Using multivariate grey model and principal component analysis to modeling the blast furnace

2011 ◽  
Author(s):  
Xueyi Liu ◽  
Wenhui Wang
Keyword(s):  
Principal Component Analysis ◽  
Blast Furnace ◽  
Principal Component ◽  
Component Analysis ◽  
Grey Model

Coke Index Analysis Based on Principal Component Analysis and Decision Tree Mining

2011 ◽  
Vol 48-49 ◽  
pp. 318-322 ◽  
Author(s):  
Hong Wei Guo ◽  
Bu Xin Su ◽  
Jian Chang ◽  
Jian Liang Zhang ◽  
Wei Chao Cao
Keyword(s):  
Principal Component Analysis ◽  
Blast Furnace ◽  
Decision Tree ◽  
Coke Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Production Index ◽  
Evaluation Indexes ◽  
Furnace Production ◽  
Blast Furnace Production

Current analysis in the relations between blast furnace production index and coke index is still using the traditional statistical analysis method，but it involves too many coke quality evaluation indexes and there are some overlap between the indexes. According to this situation, this paper puts forward a new method based on principal component analysis and decision tree mining to analyze the relations between blast furnace production index and coke index . The materials of blast furnace production mainly include ore, coke and coal, in which the coke quality index have the biggest influence on the blast furnace production index. It has profound meaning to analyze the relation between coke index and blast furnace production index to evaluate Coke quality indicators reasonably[1] and improve the blast furnace production index. Current analysis in the relations between blast furnace production index and coke index is still using the traditional statistical analysis method[2]，but it involves too many coke quality evaluation indexes and there are some overlap between the indexes. According to this situation, this paper puts forward a new method based on principal component analysis and decision-tree-based data-mining to analyze the relations between blast furnace production index and coke index. On the one hand this method can get few representative indexes from so many evaluation indexes by principal component analysis; on the other hand, decision-tree-based data-mining on the coke representative index based on the principal component analysis can get accurately quantitative relation between blast furnace production index and coke index.

A Prediction Model of Blast Furnace Slag Viscosity Based on Principal Component Analysis and K-Nearest Neighbor Regression

JOM ◽  
2020 ◽  
Vol 72 (11) ◽  
pp. 3908-3916
Author(s):  
Dewen Jiang ◽  
Jianliang Zhang ◽  
Zhenyang Wang ◽  
Chenfan Feng ◽  
Kexin Jiao ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Blast Furnace ◽  
Prediction Model ◽  
Blast Furnace Slag ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
Slag Viscosity ◽  
K Nearest Neighbor ◽  
Furnace Slag

scholarly journals Principal Component Analysis of Blast Furnace Drainage Patterns

Processes ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 519 ◽  
Author(s):  
Mauricio Roche ◽  
Mikko Helle ◽  
Henrik Saxén
Keyword(s):  
Principal Component Analysis ◽  
Blast Furnace ◽  
Principal Component ◽  
Component Analysis ◽  
Monitoring And Control ◽  
Process Data ◽  
Drainage Patterns ◽  
Process Operation ◽  
Operating Blast Furnace ◽  
And Control

Monitoring and control of the blast furnace hearth is critical to achieve the required production levels and adequate process operation, as well as to extend the campaign length. Because of the complexity of the draining, the outflows of iron and slag may progress in different ways during tapping in large blast furnaces. To categorize the hearth draining behavior, principal component analysis (PCA) was applied to two extensive sets of process data from an operating blast furnace with three tapholes in order to develop an interpretation of the outflow patterns. Representing the complex outflow patterns in low dimensions made it possible to study and illustrate the time evolution of the drainage, as well as to detect similarities and differences in the performance of the tapholes. The model was used to explain the observations of other variables and factors that are known to be affected by, or affect, the state of the hearth, such as stoppages, liquid levels, and tap duration.

Blade damage prognosis based on kernel principal component analysis and grey model using subsampled tip-timing signals

2014 ◽  
Vol 228 (17) ◽  
pp. 3178-3185 ◽  
Author(s):  
Chen Zhongsheng ◽  
Yang Yongmin ◽  
Guo Bin ◽  
Hu Zheng
Keyword(s):  
Principal Component Analysis ◽  
High Speed ◽  
Damage Index ◽  
Principal Component ◽  
Component Analysis ◽  
Vibration Monitoring ◽  
Grey Model ◽  
Kernel Principal Component Analysis ◽  
Damage Prognosis ◽  
Blade Tip

Damage prognosis of high-speed blades is very important for industrial turbomachinery. Nowadays, vibration monitoring using blade tip-timing methods is becoming promising. However, its main drawback is blade tip-timing signals are subsampled. Very few works have been done on damage prognosis using subsampled blade tip-timing signals. This paper investigates a novel method of blade damage prognosis based on kernel principal component analysis and grey model. Firstly, a nonaliasing reconstruction algorithm of subsampled blade tip-timing signals is proposed based on the Shannon theorem and wavelet packet transform. Secondly, kernel principal component analysis is done on the damage feature space and a damage index is defined by Mahalanobis distance. Then a grey model (1) model is proposed for damage prognosis. In the end, an experimental setup is built and a long time testing is done for collecting samples. The experimental results validate the superiority of the proposed method.

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