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Environmental impact minimization of a total wastewater treatment network system from a life cycle perspective
Journal of Environmental Management 90 (2009) 1454–1462
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Environmental impact minimization of a total wastewater treatment network system from a life cycle perspective Seong-Rin Lim a, Jong Moon Park b, * a
Department of Bioproducts and Biosystems Engineering, University of Minnesota, 1390 Eckles Avenue, St. Paul, MN 55108, USA Advanced Environmental Biotechnology Research Center, Department of Chemical Engineering, School of Environmental Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Pohang 790-784, South Korea b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 November 2007 Received in revised form 18 August 2008 Accepted 19 September 2008 Available online 12 November 2008
Synthesis of distributed wastewater treatment plants (WTPs) has focused on cost reduction, but never on the reduction of environmental impacts. A mathematical optimization model was developed in this study to synthesize existing distributed and terminal WTPs into an environmentally friendly total wastewater treatment network system (TWTNS) from a life cycle perspective. Life cycle assessment (LCA) was performed to evaluate the environmental impacts of principal contributors in a TWTNS. The LCA results were integrated into the objective function of the model. The mass balances were formulated from the superstructure model, and the constraints were formulated to reflect real wastewater treatment situations in industrial plants. A case study validated the model and demonstrated the effect of the objective function on the configuration and environmental performance of a TWTNS. This model can be used to minimize environmental impacts of a TWTNS in retrofitting existing WTPs in line with cleaner production and sustainable development. 2008 Elsevier Ltd. All rights reserved.
Keywords: Environmental impact Life cycle assessment (LCA) Mathematical optimization model Wastewater treatment
1. Introduction Distributed wastewater treatment plants (WTPs) have been networked to reduce initial capital investment and operating costs (Wang and Smith, 1994). The main concepts of a wastewater treatment network system are to minimize the flowrate of wastewater treated in distributed WTPs and to maximize the flowrate of wastewater bypassing distributed WTPs, with the quality of the final effluent complying with discharge permits. This is based on the assumption that total capital investment and operating costs are proportional to the flowrate of wastewater treated in distributed WTPs (Galan and Grossmann, 1998; Huang et al., 1999; Mann and Liu, 1999; Hernandez-Suarez et al., 2004). A terminal WTP has been added to the distributed WTPs to polish the wastewater treated in the distributed WTPs and to consistently meet discharge permits (Lim et al., 2008). This system consisting of distributed and terminal WTPs is named a total wastewater treatment network system (TWTNS), which is more realistic because, in the distributed WTPs, the direct discharge of untreated and diluted wastewater into waters has negative impacts on the aquatic environment, and can be prohibited by regulation (as in Korea), even though the discharged water quality meets discharge permits.
* Corresponding author. Tel.: þ82 54 279 2275; fax: þ82 54 279 8299. E-mail address: [email protected] (J.M. Park). 0301-4797/$ – see front matter 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2008.09.005
Heuristical graphical approaches have been proposed to optimize distributed wastewater treatment network systems (Wang and Smith, 1994; Kuo and Smith, 1997). These graphical methods use a concentration-composite curve to obtain a minimum wastewater flowrate to be treated, and then heuristic design methodology is used to generate and merge the independent wastewater treatment subnetworks required to remove each contaminant in wastewater streams. Mathematical optimization models have also been applied in synthesizing distributed WTPs. A search procedure has been used to yield a global or near global optimum solution on the basis of the successive solution of a relaxed linear model and an original nonconvex nonlinear problem (Galan and Grossmann, 1998). A mathematical model has been proposed to determine the optimal water use and treatment network system with the lowest freshwater consumption rate and minimum wastewater treatment capacity (Huang et al., 1999). A superstructure decomposition and parametric optimization approach has been developed to obtain a global optimal network system (Hernandez-Suarez et al., 2004). All these studies have minimized the flowrate of wastewater treated in distributed WTPs as an objective function on the basis of the assumption mentioned earlier, in order to reduce economic costs. Synthesis of WTPs is a good strategy to cost-effectively improve the performance of existing distributed and terminal WTPs. Many WTPs have been treating lower wastewater flowrates than their
