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| Fault Detection of Papermaking Wastewater Treatment Process Based on Independent Component Analysis |
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| DOI：10.11981/j.issn.1000-6842.2019.01.66 |
| Key Words:papermaking wastewater treatment process; fault detection; principal component analysis; independent component analysis |
| Fund Project:制浆造纸工程国家重点实验室开放基金资助项目（201813， 201610）；南京林业大学高层次人才科研启动基金（163105996）。 |
| Author Name | Affiliation | | YANG Chong1 | 1.Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | | SONG Liu1 | 1.Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037 | | 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 Laboratory of Pulp and Paper Engineering,South China University of Technology, Guangzhou, Guangdong Province, 510640 |
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| Abstract: |
| To monitor and control papermaking wastewater treatment process(WWTP) effectively, two common methods of multivariate statistical analysis named independent component analysis (ICA) and principal component analysis (PCA) were used to detect the sensor faults in a papermaking WWTP.The results showed that the detection rates of the bias and drifting faults using ICA were 24% and 54%, respectively.Meanwhile, the bias and drifting faults detection rates of PCA were 14% and 42%.The fault detection rates of ICA were higher than those of PCA, but neither of the two methods achieved satisfactory result of detecting the bias and drifting faults.Concerning the complete failure fault, both the fault detection rates of the two methods were 100%. |
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