Improvement of identification of blast furnace ironmaking process by outlier detection and missing value imputation

Zeng, Jiusun; Gao, Chuanhou

doi:10.1016/j.jprocont.2009.07.006

Cited by 55 publications

(42 citation statements)

References 31 publications

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“…There are seven input variables (i.e., D = 7) related to the silicon content, including the blast temperature, the blast volume, the gas permeability, the top pressure, the top temperature, the ore/coke ratio, and the pulverized coal injection. 19,20,22) The silicon content is analyzed offline and infrequently. Consequently, the soft sensor is constructed using the online measured variables.…”

Section: Silicon Content Prediction: Results and Discussionmentioning

confidence: 99%

“…And the data in different operating areas are distributed irregularly. To evaluate the prediction performance, three indices of the root-mean-square error (RMSE), relative RMSE (simply noted as RE), and the hit rate (HR) [19][20][21][22][23][24][25][26] are utilized and defined below, respectively. (11) where y q and ŷ q are the actual value and the predicted value, respectively.…”

Section: Silicon Content Prediction: Results and Discussionmentioning

confidence: 99%

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Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction

2017

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Section: Silicon Content Prediction: Results and Discussionmentioning

confidence: 99%

Section: Silicon Content Prediction: Results and Discussionmentioning

confidence: 99%

Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction

2017

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“…The process input variables correlated with the product quality (i.e., the silicon content) have been selected. 21,22,24) These input variables include the blast volume, the blast temperature, the top pressure, the gas permeability, the top temperature, the ore/coke ratio, and the pulverized coal injection. The sampling time of most of these input variables is 1 minute.…”

Section: Industrial Silicon Content Predictionmentioning

confidence: 99%

“…40) Additionally, the hit rate (HR) index is often adopted in industrial blast furnace ironmaking processes. [21][22][23][24][25][26][27][28] Three indices of RMSE, RE, and HR are defined, respectively. (18) where ŷ q and y q are the predicted value and the actual value, respectively.…”

Section: Industrial Silicon Content Predictionmentioning

confidence: 99%

“…To online predict the silicon content, various data-driven soft sensor modeling approaches, including various neural networks, [7][8][9][10][11][12][13][14] partial least squares, 14,15) fuzzy inference systems, 16) nonlinear time series analysis, [17][18][19][20] subspace identification, 21) support vector regression (SVR) and least squares SVR (LSSVR), [22][23][24] and others [25][26][27][28][29] have been investigated. A recent overview of black-box models for short-term silicon content prediction in blast furnaces can be referred to.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Liu

2017

ISIJ Int.

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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.

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Adaptive control and signal processing literature survey (No. 16)

2010

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk.

show abstract

Improvement of identification of blast furnace ironmaking process by outlier detection and missing value imputation

Cited by 55 publications

References 31 publications

Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction

Ensemble Non-Gaussian Local Regression for Industrial Silicon Content Prediction

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

Adaptive control and signal processing literature survey (No. 16)

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