S.-R. Lim, J.M. Park / Journal of Environmental Management 90 (2009) 1454–1462
Nomenclature
Sets C ¼ {cjc is a contaminant in wastewater}, c ¼ 1, 2, ., Nc WW ¼ {wwjww is a wastewater source}, ww ¼ 1, 2, ., Nm TP ¼ {tpjtp is a distributed WTP}, tp ¼ 1, 2, ., Nn TP ¼ {tpinjtpin is a distributed WTP}, tpin ¼ 1, 2, ., Nn TP ¼ {tpoutjtpout is a distributed WTP}, tpout ¼ 1, 2, ., Nn TTP ¼ {ttpjttp is a terminal WTP}, ttp ¼ 1, 2, ., Nk TTP ¼ {ttpinjttpin is a terminal WTP}, ttpin ¼ 1, 2, ., Nk TTP ¼ {ttpoutjttpout is a terminal WTP}, ttpout ¼ 1, 2, ., Nk Variables Atpout,tpin cross-sectional area of a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP Atpout,ttpin cross-sectional area of a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP Aww,tpin cross-sectional area of a pipe from a wastewater source to the inlet of a distributed WTP Aww,ttpin cross-sectional area of a pipe from a wastewater source to the inlet of a terminal WTP binary variable for the existence of a distributed WTP Btpin Btpout,tpin binary variable for the existence of a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP Btpout,ttpin binary variable for the existence of a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP binary variable for the existence of a terminal WTP Bttpin Bww,tpin binary variable for the existence of a pipe from a wastewater source to the inlet of a distributed WTP Bww,ttpin binary variable for the existence of a pipe from a wastewater source to the inlet of a terminal WTP Cc,tpin concentration at the inlet of a distributed WTP Cc,tpout concentration at the outlet of a distributed WTP Cc,ttpin concentration at the inlet of a terminal WTP Cc,ttpout concentration at the outlet of a terminal WTP EDtpout,tpin environmental effect score of pipe scrap recycling from the outlet of a distributed WTP to the inlet of a distributed WTP EDtpout,ttpin environmental effect score of pipe scrap recycling from the outlet of a distributed WTP to the inlet of a terminal WTP EDww,tpin environmental effect score of pipe scrap recycling from a wastewater source to a distributed WTP EDww,ttpin environmental effect score of pipe scrap recycling from a wastewater source to a terminal WTP environmental effect score of electricity consumption EEpt for pumping environmental effect score of electricity consumption EEwtt for wastewater treatment environmental effect score in the construction stage EEScon EESO&Mt environmental effect score in the O&M stage environmental effect score in the disposal stage EESdis EMRCt environmental effect score of M&R EPtpout,tpin environmental effect score of piping from the outlet of a distributed WTP to the inlet of a distributed WTP EPtpout,ttpin environmental effect score of piping from the outlet of a distributed WTP to the inlet of a terminal WTP EPww,tpin environmental effect score of piping from a wastewater source to a distributed WTP EPww,ttpin environmental effect score of piping from a wastewater source to a terminal WTP flowrate at the inlet of a distributed WTP Ftpin
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Ftpin,ttpin flowrate from the inlet of a distributed WTP to the inlet of a terminal WTP flowrate at the outlet of a distributed WTP Ftpout Ftpout,tpin flowrate from the outlet of a distributed WTP to the inlet of a distributed WTP Ftpout,ttpin flowrate from the outlet of a distributed WTP to the inlet of a terminal WTP flowrate at the inlet of a terminal WTP Fttpin Fttpout,dis flowrate at the outlet of a terminal WTP Fww,tpin flowrate from a wastewater source to the inlet of a distributed WTP total flowrate of wastewater treated in the distributed Ftpint WTPs HLtpout,tpin head loss through a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP HLtpout,ttpin head loss through a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP HLww,tpin head loss through a pipe from a wastewater source to the inlet of a distributed WTP HLww,ttpin head loss through a pipe from a wastewater source to the inlet of a terminal WTP power requirement for wastewater treatment in Ptpin a distributed WTP Ptpout,tpin power requirement for pumping wastewater from the outlet of a distributed WTP to the inlet of a distributed WTP Ptpout,ttpin power requirement for pumping wastewater from the outlet of a distributed WTP to the inlet of a terminal WTP power requirement for wastewater treatment in Pttpin a terminal WTP Pww,tpin power requirement for pumping wastewater from a wastewater source to the inlet of a distributed WTP Pww,ttpin power requirement for pumping wastewater from a wastewater source to the