A Fast Predicating of Nutrient Removal Efficiency in Five Steps Sequencing Batch Reactor System Using Fuzzy Logic Control Model
Saad Abualhail, Rusul Naseer, Ammar Ashor, Xi-Wu Lu
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DOI: 10.4236/eng.2010.210105   PDF    HTML     4,645 Downloads   8,650 Views  

Abstract

Removal efficiency of COD, NH4-N and PO4-P and NO3-N in five step SBR processes is widely influenced by hydraulic retention time of Anaerobic/Anoxic/Aerobic/Anoxic/Aerobic step of this system where the hydraulic retention time in each step is influence directly on removal efficiency of this system therefore the operator of this system cannot control on this system without experience or a control model. The major objective of this paper is develop a control model (Fuzzy Logic Control Model) based on fuzzy logic rule to predict the maximum removal efficiency of COD,NH4-N,PO4-P and NO3-N and minimize hydraulic retention time in each step of SBR process where the controlled variables was the hydraulic retention times in the Anaerobic/Anoxic/Aerobic/Anoxic/Aerobic step respectively and the output variables was the COD, NH4-N, PO4-P and NO3-N removal efficiency at constant ratio of C/N/P and sludge age. As a results Fuzzy logic if-then rules were used and MIMO Model was built to control COD, NH4-Nand PO4-P and NO3-N removal efficiency based on hydraulic retention time in each tank of five step SBR process where the three dimension results show that the influence of hydraulic residence time at each step of SBR system on removal efficiency COD, NH4-N, PO4-P and NO3-N. Fuzzy control model provide a suitable tool for control and fast predict of Hydraulic residence time effects on biological nutrient removal efficiency in five-step sequencing batch reactor.

