Title :
Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks
Author :
Luo, Fei ; Yu, Ren-hui ; Xu, Yu-ge ; Li, Yan
Author_Institution :
Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China
Abstract :
Effluent ammonia-nitrogen (NH3-N), chemical oxygen demand (COD) and total nitrogen (TN) removals are the most common environmental and process performance indicator for all types of wastewater treatment plants (WWTPs). In this paper, a soft computing approach based on the back propagation (BP) neural networks and fuzzy-rough sets (FR-BP) has been applied for forecasting effluent NH3-N, COD and TN concentration of a real WWTP, in which the fuzzy-rough sets theory is employed to perform input selection of neural network which can reduce the influence due to the drawbacks of BP such as low training speed and easily affected by noise and weak interdependency data. The model performance is evaluated with statistical parameters and the simulation results indicates that the FR-BP modeling approach achieves much more accurate predictions as compared with the other traditional modeling approaches.
Keywords :
backpropagation; effluents; fuzzy set theory; neural nets; rough set theory; wastewater treatment; COD concentration; NH3; ammonia-nitrogen; artificial neural network; backpropagation neural networks; chemical oxygen demand; effluent quality prediction; fuzzy rough set; soft computing approach; total nitrogen removal; wastewater treatment plant; Artificial neural networks; Chemical processes; Computer networks; Demand forecasting; Effluents; Neural networks; Nitrogen; Predictive models; Set theory; Wastewater treatment; fuzzy rough sets; input variable selection; neural network; prediction; soft computing; wastewater treatment;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2009. FSKD '09. Sixth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3735-1
DOI :
10.1109/FSKD.2009.494
