Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

Chen, Wei; Kong, Fanbei; Wang, Baoxiang; Li, Yuhan

doi:10.1080/03019233.2018.1470146

Cited by 19 publications

(8 citation statements)

References 18 publications

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“…The global comparison between two data sequences was undertaken instead of local comparison, so there is no side effect caused by subjective setting [22]. Besides, GRA is applicable to the amounts and regularity of the dataset and has the characteristic of simple calculation [23]. The steps of GRA are as follows [21][22][23]:…”

Section: Feature Selectionmentioning

confidence: 99%

“…Besides, GRA is applicable to the amounts and regularity of the dataset and has the characteristic of simple calculation [23]. The steps of GRA are as follows [21][22][23]:…”

Section: Feature Selectionmentioning

confidence: 99%

“…Thus, the learning speed of ELM can be much faster than that of traditional feedforward network algorithms, as well as better generalization performance. This algorithm can overcome the inherent defect of machine learning based on gradient descent learning theory, such as the local minima, the overtraining, and the high computing burdens [23,26]. Hence, it is suitable for online modeling with industrial data that are strongly coupled and nonlinear.…”

Section: Extreme Learning Machine Modelmentioning

confidence: 99%

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Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Zou

Zhang

Liu

et al. 2019

Metals

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Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI). To improve model performance, outliers in industrial data were eliminated by means of boxplot tool. Besides, Pearson correlation combined with grey relational analysis (GRA) was conducted to avoid multicollinearity and redundancy in input variables. The new model shows potential to evaluate online quality of steel billets. When predictive errors were within ±0.03 and ±0.025, the prediction accuracy of the R-ELM model was 94% and 89%, respectively, which was higher than that of the multiple linear regression (MLR) model and ELM model. Moreover, the effects of several key continuous casting process parameters on CSI were investigated based on the predictions of the R-ELM model via response surface analysis. The conclusions are consistent with the metallurgical mechanism, and the predictive values of the R-ELM model agree well with experimental values, which further verifies the correctness and generalization ability of the R-ELM model.

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Section: Feature Selectionmentioning

confidence: 99%

“…Besides, GRA is applicable to the amounts and regularity of the dataset and has the characteristic of simple calculation [23]. The steps of GRA are as follows [21][22][23]:…”

Section: Feature Selectionmentioning

confidence: 99%

Section: Extreme Learning Machine Modelmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Zou

Zhang

Liu

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…In this perspective, prediction algorithms are used to speed up the production process, and such efforts are being taken up by the materials science and technology community for attaining certain solutions after compilation of extensive datasets of material composition, process methods, and related properties [14][15][16]. In 2009, Brahme et al have designed an artificial neural-network-based prediction model of cold rolling textures from steel, which is used to predict fiber texture using texture intensities, carbon content, carbide content, and the amount of rolling reduction [17]. Simecek et al have developed the MECHP tool to predict the mechanical properties of hot rolled steel products.…”

Section: Introductionmentioning

confidence: 99%

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework

Choudhury

2021

Preprint

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Prediction of process route from materials with a desired set of property is one of the fundamental issues in the perspective of the materials design aspect. The parameter space is often too large to be bound since there are too many possibilities. However, in the areas with limited theoretical access, artificial learning techniques can be attempted by using the available data of the candidate materials. In this study, a computational method has been proposed to predict the different process routes of steel constituted taking composition and desired mechanical properties as inputs. First of all, historical data of the actual rolling process was collected, cleaned, and integrated. Further, the dataset is divided into four different classes based on the rolling process data. Then, to find out the essential characteristics of variables, feature correlation among various features has been calculated. A state-of-art machine learning prediction methods such as logistic regression, K- nearest neighbor, Support vector machine, and the random forest are studied to implement the prediction model. In order to avoid the overfitting of the model, k fold cross-validation is applied to the model, and achieve a realistic prediction result with an accuracy of 97%. The F1-score of the classification model is 0.86, and the kappa score is 0.95, which comply that the model has excellent learning and speculation ability and the precise forecast of steel process routes based on the given input parameters.

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“…As having an important role in manufacturing industries, the iron and steel industry also proceeds in some explorations of intelligent manufacturing from the perspective of equipment technique to planning and scheduling in order to keep competitiveness in metallurgical industry [5][6][7][8]. In addition, with the increasingly fierce market competition and the current serious overcapacity of domestic steel production, the contract orders of steel products present a trend of multi-variety, small-batch, as well as short due date.…”

Section: Introductionmentioning

confidence: 99%

Optimal Charge Planning Model of Steelmaking Based on Multi-Objective Evolutionary Algorithm

Yang

Wang

Zou

et al. 2018

Metals

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Abstract:As having an important part of coordination control in steelmaking process, traditional production planning and scheduling technologies are developed with little consideration of the metallurgy mechanism, leading to lower feasibility for actual production. Based on current situation and requirements of steel plants, this paper focuses on the investigation of the charge plan from the view of metallurgy and establishes a charge planning model concerning the minimization of both the open order amount and the difference in due dates of the orders in each charge. A modified multi-objective evolutionary algorithm is proposed to solve the charge planning model of steelmaking process. By presenting a new fitness function, based on the rule of target ranking and introducing the Elitism strategy to construct the non-inferior solution set, the quality of solutions is improved effectively and the convergence of the algorithm is enhanced remarkably. Simulation experiments are carried out on the orders from actual production, and the proposed algorithm produces a group of optimized charge plans in a short time. The quality of the solutions is better than those produced by a genetic algorithm, modified partheno-genetic algorithm, and those produced manually to some extent. The simulation results demonstrate the feasibility and effectiveness of the proposed model and the algorithm.

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Application of grey relational analysis and extreme learning machine method for predicting silicon content of molten iron in blast furnace

Cited by 19 publications

References 18 publications

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Prediction of Central Carbon Segregation in Continuous Casting Billet Using A Regularized Extreme Learning Machine Model

Processing Technique Selection for Steels Based on Mechanical Properties Using Machine Learning Framework

Optimal Charge Planning Model of Steelmaking Based on Multi-Objective Evolutionary Algorithm

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