Principal component analysis to assess the efficiency and mechanism for ultraviolet-C/polyaluminum chloride enhanced coagulation of algae-laden water

2013 ◽  
Vol 14 (3) ◽  
pp. 493-503
Author(s):  
Hua-Se Ou ◽  
Chao-Hai Wei ◽  
Yang Deng ◽  
Nai-Yun Gao
Keyword(s):  
Principal Component Analysis ◽  
Lake Taihu ◽  
Principal Component ◽  
Component Analysis ◽  
Polyaluminum Chloride ◽  
Algae Biomass ◽  
Principal Factors ◽  
P Factor ◽  
Uv C

Polyaluminum chloride (PAC) coagulation, short wavelength ultraviolet (UV-C at 254 nm) treatment and UV-C/PAC treatment of algae-laden water from Meiliang Gulf (Lake Taihu) were conducted. The intrinsic characteristics and correlations of chromophoric dissolved organic matter (CDOM), algae biomass and other contaminants were investigated. Parallel factor analysis determined four CDOM components, and principal component analysis (PCA) identified three integrated principal factors. PCA Factor 1 had correlations with dissolved organic carbon (DOC) (r = 0.798), UV254 (0.645), humic-like CDOM component C3 (0.892) and C4 (0.876), and Factor 2 correlated with in vivo chlorophyll-a (0.768), microcystin-LR (0.713), protein-like C1 (0.782) and C2 (0.759). Factor 3 had correlations with NH3-N (0.851) and total phosphorus (T-P) (0.716). The variation in PCA factor scores revealed that single PAC coagulation preferentially removed algal cells (Factor 2) but had little effect on NH3-N and T-P (Factor 3). Single UV-C treatment induced degradation of protein-like CDOM and decrease of algal biomass (Factor 2), followed by humic-like CDOM increase from ruptured cells (Factors 1 and 3). Compared with single PAC coagulation (20 mg L−1), the combined UV-C/PAC treatment (700 mJ cm−2 + 20 mg L−1) enhanced the removal of PCA Factors 1, 2 and 3 (15%, 32% and 10%).

scholarly journals Construction of Training Sets for Valid Calibration of in Vivo Cyclic Voltammetric Data by Principal Component Analysis

2015 ◽  
Vol 87 (22) ◽  
pp. 11484-11491 ◽  
Author(s):  
Nathan T. Rodeberg ◽  
Justin A. Johnson ◽  
Courtney M. Cameron ◽  
Michael P. Saddoris ◽  
Regina M. Carelli ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Cyclic Voltammetric ◽  
Training Sets

scholarly journals Forecasting Models for the Economic Security of Regional Clusters of the National Economy

2018 ◽  
Vol 81 (2) ◽  
pp. 72-81
Author(s):  
N. S. Ivanova
Keyword(s):  
Principal Component Analysis ◽  
National Economy ◽  
Principal Component ◽  
Component Analysis ◽  
Economic Security ◽  
Internal Factors ◽  
Forecasting Models ◽  
Regional Clusters ◽  
Regional Cluster ◽  
Principal Factors

Introduction. The principal internal factors of the economic security of regions are defined by principal component analysis and their correlation with respective indicators is determined. Four principal factors (components) of the economic security of regions are identified. Forecasting models for the economic security of regions of the national economy are constructed by regional cluster using the results of the study. The study is performed by data on main socio-economic indicators of 24 Ukrainian regions over 2008–2015 and by economic security clusters (classified as “good”, “sufficient” and “satisfactory”) defined in previous studies. The purpose of the study is to construct sets of forecasting models for the economic security of the national economy by regional cluster. Methods. Methods of theoretical generalization are used to study and systematize the factors of the economic security of regions; multidimensional statistical methods such as principal component analysis are used to define their correlation with the respective indicators; the method of comparison allows for defining common and distinctive features of the results of computation of the economic security of regions using the constructed models. Results. Principal component analysis used for defining the internal factors of economic security of regions and their correlations with the respective indicators allows for identifying four principal factors (components) of the economic security of regions. Results of principle component analysis show that the significance of the first four latent factors for the overall sample is 86.86% of the dispersion of the analyzed variables (13.14% error); for “good” cluster it is 87.3% (12.7% error); for “sufficient” cluster it is 88.03% (11.97% error); for “satisfactory” cluster it is 86.0% (14.0% error). Four sets of forecasting models are constructed, each consisting of a mathematical description of four principal components for each sample. The implementation of the constructed models confirms the inclusion of the regions to the respective regional clusters. The dynamics of the indicators shows the economic situation and processes in the regions in the respective periods.  Conclusion. The applied character of the constructed models is substantiated by their practical implementation using actual socio-economic data for the regions represented in each of the regional clusters (Dnipropetrovsk, Vinnytsia, and Volyn regions), over the period of 2008–2015. Further studies should focus on making the scale of the economic security performance for the constructed forecasting models, to determine the performance and estimate the probability of insecurity occurrences in the economy using expected (planned) figures of economic development in a region.

scholarly journals PO-0827: Principal component analysis for deviation detection in 3D in vivo EPID dosimetry

2016 ◽  
Vol 119 ◽  
pp. S391
Author(s):  
R.A. Rozendaal ◽  
B. Mijnheer ◽  
I. Olaciregui-Ruiz ◽  
P. Gonzalez ◽  
J.J. Sonke ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Epid Dosimetry
Sign in / Sign up

Export Citation Format

Share Document