Extracting the core indicators of pulverized coal for blast furnace injection based on principal component analysis

2013 ◽  
Vol 20 (3) ◽  
pp. 246-252 ◽  
Author(s):  
Hong-wei Guo ◽  
Bu-xin Su ◽  
Jian-liang Zhang ◽  
Meng-yi Zhu ◽  
Jian Chang
Keyword(s):  
Principal Component Analysis ◽  
Blast Furnace ◽  
Principal Component ◽  
Component Analysis ◽  
Pulverized Coal ◽  
The Core

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 Doppler Radar Data. Part I: Geometric Connections between Eigenvectors and the Core Region of Atmospheric Vortices

2005 ◽  
Vol 62 (11) ◽  
pp. 4027-4042 ◽  
Author(s):  
Paul R. Harasti ◽  
Roland List
Keyword(s):  
Principal Component Analysis ◽  
Doppler Radar ◽  
Principal Component ◽  
Component Analysis ◽  
Radar Data ◽  
Core Region ◽  
Doppler Velocity ◽  
Maximum Wind ◽  
The Core ◽  
Atmospheric Vortices

Abstract This is the first in a three-part series of papers that present the first applications of principal component analysis (PCA) to Doppler radar data. Although this novel approach has potential applications to many types of atmospheric phenomena, the specific goal of this series is to describe and verify a methodology that establishes the position and radial extent of the core region of atmospheric vortices. The underlying assumption in the current application is that the streamlines of the nondivergent component of the horizontal wind are predominantly circular, which is a characteristic often observed in intense vortices such as tropical cyclones. The method employs an S2-mode PCA on the Doppler velocity data taken from a single surveillance scan and arranged sequentially in a matrix according to the range and azimuth coordinates. Part I begins the series by examining the eigenvectors obtained from such a PCA applied to a Doppler velocity model for a modified, Rankine-combined vortex, where the ratio of the radius of maximum wind to the range from the radar to the circulation center is varied over a wide range of values typically encountered in the field. Results show that the first two eigenvectors within the eigenspace of range coordinates represent over 99% of the total variance in the data. It is also demonstrated that the coordinates of particular cusps in the curves of the eigenvector coefficients plotted against their indices are geometrically related to both the position of circulation center and the radius of maximum wind.

scholarly journals Diversity of Pangasiid Catfishes From Sumatra

2016 ◽  
Vol 12 (2) ◽  
pp. 83
Author(s):  
Rudhy Gustiano ◽  
Laurent Pouyaud
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Analysis Data ◽  
Component Analysis ◽  
The Body ◽  
The Core ◽  
Body Conformation ◽  
Scatter Plots ◽  
Distribution And Ecology ◽  
Point To Point

<p>One of the utmost importance catfish group for fisheries and aquaculture in Southeast Asia is pangasiids. The main constrain to cultivate wild species and optimize the production of cultured species was due to the poorly documented of the genetic resources. In the current study, it presents the diversity of pangasiids catfishes from Sumatra. Nine hundreds and ninety nine specimens formed the core of the material examined during this study. On each specimen, 35 point to point measurement, covering the possible variation of the body conformation were taken using dial calipers. Data were subjected to principal component analysis. Data analysis consisted in characterizing groups from scatter plots between pairs of structuring characters for subsequent use in generic identifycation keys. Four genera with seven species exist in four main rivers, Indragiri; Batang Hari; Musi; Way Rarem, in Sumatra. They are Helicophagus typus, H. Waandersii, Pteropangasius micronemus, Pangasius polyuranodon, P kunyit, P. djamba,l and P. nasutus. The diagnosis of the species, identification key, distribution and ecology were given.</p><p> </p><p><strong>Abstrak</strong></p><p>Salah satu group catfish (ikan berkumis) penting untuk perikanan tangkap dan budi daya di Asia Tenggara adalah famili Pangasiidae. Kendala utama untuk membudidayakan spesies dari alam dan meningkatkan produksi ikan budi daya adalah kurangnya informasi tentang plasma nutfah. Studi yang dilakukan menyajikan keragaman pangasius catfish dari Sumatra. Sejumlah 999 spesimen digunakan sebagai bahan uji. Pada setiap spesimen dilakukan 35 pengukuran menggunakan jangka sorong untuk menggambarkan keragaman bentuk tubuh. Data diuji dengan principal component analysis. Analisis data terdiri dari karakterisasi group menggunakan sebaran data antara pasangan-pasangan karakter pengukuran untuk menghasilkan kunci identifikasi. Empat genera dengan 7 spesies didapatkan dari 4 sungai utama (Indragiri, Batang Hari, Musi, dan Way Rarem) di Sumatra. Spesies-spesies tersebut adalah Helicophagus typus, H. waandersii, Pteropangasius micronemus, Pangasius polyuranodon, P. kunyit, P. djambal, dan P. nasutus. Diagnosis dari spesies-spesies tersebut, kunci identifikasi, distribusi, dan ekologi dipaparkan dalam makalah ini.</p>

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.

Sign in / Sign up

Export Citation Format

Share Document