[1]胡小兵,张琳,汪坤,等.基于絮体结构指标的丝状菌膨胀污泥SVI预测[J].中国给水排水,2022,38(1):16-22.
　HU Xiao-bing,ZHANG-Lin,WANG-Kun,et al.Sludge Volume Index Prediction of Filamentous Bulking Sludge Based on Floc Structure Index[J].China Water & Wastewater,2022,38(1):16-22.
点击复制

基于絮体结构指标的丝状菌膨胀污泥SVI预测

中国给水排水[ISSN:1000-4062/CN:12-1073/TU] 卷: 第38卷 期数: 2022年第1期 页码: 16-22 栏目: 出版日期: 2022-01-01
Title:
Sludge Volume Index Prediction of Filamentous Bulking Sludge Based on Floc Structure Index
作者:

胡小兵1,2， 张琳1， 汪坤1， 韦京云1， 林睿1， 张诗华1,2，
钟梅英1， 谢俊霞1

（1.安徽工业大学 建筑工程学院，安徽 马鞍山 243032；2.生物膜法水质净化及利用技术教育部工程研究中心，安徽 马鞍山 243032）
Author(s):
HU Xiao-bing1，2， ZHANG-Lin1， WANG-Kun1， WEI Jing-yun1， LIN Rui1， ZHANG　Shi-hua1，2， ZHONG Mei-ying1， XIE Jun-xia1
（1. College of Architectural Engineering， Anhui University of Technology， Ma’anshan 243032， China； 2. Engineering Research Center of Biofilm Water Purification and Utilization Technology ， Ma’anshan 243032， China）
关键词:
丝状菌膨胀污泥;  絮体结构指标;  污泥容积指数;  主成分分析法;  预测函数
Keywords:
filamentous bulking sludge;  indicator of floc structure;  sludge volume index (SVI);  principal component analysis;  predictive function
摘要:
为了建立更加准确的膨胀活性污泥沉降性表征方法，筛选出5个反映污泥微观絮体结构的特征指标：絮体大小（SZ）、伸长性（ST）、密实性（CP）、规则性（RG）和丝状菌（FL），分析众多特征参数降维的可能性，建立丝状菌膨胀污泥絮体结构特征指标体系，以偏最小二乘回归法（PLS）探索膨胀污泥絮体结构特征指标与污泥容积指数（SVI）的关系，建立丝状菌膨胀污泥的SVI预测函数。结果表明，污泥絮体特征参数之间具有较好的相关性，可采用主成分分析法（PCA）进行降维。污泥絮体结构特征指标与丝状菌膨胀活性污泥的SVI之间有较强的线性相关性（R2=0.779），据此建立了SVI预测模型，丝状菌膨胀污泥的SVI实测值与预测值呈现较强的线性相关性（R2=0.801），模型能较好地预测丝状菌污泥膨胀。
Abstract:
Five comprehensive indicators, namely floc size (SZ), stretchability (ST), compactness (CP), regularity (RG) and filamentous bacteria (FL), reflecting the microscopic floc structure characteristics of activated sludge were selected to establish a more accurate method for characterizing the settleability of bulking activated sludge. The possibility of dimensionality reduction of many characteristic parameters was analyzed, and the structural characteristic index system of filamentous bulking sludge floc was established. The relationship between the structural characteristics of bulking sludge floc and sludge volume index (SVI) was explored by using partial least squares regression (PLS) method, so as to establish a SVI prediction function of bulking sludge. There were good correlations among the characteristic parameters of sludge floc, and principal component analysis (PCA) method could be employed to reduce the dimensionality. There was a strong linear correlation (R2=0.779) between the structural characteristics of sludge floc and SVI of filamentous bulking activated sludge, and a SVI prediction model was established based on the linear correlations. There was a strong linear correlation between the measured and the predicted SVI value of filamentous bulking sludge (R2=0.801), indicating that the model could predict filamentous bulking sludge well.
更新日期/Last Update: 2022-01-01