Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

International Journal of Automation and Computing

Volumn 9, Issue 6, 2012, Pages 627-633

  Zhao, Li Jie ^a,b   Chai, Tian You ^b   Yuan, De Cheng ^a  

^a SHENYANG UNIVERSITY OF CHEMICAL TECHNOLOGY   (China)

^b STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

Author keywords

effluent quality prediction; extreme learning machine; genetic algorithm; selective ensemble model; Wastewater treatment process

Indexed keywords

COMPONENT MODEL; COMPUTATION COMPLEXITY; EFFLUENT QUALITY; EXTREME LEARNING MACHINE; GENERALIZATION PERFORMANCE; INDUSTRIAL WASTEWATER TREATMENT PLANTS; OPERATING EFFICIENCY; PARTIAL LEAST SQUARE (PLS); RELIABLE MEASUREMENT; SELECTIVE ENSEMBLES; WASTEWATER TREATMENT PLANTS; WASTEWATER TREATMENT PROCESS;

COMPUTER SIMULATION; ENERGY UTILIZATION; GENETIC ALGORITHMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; WASTEWATER TREATMENT; WATER QUALITY; WATER TREATMENT PLANTS;

KNOWLEDGE ACQUISITION;

EID: 84871280437     PISSN: 14768186     EISSN: 17518520     Source Type: Journal    
DOI: 10.1007/s11633-012-0688-3     Document Type: Article

References (20)

1. D. S. Lee, C. O. Jeon, J. M. Park, K. S. Chang. Hybrid neural network modeling of a full-scale industrial wastewater treatment process. Biotechnology and Bioengineering, vol. 78, no. 6, pp. 670-682, 2002.
2. E. Belia, Y. Amerlinck, L. Benedetti, B. Johnson, G. Sin, P. A. Vanrolleghem, K. V. Gernaey, S. Gillot, M. B. Neumann, L. Rieger, A. Shaw, K. Villez. Wastewater treatment modelling: Dealing with uncertainties. Water Science & Technology, vol. 60, no. 8, pp. 1929-1941, 2009.
3. L. J. Zhao, T. Y. Chai. Wastewater BOD forecasting model for optimal operation using robust time-delay neural network. In Proceedings of the Second International Conference on Advances in Neural Networks, Springer, Berlin, Heidelberg, vol. 3498, pp. 1028-1033, 2005.
4. D. S. Lee, P. A. Vanrolleghem. Monitoring of a sequencing batch reactor using adaptive multiblock principal component analysis. Biotechnology and Bioengineering, vol. 82, no. 4, pp. 489-497, 2003.
5. J. Shi, X. G. Liu. Product quality prediction by a neural soft-sensor based on MSA and PCA. International Journal of Automation and Computing, vol. 3, no. 1, pp. 17-22, 2006.
6. L. Meng, Q. H. Wu. Fast training of support vector machines using error-center-based optimization. International Journal of Automation and Computing, vol. 2, no. 1, pp. 6-12, 2005.
7. L. K. Hansen, P. Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-1001, 1990.
8. L. Breiman. Bagging predictors. Machine Learning, vol. 24, no. 2, pp. 123-140, 1996.
9. R. E. Schapire. The strength of weak learnability. Machine Learning, vol. 5, no. 2, pp. 197-227, 1990.
10. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, G. E. Hinton. Adaptive mixtures of local experts. Neural Computation, vol. 3, no. 1, pp. 79-87, 1991.
11. P. Kadlec. On Robust and Adaptive Soft Sensors, Ph. D. dissertation, Bournemouth University, Poole, 2009.
12. P. Kadlec, R. Grbic, B. Gabrys. Review of adaptation mechanisms for data-driven soft sensors. Computers and Chemical Engineering, vol. 35, no. 1, pp. 1-24, 2011.
13. R. L. Kodell, B. A. Pearce, S. Baek, H. Moon, H. Ahn, J. F. Young, J. J. Chen. A model-free ensemble method for class prediction with application to biomedical decision making. Artificial Intelligence in Medicine, vol. 46, no. 3, pp. 267-276, 2009.
14. Z. H. Zhou, J. X. Wu, W. Tang. Ensembling neural networks: Many could be better than all. Artificial Intelligence, vol. 137, no. 1-2, pp. 239-263, 2002.
15. G. B. Huang, Q. Y. Zhu, C. K. Siew. Extreme learning machine: Theory and applications. Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
16. G. B. Huang, H. M. Zhou, X. J. Ding, R. Zhang. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, vol. 42, no. 2, pp. 513-529, 2011.
17. Y. Lan, Y. C. Soh, G. B. Huang. Ensemble of online sequential extreme learning machine. Neurocomputing, vol. 72, no. 13-15, pp. 3391-3395, 2009.
18. L. J. Zhao, D. C. Yuan, T. Y. Chai, J. Tang. KPCA and ELM ensemble modeling of wastewater effluent quality indices. Procedia Engineering, vol. 15, pp. 5558-5562, 2011.
19. L. J. Zhao, D. C. Yuan, J. Tang, W. Wang, T. Y. Chai. Nonlinear robust PLS modeling of wastewater effluent quality indices. Journal of Software, vol. 6, no. 6, pp. 1067-1074, 2011.
20. C. R. Houck, J. A. Joines, M. G. Kay. A genetic Algorithm for Function Optimization: A Matlab Implementation, Technical Report NCSU-IE-TR-95-09, North Carolina State University, Raleigh, NC, USA, 1995.