|
|
Characteristic indexes of floc structure in activated sludge based on dimensionality reduction methods |
HU Xiao-bing1,2, ZHU Rong-fang1, YE Xing3, XIE Rui-tao1, TANG Su-lan1, DAI Bo3 |
1. College of Architectural Engineering, Anhui University of Technology, Ma'anshan 243032, China;
2. Engineering Research Center of Water Purification and Utilization Technology based on Biofilm Process, Ministry of Education, Ma'anshan 243032, China;
3. College of Energy and Environment, Anhui University of Technology, Ma'anshan 243000, China |
|
|
Abstract In order to establish characteristic indexes of floc structure in activated sludge, 19microscopic parameters used for description of floc structure were divided into four groups: floc size (SZ), compactness (CP), regulation (RG) and filamentous microbes (FL). These four groups included 4, 5, 8, 2indexes, respectively. Principal component analysis method (PCA, linear dimension reduction) and Isometric mapping method (Isomap, nonlinear dimension reduction) were used to reduce dimensions of these parameters of floc structure. By comparing decrease range and effectiveness of dimension reduction with two methods above, the characteristics indexes of floc structure were established. After treatment of dimension reduction with PCA, the group index of SZ, FL of floc structure can be characterized by 1comprehensive index, so can the group index FL, but for the group index CP, RG, each of them need 3comprehensive indexes to represent their characteristics. The decrease range of dimension reduction of SZ, CP, RG, FL are 0.750, 0.400, 0.625 and 0.500, respectively. The dimensionality of floc structure reduced by Isomap method can be characterized by 1comprehensive index for each group, the decrease range of dimension reduction of SZ, CP, RG, FL are 0.750, 0.800, 0.875 and 0.500, respectively. Therefore, the comprehensive indexes with Isomap dimension reduction are more accurate, concise to describe floc structure characteristics than those with PCA dimension reduction and more suitable for being characteristics indexes of floc structure in activated sludge.
|
Received: 23 September 2016
|
|
|
|
|
[1] |
阮晓东,刘俊新.活性污泥絮体的分形结构分析 [J]. 环境科学, 2013,34(4):1457-1463.
|
[2] |
龙腾锐,何 强,林 刚.活性污泥中丝状菌与絮体结构的关系研究 [J]. 中国给水排水, 2000,16(2):5-8.
|
[3] |
Han Y, Liu J, Guo X, et al. Micro-environment characteristics and microbial communities in activated sludge flocs of different particle size [J]. Bioresource Technology, 2012,124:252-258.
|
[4] |
Jin B, Wilen B M, Lant P. Impacts of morphological, physical and chemical properties of sludge flocs on dewaterability of activated sludge [J]. Chemical Engineering Journal, 2004,98: 115-126.
|
[5] |
沈韫芬,章宗涉,顾曼如,等.微型生物监测新技术 [M]. 北京:中国建筑工业出版社, 1990.
|
[6] |
Costa J C, Mesquita D P, Amaral A L, et al. Quantitative image analysis for the characterization of microbial aggregates in biological wastewater treatment: a review [J]. Environmental Science and Pollution Research International, 2013,20(9):5887- 5912.
|
[7] |
Chung H Y, Lee D J. Porosity and interior structure of flocculated activated sludge floc [J]. Journal of Colloid and Interface Science, 2003,267(1):136-143.
|
[8] |
He W P, Nan J, Li H Y, at el. Characteristics analysis on temporal evolution of floc size and structure in low-shear flow [J]. Water Research, 2012,46(2):509-520.
|
[9] |
宣科佳,王毅力,魏科技,等.A2/O工艺中好氧污泥絮体的分形结构与理化特征分析 [J]. 环境科学, 2009,30(7):2013-2021.
|
[10] |
Vahedi A, Gorczyca B. Application of fractal dimensions to study the structure of flocs formed in lime softening process [J]. Water Research, 2011,45(2):545-556.
|
[11] |
李振亮,张代钧,卢培利,等.活性污泥絮体粒径分布与分形维数的影响因素 [J]. 环境科学, 2013,34(10):3975-3980.
|
[12] |
Koivuranta E, STOOR T, Hattuniemi J, et al. On-line optical monitoring of activated sludge floc morphology [J]. Journal of Water Process Engineering, 2015,5:28-34.
|
[13] |
Ollakka H, Ruuska J, Taskila S. The application of principal component analysis for bioheapleaching process - Case study: Talvivaara mine [J]. Minerals Engineering, 2016,95:48-58.
|
[14] |
刘 潇,薛 莹,纪毓鹏,等.基于主成分分析法的黄河口及其邻近水域水质评价 [J]. 中国环境科学, 2015,35(10):3187-3192.
|
[15] |
Shi J H, Song W X. Sparse principal component analysis with measurement errors [J]. Journal of Statistical Planning and Inference, 2016,175:87-99.
|
[16] |
Zhou C, Wang L, Zhang Q, et al. Face recognition based on PCA image reconstruction and LDA [J]. Optik-International Journal for Light and Electron Optics, 2013,124(22):5599-5603.
|
[17] |
尚 文,杨永兴,韩大勇.基于PCA的滇西北高原纳帕海湿地退化过程分析及其评价 [J]. 生态学报, 2013,33(15):4776-4789.
|
[18] |
张新喜,完颜健飞,胡小兵,等.基于活性污泥絮体微观参数的污泥沉降性能判别 [J]. 环境科学学报, 2015,35(12):3815-3823.
|
[19] |
Zheng Y, Fang B, Tang Y Y, et al. Learning orthogonal projections for Isomap [J]. Neurocomputing, 2013,103:149-154.
|
[20] |
Bu Y, Chen F, Pan J. Stellar spectral subclasses classification based on Isomap and SVM [J]. New Astronomy, 2014,28:35-43.
|
[21] |
Orsenigo C. An improved set covering problem for Isomap supervised landmark selection [J]. Pattern Recognition Letters, 2014,49:131-137.
|
[22] |
Yoon J C, Lee I K. Visualization of graphical data in a user-specified 2D space using a weighted Isomap method [J]. Graphical Models, 2014,76(2):103-114.
|
[23] |
卜育德,潘景昌,陈福强.基于Isomap算法的恒星光谱离群点挖掘 [J]. 光谱学与光谱分析, 2014,34(1):267-273.
|
[24] |
丁 玲,唐 娉,李宏益.基于ISOMAP的高光谱遥感数据的降维与分类 [J]. 红外与激光工程, 2013,42(10):2707-2711.
|
[25] |
Cheng C. Multiscale imaging, modeling, and principal component analysis of gas transport in shale reservoirs [J]. Fuel, 2016, 182:761-770.
|
[26] |
Yang B, Xiang M, Zhang Y P. Multi-manifold discriminant Isomap for visualization and classification [J]. Pattern Recognition, 2016,55:215-230.
|
[27] |
倪 艳. Isomap算法在地震属性参数降维中的应用 [D]. 四川:成都理工大学, 2007.
|
|
|
|