Ann and regression modeling for dissolved solids (DS) removal from industrial effluents

Pollution Research

Volumn 28, Issue 2, 2009, Pages 165-170

  Jain, R K ^a   Pendharkar, Umesh ^a   Srivastava, J K ^a   Dave, R K ^a  

^a UJJAIN ENGINEERING COLLEGE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DISSOLVED MATTER; INDUSTRIAL WASTE; PERFORMANCE ASSESSMENT; POLLUTANT REMOVAL; REGRESSION ANALYSIS; WASTE TREATMENT; WASTEWATER;

EID: 76049126912     PISSN: 02578050     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (21)

1. Ackley, Bradshaw, Recknagel, Golikov, 1985. Neural network for Signal Processing Application, Computer Science and Informatics, 27(3): 34-58.
2. Bhat N. and McAvoy T.J. 1990. Use of neural nets for dynamic modeling and control of chemical process systems. Comput Chem. Engineering, 14(4/5): 573.
3. Easton, K, John, 1967. Electrolytic decomposition of concentrated cyanide plating waste. Environmental Technology, 73: 250-260.
4. Gurusamy Annadurai, Jiunn-FwuLee 2007. Application of artifical neural network model for the development of optimized complex medium for phenol degradation using pseudomonas pictorum (NICM 2074). Journal of Biodegradation 18: 383-392.
5. Hoskins J.C and Himmelblau D.M. 1998. Artificial neural networks models of knowledge representation in chemical engineering. Comput Chem. Engineering 12: 881.
6. Joseph. B, Wang F.H., Shiekh S.S. 1992. Exploratory Date Analysis: A Comparison of statistical methods with artificial neural networks. Computers Chem. Engng Vol 16(4): 413-423.
7. Khandelwal, K.K. Barduhn, A.J. and Grove, C.S. 1959. Kinetics of ozonation of cyanides. Ozone chemistry and technology; American Chemical Society. Advances in Chemistry series. Wasshington. 78: 21.
8. Lee, D.S., Joen, C.O. Park, J.M., change, K.S. 2002. Hybrid Neural Network modelling of a full scale industrial waste water treatment process. Bioeng, 78: 670-682.
9. Nayef Al-Mutarifi, Nabil Kartam, Parviz Koushki, and Mubarek Al-Mutairi, 2004. Modeling and Predicting Biological Performance of contact stalization process using Artificial Neural Networks. Journal of Computing in Civil Engineering (c) ASCE Vol 18(4): 341-349.
10. Rahim, Chu, and Bose, 1993. Speech Recognition, www.speechozone.com/recognition.html
11. Rice, and Netzer, 1982. Application of ozonation in different field. Journal of Hazardous Materials (Elsevier), B129: 253-259.
12. Roth, J.K. Ozone used in aqueous solutions", www.ozoneaqu.com/sol. html
13. Sameer Al - Asheh, Farouq Sabri Mjalli and Hussan E.Alfadala, 2007. Forecasting influent - Effluent Wastewater Treatment Plant Using Time Series Analysis and Artificial Neural Network Techniques. Chemical Product and Process Modeling 2 issue 3.
14. Seppalaa, C.T., Harrisb, T.J., Baconb, D.W. 2002. Time series methods for dynamic analysis of multiple controlled variables. Journal of Process control 12: 25-276.
15. Smith, D.W., D.W. Stanley, S. J. Baxter, C.W., 2004. A comparison of artificial neural networks and multiple regression methods for the analysis of pilot scale data. Journal of Environmental Engg. & Sci. 3, no. supp 1, S45-S58
16. Sondak, N.E. and Dodge, B.F. 1961. The oxidation of cyanide bearing plating wastes by ozone. Plating. 48: 173-180, 280-284.
17. Sameer Al-Ashet et al 2007. Forecasting influent-Effluent Wastewater Treatment Plant using time series analysis and artificial neural network techniques, chemical product and process modelling 2 (3): 2007, Art3.
18. SNNS User Manual, Version 4.2 and JNNS user manual version 1.1.
19. Tyler, R.G, Maske, W., Westin, M.J. and Mathews, W. 1951. "Ozonation of Cyanide Wastes" Sewage industry wastes. 23: 1150-1153.
20. Tyler, Walker, and Zabban, 1953. Material balance relating to ozone consumption for oxidation of cyanide" waste research 20, pp 128-150.
21. Upadhyay Kanjan 2002. Oxidation of toxic waste water from electroplating industry by ozonation process. Ph. D. thesis, UEC Ujjain.