inlet of a terminal WTP TEES total environmental effect score during the life cycle vtpout,tpin optimal velocity through a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP vtpout,ttpin optimal velocity through a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP vww,tpin optimal velocity through a pipe from a wastewater source to the inlet of a distributed WTP vww,ttpin optimal velocity through a pipe from a wastewater source to the inlet of a terminal WTP Parameters regression parameter for an environmental effect score ade of pipe scrap recycling regression parameter for an optimal velocity aop regression parameter for an environmental effect score ape of piping regression parameter for an environmental effect score bde of pipe scrap recycling regression parameter for an optimal velocity bop regression parameter for an environmental effect score bpe of piping concentration of a wastewater source Cc,ww d coefficient for an M&R cost f friction factor g acceleration of gravity additional head required to take into account the Ha elevation of WTPs hmotor motor efficiency hpump pump efficiency
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ltpout,tpin Pipe length from the outlet of a distributed WTP to the inlet of a distributed WTP ltpout,ttpin Pipe length from the outlet of a distributed WTP to the inlet of a terminal WTP lww,tpin pipe length from a wastewater source to the inlet of a distributed WTP lww,ttpin pipe length from a wastewater source to the inlet of a terminal WTP contaminant load of a distributed WTP Ltpin contaminant load of a terminal WTP Lttpin r density of wastewater removal efficiency of a distributed WTP Rc,tp removal efficiency of a terminal WTP Rc,ttp t service lifetime operating hours per annum top unit environmental effect score of electricity UEe consumption Superscript max maximum
design capacities and have superfluous treatment capacities. This is because wastewater generation rates have been decreased by water reuse, recycling and recirculation which are used to reduce freshwater consumption rates. Therefore, the synthesis can be employed to effectively manage the resources and costs associated with wastewater treatment by fully utilizing the superfluous capacities. However, it should be mentioned that the synthesis should also take into account the environmental performance of a wastewater treatment system in order to be in line with cleaner production and sustainable development. The environmental and economic feasibility of a TWTNS was evaluated and analyzed to show the effect of the wastewater treatment network synthesis performed to reduce economic costs (Lim et al., 2008). The result showed that the environmental impacts of the TWTNS were greater than those of the conventional wastewater treatment system. Therefore, a new mathematical optimization model is required to synthesize the most environmentally friendly wastewater treatment network system. Much effort has been made to reduce environmental impacts of systems, processes, products and services over their life cycle. Design for environment (DfE), eco-design, and cleaner production have become more important concepts in many industries to enhance their environmental and economic performance (Dewulf et al., 2004; Donnelly et al., 2006; Kjaerheim, 2005). A life cycle perspective is required to take into account tradeoffs among the environmental impacts incurred throughout all life cycle stages. This is because improving the tradeoffs of one stage can worsen the tradeoffs of other stages. Therefore, tradeoffs among environmental impacts should be optimized to minimize the total environmental impacts associated with systems, processes, products and services. Life cycle assessment (LCA) is a systematic methodology to evaluate environmental impacts incurred during the life cycle (Friedrich, 2002; Carpentieri et al., 2005). LCA quantifies environmental impacts from all inputs and outputs through the system boundary, which enables to identify principal contributors to the total environmental impacts of the system. These principal contributors, so-called hot spots, are primary targets to focus on to effectively reduce total environmental impacts. DfE, eco-design and cleaner production have been employed to decrease the environmental impacts of principal contributors. This study developed a mathematical optimization model to retrofit existing distributed and terminal WTPs into an environmentally friendly TWTNS from the life cycle perspective. The
min
minimum
Acronyms DfE design for environment EES environmental effect score ELU environmental load unit ENS environmental effect score-minimized total wastewater treatment network system FNS flowrate-minimized total wastewater treatment network system LCA life cycle assessment LCI life cycle inventory analysis LCIA life cycle impact assessment M&R maintenance and repair MINLP mixed integer nonlinear programming O&M operations and maintenance TEES total environmental effect score TWTNS total wastewater treatment network system WTP wastewater treatment plant
