An integrated model for structure optimization and technology screening of urban wastewater systems

doi:10.1007/s11783-015-0792-z

Front. Environ. Sci. Eng.

2015, Vol. 9

Issue (6) : 1036-1048 https://doi.org/10.1007/s11783-015-0792-z

RESEARCH ARTICLE

An integrated model for structure optimization and technology screening of urban wastewater systems

Yue HUANG,Xin DONG,Siyu ZENG(

),Jining CHEN

School of Environment, Tsinghua University, Beijing 100084, China

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Abstract

The conventional approach to wastewater system design and planning considers each component separately and does not provide the optimum performance of the entire system. However, the growing concern for environmental protection, economic efficiency, and sustainability of urban wastewater systems requires an integrated modeling of subsystems and a synthetic evaluation of multiple objectives. In this study, a multi-objective optimization model of an integrated urban wastewater system was developed. The model encompasses subsystems, such as a sewer system, stormwater management, municipal wastewater treatment, and a wastewater reclamation system. The non-dominated sorting genetic algorithm (NSGA-II) was used to generate a range of system design possibilities to optimize conflicting environmental and economic objectives. Information from a knowledge base, which included rules for generating treatment trains as well as the performance characteristics of commonly used water pollution control measures, was utilized. The trade-off relationships between the objectives, total water pollution loads to the environment, and life cycle costs (which consist of investment as well as operation and maintenance costs), can be illustrated using Pareto charts. The developed model can be used to assist decision makers in the preliminary planning of system structure. A benchmark city was constructed to illustrate the methods of multi-objective controls, highlight cost-effective water pollution control measures, and identify the main pressures on urban water environment.

Keywords urban wastewater system integrated modeling multi-objective optimization non-dominated sorting genetic algorithm (NSGA-II)

Corresponding Author(s): Siyu ZENG

Online First Date: 01 June 2015 Issue Date: 23 November 2015

Cite this article:

Yue HUANG,Xin DONG,Siyu ZENG, et al. An integrated model for structure optimization and technology screening of urban wastewater systems[J]. Front. Environ. Sci. Eng., 2015, 9(6): 1036-1048.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-015-0792-z
https://academic.hep.com.cn/fese/EN/Y2015/V9/I6/1036

Fig.1 Conceptualized structure of the integrated system

Fig.2 Framework of the integrated optimization model

categories	symbols	description	value ranges
system structure (continuous variables)	α 1	fraction of combined sewer system	R 1 = [0,1]
	α 2	fraction of collected domestic wastewater in sewer pipes	R 2 = [0.8,1]
	α 3	fraction of collected rainwater runoff in sewer pipes	R 3 = [0,1]
	α 4	interception ratio of the intercepting combined sewer system	R 4 = [1,3]
	α 5	fraction of treated roof runoff	R 5 = [0,0.5]
	α 6	fraction of treated permeable pavement runoff	R 6 = [0,0.5]
	α 7	fraction of treated commercial and residential district runoff	R 7 = [0,0.5]
	α 8	fraction of treated green space runoff	R 8 = [0,0.5]
	α 9	fraction of treated CSO	R 9 = [0,1]
	α 10	fraction of reclaimed wastewater	R 10 = [0,1]
treatment measures (integer variables)	k B M P	serial number of selected BMP/LID treatment trains	Γ B M P = { 1 , 2 , ⋯ , 20 }
	k C S O	serial number of selected CSO treatment trains	Γ C S O = { 1 , 2 , ⋯ , 6 }
	k W W T P	serial number of selected WWTP treatment trains	Γ W W T P = { 1 , 2 , ⋯ , 30 }
	k W R T P	serial number of selected WRTP treatment trains	Γ W R T P = { 1 , 2 , ⋯ , 39 }
parameters	E R , 1	runoff quality (SS, COD, NH₃–N, TN, and TP) of road area in vector form (mg/L)	[313.5, 235.3, 3.84, 6.8, 0.61]^a)
	E R , 2	runoff quality of roof area in vector form (mg/L)	[35.9, 72.74, 3.18, 6.6, 0.38]^a)
	E R , 3	runoff quality of residential and business areas in vector form (mg/L)	[186.5, 273, 3.37, 7.97, 0.58]^a)
	E R , 4	runoff quality of green area in vector form (mg/L)	[41.8, 113.13, 0.9, 4.55, 0.47]^a)
	E R , u	runoff quality of unconstructed district in vector form (mg/L)	[99.7, 119.9, 1.77, 3.26, 0.16]^a)
	E u	runoff coefficient of unconstructed district	0.35^b)
	E 5 ~ 8	runoff coefficient of four land-use types	[0.9, 0.9, 0.75, 0.3]^b)
	P r e u s e	price of reused wastewater (CNY/t)	1^c)
	S W W T P	upper limits of WWTP effluent quality ( C W W T P ) in vector form (mg/L)	[20, 60, 8, 20,1]^d)
	S W R T P	upper limits of reclaimed water quality ( C W R T P ) in vector form (mg/L)	[10, 50, 5, 15, 0.5]^e)
definitions and values of input variables (benchmark city)	A	size of urban area (km²)	518.32
	D	area of constructed district (km²)	118.01
	P	proportions of road, roof, residential and business areas, and green area to the constructed district (%)	[15.7, 18.5, 25.2, 10.7]
	P o p	population (person)	1000000
	R	monthly rainfall in vector form (mm/month)	[12.9, 17.6, 26.9, 39.8, 64.4, 97.8, 114.9, 96.5, 62.4, 35.9, 13.5, 7.2]
	D AG	reclaimed water demand for agricultural use (10⁶ m³/a)	336.7
	D M U	reclaimed water demand for municipal use (10⁶ m³/a)	21.9
	D R U	reclaimed water demand for recreational use (10⁶ m³/a)	15
	D I U	reclaimed water demand for industrial use (10⁶ m³/a)	4.33
	E D	water consumption per capita per day (L/p·d^–1)	160
	E W	pollutant discharge per capita per day in vector form (g/p·d^–1)	[52.5, 54, 7.4, 9.3, 0.66]
	r d	water use depletion rate (%)	10

Tab.1 Summary of the variables and parameters of the model

Tab.2 Treatment processes and measures included in the knowledge base of the model

Tab.3 Base case values of the decision variables

Fig.3 The generated optimal results: (a) the generated Pareto optimal front; (b) total cost allocation under solutions A, B, C, and D

Fig.4 Pollutant reduction contributed by the subsystems under solutions A, B, C, and D. (a) Reduction of SS; (b) reduction of COD; (c) reduction of NH₃–N; (d) reduction of TN; (e) reduction of TP

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