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Dynamic Process Monitoring of Wastewater Treatment Systems
  
DOI：10.11980/j.issn.0254-508X.2019.02.009
Key Words:wastewater treatment processes  fault detection  dynamic process  dynamic principal component analysis  dynamic independent component analysis
Fund Project:南京林业大学大学生创新训练计划项目（2017NFUSPITP353）；制浆造纸工程国家重点实验室开放基金资助项目（201813）；南京林业大学高层次人才科研启动基金（163105996）。
Author NameAffiliation
LIU Hongbin1，2，* 1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources， Nanjing Forestry University， Nanjing， Jiangsu Province， 210037； 2. State Key Lab of Pulp and Paper Engineering， South China University of Technology， Guangzhou, Guangdong Province， 510640 
CHEN Qin1 1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources， Nanjing Forestry University， Nanjing， Jiangsu Province， 210037 
ZHANG Hao1 1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources， Nanjing Forestry University， Nanjing， Jiangsu Province， 210037 
YANG Chong1 1. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources， Nanjing Forestry University， Nanjing， Jiangsu Province， 210037 
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Abstract:Fault detection is an important part in wastewater treatment processes. Independent component analysis(ICA）， as a method of multivariate statistical analysis， decomposed mixed information into linear combination of independent components， which can effectively extract the main information features of the process. Compared with principal components analysis(PCA）， ICA can extract more information from original date. In view of the dynamic characteristics of continuous production， dynamic independent component analysis(DICA） was proposed to improve the process monitoring ability of ICA. The results showed that the fault detection rates of DICA were optimized by 7.15%， 18.58%， and 12.86% for bias， drifting and complete faults in the wastewater data， respectively. The fault detection rates of DICA for the three kinds of faults were as high as 88.57%， 84.29%， and 82.86%， respectively. It indicates that DICA analysis method could significantly improve the process monitoring．
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