objective function of the model was formulated with principal contributors to environmental impacts in all life cycle stages. The mass balances were formulated on the basis of the superstructure model, and the constraints were formulated to reflect real wastewater treatment situations in industrial plants. LCA was used to obtain linear regression equations from the relationship between the specifications of contributors and their environmental effect scores (EESs) indicating the amount of environmental impacts: the linear relationship is employed to simplify the mathematical model. A case study was performed to validate the mathematical optimization model by demonstrating the environmental performance of an EES-minimized TWTNS (ENS). The ENS was compared to the TWTNS (FNS) optimized by minimizing a total flowrate of wastewater treated in distributed WTPs. The EESs of principal contributors in each system were estimated and compared to examine the effects of the proposed model used to minimize a total EES on the configuration and characteristics of a TWTNS.
2. Mathematical optimization model 2.1. Superstructure model A TWTNS is generated on the basis of the generalized superstructure model (Lim et al., 2008). The superstructure model is presented in Supplementary Material (Fig. S-1). This superstructure model includes all possible interconnections: (1) from the wastewater sources to the inlets of the distributed and terminal WTPs to allocate wastewater into all the WTPs; and (2) from the outlets of the distributed WTPs to their inlets to dilute the contaminant concentration of influents to the distributed WTPs, and (3) from the outlets of the distributed WTPs to the inlets of the terminal WTPs to polish the wastewater treated in the distributed WTPs. The existence of each interconnections in the superstructure model and the operation of each WTP in the distributed WTPs are expressed as binary variables in the mathematical optimization model: ‘‘0’’ means that the interconnection is not necessary or the WTP does not need to be operated; and ‘‘1’’ means that the interconnection is required to transfer wastewater or the WTP is required to treat wastewater. Wastewater in all the streams is transferred by pumps. It is assumed that the mixers combine all possible streams into one stream and that the splitters divide a given stream into all possible streams.
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2.2. Mathematical formulation The mathematical optimization model minimizing the environmental impacts of a TWTNS is a mixed integer nonlinear programming (MINLP) model because bilinear variables are included in the mass balances of contaminants and because binary variables are required to express whether the streams exist or whether the WTPs are operated. Sets are defined for this mathematical formulation as follows: C, a set of contaminants in wastewater; WW, wastewater sources; TP, distributed WTPs; and TTP, terminal WTPs. 2.2.1. Objective function The sum of EESs over the life cycle is minimized as the objective function. The total EES (TEES) is composed of the EESs of principal contributors to environmental impacts in the stages of construction, operations and maintenance (O&M), and disposal. The objective function is as follows:
Minimize TEES ¼ EEScon þ
t X
The principal contributors in the O&M stage are electricity consumption for pumping and for wastewater treatment, and maintenance and repair (M&R). The EES of electricity consumption for pumping is estimated from the power requirement which is calculated with the flowrate and pressure required for pumping. The pressure is the sum of the head loss through the pipeline and the additional head required to take into account the elevations of WTPs. The head loss is calculated by using the Darcy–Weisbach equation (McGhee, 1991). Electricity consumption for wastewater treatment is required to operate equipment such as agitators, flocculators and sludge scrapers in distributed and terminal WTPs. The EES of M&R is assumed to be proportional to the EES of piping in the construction stage. Equations used to estimate the EES in the O&M stage are as follows:
EEStO&M ¼ EEtp þ EEtwt þ EMRCt
þ EESdis
(1)
EEtp ¼ @
t¼1
X
EEScon ¼
X
þ
X
X
EPww;tpin þ
ww˛WW tpin˛TP
X
X
X
EPtpout;ttpin
ð2Þ
tpout˛TP ttpin˛TTP
EPww;tpin ¼ ape Aww;tpin þ bpe lww;tpin Bww;tpin EPww;ttpin ¼ ape Aww;ttpin þ bpe lww;ttpin Bww;ttpin EPtpout;tpin ¼ ape Atpout;tpin þ bpe ltpout;tpin Btpout;tpin EPtpout;ttpin ¼ ape Atpout;ttpin þ bpe ltpout;ttpin Btpout;ttpin
(3)
where B means the binary variable to formulate the existence of a pipe.
