Abstract
Many process variables are included in the wastewater treatment process. The realization of real-time detection of as many process variables as possible is of great significance for improve the quality of wastewater treatment. Due to cost constraints, it is difficult to achieve real-time monitoring of some important process variables. Soft measurement scheme, which modeling the relationship between easy-to-measure variables and hard-to-measure variables to achieve the best estimate of the latter is a common solution. This paper proposes a key variables soft measurement scheme for wastewater treatment process based on hierarchical extreme learning machine (HELM). The proposed scheme takes some of the known variables that are easy to measure as inputs, and then implement a best estimate of unknown variables that are difficult to measure. At the same time, the selective moving window strategy (SMW) is used to update the training datasets. Experiments show that the proposed scheme has excellent performance on many key indicators.
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References
Huang, M., Ma, Y., Wan, J., Chen, X.: A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process. Appl. Soft Comput. 27, 1–10 (2015). https://doi.org/10.1016/j.asoc.2014.10.034
Trends in extreme learning machines: a review. Neural Netw. 61, 32–48 (2015). https://doi.org/10.1016/j.neunet.2014.10.001
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541). vol. 2, pp. 985–990, July 2004. https://doi.org/10.1109/IJCNN.2004.1380068
Tang, J., Deng, C., Huang, G.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2016). https://doi.org/10.1109/TNNLS.2015.2424995
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006). https://doi.org/10.1016/j.neucom.2005.12.126. Neural Networks
Marquardt, D.W., Snee, R.: Ridge regression in practice. American Statistician - AMER STATIST 29, 3–20 (1975). https://doi.org/10.1080/00031305.1975.10479105
Huang, G.B.: Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Netw. 14(2), 274–281 (2003). https://doi.org/10.1109/TNN.2003.809401
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006). https://doi.org/10.1126/science.1127647
Xianghua, C., Ouguan, X., Hongbo, Z.: Recursive PLS soft sensor with moving window for online PX concentration estimation in an industrial isomerization unit. In: 2009 Chinese Control and Decision Conference, pp. 5853–5857, June 2009. https://doi.org/10.1109/CCDC.2009.5195246
Xiong, W., Shi, X.: Soft sensor modeling with a selective updating strategy for gaussian process regression based on probabilistic principle component analysis. J. Frankl. Inst. 355(12), 5336–5349 (2018). https://doi.org/10.1016/j.jfranklin.2018.05.017
Kadlec, P., Gabrys, B., Strandt, S.: Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33(4), 795–814 (2009). https://doi.org/10.1016/j.compchemeng.2008.12.012
Zhang, Z., Wang, T., Liu, X.: Melt index prediction by aggregated rbf neural networks trained with chaotic theory. Neurocomputing 131, 368–376 (2014). https://doi.org/10.1016/j.neucom.2013.10.006
Gonzaga, J., Meleiro, L., Kiang, C., Filho, R.M.: Ann-based soft-sensor for real-time process monitoring and control of an industrial polymerization process. Comput. Chem. Eng. 33(1), 43–49 (2009). https://doi.org/10.1016/j.compchemeng.2008.05.019
Yoo, C.K., Lee, I.B.: Soft sensor and adaptive model-based dissolved oxygen control for biological wastewater treatment processes. Environ. Eng. Sci. 21(3), 331–340 (2004). https://doi.org/10.1089/109287504323066978
Zyngier, D., Araujo, O., Lima, E.: Soft sensors with white- and black-box approaches for a wastewater treatment process. Braz. J. Chem. Eng. 17(4–7), 433–440 (2000). https://doi.org/10.1590/S0104-66322000000400008
Kim, Y., Bae, H., Poo, K., Kim, J., Moon, T., Kim, S., Kim, C.: Soft sensor using PNN model and rule base for wastewater treatment plant. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.L., Yin, H. (eds.) Advances in Neural Networks - ISNN 2006, pp. 1261–1269. Springer, Heidelberg (2006)
Kaneko, H., Funatsu, K.: Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants. Chemom. Intell. Lab. Syst. 137, 57–66 (2014). https://doi.org/10.1016/j.chemolab.2014.06.008
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)
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Zhao, F., Liu, M., Jia, B., Jiang, X., Ren, J. (2020). Key Variables Soft Measurement of Wastewater Treatment Process Based on Hierarchical Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_6
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DOI: https://doi.org/10.1007/978-3-030-23307-5_6
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