Process Monitoring of Filter Press in Hydrometallurgy Based on PCA

2015 ◽  
Vol 738-739 ◽  
pp. 844-848
Author(s):  
Qing Kai Wang ◽  
Xiao Yu Zou ◽  
Shu Wang ◽  
Da Kuo He ◽  
Yang Zhou ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Missing Data ◽  
Process Monitoring ◽  
Test Data ◽  
Principal Component ◽  
Sampling Period ◽  
Component Analysis ◽  
Filter Press ◽  
Metallurgical Technology

Hydrometallurgy is a popular metallurgical technology. Filter press is common but vital to the production of hydrometallurgy. Hence, the process monitoring of filter press is of great significance for hydrometallurgy. Due to data analysis and related knowledge of filter press, Principal component analysis (PCA) is applied to process monitoring of the filter press via two traditional statistics. However, modeling and test data collected from actual production suffers from outliers, missing data, inconsistent sampling period between variables. Based on these practical problems, corresponding data proceeding technique is proposed. The final application simulation illustrates the validity of the proposed method.

Online reduced kernel principal component analysis for process monitoring

2018 ◽  
Vol 61 ◽  
pp. 1-11 ◽  
Author(s):  
Radhia Fezai ◽  
Majdi Mansouri ◽  
Okba Taouali ◽  
Mohamed Faouzi Harkat ◽  
Nasreddine Bouguila
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis

Uncertain Nonlinear Process Monitoring Using Interval Ensemble Kernel Principal Component Analysis

2021 ◽  
Vol 25 (1) ◽  
pp. 101-109
Author(s):  
Xianrui Wang ◽  
Guoxin Zhao ◽  
Yu Liu ◽  
Shujie Yang ◽  
◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Nonlinear Process ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Gaussian Kernel ◽  
Bayesian Decision ◽  
Symbolic Data ◽  
Nonlinear Process Monitoring

To solve uncertainties in industrial processes, interval kernel principal component analysis (IKPCA) has been proposed based on symbolic data analysis. However, it is experimentally discovered that the performance of IKPCA is worse than that of other algorithms. To improve the IKPCA algorithm, interval ensemble kernel principal component analysis (IEKPCA) is proposed. By optimizing the width parameters of the Gaussian kernel function, IEKPCA yields better performances. Ensemble learning is incorporated in the IEKPCA algorithm to build submodels with different width parameters. However, the multiple submodels will yield a large number of results, which will complicate the algorithm. To simplify the algorithm, a Bayesian decision is used to convert the result into fault probability. The final result is obtained via a weighting strategy. To verify the method, IEKPCA is applied to the Tennessee Eastman (TE) process. The false alarm rate, fault detection rate, accuracy, and other indicators used in the IEKPCA are compared with those of other algorithms. The results show that the IEKPCA improves the accuracy of uncertain nonlinear process monitoring.

Reaction Monitoring in Explorative Organic Synthesis Using Fiber-Optical NIR Spectroscopy and Principal Component Analysis

1996 ◽  
Vol 50 (12) ◽  
pp. 1541-1544 ◽  
Author(s):  
Hans-René Bjørsvik
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Latent Variables ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Quantitative Information ◽  
Chemistry Laboratory ◽  
Reaction Profile

A method of combining spectroscopy and multivariate data analysis for obtaining quantitative information on how a reaction proceeds is presented. The method is an approach for the explorative synthetic organic laboratory rather than the analytical chemistry laboratory. The method implements near-infrared spectroscopy with an optical fiber transreflectance probe as instrumentation. The data analysis consists of decomposition of the spectral data, which are recorded during the course of a reaction by using principal component analysis to obtain latent variables, scores, and loading. From the scores and the corresponding reaction time, it is possible to obtain a reaction profile. This reaction profile can easily be recalculated to obtain the concentration profile over time. This calculation is based on only two quantitative measurements, which can be (1) measurement from the work-up of the reaction or (2) chromatographic analysis from two withdrawn samples during the reaction. The method is applied to the synthesis of 3-amino-propan-1,2-diol.

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