Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method

Bioresource Technology

Volumn 157, Issue , 2014, Pages 161-165

  Zhang, Rui ^a   Xie, Wen Ming ^a   Yu, Han Qing ^a   Li, Wen Wei ^a  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Decision factor; Effluent quality; Multi objective optimization; Treatment cost; Wastewater treatment plant

Indexed keywords

COSTS; MULTIOBJECTIVE OPTIMIZATION; SEWAGE PUMPING PLANTS; TOXICITY; WATER QUALITY; WATER TREATMENT PLANTS;

CONFLICTING OBJECTIVES; DECISION FACTORS; EFFLUENT QUALITY; MUNICIPAL WASTEWATER TREATMENT PLANTS; OPTIMIZED DESIGNS; RELATIONSHIP MODEL; TREATMENT COSTS; WASTEWATER TREATMENT PLANTS;

EFFLUENT TREATMENT;

AMMONIA; NITRATE; NITROGEN; WASTE WATER;

ALGORITHM; COMPARATIVE STUDY; EFFLUENT; MUNICIPAL SOLID WASTE; OPTIMIZATION; WASTE TREATMENT; WASTEWATER;

ALGORITHM; ARTICLE; CHEMICAL OXYGEN DEMAND; COST MINIMIZATION ANALYSIS; EFFLUENT; MULTIOBJECTIVE OPTIMIZATION MODEL; MUNICIPAL SOLID WASTE; PRIORITY JOURNAL; PROCESS OPTIMIZATION; QUALITY CONTROL; WASTE WATER TREATMENT PLANT; CHEMISTRY; CHINA; CITY; COMPUTER SIMULATION; COST; ECONOMICS; PROCEDURES; SEWAGE; SUPPORT VECTOR MACHINE; WASTE WATER; WATER MANAGEMENT;

CHINA; CITIES; COMPUTER SIMULATION; COSTS AND COST ANALYSIS; SUPPORT VECTOR MACHINES; WASTE DISPOSAL, FLUID; WASTE WATER; WATER PURIFICATION;

EID: 84894075886     PISSN: 09608524     EISSN: 18732976     Source Type: Journal    
DOI: 10.1016/j.biortech.2014.01.103     Document Type: Article

References (26)

1. APHA Standard Methods for the Examination of Water and Wastewater 1995, American Public Health Association, Washington, DC. nineteenth ed.
2. Coello C.A.C. Evolutionary multi-objective optimization: a historical view of the field. IEEE Comput. Intell. M. 2006, 1:28-36.
3. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20:273-297.
4. El Shorbagy W.E., Radif N.N., Droste R.L. Optimization of A(2)O BNR processes using ASM and EAWAG Bio-P models: model performance. Water Environ. Res. 2013, 85:2271-2284.
5. Fang F., Ni B.J., Li W.W., Sheng G.P., Yu H.Q. A simulation-based integrated approach to optimize the biological nutrient removal process in a full-scale wastewater treatment plant. Chem. Eng. J. 2011, 174:635-643.
6. Fenu A., Guglielmi G., Jimenez J., Sperandio M., Saroj D., Lesjean B., Brepols C., Thoeye C., Nopens I. Activated sludge model (ASM) based modeling of membrane bioreactor (MBR) processes: a critical review with special regard to MBR specificities. Water Res. 2010, 44:4272-4294.
7. Henze M. Activated Sludge Models ASM1, ASM2, ASM2d and ASM3 2000, IWA Publishing, London.
8. Koziel S., Bandler J.W. Space-mapping optimization with adaptive surrogate model. IEEE Trans. Microw. Theory 2007, 55:541-547.
9. Lamas W.D. Fuzzy thermoeconomic optimisation applied to a small waste water treatment plant. Renew. Sust. Energ. Rev. 2013, 19:214-219.
10. Li W.W., Sheng G.P., Zeng R., Liu X.W., Yu H.Q. China's wastewater discharge standards in urbanization evolution, challenges and implications. Environ. Sci. Pollut. Res. 2012, 19:1422-1431.
11. Ma Y.W., Huang M.Z., Wan J.Q., Wang Y., Sun X.F., Zhang H.P. Prediction model of DnBP degradation based on BP neural network in AAO system. Bioresour. Technol. 2011, 102:4410-4415.
12. Maneeratana K., Boonlong K., Chaiyaratana N. Multi-objective optimisation by co-operative co-evolution. Parallel Problem Solving from Nature - PPSN VIII 2004, 772-781. Springer-Verlag, Berlin. X. Yao, E. Burke, J.A. Lozano, J. Smith, J.J. MereloGuervos, J.A. Bullinaria, J. Rowe, P. Tino, A. Kaban, H.P. Schwefel (Eds.).
13. Marler R.T., Arora J.S. Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 2004, 26:369-395.
14. Matic D., Kulic F., Pineda-Sanchez M., Kamenko I. Support vector machine classifier for diagnosis in electrical machines: application to broken bar. Expert Syst. Appl. 2012, 39:8681-8689.
15. Qian Z., Seepersad C.C., Joseph V.R., Allen J.K., Wu C.F.J. Building surrogate models based on detailed and approximate simulations. J. Mech. Des. 2006, 128:668-677.
16. Qu J.H., Fan M.H. The current state of water quality and technology development for water pollution control in China. Crit. Rev. Env. Sci. Tec. 2010, 40:519-560.
17. Reichert P. Aquasim - a tool for simulation and data-analysis of aquatic systems. Water Sci. Technol. 1994, 30:21-30.
18. Shanmugaprakash M., Sivakumar V. Development of experimental design approach and ANN-based models for determination of Cr(VI) ions uptake rate from aqueous solution onto the solid biodiesel waste residue. Bioresour. Technol. 2013, 148:550-559.
19. Sidiras D., Batzias F., Ranjan R., Tsapatsis M. Simulation and optimization of batch autohydrolysis of wheat straw to monosaccharides and oligosaccharides. Bioresour. Technol. 2011, 102:10486-10492.
20. Tan K.C., Lee T.H., Khor E.F. Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons. Artif. Intell. Rev. 2002, 17:253-290.
21. Tripathi P.K., Bandyopadhyay S., Pal S.K. Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf. Sci. 2007, 177:5033-5049.
22. Wu C.H., Maurer C., Wang Y., Xue S.Z., Davis D.L. Water pollution and human health in China. Environ. Health Perspect. 1999, 107:251-256.
23. Xie W.M., Zhang R., Li W.W., Ni B.J., Fang F., Sheng G.P., Yu H.Q., Song J., Le D.Z., Bi X.J., Liu C.Q., Yang M. Simulation and optimization of a full-scale Carrousel oxidation ditch plant for municipal wastewater treatment. Biochem. Eng. J. 2011, 56:9-16.
24. Yang M., Sun P., Wang R., Han J., Wang J., Song Y., Cai J., Tang X. Simulation and optimization of ammonia removal at low temperature for a double channel oxidation ditch based on fully coupled activated sludge model (FCASM): a full-scale study. Bioresour. Technol. 2013, 143:538-548.
25. Yetilmezsoy K. Integration of kinetic modeling and desirability function approach for multi-objective optimization of UASB reactor treating poultry manure wastewater. Bioresour. Technol. 2012, 118:89-101.
26. Zafar M., Kumar S., Kumar S., Dhiman A.K. Artificial intelligence based modeling and optimization of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) production process by using Azohydromonas lata MTCC 2311 from cane molasses supplemented with volatile fatty acids: a genetic algorithm paradigm. Bioresour. Technol. 2012, 104:631-641.