Data-Driven Time Discrete Models for Dynamic Prediction of the Hot Metal Silicon Content in the Blast Furnace—A Review

Liu

2017

ISIJ Int.

Development of accurate soft sensors for online quality prediction (e.g., silicon content) in an industrial blast furnace is a difficult task. A novel just-in-time-learning (JITL) prediction approach using adaptive feature-weighting for similar samples is developed. First, a dual-objective joint-optimization framework is proposed to introduce both input and output information into the model. Then, a suitable similarity criterion with feature weighting strategy is formulated, which is not considered in conventional JITL methods. Moreover, the trade-off parameter in the joint-optimization problem can be chosen automatically, without the time-consuming cross-validation procedure. The proposed method is applied to online predict the silicon content in an industrial blast furnace in China. Compared with other JITL-based soft sensors, better prediction performance has been obtained.

Section: Industrial Silicon Content Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Weighting Just-in-Time-Learning Quality Prediction Model for an Industrial Blast Furnace

Liu

2017

ISIJ Int.

“…Its main idea is to embed the inputs into a feature space through a high-dimension mapping, so that an optimal decision hyperplane can be found among the high-dimension embedded data points [15]. In order to find a decision rule with good generalization capability, the socalled support vectors (SVs), including a small subset of the training data, are selected to support the optimal hyperplane [16].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the hot metal silicon content in blast furnace using support vector machine optimized by an improved particle swarm optimizer

Xia

Tang

et al. 2015

Neural Comput & Applic

As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems to be solved, such as the modeling problem. This paper adopts support vector regression (SVR) to construct the prediction model of blast furnace silicon content. To ensure a good generalization performance for the given datasets, it is important to select proper parameters for SVR. In view of this problem, a new particle swarm optimizer called DMS-PSO-CLS is presented to optimize the parameters of SVR. In DMS-PSO-CLS, a new cooperative learning strategy is hybridized with DMS-PSO, which makes particle information be used more effectively for generating better-quality solutions. DMS-PSO-CLS takes merits of the DMS-PSO and the cooperative learning strategy so that both the convergence speed and the convergence precision can be improved. Experimental results show that DMS-PSO-CLS can find the optimal parameters of SVR with high speed and the SVR model optimized by DMS-PSO-CLS can achieve a good regression precision on the predictive problem of blast furnace.

“…Support vector machine (SVM) is a popular machine learning method. Its main idea is to embed the inputs into a feature space through a high dimension mapping, and then find an optimal decision hyperplane among the high dimension embedded data points [19]. The essence of SVM is a structure risk minimization principle.…”

Section: Introductionmentioning

confidence: 99%

Wiener model identification of blast furnace ironmaking process based on Laguerre filter and linear programming support vector regression

Xia

2014 International Joint Conference on Neural Networks (IJCNN)

Tang

et al. 2014

As a highly complex multi-input and multi-output system, blast furnace plays an important role in industrial development. Although much research has been done in the past few decades, there still exist many problems, such as the modeling and control problems. In view of these reasons, this paper is concerned with developing a Wiener model to predict the silicon content of blast furnace. Unlike traditional Wiener model, this paper avoids the optimization of high number of model parameters. The Wiener model here is composed of a basis filter filter expansion named Laguerre filter and a linear programming support vector regression (LP-SVR). They are used to represent the linear dynamic component and the nonlinear static element. Take the advantages that Laguerre filter can approximate linear systems with a lower model and order and LP-SVR can achieve a sparse solution, the proposed Wiener model not only improves the prediction accuracy but also reduces the computation complexity. Simulation results show that this Wiener model is suitable for the prediction of blast furnace silicon content.