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.

Supervisory control of wastewater treatment plants by combining principal component analysis and fuzzy c-means clustering

2001 ◽  
Vol 43 (7) ◽  
pp. 147-156 ◽  
Author(s):  
C. Rosen ◽  
Z. Yuan
Keyword(s):  
Principal Component Analysis ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Principal Component ◽  
Component Analysis ◽  
Biological Wastewater Treatment ◽  
Monitoring And Control ◽  
Process Data ◽  
Fuzzy C Means

In this paper a methodology for integrated multivariate monitoring and control of biological wastewater treatment plants during extreme events is presented. To monitor the process, on-line dynamic principal component analysis (PCA) is performed on the process data to extract the principal components that represent the underlying mechanisms of the process. Fuzzy c-means (FCM) clustering is used to classify the operational state. Performing clustering on scores from PCA solves computational problems as well as increases robustness due to noise attenuation. The class-membership information from FCM is used to derive adequate control set points for the local control loops. The methodology is illustrated by a simulation study of a biological wastewater treatment plant, on which disturbances of various types are imposed. The results show that the methodology can be used to determine and co-ordinate control actions in order to shift the control objective and improve the effluent quality.

Assessing irreversible fouling behavior of membrane foulants in the ultrafiltration of natural water using principal component analysis of fluorescence excitation-emission matrices

2011 ◽  
Vol 11 (2) ◽  
pp. 179-185 ◽  
Author(s):  
R. H. Peiris ◽  
H. Budman ◽  
R. L. Legge ◽  
C. Moresoli
Keyword(s):  
Principal Component Analysis ◽  
Particulate Matter ◽  
River Water ◽  
Membrane Fouling ◽  
Principal Component ◽  
Drinking Water Treatment ◽  
Component Analysis ◽  
Monitoring And Control ◽  
Fluorescence Excitation ◽  
Natural River Water

Natural river water is comprised of different foulant components such as natural organic matter and colloidal/particulate matter. Both individual and combined contributions of these foulant components results in different fouling behaviour. The ability to characterize these contributions that lead to reversible and irreversible membrane fouling would be beneficial for the implementation of fouling monitoring and control strategies for membrane-based drinking water treatment operations. A fluorescence excitation-emission matrix and principal component analysis-based approach was able to qualitatively estimate the accumulation of humic substances (HS)-, protein- and colloidal/particulate matter-like foulant components in membranes during the ultrafiltration (UF) of natural river water. A bench-scale flat sheet UF cross-flow set-up and successive permeation and membrane backwashing cycles were used. Analysis of the accumulation of these foulant components revealed that the increased levels of colloidal/particulate matter accumulation in the membranes appeared to have increased the extent of irreversible fouling by HS-like matter whereas lower irreversible fouling by protein-like matter was observed with increased colloidal/particulate matter accumulation. The results also indicate that the combined contributions by these foulants are important in the fouling of membranes during the UF of river water.

Combining multiway principal component analysis (MPCA) and clustering for efficient data mining of historical data sets of SBR processes

2008 ◽  
Vol 57 (10) ◽  
pp. 1659-1666 ◽  
Author(s):  
Kris Villez ◽  
Magda Ruiz ◽  
Gürkan Sin ◽  
Joan Colomer ◽  
Christian Rosén ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Process Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Process Data ◽  
Nitrogen And Phosphorus ◽  
Online Data ◽  
Data Set

A methodology based on Principal Component Analysis (PCA) and clustering is evaluated for process monitoring and process analysis of a pilot-scale SBR removing nitrogen and phosphorus. The first step of this method is to build a multi-way PCA (MPCA) model using the historical process data. In the second step, the principal scores and the Q-statistics resulting from the MPCA model are fed to the LAMDA clustering algorithm. This procedure is iterated twice. The first iteration provides an efficient and effective discrimination between normal and abnormal operational conditions. The second iteration of the procedure allowed a clear-cut discrimination of applied operational changes in the SBR history. Important to add is that this procedure helped identifying some changes in the process behaviour, which would not have been possible, had we only relied on visually inspecting this online data set of the SBR (which is traditionally the case in practice). Hence the PCA based clustering methodology is a promising tool to efficiently interpret and analyse the SBR process behaviour using large historical online data sets.

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

Hot Rolling State Analysis Based on Kernel Principal Component Analysis and Information Entropy

2012 ◽  
Vol 572 ◽  
pp. 7-12
Author(s):  
Fei He ◽  
Quan Yang ◽  
Bao Jian Wang
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Information Entropy ◽  
Hot Rolling ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Process Data ◽  
Feature Extraction Method ◽  
Information Metric

With more and more process data acquired from manufacturing process, extracting useful information to build empirical models of past successful operations is urgently required to get higher product quality. Clustering is the important data mining methods, where feature extraction is a significant factor to ensure the accurate rate of clustering and classification. As a common non-linear feature extraction method, kernel principal component analysis (KPCA) uses the variance as the information metric, but the variance is not always effective in some cases. Since information entropy is nonlinear and can effectively represent the dependencies of features, the Renyi entropy is used as the information metric to extract the feature in this paper. Simulation data, Tennessee Eastman and hot rolling process data are used for model validation. As a result the proposed method has better performance on feature extraction, compared with traditional KPCA.

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