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

Jie Yang; Yuchen Zhang; Lei Zhou; Fengshan Zhang; Yi Jing; Mingzhi Huang; Hongbin Liu

doi:10.1016/j.jobab.2021.04.003

Volume 7 Issue 1

Feb. 2022

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Article Navigation > Journal of Bioresources and Bioproducts > 2022 > 7(1): 73-82

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, 2022, 7(1): 73-82. 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, 2022, 7(1): 73-82. 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, 2022, 7(1): 73-82. doi: 10.1016/j.jobab.2021.04.003

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

doi: 10.1016/j.jobab.2021.04.003

a.
Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China
b.
Laboratory for Comprehensive Utilization of Paper Waste of Shandong Province, Shandong Huatai Paper Co. Ltd., Dongying 257335, China
c.
SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, School of Environment, South China Normal University, Guangzhou 510006, China

More Information

Corresponding author: E-mail address: hongbinliu@njfu.edu.cn (H. Liu)
Received Date: 2020-11-01
Accepted Date: 2021-01-24
Rev Recd Date: 2021-01-16

Available Online: 2021-04-07

Publish Date: 2022-02-20

Abstract

Abstract

Environmental problems have attracted much attention in recent years, especially for papermak-ing 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 mod-els, 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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References(27)

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