A simulation-based integrated approach to optimize the biological nutrient removal process in a full-scale wastewater treatment plant

The biological nutrient removal in anaerobic–anoxic–oxic (A/A/O) process in a full-scale wastewater treatment plant is simulated and later optimized by means of an integrated system model, which combines an extended model derived from activated sludge model No. 3 with support vector machine (SVM) and accelerating genetic algorithm (AGA). The data generated by the mechanistic model are used for establishing the relationship between the operating conditions and effluent water quality by SVM. The AGA approach is used to optimize the operating conditions by taking into account the effluent quality. Through the optimization of the integrated model, the volume of the anoxic tank could be reduced by about 11% and the internal recycle ratio could be decreased from 300% to 250–260%, while the effluent quality would still meet the discharging standards. The results demonstrate that the integration of the mechanistic model, SVM model and AGA approach is an effective strategy for the optimization of complex biological processes.

[1]  Gürkan Sin,et al.  Multi-criteria evaluation of wastewater treatment plant control strategies under uncertainty. , 2008, Water research.

[2]  MohammadSajjad Khan,et al.  Application of Support Vector Machine in Lake Water Level Prediction , 2006 .

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[4]  L Rieger,et al.  The EAWAG Bio-P module for activated sludge model No. 3. , 2001, Water research.

[5]  Glen T. Daigger,et al.  Biological wastewater treatment. , 2011 .

[6]  Hanqing Yu,et al.  A thermodynamic analysis of the activated sludge process: Application to soybean wastewater treatment in a sequencing batch reactor , 2009 .

[7]  Hanqing Yu,et al.  Modeling and simulation of the formation and utilization of microbial products in aerobic granular sludge , 2009 .

[8]  Mogens Henze,et al.  Activated sludge models ASM1, ASM2, ASM2d and ASM3 , 2015 .

[9]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[10]  Krist V. Gernaey,et al.  Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study , 2007, Environ. Model. Softw..

[11]  Tzu-Yi Pai,et al.  Modeling nitrite and nitrate variations in A2O process under different return oxic mixed liquid using an extended model , 2007 .

[12]  E. Colleran,et al.  Evaluation of the parameters affecting nitrogen and phosphorus removal in anaerobic/anoxic/oxic (A/A/O) biological nutrient removal systems , 2000 .

[13]  Seyed Nezameddin Ashrafizadeh,et al.  Prediction of cell voltage and current efficiency in a lab scale chlor-alkali membrane cell based on support vector machines , 2009 .

[14]  M. V. van Loosdrecht,et al.  Kinetic model of a granular sludge SBR: Influences on nutrient removal , 2007, Biotechnology and bioengineering.

[15]  J. Villaseñor,et al.  Influence of industrial discharges on the performance and population of a biological nutrient removal process , 2007 .

[16]  Alejandro Rivas,et al.  Model-based optimisation of Wastewater Treatment Plants design , 2008, Environ. Model. Softw..

[17]  H Steinmetz,et al.  Optimisation potential for a SBR plant based upon integrated modelling for dry and wet weather conditions. , 2009, Water science and technology : a journal of the International Association on Water Pollution Research.

[18]  Jacek Makinia,et al.  Long-term simulation of the activated sludge process at the Hanover-Gümmerwald pilot WWTP. , 2005, Water research.

[19]  M. Wentzel,et al.  Batch test for characterisation of the carbonaceous materials in municipal wastewaters , 1999 .

[20]  Bingtao Zhao,et al.  Modeling pressure drop coefficient for cyclone separators: A support vector machine approach , 2009 .

[21]  K. Kennedy,et al.  Optimization of municipal wastewater biological nutrient removal using ASM2d , 2007 .

[22]  W. Gujer,et al.  Activated sludge model No. 3 , 1995 .

[23]  Joaquim Comas,et al.  Biological nutrient removal in an MBR treating municipal wastewater with special focus on biological phosphorus removal. , 2010, Bioresource technology.

[24]  ChangKyoo Yoo,et al.  Optimization of biological nutrient removal in a SBR using simulation-based iterative dynamic programming , 2008 .

[25]  Say Kee Ong,et al.  Comparison of recirculation configurations for biological nutrient removal in a membrane bioreactor. , 2008, Water research.

[26]  B. Dong,et al.  Applying support vector machines to predict building energy consumption in tropical region , 2005 .

[27]  Juan A. Baeza,et al.  Effect of internal recycle on the nitrogen removal efficiency of an anaerobic/anoxic/oxic (A2/O) wastewater treatment plant (WWTP) , 2004 .

[28]  I-Fan Chang,et al.  Support vector regression for real-time flood stage forecasting , 2006 .

[29]  M C M van Loosdrecht,et al.  Experience with guidelines for wastewater characterisation in The Netherlands. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[30]  G. A. Ekama,et al.  A general kinetic model for biological nutrient removal activated sludge systems: Model evaluation , 2007 .

[31]  Han-Qing Yu,et al.  Estimating the kinetic parameters of activated sludge storage using weighted non-linear least-squares and accelerating genetic algorithm. , 2009, Water research.

[32]  Bernard De Baets,et al.  Multi-criteria analysis of wastewater treatment plant design and control scenarios under uncertainty , 2010, Environ. Model. Softw..

[33]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[34]  Peter Reichert,et al.  Concepts underlying a computer program for the identification and simulation of aquatic systems , 1994 .