Prediction of anoxic sulfide biooxidation under various HRTs using artificial neural networks

Biomedical and Environmental Sciences

Volumn 20, Issue 5, 2007, Pages 398-403

  Mahmood, Qaisar ^a   Zheng, Ping ^a   Wu, Dong Lei ^a   Wang, X U Sheng ^a   Yousaf, Hayat ^a   Ul Islam, Ejaz ^a   Hassan, Muhammad Jaffar ^a   Jilani, Ghulam ^b   Azim, Muhammad Rashid ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b PMAS ARID AGRICULTURE UNIVERSITY   (Pakistan)

^c Federal Government Postgraduate College for Men Islamabad   (Pakistan)

Author keywords

Artificial neural networks; ASO reactor; Effluent nitrite prediction; Effluent sulfide prediction; Principal components analysis; Wastewater treatment

Indexed keywords

SULFATE; SULFIDE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOREACTOR; CHEMISTRY; METHODOLOGY; OXIDATION REDUCTION REACTION; SEWAGE; TIME;

BIOREACTORS; NEURAL NETWORKS (COMPUTER); OXIDATION-REDUCTION; SULFATES; SULFIDES; TIME FACTORS; WASTE DISPOSAL, FLUID;

EID: 37049022813     PISSN: 08953988     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. Mahmood Q, Ping Z, Jing C, et al. (2007). Anoxic sulfide biooxidation using nitrite as electron acceptor, J. Hazard. Mater, doi:10.1016/j.jhazmat.2007.01.002
2. Belanche L, Sànchez M, Cortés U, et al. (1992). A knowledge-based system for the diagnosis of waste-water treatment plants. Proc. Of 5th IEA/AIE-92. Lecture Notes in Artificial Intelligence 604, 324-336.
3. Bishop C (1995). Neural Networks for Pattern Recognition. New York: Oxford University Press.
4. Iglesias A, Varela B A, Cotos J M, et al. (2004). A comparison between functional networks and artificial neural networks for the prediction of fishing catches. Neural Computing and Applications 13(1), 24-31.
5. Tutu H, Cukrowska E M, Dohnal V, et al. (2005). Application of artificial neural networks for classification of uranium distribution in the Central Rand goldfield, South Africa, Environmental Modeling & Assessment 10(2), 143-152.
6. Leitão R C, van Haandel A C, Zeeman G et al. (2006). The effects of operational and environmental variations on anaerobic wastewater treatment systems: A review. Bioresource Technology 97(9), 1105-1118.
7. McCulloch W S, Pitts W (1943). A logical calculus of the ideas immanent in nervous activity. Bull math Biophys 5, 115-133.
8. Hrycej T (1997) Neurocontrol Towards an Industrial Control Methodology in Biological Wastewater Treatment Control. pp. 331-362. John Wiley and Sons Inc. USA.
9. Hamoda M F, Al-Ghusain I A, Hassan A H (1999). Integrated wastewater treatment plant performance evaluation using artificial neural networks. Water Sci Tech 40(7), 55.
10. Lee D S, Park J M (1999). Neural network modeling for on-line estimation of nutrient dynamics in a sequentially-operated batch reactor. J Biotech 75, 229.
11. Cote M, Grandijean BPA, Lessard P, Yhibault J (1995). Dynamic modeling of the activated sludge process: improving prediction using neural networks. Wat Res 29(4), 995.
12. Häck M, Köhne M (1996). Estimation of wastewater process parameters using neural networks. Water Sci Technol 33(1), 101.
13. Gontarski C A, Rodrigues P R, Mori M, et al. (2000).
14. Simulation of an industrial wastewater treatment plant using artificial neural networks. Comput Chem Eng 24, 1719.
15. Pu H, Hung Y (1995). Use of artificial neural networks: Predicting trickling filter performance in a municipal wastewater treatment plant. Envir Manag Health 6(2), 16.
16. Wilcox S J, Hawkes D L, Hawkes F R, et al. (1995). A neural network, based on bicarbonate monitoring, to control anaerobic digestion. Wat Res 29(6), 1465.
17. Zhao H, Hao O I, Fellow ASCE, et al. (1997). Modeling nutrient dynamics in sequencing batch reactor. Journal of Environ Eng 123(4), 863.