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

An improved multi-objective optimization (MOO) model was established and used for simultaneously optimizing the treatment cost and multiple effluent quality indexes (including effluent COD, NH4(+)-N, NO3(-)-N) of a municipal wastewater treatment plant (WWTP). Compared with previous models that were mainly based on the use of fixed decision factors and did not taken into account the treatment cost, this model introduces a relationship model based on back propagation algorithm to determine the set of decision factors according to the expected optimization targets. Thus, a more flexible and precise optimization of the treatment process was allowed. Moreover, a MOO of conflicting objectives (i.e., treatment cost and effluent quality) was achieved. Applying this method, an optimal balance between operating cost and effluent quality of a WWTP can be found. This model may offer a useful tool for optimized design and control of practical WWTPs.

[1]  Dragan Matic,et al.  Support vector machine classifier for diagnosis in electrical machines: Application to broken bar , 2012, Expert Syst. Appl..

[2]  Carolyn Conner Seepersad,et al.  Building Surrogate Models Based on Detailed and Approximate , 2004, DAC 2004.

[3]  Ronald L Droste,et al.  Optimization of A(2)O BNR processes using ASM and EAWAG Bio-P models: model performance. , 2013, Water environment research : a research publication of the Water Environment Federation.

[4]  Kaan Yetilmezsoy,et al.  Integration of kinetic modeling and desirability function approach for multi-objective optimization of UASB reactor treating poultry manure wastewater. , 2012, Bioresource technology.

[5]  M. Zafar,et al.  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. , 2012, Bioresource technology.

[6]  Sanghamitra Bandyopadhyay,et al.  Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients , 2007, Inf. Sci..

[7]  Yan Wang,et al.  Prediction model of DnBP degradation based on BP neural network in AAO system. , 2011, Bioresource technology.

[8]  P. Reichert.  AQUASIM-a tool for simulation and data analysis of aquatic systems , 1994 .

[9]  Kittipong Boonlong,et al.  Multi-objective Optimisation by Co-operative Co-evolution , 2004, PPSN.

[10]  Rajiv Ranjan,et al.  Simulation and optimization of batch autohydrolysis of wheat straw to monosaccharides and oligosaccharides. , 2011, Bioresource technology.

[11]  Min Yang,et al.  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. , 2013, Bioresource technology.

[12]  Han-Qing Yu,et al.  A simulation-based integrated approach to optimize the biological nutrient removal process in a full-scale wastewater treatment plant , 2011 .

[13]  A. Fenu,et al.  Activated sludge model (ASM) based modelling of membrane bioreactor (MBR) processes: a critical review with special regard to MBR specificities. , 2010, Water research.

[14]  Wendell de Queiroz Lamas.  Fuzzy thermoeconomic optimisation applied to a small waste water treatment plant , 2013 .

[15]  Carlos A. Coello Coello,et al.  Evolutionary multi-objective optimization: a historical view of the field , 2006, IEEE Comput. Intell. Mag..

[16]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[17]  Maohong Fan,et al.  The Current State of Water Quality and Technology Development for Water Pollution Control in China , 2010 .

[18]  Han-Qing Yu,et al.  China’s wastewater discharge standards in urbanization , 2012, Environmental Science and Pollution Research.

[19]  Rui Zhang,et al.  Simulation and optimization of a full-scale Carrousel oxidation ditch plant for municipal wastewater treatment , 2011 .

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

[21]  M Shanmugaprakash,et al.  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. , 2013, Bioresource technology.

[22]  Tong Heng Lee,et al.  Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons , 2004, Artificial Intelligence Review.

[23]  A. E. Greenberg,et al.  Standard methods for the examination of water and wastewater : supplement to the sixteenth edition , 1988 .

[24]  S. Koziel,et al.  Space-Mapping Optimization With Adaptive Surrogate Model , 2007, IEEE Transactions on Microwave Theory and Techniques.

[25]  D L Davis,et al.  Water pollution and human health in China. , 1999, Environmental health perspectives.

[26]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.