Volume 9 Number 6
December 2012
Article Contents
Li-Jie Zhao, Tian-You Chai and De-Cheng Yuan. Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants. International Journal of Automation and Computing, vol. 9, no. 6, pp. 627-633 , 2012. doi: 10.1007/s11633-012-0688-3
Cite as: Li-Jie Zhao, Tian-You Chai and De-Cheng Yuan. Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants. International Journal of Automation and Computing, vol. 9, no. 6, pp. 627-633 , 2012. doi: 10.1007/s11633-012-0688-3

Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants

Author Biography:
  • Tian-You Chai received his Ph. D. de-gree in control theory and engineering from Northeastern University, China in 1985, and became a professor in 1988 in the same university.

  • Corresponding author: Li-Jie Zhao
  • Received: 2012-02-20
Fund Project:

This work was supported by National Natural Science Foundation of China (Nos. 61203102 and 60874057) and Postdoctoral Science Foun-dation of China (No. 20100471464).

通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants

  • Corresponding author: Li-Jie Zhao
Fund Project:

This work was supported by National Natural Science Foundation of China (Nos. 61203102 and 60874057) and Postdoctoral Science Foun-dation of China (No. 20100471464).

Abstract: Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process. Due to the low accuracy and unstable performance of the traditional effluent quality measurements, we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions. Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms. Ensemble extreme learning machine models overcome variations in different trials of simulations for single model. Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance. The proposed method is verified with the data from an industrial wastewater treatment plant, located in Shenyang, China. Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square, neural network partial least square, single extreme learning machine and ensemble extreme learning machine model.

Li-Jie Zhao, Tian-You Chai and De-Cheng Yuan. Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants. International Journal of Automation and Computing, vol. 9, no. 6, pp. 627-633 , 2012. doi: 10.1007/s11633-012-0688-3
Citation: Li-Jie Zhao, Tian-You Chai and De-Cheng Yuan. Selective Ensemble Extreme Learning Machine Modeling of Effluent Quality in Wastewater Treatment Plants. International Journal of Automation and Computing, vol. 9, no. 6, pp. 627-633 , 2012. doi: 10.1007/s11633-012-0688-3
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