doi: 10.4304/jsw.6.6.1067-1074
Nonlinear Robust PLS Modeling of Wastewater Effluent Quality Indices
- 1 Shenyang University of chemical technology / Provincial key laboratory of control technology of chemical industry process, Shenyang, P.R.China
- 2 Northeastern University / Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Shenyang, P.R.China
Abstract
To solve the strong nonlinearity and data deterioration due to missing, outliers contained in the training data, this paper combines robust EMPCA (Expectation Maximization Principle Component Analysis) and the error-based input weights updating NNPLS (Neural Network Partial Least Square) to build a nonlinear and robust model as a software sensor of effluent quality indices for the anoxic-aeration activated sludge with nitrogen removal process in wastewater treatment pants. As the first step, data preprocessing based on the modified robust EMPCA is used to eliminate gross error, estimate missing data. Then an error-based input weights updating NNPLS (EB-NNPLS) is further used to predict effluent quality indices. This study compares the performance of partial least squares (PLS) regression analysis, polynomial PLS, NNPLS and EB-NNPLS with robust nonlinearity for the prediction of effluent quality. Simulations results for industrial process data show that the proposed method outperforms basic PLS, the polynomial PLS and NNPLS for the prediction of effluent quality indices.
Keywords
- Principal
- Component
- Analysis;
- Partial
- Le
How to Cite
Lijie Zhao, Decheng Yuan, Jian Tang, Wei Wang, and Tianyou Chai, "Nonlinear Robust PLS Modeling of Wastewater Effluent Quality Indices," Journal of Software, vol. 6, no. 6, pp. 1067-1074, 2011. https://doi.org/ 10.4304/jsw.6.6.1067-1074
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).