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Jie Yang, Yuchen Zhang, Lei Zhou, Fengshan Zhang, Yi Jing, Mingzhi Huang, Hongbin Liu. Quality-related monitoring of papermaking wastewater treatment processes using dynamic multiblock partial least squares[J]. Journal of Bioresources and Bioproducts. doi: 10.1016/j.jobab.2021.04.003
Citation: Jie Yang, Yuchen Zhang, Lei Zhou, Fengshan Zhang, Yi Jing, Mingzhi Huang, Hongbin Liu. Quality-related monitoring of papermaking wastewater treatment processes using dynamic multiblock partial least squares[J]. Journal of Bioresources and Bioproducts. doi: 10.1016/j.jobab.2021.04.003

Quality-related monitoring of papermaking wastewater treatment processes using dynamic multiblock partial least squares

doi: 10.1016/j.jobab.2021.04.003
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  • Corresponding author: Corresponding author. Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province, Shandong Huatai Paper Co. Ltd., Dongying 257335, China
    Email address: hongbinliu@njfu.edu.cn (Hongbin Liu)
  • Received Date: 2020-11-01
  • Accepted Date: 2021-01-24
  • Rev Recd Date: 2021-01-16
  • Environmental problems have attracted much attention in recent years, especially for papermaking wastewater discharge. To reduce the loss of effluence discharge violation, quality-related multivariate statistical methods have been successfully applied to achieve a robust wastewater treatment system. In this work, a new dynamic multiblock partial least squares (DMBPLS) is proposed to extract the time-varying information in a large-scale papermaking wastewater treatment process. By introducing augmented matrices to input and output data, the proposed method not only handles the dynamic characteristic of data and reduces the time delay of fault detection, but enhances the interpretability of model. In addition, the DMBPLS provides a capability of fault location, which has certain guiding significance for fault recovery. In comparison with other models, the DMBPLS has a superior fault detection result. Specifically, the maximum fault detection rate of the DMBPLS is improved by 35.93% and 12.5% for bias and drifting faults, respectively, in comparison with partial least squares (PLS).


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