¼
Fww;tpin Fww;ttpin ; Aww;ttpin ¼ ; Atpout;tpin vww;tpin vww;ttpin Ftpout;tpin Ftpout;ttpin ; Atpout;ttpin ¼ vtpout;tpin vtpout;ttpin
Ptpout;tpin
tpout˛TP tpin˛TP
þ
X
X
1 Ptpout;ttpin AUEe $top
tpout˛TP ttpin˛TTP
HLww;ttpin ¼ f
!0:5
lww;ttpin p g Aww;ttpin
(4)
vww;tpin ¼ aop Fww;tpin þ bop ; vww;ttpin ¼ aop Fww;ttpin þ bop ; vtpout;tpin ¼ aop Ftpout;tpin þ bop ; vtpout;ttpin ¼ aop Ftpout;ttpin þ bop (5) The optimal velocity in the pipeline is assumed to be linear with the flowrate as a way to express their relationship, because designating the optimal velocity as a parameter cannot take into account the effect of pipe diameter on the variability of head loss and because an exact equation for their relationship cannot be obtained even from nonlinearity without the distance of the pipe. It should be noted that a regression equation for the optimal velocity from flowrate, pipe diameter and the distance of the pipe was not taken into account here because this makes the mathematical model more complicated and is not the scope of this study.
HLtpout;ttpin ¼ f
v2ww;tpin
!0:5
ltpout;tpin p HLtpout;tpin ¼ f g Atpout;tpin
where
Aww;tpin ¼
X
X
Pww;ttpin
ww˛WW ttpin˛TTP
lww;tpin p HLww;tpin ¼ f g Aww;tpin
EPtpout;tpin
X
þ
X
X
Pww;tpin þ
EPww;ttpin
ww˛WW ttpin˛TTP
tpout˛TP tpin˛TP
þ
X
X
ww˛WW tpin˛TP
The EES in the construction stage originates from piping for the streams shown in the superstructure model. The EES of piping is estimated using the linear regression equation obtained in the LCA (the R2 of this regression is 0.974, as shown in the case study). Equations used to estimate the EES of piping are as follows:
(6)
where
0 EEStO&M
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4
v2ww;ttpin
!0:5
ltpout;ttpin p g Atpout;ttpin
Bww;tpin
4
v2tpout;tpin
!0:5
Bww;ttpin
4
(8) Btpout;tpin
v2tpout;ttpin 4
Btpout;ttpin
rFww;tpin g HLww;tpin þ Ha 1 hpump h 1000 motor rFww;ttpin g HLww;ttpin þ Ha 1 Pww;ttpin ¼ hpump h 1000 motor rFtpout;tpin g HLtpout;tpin þ Ha 1 Ptpout;tpin ¼ hpump h 1000 motor rFtpout;ttpin g HLtpout;ttpin þ Ha 1 Ptpout;ttpin ¼ hpump hmotor 1000
Pww;tpin ¼
0 EEtwt ¼ @
X
Ptpin Btpin þ
tpin˛TP
X
(9)
1 Pttpin Bttpin AUEe $top
(10)
ttpin˛TTP
where B means the binary variable to formulate the existence of a WTP.
