Nitrate control strategies in an activated sludge wastewater treatment process

Wenhao Shen; Erpan Tao; Xiaoquan Chen; Dawei Liu; Hongbin Liu

Korean Journal of Chemical Engineering, Vol.31, No.3, 386-392, March, 2014

DOI10.1007/s11814-013-0237-y Export Citation

Nitrate control strategies in an activated sludge wastewater treatment process

Wenhao Shen^†, Erpan Tao, Xiaoquan Chen, Dawei Liu, and Hongbin Liu¹

State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, 510640, Guangzhou, P. R. China
¹Department of Environmental Science and Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Korea

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We studied nitrate control strategies in an activated sludge wastewater treatment process (WWTP) based on the activated sludge model. Two control strategies, back propagation for proportional-integral-derivative (BP-PID) and adaptive-network based fuzzy inference systems (ANFIS), are applied in the WWTP. The simulation results show that the simple local constant setpoint control has poor control effects on the nitrate concentration control. However, the ANFIS (4*1) controller, which considers not only the local constant setpoint control of the nitrate concentration, but also three important indices in the effluent--ammonia concentration, total suspended sludge concentration and total nitrogen concentration--demonstrates good control performance. The results also prove that ANFIS (4*1) controller has better control performance than that of the controllers PI, BP-PID and ANFIS (2*1), and that the ANFIS (4*1) controller is effective in improving the effluent quality and maintaining the stability of the effluent quality.

Keywords:Nitrate;Control;Wastewater;Simulation;Environment;Numerical Analysis

[References]

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Gomes J, Menawat AS, Chem. Eng. Sci., 1, 55 (2000)
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[Referenced By]

[1] Copp JB, Luxembourg: Office for Official Publication of the European Community (2002)

[2] Nyberg U, Andersson B, Aspegren H, Water Sci. Technol., 12, 33 (1996)

[3] Purtschert I, Siegrist H, Gujer W, Water Sci. Technol., 12, 33 (1996)

[4] Ma Y, Peng YZ, Jeppsson U, Control Eng. Pract., 11, 14 (2006)

[5] Ma Y, Peng YZ, Wang XL, Wang SY, Environ. Modell. Softw., 6, 21 (2006)

[6] Yuan ZG, Adrian O, Pernille I, Water Sci. Technol., 4/5, 45 (2002)

[7] Stare A, Vrecko D, Hvala N, Strmcnik S, Water Res., 9, 41 (2007)

[8] Liu HB, Yoo CK, Korean J. Chem. Eng., 3, 28 (2011)

[9] Balslev P, Lynggaard-Jensen A, Nickelsen C, Water Sci. Technol., 1, 33 (1996)

[10] Londong J, Water Sci. Technol., 5/6, 26 (1992)

[11] Lindberg CF, Carlsson B, Water Sci. Technol., 3/4, 34 (1996)

[12] Samuelsson P, Carlsson B, Water Sci. Technol., 1, 43 (2001)

[13] Yuan ZG, Bogaert H, Vanrolleghem P, Thoeye C, Vansteenkiste G, Verstraete W, J. Environ. Eng., 11, 123 (1997)

[14] Yuan Z, Keller J, Water Sci. Technol., 11/12, 48 (2003)

[15] Shen WH, Chen XQ, Corriou JP, Comput. Chem. Eng., 12, 32 (2008)

[16] Shen WH, Chen XQ, Pons MN, Corriou JP, Chem. Eng. J., 1-2, 155 (2009)

[17] Alex J, Beteau JF, Copp JB, Hellinga C, Jeppsson U, Marsililibelli S, Pons MN, Spanjers H, Vanhooren H, Proceedings of ECC’99; Karlsruhe, Germany (1999)

[18] Alex J, Jumar U, Tschepetzki R, Third IEEE Conference, CCA, Proc. Glasgow, UK (1994)

[19] Bidstrup SM, Grady CPL, J. Wat. Pollut. Cont. Fed., 3, 60 (1988)

[20] Jeppsson U, Rosen C, Alex J, Copp J, Gernaey KV, Pons MN, Vanrolleghem PA, Water Sci. Technol., 1, 53 (2006)

[21] Vrecko D, Hvala N, Carlsson B, Water Sci. Technol., 12, 47 (2003)

[22] Henze M, IWA Publishing, London (2000)

[23] Turmel VJ, Williams D, Jones KO, Control ’98. UKACC International Conference (1998)

[24] Gomes J, Menawat AS, Chem. Eng. Sci., 1, 55 (2000)

[25] Holenda B, Domokos E, Redey A, Fazakas J, Comput. Chem. Eng., 6, 32 (2008)

[26] Campolucci P, Uncini A, IEEE Trans. on Neural Networks, 10, 253 (1999)

[27] Jang JSR, IEEE Trans. Syst., Man Cybernetics, 3, 23 (1993)

[28] Jang JSR, Sun CT, Mizutani E, IEEE Trans. Autom. Control, 42 (1997)