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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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  • [1]
    Baffi, G., Martin, E.B., Morris, A.J., 2000. Non-linear dynamic projection to latent structures modelling. Chemom. Intell. Lab. Syst. 52, 5–22. doi: 10.1016/S0169-7439(00)00083-6
    Carey, R.O., Migliaccio, K.W., 2009. Contribution of wastewater treatment plant effluents to nutrient dynamics in aquatic systems: a review. Environ. Manage. 44, 205–217. doi: 10.1007/s00267-009-9309-5
    Choi, S.W., Lee, I.B., 2005. Multiblock PLS-based localized process diagnosis. J. Process. Control. 15, 295–306. doi: 10.1016/j.jprocont.2004.06.010
    Dong, J., Zhang, K., Huang, Y., Li, G., Peng, K.X., 2015. Adaptive total PLS based quality-relevant process monitoring with application to the Tennessee Eastman process. Neurocomputing154, 77–85. http://dx.doi.org/10.1016/j.neucom.2014.12.017
    Du, Z.M., Jin, X.Q., Wu, L.Z., 2007. Fault detection and diagnosis based on improved PCA with JAA method in VAV systems. Build. Environ. 42, 3221–3232. http://dx.doi.org/10.1016/j.buildenv.2006.08.011
    Ge, Z.Q., Song, Z.H., Gao, F.R., 2013. Review of recent research on data-based process monitoring. Ind. Eng. Chem. Res. 52, 3543–3562. http://dx.doi.org/10.1021/ie302069q
    Geladi, P., Kowalski, B.R., 1986. Partial least-squares regression: a tutorial. Anal. Chimica Acta185, 1–17. http://dx.doi.org/10.1016/0003-2670(86)80028-9
    Harrou, F., Nounou, M.N., Nounou, H.N., Madakyaru, M., 2015. PLS-based EWMA fault detection strategy for process monitoring. J. Loss Prev. Process. Ind. 36, 108–119. http://dx.doi.org/10.1016/j.jlp.2015.05.017
    Jiao, J.F., Yu, H., Wang, G., 2016. A quality-related fault detection approach based on dynamic least squares for process monitoring. IEEE Trans. Ind. Electron. 63, 2625–2632. http://dx.doi.org/10.1109/TIE.2015.2497204 doi: 10.1109/TED.2016.2556749
    Kim, M., Liu, H.B., Kim, J.T., Yoo, C., 2013. Sensor fault identification and reconstruction of indoor air quality (IAQ) data using a multivariate non-Gaussian model in underground building space. Energy Build. . 66, 384–394. http://dx.doi.org/10.1016/j.enbuild.2013.07.002
    Kim, M., Liu, H.B., Kim, J.T., Yoo, C., 2014. Evaluation of passenger health risk assessment of sustainable indoor air quality monitoring in metro systems based on a non-Gaussian dynamic sensor validation method. J. Hazard. Mater. 278, 124–133. doi: 10.1016/j.jhazmat.2014.05.098
    Lee, D.S., Lee, M.W., Woo, S.H., Kim, Y.J., Park, J.M., 2006. Nonlinear dynamic partial least squares modeling of a full-scale biological wastewater treatment plant. Process. Biochem. 41, 2050–2057. http://dx.doi.org/10.1016/j.procbio.2006.05.006
    Lin, B., Recke, B., Knudsen, J.K.H., Jørgensen, S.B., 2007. A systematic approach for soft sensor development. Comput. Chem. Eng. 31, 419–425. http://dx.doi.org/10.1016/j.compchemeng.2006.05.030
    Liu, H.B., Chang, K.H., Yoo, C., 2012. Multi-objective optimization of cascade controller in combined biological nitrogen and phosphorus removal wastewater treatment plant. Desalination Water Treat. . 43, 138–148. http://dx.doi.org/10.1080/19443994.2012.672164
    Liu, H.B., Huang, M.Z., Kim, J.T., Yoo, C., 2013a. Adaptive neuro-fuzzy inference system based faulty sensor monitoring of indoor air quality in a subway station. Korean J. Chem. Eng. 30, 528–539. http://dx.doi.org/10.1007/s11814-012-0197-7
    Liu, H.B., Yang, C., Huang, M.Z., Yoo, C., 2020. Multivariate statistical monitoring of subway indoor air quality using dynamic concurrent partial least squares. Environ. Sci. Pollut. Res. 27, 4159–4169. http://dx.doi.org/10.1007/s11356-019-06935-9
    Liu, Q., Qin, S.J., Chai, T.Y., 2013b. Decentralized fault diagnosis of continuous annealing processes based on multilevel PCA. IEEE Trans. Autom. Sci. Eng. 10, 687–698. http://dx.doi.org/10.1109/TASE.2012.2230628
    MacGregor, J.F., Jaeckle, C., Kiparissides, C., Koutoudi, M., 1994. Process monitoring and diagnosis by multiblock PLS methods. Aiche J. . 40, 826–838. http://dx.doi.org/10.1002/aic.690400509
    Misra, M., Yue, H.H., Qin, S.J., Ling, C., 2002. Multivariate process monitoring and fault diagnosis by multi-scale PCA. Comput. Chem. Eng. 26, 1281–1293. http://dx.doi.org/10.1016/S0098-1354(02)00093-5
    Qin, S.J., 2012. Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control. 36, 220–234. http://dx.doi.org/10.1016/j.arcontrol.2012.09.004
    Qin, S.J., Valle, S., Piovoso, M.J., 2001. On unifying multiblock analysis with application to decentralized process monitoring. J. Chemom. 15, 715–742. http://dx.doi.org/10.1002/cem.667
    Qin, S.J., Zheng, Y.Y., 2013. Quality-relevant and process-relevant fault monitoring with concurrent projection to latent structures. Aiche J. . 59, 496–504. http://dx.doi.org/10.1002/aic.13959
    Tao, E.P., Shen, W.H., Liu, T.L., Chen, X.Q., 2013. Fault diagnosis based on PCA for sensors of laboratorial wastewater treatment process. Chemom. Intell. Lab. Syst. 128, 49–55. http://dx.doi.org/10.1016/j.chemolab.2013.07.012
    Wise, B.M., Gallagher, N.B., 1996. The process chemometrics approach to process monitoring and fault detection. J. Process. Control. 6, 329–348. http://dx.doi.org/10.1016/0959-1524(96)00009-1
    Xin, C., Shi, X.Q., Wang, D.S., Yang, C., Li, Q., Liu, H.B., 2020. Multi-grained cascade forest for effluent quality prediction of papermaking wastewater treatment processes. Water Sci. Technol. 81, 1090–1098. https://doi.org/10.2166/wst.2020.206
    Yin, S., Gao, H., Qiu, J., Kaynak, O., 2017. Fault detection for nonlinear process with deterministic disturbances: a just-in-time learning based data driven method. IEEE Trans. Cybern. . 47, 3649–3657 doi: 10.1109/TCYB.2016.2574754
    Zhu, Q.Q., Liu, Q., Qin, S.J., 2017. Concurrent quality and process monitoring with canonical correlation analysis. J. Process. Control.60, 95–103. http://dx.doi.org/10.1016/j.jprocont.2017.06.017
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