EMRCt ¼ d$EEScon
(11)
The EES in the disposal stage is derived from the recycling of the pipeline built in the construction stage. This EES is estimated using the linear regression equation for the relationship between the unit EES of pipe scrap recycling and the cross-sectional area of the pipeline (the R2 of this regression is 0.974 as shown in the case
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study). Equations used to estimate the EES in the disposal stage are as follows:
X
EESdis ¼
X
ww˛WW tpin˛TP
þ
X
EDww;ttpin
ww˛WW ttpin˛TTP
X
X
X
EDww;tpin þ EDtpout;tpin þ
tpout˛TP tpin˛TP
X
X
EDtpout;ttpin
tpout˛TP ttpin˛TTP
(12)
where
EDww;tpin ¼ ade Aww;tpin þ bde lww;tpin Bww;tpin EDww;ttpin ¼ ade Aww;ttpin þ bde lww;ttpin Bww;ttpin EDtpout;tpin ¼ ade Atpout;tpin þ bde ltpout;tpin Btpout;tpin EDtpout;ttpin ¼ ade Atpout;ttpin þ bde ltpout;ttpin Btpout;ttpin
(13)
2.2.2. Mass balances and constraints The mass balances in this model are formulated on the basis of the superstructure model described above, and the constraints are formulated to reflect the real wastewater treatment situations in industrial plants. Equations for the mass balances and constraints are as follows. For the overall mass balance of the TWTNS,
X
X
X
Fww;tpin þ
ww˛WW tpin˛TP
X
X
Fttpout;dis ¼ 0
ð14Þ
ttpout˛TTP
For the mass balances of the distributed WTP inlet mixers,
X
X
Fww;tpin þ
ww˛WW
Ftpout;ttpin Ftpin ¼ 0
(15)
tpout˛TP
X
Fww;tpin Cc;ww þ
ww˛WW
X
Ftpout;ttpin Cc;tpout Ftpin Cc;tpin ¼ 0
tpout˛TP
(16) For the mass balances of the distributed WTPs,
Ftpin Ftpout ¼ 0
(17)
Ftpin Cc;tpin 1 Rc;tp Ftpout Cc;tpout ¼ 0
(18)
For the mass balances of the distributed WTP outlet splitters,
X
Ftpout
X
Ftpout;tpin
tpin˛TP
Ftpout;ttpin ¼ 0
(19)
ttpin˛TTP
For the mass balances of the terminal WTP inlet mixers,
X
Ftpout;ttpin þ
tpout˛TP
X tpout˛TP
X
Fww;ttpin Fttpin ¼ 0
(20)
ww˛WW
Ftpout;ttpin Cc;tpout þ
X
Fww;ttpin Cc;ww
ww˛WW
Fttpin Cc;ttpin ¼ 0
ð21Þ
For the mass balances of the terminal WTPs,
Fttpin Fttpout;dis ¼ 0 Fttpin Cc;ttpin 1 Rc;ttp Fttpout;dis Cc;ttpout ¼ 0
(24)
Ftpin 0
(25)
Ftpin Cc;tpin Lmax tpin 0
(26)
max Fttpin Fttpin Bttpin 0
(27)
Fttpin 0
(28)
Fttpin Cc;ttpin Lmax ttpin 0
(29)
max Fww;tpin Fww;tpin Bww;tpin 0 max Bww;ttpin 0 Fww;ttpin Fww;ttpin max Btpout;tpin 0 Ftpout;tpin Ftpout;tpin max Btpout;ttpin 0 Ftpout;ttpin Ftpout;ttpin
(30)
min Bww;tpin 0 Fww;tpin Fww;tpin min Fww;ttpin Fww;ttpin Bww;ttpin 0 min Ftpout;tpin Ftpout;tpin Btpout;tpin 0
(31)
min Ftpout;ttpin Ftpout;ttpin Btpout;ttpin 0
Fww;ttpin
ww˛WW ttpin˛TTP
max Ftpin Ftpin Btpin 0
(22) (23)
For the constraints of flowrates and loads on the distributed and terminal WTPs,
For the constraint of concentrations on the discharge quality of the terminal WTP required to consistently meet the discharge limits of environmental regulations, max Cc;ttpout Cc;ttpout 0
(32)
In this mathematical model, the number of the continuous variables is 7(mn þ mk þ n2 þ nk þ 1) þ (n þ k)(2c þ 3), the number of the binary variables is mn þ mk þ n2 þ nk þ n þ k, and the number of the constraints is 2(mn þ mk þ n2 þ nk) þ 3nc(n þ k) þ 5n þ 4k, where the number of the contaminants, wastewater sources, distributed WTPs, and terminal WTPs are c, m, n and k, respectively. 