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.
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This work was supported by National Natural Science Foundation of China (Nos. 61203102 and 60874057) and Postdoctoral Science Foundation of China (No. 20100471464).
Li-Jie Zhao received bachelor degree and M. Sc. degree from Shenyang Institute of Chemical Technology, China in 1996 and 1999, respectively. She received her Ph.D. degree in control theory and control engineering from Northeastern University, China in 2006. Now, she is a post-doctor in Northeastern University and an associate professor in Shenyang University of Chemical Technology, China. She has published about 40 refereed journal and conference papers.
Her research interests include modeling, fault diagnosis and optimization of complex industrial processes.
Tian-You Chai received his Ph.D. degree in control theory and engineering from Northeastern University, China in 1985, and became a professor in 1988 in the same university. He is the founder and director of the Automation Research Center, which became a National Engineering and Technology Research Center in 1997. He served as a member of Technical Board and Chairman of International Federation of Automatic Control (IFAC) Coordinating Committee on Manufacturing and Instrumentation during 1996 to 1999. Since 2010, he has served as head of Department of Information Sciences of National Natural Science Foundation of China (NSFC). He is a member of Chinese Academy of Engineering, IEEE fellow, IFAC fellow as well as Academician of International Eurasian Academy of Sciences. He has published two monographs and 84 peer reviewed international journal papers and around 219 international conference papers. He has also been invited to deliver more than 20 plenary speeches in international conferences of IFAC and IEEE. For his contributions, he has won numerous awards including three National Science and Technology Progress Awards, the Technological Science Progress Award from Ho Leung Ho Lee Foundation in 2002, the Science and Technology Honor Prize of Liaoning Province in 2003, and honor of “National Advanced Worker” in 2005, respectively. He received the 2007 Industry Award for Excellence in Transitional Control Research from IEEE Control Systems Society. In addition, he won 2010 Yang Jia-Chi Science and Technology Award from Chinese Association of Automation.
His research interests include adaptive control, intelligent decoupling control, and integrated automation of complex industrial processes.
De-Cheng Yuan received his B.Sc. and M.Sc. degrees in chemical engineering from Beijing Institute of Technology, China in 1982 and 1988, respectively, and the Ph.D. degree in mechatronics from Shenyang Institute of Automation, Chinese Academy of Science, China in 2004. Currently, he is a professor in Department of Information Engineering at Shenyang University of Chemical Technology and Ph.D. supervisor of Northeastern University, China. He has published about 60 refereed journal and conference papers.
His research interests include chemical system engineering, modeling, monitoring and optimization of industrial process.
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Zhao, LJ., Chai, TY. & Yuan, DC. Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants. Int. J. Autom. Comput. 9, 627–633 (2012). https://doi.org/10.1007/s11633-012-0688-3
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DOI: https://doi.org/10.1007/s11633-012-0688-3