skip to main content

Optimal Geometric Configuration for Power Consumption in Baffled Surface Aeration Tanks

*Bimlesh Kumar  -  Civil Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, India
Thiyam Tamphasana Devi  -  Civil Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, India
Ajey Kumar Patel  -  Department of Civil Engineering, Indian Institute of Science, Bangalore-56012, India
Ankit Bhatla  -  Civil Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, India

Citation Format:

The power usage in mass transfer operations is very important in judging the aeration performance of the aerator on which geometry of the aeration tank imparts a major effect. Optimal geometric conditions are also needed to scale up the laboratory result to the field installation. Finding geometrical optimal conditions of a surface aeration system through experiments involves physical constraints and classically parameters can be optimized by varying one variable at one time and keeping others at constant. In the real experimental process, it is not possible to vary all others geometric parameters simultaneously. In such a case, the model of the system is built through computer simulation, assuming that the model will result in adequate determination of the optimum conditions for the real system. In this paper, functional model of power consumption in the surface aeration systems has been obtained by using neural network technique. Predictability capability of such functional model has been found satisfactorily. In process of optimization, the pertinent dynamic parameter is divided into a finite number of segments over the entire range of observations. For each segment of the dynamic parameter, the neural network model is optimized for the geometrical parameters spanning over the entire range of observations. ©2010 BCREC UNDIP. All rights reserved

(Received: 29th May 2010, Revised: 12nd August 2010, Accepted: 7th September 2010)

[How to Cite: B. Kumar, T.T. Devi, A.K. Patel, A. Bhatla. (2010). Optimal Geometric Configuration for Power Consumption in Baffled Surface Aeration Tanks. Bulletin of Chemical Reaction Engineering and Catalysis, 5 (2): 87-93. doi:10.9767/bcrec.5.2.795.87-93]

[DOI: || or local:]

Fulltext View|Download
Keywords: Optimization, power number, RBF model, Reynolds number, surface aerators

Article Metrics:

  1. Rao, A.R.K., BharathiLaxmi, B.V., and Narasiah, K.S. 2004. Simulation of Oxygen Transfer Rates in Circular aeration Tanks. Water Qual. Res. J. Canada 39: 237-244
  2. Nagata, S. 1975. Mixing Principles and applications, John-willey & sons
  3. Hwang, H.J. and Stenstrom, M.K. 1985. Evaluation of Fine-Bubble Alpha Factors in Near-Full Scale Equipment. J. Water Pollution Control Federation 57: 1-12
  4. Vasel, J.L. 1988. Contribution á l’étude des transferts d'oxygène en gestion des eaux. PhD Thesis, Fondation Universitaire Luxemourgeoise, Luxembourg, Arlon
  5. Wesner, G.M., Ewing, L.J., Lineck, T.S., and Hinrichs, D.J. 1977. Energy Conservation in Municipal Wastewater Treatment, EPA-430/9-77-01 1, NTIS No. PB81-165391, U .S. EPA Report, Washington, DC
  6. Cook A.L., and Carr, C.C. 1947. Elements of Electrical Engineering. 5th ed. Wiley, New York
  7. Uhl, V.W., and Gray, J.B. 1966. Mixing: Theory and practice. Academic Press
  8. Horvath, I. 1984. Modelling in the technology of wastewater treatment. Pergamon, Tarrytown, N.Y
  9. Rao, A.R.K., and Kumar, B. 2009. Resistance characteristics of Surface Aerator. Journal of Hydraulic Engineering 135: 38-44
  10. Rao, A.R.K. 1999. Prediction of reaeration rates in square, stirred tanks. Journal of Environmental Engineering 125: 215-233
  11. Rao, A.R.K., and Kumar, B. 2007. Scale-up Criteria of Square Surface Aerators. Biotechnology and Bioengineering 96: 464-470
  12. Rao, A.R.K., and Kumar, B. 2008. Scaling-Up the Geometrically Similar Unbaffled Circular Tank Surface Aerator, Chemical Engineering and Technology 31: 287-293
  13. Kleijnen, J.P.C., and Sergeant, R.G. 2000. A Methodology for Fitting and Validating Metamodels in Simulation. European Journal of Operational Research 120: 14-29
  14. Haykin, S. 1994. Neural networks: a comprehensive foundation. Macmillan, New York
  15. Poggio, T., and Girosi, F. 1990. Networks for approximation and learning. Proc. IEEE 78: 1481–1497
  16. Allison, J. 1993. Multiquadric radial basis functions for representing multidimensional high energy physics data. Computer physics communications 77: 377-395
  17. Yuhong, Z., and Wenxin, H. 2008. Application of artificial neural network to predict the friction factor of open channel flow. Communications in Nonlinear Science and Numerical Simulation14: 2373-2378
  18. Nishikawa, M., Ashiwake, K., Hashimoto, N., and Nagata, S. 1979. Agitation power and mixing time in off-centering mixing. Int. Chem. Eng. 19: 153-159
  19. Patil, S.S., Deshmukh, N.A., and Joshi, J.B. 2004. Mass-Transfer Characteristics of Surface Aerators and Gas-Inducing Impellers. Ind. Eng. Chem. Res. 43: 2765-2774
  20. Mishra, V.P., and Joshi, J.B. 1993. Flow generated by a disc turbine: part III. Effect of impeller diameter, impeller location and comparison with other radial flow turbines. Chemical Engineering Research & Design 71: 563-573
  21. Udayasimha, L. 1991. Experimental studies on oxygen transfer by surface aeration. PhD Thesis, IISc, Bangalore

Last update:

No citation recorded.

Last update:

No citation recorded.