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Application of principal component analysis (PCA) to the assessment of parameter correlations in the partial-nitrification process using aerobic granular sludge

Title
Application of principal component analysis (PCA) to the assessment of parameter correlations in the partial-nitrification process using aerobic granular sludge
Author
CUI FENGHAO
Keywords
Aerobic granular sludge; Microbial communities; Nitrogen removal; Partial-nitrification; Principal component analysis
Issue Date
2021-06
Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Citation
JOURNAL OF ENVIRONMENTAL MANAGEMENT, v. 288, Page. 1-9
Abstract
For the first time, principal component analysis (PCA) was used to extract relevant information hidden in the partial-nitrification process using aerobic granular sludge. The objectives of this research are (a) to determine total ammonia nitrogen (TAN), total nitrite nitrogen (NO2–N), nitrate nitrogen (NO3–N), and other water quality parameters; (b) to identify the diversity of nitrification and denitrification bacterial community of wastewater samples during the partial-nitrification process using aerobic granular sludge and; (c) to analyze the correlation of available parameters using PCA. The nitrite accumulation ratio was determined from TAN, NO2–N, and NO3–N. Other water quality parameters were mixed liquor volatile suspended solids (MLVSS), alkalinity, total nitrogen (TN) and sludge volume index (SVI), pH, and dissolved oxygen (DO). The identification of bacterial community was conducted using 16S rRNA gene-based pyrosequencing by GS Junior Sequencing system. The water quality parameters were computed for PCA using software MATLAB. A nitrite accumulation ratio (NAR) between 0.55 and 0.85 was determined while maintaining the aerobic granular sludge's compact and dense structure. The PCA was used to reduce the data dimensionality from the original 8 variables to 2 principal components explaining 75% of the total data variance. Applying PCA to the data analysis in biological wastewater treatment can support detecting data anomalies and separating useful information from unwanted interferences.
URI
https://www.sciencedirect.com/science/article/pii/S0301479721004709https://repository.hanyang.ac.kr/handle/20.500.11754/171622
ISSN
0301-4797
DOI
10.1016/j.jenvman.2021.112408
Appears in Collections:
OFFICE OF ACADEMIC AFFAIRS[E](교무처) > Center for Creative Convergence Education(창의융합교육원) > Articles
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