| Title |
Estimation of a wastewater component using a hybrid artificial neural network in a wastewater treatment process |
| Authors |
최동진(Dong Jin Choi),박희경(Hee Kyung Park) |
| Abstract |
For control and automation of biological wastewater treatment process. lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to be overcome. The accuracy of many hardware sensors is not sufficient and maintenance problems such as electrode fouling often give trouble. This article deals with development of software sensor technique which estimates the target water quality parameter from other parameters using the correlation between water quality parameters. We propose a hybrid neural network that combines conventional statistical methods as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and the hybrid technique that combines principal component analysis (PCA) as a preprocessing stage are applied to data from industrial wastewater process. The proposed hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment process. |