H. Liu et al. : Effluent Quality Prediction of Papermaking WWTPs Using SEL
REFERENCES [1] H. Liu, K.-H. Chang, and C. Yoo, ‘‘Multi-objective optimization of cas- cade controller in combined biological nitrogen and phosphorus removal wastewater treatment plant,’’ Desalination Water Treatment , vol. 43, nos. 1–3, pp. 138–148, Apr. 2012. [2] H. Haimi, F. Corona, M. Mulas, L. Sundell, M. Heinonen, and R. Vahala, ‘‘Shall we use hardware sensor measurements or soft-sensor estimates? Case study in a full-scale WWTP,’’ Environ. Model. Softw. , vol. 72, pp. 215–229, Oct. 2015. [3] P. Kadlec, B. Gabrys, and S. Strandt, ‘‘Data-driven soft sensors in the process industry,’’ Comput. Chem. Eng. , vol. 33, no. 4, pp. 795–814, Apr. 2009. [4] C. Shang, F. Yang, D. Huang, and W. Lyu, ‘‘Data-driven soft sensor devel- opment based on deep learning technique,’’ J. Process Control , vol. 24, no. 3, pp. 223–233, Mar. 2014. [5] H. Liu, C. Yang, B. Carlsson, S. J. Qin, and C. Yoo, ‘‘Dynamic nonlinear partial least squares modeling using Gaussian process regression,’’ Ind. Eng. Chem. Res. , vol. 58, no. 36, pp. 16676–16686, Aug. 2019. [6] X. Yang, Y. Zou, J. Tang, J. Liang, and M. Ijaz, ‘‘Evaluation of short-term freeway speed prediction based on periodic analysis using statistical mod- els and machine learning models,’’ J. Adv. Transp. , vol. 2020, pp. 1–16, Jan. 2020. [7] A. Shokry, P. Vicente, G. Escudero, M. Pérez-Moya, M. Graells, and A. Espuña, ‘‘Data-driven soft-sensors for online monitoring of batch pro- cesses with different initial conditions,’’ Comput. Chem. Eng. , vol. 118, pp. 159–179, Oct. 2018. [8] F. A. A. Souza, R. Araújo, and J. Mendes, ‘‘Review of soft sensor methods for regression applications,’’ Chemometric Intell. Lab. Syst. , vol. 152, pp. 69–79, Mar. 2016. [9] Y. Wang, H. Cao, Y. Zhou, and Y. Zhang, ‘‘Nonlinear partial least squares regressions for spectral quantitative analysis,’’ Chemometric Intell. Lab. Syst. , vol. 148, pp. 32–50, Nov. 2015. [10] H. Liu, C. Yang, M. Huang, and C. Yoo, ‘‘Soft sensor modeling of industrial process data using kernel latent variables-based relevance vector machine,’’ Appl. Soft Comput. , vol. 90, pp. 1–10, May 2020. [11] H. Mingzhi, Y. Ma, W. Jinquan, and W. Yan, ‘‘Simulation of a paper mill wastewater treatment using a fuzzy neural network,’’ Expert Syst. Appl. , vol. 36, no. 3, pp. 5064–5070, Apr. 2009. [12] S. H. Hong, M. W. Lee, D. S. Lee, and J. M. Park, ‘‘Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks,’’ Biochem. Eng. J. , vol. 35, no. 3, pp. 365–370, Aug. 2007. [13] J. Wan, M. Huang, Y. Ma, W. Guo, Y. Wang, H. Zhang, W. Li, and X. Sun, ‘‘Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system,’’ Appl. Soft Comput. , vol. 11, no. 3, pp. 3238–3246, Apr. 2011. [14] Z. Ge and Z. Song, ‘‘Nonlinear soft sensor development based on relevance vector machine,’’ Ind. Eng. Chem. Res. , vol. 49, no. 18, pp. 8685–8693, Sep. 2010. [15] J. Qu and M. J. Zuo, ‘‘An LSSVR-based algorithm for online system condition prognostics,’’ Expert Syst. Appl. , vol. 39, no. 5, pp. 6089–6102, Apr. 2012. [16] Z.-J. Liu, J.-Q. Wan, Y.