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S. Abualhail, R. Naseer, A. Ashor and X. Lu, "A Fast Predicating of Nutrient Removal Efficiency in Five Steps Sequencing Batch Reactor System Using Fuzzy Logic Control Model," Engineering, Vol. 2 No. 10, 2010, pp. 820-831. doi: 10.4236/eng.2010.210105.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] A. Uygur and F. Kargi, “Hydraulic Residence Time Effects in Biological Nutrient Removal Using Five-Step Sequencing Batch Reactor,” Enzyme and Microbial Technology, Vol. 35, No. 2-3, 2004, pp. 167-172.
[2] S. Fujii, “Theoretical Analysis on Nitrogen Removal of the Step-Feed Anoxic-Oxic Activated Sludge Process and Its Application for the Optimal Operation,” Water Science and Technology, Vol. 34, No. 1-2, 1996, pp. 459 -466.
[3] L. Larrea, A. Larrea, E. Ayesa, J. Rodrigo, M. Lo-pez- Carrasco and J. Cortacans, “Development and Verification of Design and Operation Criteria for the Step Feed Process with Nitrogen Removal,” Water Science and Technology, Vol. 43, No. 1, 2001, pp. 261-268.
[4] G. Zhu and Y. Peng, “Theoretical Evaluation on Nitrogen Removal of Step-Feed Anoxic/Oxic Activated Sludge Process,” Journal of Harbin Institute of Technology, Vol. 13, No. 3, 2006, pp. 99-102.
[5] J. Fillos, V. Diyamandoglu, L. Carrio and L. Robinson, “Full-Scale Evaluation of Biological Nitrogen Removal in the Step-Feed Activated Sludge Process,” Water Environment Research, Vol. 68, No. 2, 1996, pp. 132-142.
[6] S. Schlegel, “Operational Results of wastewater Treatment Plants with Biological N and P Elimination,” Water Science and Technology, Vol. 25, No. 4-5, 1992, pp. 241 -247.
[7] E. Gorgun, N. Artan, D. Orhon and S. Sozen, “Evaluation of Nitrogen Removal by Step Feeding in Large Treatment Plants,” Water Science and Technology, Vol. 34, No. 1-2, 1996, pp. 253-260.
[8] S. Wang, L. Yu, G. Man, H. Zhu, D. Peng and X. Wang, “A Pilot Study on a Step-Feeding Anoxic/Oxic Activated Sludge System,” Water Science and Technology, Vol. 53, No. 9, 2006, pp. 95-101.
[9] T. Kalker, C. Van Goor, P. Roeleveld, M. Ruland and R. Babuska, “Fuzzy Control of Aeration in an Activated Sludge Wastewater Treatment Plant: Design, Simulation and Evaluation,” Water Science and Technology, Vol. 39, No. 4, 1999, pp. 71-78.
[10] M. Fiter, D. Guell, J. Comas, J. Colprim, M. Poch and I. Rodriguez-Roda, “Energy Saving in a Wastewater Treatment Process: An Application of Fuzzy Logic Control,” Environmental Technology, Vol. 26, No. 11, 2005, pp. 1263-1270.
[11] J. Ferrer, M. Rodrigo, A. Seco and J. Penya-Roja, “Energy Saving in the Aeration Process by Fuzzy Logic Control,” Water Science and Technology, Vol. 38, No. 3, 1998, pp. 209-217.
[12] Y. Tsai, C. Ouyang, M. Wu and W. Chiang, “Effluent Suspended Solid Control of Activated Sludge Process by Fuzzy Control Approach,” Water Environment Research, Vol. 68, No. 6, 1996, pp. 1045-1053.
[13] M. Yong, P. Yong-Zhen, W. Xiao-Lian, W. Shu-Ying, “Intelligent Control Aeration and External Carbon Addition for Improving Nitrogen Removal,” Environmental Modelling and Software, Vol. 21, 2006, pp. 821-828.
[14] E. Murnleitner, T. Becker and A. Delgado, “State Detection and Control of Overloads in the Anaerobic Wastewater Treatment Using Fuzzy Logic,” Water Research, Vol. 36, No. 1, 2002, pp. 201-211.
[15] Y. Peng, J. Gao, S. Wang and M. Sui, “Use of pH as Fuzzy Control Parameter for Nitrification under Different Alkalinity in SBR Process,” Water Science and Technology, Vol. 47, No. 11, 2003, pp. 77-84.
[16] A. Traoré, S. Grieu, S. Puig, L. Corominas, F. Thiery, M. Polit and J. Colprim, “Fuzzy Control of Dissolved Oxygen in a Sequencing Batch Reactor Pilot Plant,” Chemical Engineering Journal, Vol. 111, No. 1, 2005, pp. 13-19.
[17] U. Meyer and H. J. P?pel, “Fuzzy-Control for Improved Nitrogen Removal and Energy Saving in WWWT-Plants with Pre-Denitrification,” Water Science and Technology, Vol. 47, No. 11, 2003, pp. 69-76.
[18] E. F. Carrasco, J. Rodríguez, A. Pu?al, E. C. Roca and J. M. Lema, “Rule-Based Diagnosis and Supervision of a Pilotscale Wastewater Treatment Plant Using Fuzzy Logic Techniques,” Expert Systems with Applications, Vol. 22, No. 1, 2002, pp. 11-20.
[19] A. Pu?al, J. Rodríguez, E. F. Carrasco, E. Roca and J. M. Lema, “An Expert System for Monitoring and Diagnosis of Anaerobic Wastewater Treatment Plants,” Water Research, Vol. 36, No. 10, 2002, pp. 2656-2666.
[20] J. Flores, B. Arcay and J. Arias, “An Intelli-gent System for Distributed Control of an Anaerobic Wastewater Treatment Process,” Artificial Intelligence, Vol. 13, No. 4, 2000, pp. 485-494.
[21] M. Polit, M. Estaben and P. Labat, “A Fuzzy Model for an Anaerobic Digester, Comparison with Experi-mental Results,” Artificial Intelligence, Vol. 15, No. 5, 2002, pp. 385-390.
[22] L. A. Zadeh, “Fuzzy Logic Computing with Words,” IEEE Transactions—Fuzzy Systems, Vol. 4, No. 2, 1996, pp. 103-111.
[23] B. Olsson and B. Newell, “Wastewater Treatment Systems—Modelling, Diagnosis and Control,” IWA Publishing, London, 1999.
[24] G. G. Patry and D. Chapman, “Dynamic Modeling and Expert Systems in Wastewater Engineering,” Lewis Publishers, Chelsea, 1989.
[25] M. W. Barnett, G. G. Patry and M. Hiraoka, “Knowledge-Based (Expert) Systems for the Activated Sludge Process,” In: J. F. Andrews, Ed., Dynamics and Control of the Activated Sludge Process, Technomic Publishing Company, Lancaster, 1992, pp. 231-243.
[26] J. Irene, C. Julián, L. Javier and A. B. Juan, “Start-Up of a Nitrification System with Automatic Control to Treat Highly Concentrated Ammonium Wastewater: Experi-mental Results and Modeling,” Chemical Engineering Journal, Vol. 144, No. 3, 2008, pp. 407-419.
[27] C. Y. Wu, Z. Q. Chen, X. H. Liu and Y. Z. Peng, “Nitrification-Denitrification via Nitrite in SBR Using Real -Time Control Strategy when Treating Domestic Wastewater,” Biochemical Engineering Journal, Vol. 36, No. 2, 2007, pp. 87-92.
[28] E. Murnleitner, T. M. Becker and A. Delgado, “State Detection and Control of Overloads in the Anaerobic Wastewater Treatment Using Fuzzy Logic,” Water Research, Vol. 36, No. 1, 2002, pp. 201-211.
[29] S. Marsili-Libelli, “Control of SBR Switching by Fuzzy Pattern Recognition,” Water Research, Vol. 40, No. 5, 2006, pp. 1095-1107.
[30] E. H .Mamdani, “Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Systems,” Fuzzy Sets and Systems, Vol. 26, No. 1, 1977, pp. 1182-1191.
[31] L. A. Zadeh, “Outline of a New Approach to the Analysis of Complex Systems and Decision Process,” IEEE Transactions of Systems, Man and Cybernetics, No. 1, 1973, pp. 28-44.
[32] R. R. Yager, “A General Class of Fuzzy Connectives,” Fuzzy Sets and Systems, Vol. 4, No. 3, 1980, pp. 235-242.
[33] C. Betroluzza, N. Corral and A. Salas, “On a New Class of Distances between Fuzzy Numbers,” Mathware and Soft Computing, Vol. 2, No. 2, 1995, pp. 71-84.
[34] L. T. Tran and L. Duckstein, “Comparison of Fuzzy Numbers Using a Fuzzy Distance Measure,” Fuzzy Sets and Systems, Vol. 130, No. 3, 2002, pp. 331-341.

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