles. In order to investigate the effectiveness of our predictor, we compare it with several competitors proposed in literature on a real-world dataset. The experimental result has approved the superiority of our predictor.

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Section: B Ensemble Generationmentioning

confidence: 99%

“…As discussed in [6], there are two ways of aggregation when using two ensemble generation methods in one framework. One aggregation rule is the one that used in FIGURE 2, i.e.…”

Section: B Ensemble Generationmentioning

confidence: 99%

Section: B Baselines and Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Molten Steel Temperature Prediction in Ladle Furnace Using a Dynamic Ensemble for Regression

Qiao

Wang

2021

IEEE Access

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“…As a result, empirical modeling approaches have been extensively used in developing the temperature prediction models of molten steel in LF. In empirical modeling, the model is developed exclusively from the production data without the need to invoke the phenomenology of the process [5][6][7][8]. Thus, the time-consuming and expensive nature associated with the development of a suitable mechanistic prediction model can be averted.…”

Section: Introductionmentioning

confidence: 99%

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Yang

Zhang

2020

Neural Comput & Applic

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This article presents a hybrid model for predicting the temperature of molten steel in a ladle furnace (LF). Unique to the proposed hybrid prediction model is that its neural network-based empirical part is trained in an indirect way since the target outputs of this part are unavailable. A modified cuckoo search (CS) algorithm is used to optimize the parameters in the empirical part. The search of each individual in the traditional CS is normally performed independently, which may limit the algorithm’s search capability. To address this, a modified CS, information interaction-enhanced CS (IICS), is proposed in this article to enhance the interaction of search information between individuals and thereby the search capability of the algorithm. The performance of the proposed IICS algorithm is first verified by testing on two benchmark sets (including 16 classical benchmark functions and 29 CEC 2017 benchmark functions) and then used in optimizing the parameters in the empirical part of the proposed hybrid prediction model. The proposed hybrid model is applied to actual production data from a 300 t LF at Baoshan Iron & Steel Co. Ltd, one of China's most famous integrated iron and steel enterprises, and the results show that the proposed hybrid prediction model is effective with comparatively high accuracy.

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Applications and Progress of Machine Learning Techniques in the Ladle Furnace Refining Process: A Review

Wang,

Xie,

Wang

et al. 2024

steel research int.

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Machine learning techniques have now become key technologies in optimizing the ladle furnace (LF) refining process, significantly improving prediction accuracy and operational efficiency in various aspects such as temperature control, slag optimization, and alloying processes. This article first reviews the principles of neural networks, utilizing their significant advantages in data processing capability and model generalization to explore the current applications and development trends of machine learning techniques in the LF refining process. It analyzes the advantages of hybrid dynamic models and ensemble learning methods in improving prediction accuracy and handling complex data. Additionally, this article explains the optimization of the LF refining process by combining machine learning techniques with traditional methods to reduce costs and improve production efficiency. It demonstrates the application effects and potential value of machine learning techniques in actual production, discusses the strategies and challenges in real‐time control and prediction accuracy, and envisions the future development direction of intelligent models for the LF refining process, providing a scientific basis and technical reference for the subsequent development of LF refining technology.

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Molten steel temperature prediction model based on bootstrap Feature Subsets Ensemble Regression Trees

Cited by 22 publications

References 24 publications

Molten Steel Temperature Prediction in Ladle Furnace Using a Dynamic Ensemble for Regression

Molten Steel Temperature Prediction in Ladle Furnace Using a Dynamic Ensemble for Regression

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Applications and Progress of Machine Learning Techniques in the Ladle Furnace Refining Process: A Review

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