Mathematical Model and Advance Control for Activated Sludge Process in Sequencing Batch Reactor

  • Ahmmed Ibrehem Department of Chemical Engineering, University of Malaysia, Kuala Lumpur, MALAYSIA
  • Mohammed Azlan Hussain Department of Chemical Engineering, University of Malaysia, Kuala Lumpur, MALAYSIA
Keywords: Sequencing batch reactor, Mathematical Model, Dynamic studies, control system

Abstract

This paper presents the results of a modeling and simulation study of an activated sludge process in a sequencing batch reactor (SBR), with emphasis on total nitrogen removal. This study focuses on the effect of dissolved oxygen (DO) and effluent chemical oxygen demand (COD). Neural-network based redictive controller (MPC) is implemented to control the system for the DO set point and give better and acceptable results when compared with the conventional PID controller

References

1. Ahmmed S Ibrehem, Mohamed Azlan Hussain, Nayef Mohamed Ghasem, (2008). Mathematical Model and Advanced Control for Gas Phase Olefin Polymerization in Fluidized-Bed Catalytic Reactors, Chinese Journal of Chemical Engineering, 16(1), page 84 89.
2. Capodaglio, P.S. Mikkelsen Ráduly, B., K.V. Gernaey, A.G. (1991). Artificial Neural Networks for Rapid WWTP Performance Evaluation: Methodology and Case Study. Environmental Modelling & Software 22(8), 1208-1216, 2007.
3. Côté, P., Buisson, H., and Praderie, M. (1995). Immersed Membranes Activated Sludge Process Applied to The Treatment of Municipal Wastewater. Water Sci. Technol., 38:4-5, 437 442.
4. Cohen, A., Janssen, G., Brewster, S. D., Seeley, R., Boogert, A. A., Graham, A. A., Mardani, M. R., Clarke, N., Kasabov, N. K. (1997). Application of Computational Intelligence for On-Line Control of A Sequencing Batch Reactor (SBR) at Morrinsville Sewage Treatment Plant. Wat. Sci. Tech., 35, 63.
5. Carlsson, D. (1992). Adaptive Economic Optimization of Thinnings and Rotation Period in A Mixed- Species Stand. Working control.,15, 157-169.
6. D. Orhon, (1986). Activated Sludge Separation Problems Environment International, 20 , 75-79.
7. Dold and Marais, (1986). Activated Sludge Process, Journal of Prog, wat.tech.12:47-77.
8. Fu C.S., Poch M. (1995). System Identification and Real-Time Pattern Recognition by Neural Networks for An Activated Sludge Process, Environment International, 21 , 57-59.
9. Henze M., Grady C.P.L, Gujer W., Marais G.V.R., and Matsuno T., (1987). Activated Sludge Model No.1 (ASM 1), IAWPRC Scientific and Technical Report No. 1 London: IAWPRC.
10. Hussain M.A.,(1999). Review of the Applications of Neural Network in Chemical Process Control- Simulation and Online Implementation, Artificial Intelligence in Engineering, 13, 55-68.
11. Holmberg U., Olsson G. and Andersson B. (1989). Simultaneous DO Control and Respiration Estimation. Wat. Sci. Tech., 21, 1185-1195.
12. Lindberg and Carlsson (1996). Estimation of The Respiration Rate and Oxygen Transfer Function Utilizing A Slow DO Sensor. Water, Science and Technology, vol 33, No 1, pp 325-333.
13. Haykin, S., (1999). Neural Networks: A Comprehensive Foundation. Mcmillan College Publishing Company, New York.
14. 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.
15. Lindberg C.F. (1997). Control and Estimation Strategies Applied to The Activated Sludge Process. PhD Thesis,Uppsala University, Dept. of Material Science, Systems and Control Group, 214pp. Uppsala. ISBN 91-506-1202-6.
16. Isaac JT, Nicoll RA, Malenka RC (1995). Evidence for Silent Synapses: Implications for The Expression of LTP. Neuron 15:427-434.
17. Lau PS, Tam NFY, Wong YS(1995). Effect of Algal Density on Nutrient Removal from Primary Settled Wastewater. Environ. Pollut., 89: 59-66.
18. Olsson, G., and Newell . (1999). Wastewater Treatment Systems: Modelling, Diagnosis and Control. IWA Publishing, London.
19. Olsson G, Newell B. (1999). Wastewater Treatment System: Modelling, Diagnosis and Control. Water, Science and Technology, 37, 9, 24-31.
20. Yoo and Lee (2003). Self-Adaptive Neuro-Fuzzy Systems with Fast Parameter Learning for Autonomous Underwater Vehicle Control, International Journal of Advanced Robotics, Vol. 15, No. 5, pp. 589- 608.
21. Yoo, H.Y., Kim, S.S., and Rho, H.M. (1999). Overexpression and Simple Purification of Human Superoxide Dismutase (SOD1) in Yeast and its Resistance to Oxidative Stress. J. Biotechnol. 68, 29-35.
22. Zhao, J., & Kanellakopoulos, I. (1994). Adaptive Control of Discretetime Strict-Feedback Nonlinear Systems. Proceedings of 1994 American Control Conference, New Mexico. 828-832.
23. Zhang and Stephen (1999). Wastewater Treatment Systems: Modelling, Diagnosis and Control. IWA Publishing, American Journal.
Published
2009-12-31
How to Cite
Ibrehem, A., & Hussain, M. A. (2009). Mathematical Model and Advance Control for Activated Sludge Process in Sequencing Batch Reactor. ASEAN Journal of Chemical Engineering, 9(1), 32-46. Retrieved from https://journal.ugm.ac.id/v3/AJChE/article/view/7709
Issue
Vol 9 No 1 (2009)
Section
Articles

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