Application of Least Squares Support Vector Machines to Predict the Silicon Content in Blast Furnace Hot Metal

Jian, Ling; Gao, Chuanhou; Li, Lei; Zeng, Jiusun

doi:10.2355/isijinternational.48.1659

Cited by 53 publications

(18 citation statements)

References 21 publications

(21 reference statements)

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“…Nurkkala et al [9] proposed a systematic method by addressing the neural network complexity and the choice of relevant inputs, and then gave a good illustration on the silicon prediction. Jian et al [12], [13] applied support vector machines (Abbr. SVM, more abbreviation symbols may be found in Appendix A) to tackle the silicon prediction problem.…”

Section: Introductionmentioning

confidence: 99%

Constructing Multiple Kernel Learning Framework for Blast Furnace Automation

Jian

Gao

Xia

2012

IEEE Trans. Automat. Sci. Eng.

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This paper constructs the framework of the reproducing kernel Hilbert space for multiple kernel learning, which provides clear insights into the reason that multiple kernel support vector machines (SVM) outperform single kernel SVM. These results can serve as a fundamental guide to account for the superiority of multiple kernel to single kernel learning. Subsequently, the constructed multiple kernel learning algorithms are applied to model a nonlinear blast furnace system only based on its input-output signals. The experimental results not only confirm the superiority of multiple kernel learning algorithms, but also indicate that multiple kernel SVM is a kind of highly competitive data-driven modeling method for the blast furnace system and can provide reliable indication for blast furnace operators to take control actions. Note to Practitioners-This paper is motivated by the problem of predicting the silicon content in blast furnace hot metal, whichis an open problem for realizing blast furnace automation. Here, based on the single kernel and multiple kernel SVM, we pay special attention to the silicon trend prediction since it can provide more direct guideline for taking control action in the blast furnace operation. Theoretically, we have given the detailed reasons that multiple kernel SVM is superior to single kernel SVM, which can improve the transparency of multiple kernel learning algorithm. The experimental results, not only confirm the superiority of multiple kernel learning algorithms, but also indicate that multiple kernel SVM is a kind of highly competitive data-driven modeling method for the blast furnace system and can provide reliable indication for blast furnace operators to take control actions.Index Terms-Data-driven, multiple kernel support vector machines (SVM), nonlinear blast furnace system, quadratically constrained quadratic programming, reproducing kernel Hilbert space.

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Section: Introductionmentioning

confidence: 99%

Constructing Multiple Kernel Learning Framework for Blast Furnace Automation

Jian

Gao

Xia

2012

IEEE Trans. Automat. Sci. Eng.

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“…Since the silicon content in hot metal is very important, it is natural to take it as the BF output. 3,4,8,9,11) Generally, the variables involved in the compressed air, the charged solid raw materials and some other hot metal components are thought to be the most relevant ones. They are selected as input variables.…”

Section: Application and Validationmentioning

confidence: 99%

“…Today, as a BF system is concerned, data-driven modeling is being broadly investigated and exhibits great potential in describing the complex behavior of the BF process. Candidate data-driven modeling tools include state space, 3) neural networks, 4) genetic programming, 5) partial least squares, 6) fuzzy set, 7) chaos, 8) support vector machine (SVM), [9][10][11] generalized Gaussian regularization network, 12) and generalized autoregressive conditional heteroskedastic model. 13) These data-driven methods are expected to be able to improve the BF model accuracy and also to shed more light on how to optimize the BF process operation in the future.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking Process

et al. 2015

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Blast furnace system is one of the most complex industrial systems and, as such, there are still many unsolved theoretical and experimental difficulties, such as silicon prediction. For this reason, based on recursive updating algorithm, an adaptive least squares support vector machine (LS-SVM) predictor is presented for prediction task of silicon content in blast furnace (BF) hot metal. The predicator employs recursive updating algorithm to get the precise solution of the latest LS-SVM model and avoid the long process of running through the whole model. Theoretically, the computational complexity is reduced significantly from O(n 3 m + m 4 ) to O(n 3 + m 3 ). Experiments on two different BF data sets demonstrate that the proposed adaptive LS-SVM predicator is suitable for the task of predicting BF ironmaking process for its high hitting percentage and time saving.

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“…The input variable selection problem has traditionally been tackled by using process knowledge, but the choice should also include the pertinent time lags of the variables to facilitate the modeling task. 4,5,7,10,11,16) Some authors have applied exhaustive search among linear models 8) or the partial least squares method to find the most relevant ones. 17) Techniques based on evolving or pruning neural networks have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…Second, due to the complexity of the task, the prediction of the silicon content has been used as a benchmark problem for an array of numerical algorithms. Initially, linear time series analysis, [4][5][6][7][8] and later also nonlinear techniques, including neural networks, support vector machines and Wiener models [9][10][11][12] have been used. Chaos-based techniques have also indicated the complex dynamics of the system at hand.…”

Section: Introductionmentioning

confidence: 99%

Blast Furnace Dynamics Using Multiple Autoregressive Models with Exogenous Inputs

Nurkkala¹,

Pettersson²,

Saxén³

2012

ISIJ Int.

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Autoregressive models with exogenous inputs are useful tools for analyzing systems with unknown dynamics, but are limited by the assumption that the relations between inputs and output(s) are linear. For complex systems with nonlinear or abruptly changing dynamics it is possible to modify the technique by allowing for multiple local models and designing a strategy for switching between them. A method by which this can be realized is developed in the paper. The technique is applied on a complex problem in the metallurgical industry, i.e., the prediction of hot metal silicon content in the blast furnace. A set of local models is developed for different parts of a training set, using a statistical criterion for model selection. The resulting local models are then applied to predict future values of the silicon content. It is demonstrated that the method is capable to develop models, among which a proper choice can be made for prediction. The potential of multi-step predictions is also studied. Finally, some conclusions concerning the method and the results are drawn.

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Application of Least Squares Support Vector Machines to Predict the Silicon Content in Blast Furnace Hot Metal

Cited by 53 publications

References 21 publications

Constructing Multiple Kernel Learning Framework for Blast Furnace Automation

Constructing Multiple Kernel Learning Framework for Blast Furnace Automation

Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking Process

Blast Furnace Dynamics Using Multiple Autoregressive Models with Exogenous Inputs

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