-W. Ma, and Y. Wang, ‘‘Online prediction of effluent COD in the anaerobic wastewater treatment system based on PCA-LSSVM algorithm,’’ Environ. Sci. Pollut. Res. , vol. 26, no. 13, pp. 12828–12841, May2019. [17] R. Polikar, ‘‘Ensemble based systems in decision making,’’ IEEE Circuits Syst.Mag. , vol. 6, no. 3, pp. 21–45, Mar. 2006. [18] S. Džeroski and B. Ženko, ‘‘Is combining classifiers with stacking better than selecting the best one?’’ Mach. Learn. , vol. 54, no. 3, pp. 255–273, Mar. 2004. [19] L. Breiman, ‘‘Bagging predictors,’’ Mach. Learn. , vol. 24, no. 2, pp. 123–140, Aug. 1996. [20] Y. Freund, R. E. Schapire, and N. Abe, ‘‘A short introduction to boosting,’’ J. Jpn. Soc. Artif. Intell. , vol. 14, no. 5, pp. 771–780, 1999. [21] D. H. Wolpert, ‘‘Stacked generalization,’’ Neural Netw. , vol. 5, no. 2, pp. 241–259, Jan. 1992. [22] T. G. Dietterich, ‘‘An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization,’’ Mach. Learn. , vol. 40, no. 2, pp. 139–157, 2000. [23] L. Breiman, ‘‘Using iterated bagging to debias regressions,’’ Mach. Learn. , vol. 45, no. 3, pp. 261–277, 2001. [24] H. Parvin and H. Alizadeh, ‘‘Classifier ensemble based class weightening,’’ Amer. J. Sci. Res. , vol. 19, pp. 84–90, May 2011.
[25] K. M. Ting and I. H. Witten, ‘‘Issues in stacked generalization,’’ J. Artif. Intell. Res. , vol. 10, pp. 271–289, May 1999. [26] F. Divina, A. Gilson, F. Gómez-Vela, M. G. Torres, and J. Torres, ‘‘Stacking ensemble learning for short-term electricity consumption forecasting,’’ Energies , vol. 11, pp. 949–979, Apr. 2018. [27] M. A. Khairalla, X. Ning, N. T. Al-Jallad, and M. O. El-Faroug, ‘‘Short- term forecasting for energy consumption through stacking heterogeneous ensemble learning model,’’ Energies , vol. 11, no. 6, pp. 1605–1625, Jun. 2018. [28] W. Sun and B. Trevor, ‘‘A stacking ensemble learning framework for annual river ice breakup dates,’’ J. Hydrol. , vol. 561, pp. 636–650, Jun. 2018. [29] H. Liu and C. Yoo, ‘‘A robust localized soft sensor for particulate mat- ter modeling in Seoul metro systems,’’ J. Hazardous Mater. , vol. 305, pp. 209–218, Mar. 2016. [30] I.-G. Chong and C.-H. Jun, ‘‘Performance of some variable selection meth- ods when multicollinearity is present,’’ Chemometric Intell. Lab. Syst. , vol. 78, nos. 1–2, pp. 103–112, Jul. 2005. [31] H. Yasin, R. E. Caraka, and A. Hoyyi, ‘‘Prediction of crude oil prices using support vector regression (SVR) with grid search-cross validation algorithm,’’ Global J. Pure Appl. Math. , vol. 12, no. 4, pp. 3009–3020, Apr. 2016.
HONGBIN LIU received the M.S. degree from the South China University of Technology, Guangzhou, China, in 2009, and the Ph.D. degree from Kyung Hee University, Seoul, South Korea, in 2013. He has been an Associate Professor with Nanjing Forestry University, Nanjing, China, since 2015. He is the author of more than 60 journal articles. His research interests include process modeling, process monitoring, fault detection and diagnosis, and wastewater treatment.
CHENXIN received the B.S. degree from Nanjing Forestry University, Nanjing, China, in 2018, where he is currently pursuing the M.S. degree in process modeling and process monitoring direction.
HAO ZHANG received the B.S. degree from Nanjing Forestry University, Nanjing, China, in 2018, where he is currently pursuing the M.S. degree in process modeling and monitoring for wastewater treatment processes.
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