3. Life cycle assessment An LCA methodology was used to quantitatively evaluate the environmental impacts associated with principal contributors. The specifications of contributors and their LCA results were fitted to obtain linear regression equations. The principal contributors assessed in the LCA consisted of (1) piping in the construction stage, (2) electricity for pumping and for wastewater treatment and M&R in the O&M stage, and (3) pipe scrap recycling in the disposal stage (Lim et al., 2008). Existing distributed and terminal WTPs, and existing pump pits in a TWTNS were excluded from the system boundary for the LCA because the civil structures already existed. However, the system boundary included the electricity consumption required to operate mechanical equipment (agitators, flocculators and sludge scrapers) in the distributed and terminal WTPs. Chemicals consumption and sludge generation were excluded in the LCA because the dosages of coagulants and flocculants used and the quantity of sludge generated were assumed to be proportional to contaminant loads, which are not changed from wastewater treatment network synthesis. In other words, environmental impacts from chemicals consumption and sludge generation in a TWTNS are equal to those in other alternatives. This assumption could simplify the mathematical optimization model. It should be mentioned that it is difficult to accurately estimate the amounts of chemicals used and sludge generated because the properties of wastewater such as pH and
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alkalinity affect the dosage of coagulants and flocculants and the quantity of sludge generated. The LCA procedure was in accordance with the ISO 14040 series of standards (ISO, 1997). The life cycle inventory analysis (LCI) used the GaBi 4.0 (IKP and PE Europe GMBH, 2004) and Ecoinvent v1.2 (Swiss Center for Life Cycle Inventories, 2005) databases: the data of a process included all inputs and outputs associated with a process itself and its supply chains. The life cycle impact assessment (LCIA) evaluated the significance of potential environmental impacts on the basis of the LCI results. The EPS 2000 methodology (Steen, 1999) was employed for the classification and characterization in the LCIA to evaluate the EES of contributors. This methodology uses a single unit, i.e. ELU (Environmental Load Unit) regardless of environmental impact categories such as emissions to air, water, or soil, and impacts on humans. The single unit makes it easy to evaluate tradeoffs among the environmental impacts of different environment impact categories. The EESs of the contributors from the LCIA results and their specifications were fitted to generate linear regression equations. 3.1. Construction stage Environmental impacts incurred from piping were evaluated repeatedly for various diameters of pipes in order to obtain a linear regression equation. The system and its function for the LCA of the piping were defined as the carbon steel pipeline required to transfer wastewater. The reference flow was set to the pipeline of 1 m to obtain its unit EES, which was used to obtain its regression equation with its cross-sectional area and then this equation was integrated into the mathematical model to estimate the total environmental impacts of piping. The system boundary included the processes required to construct the pipeline, as shown in Fig. 1(a). Pipe is made from hot rolled strip with squared or slightly beveled edges, which has supply chains such as the extraction of iron ore, lime and coal as well as integrated steel processes. The strip is drawn through rolls forming a cylindrical shape. The edges
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are pressed together forming a butt-weld with an electric welder. The pipe is transported from a pipe manufacturer to a wastewater treatment system and then is welded to construct the pipeline. The LCA results of piping were used to generate a linear regression equation which enables prediction of the unit EES of the pipeline with the cross-sectional area of the pipe. Equipment for pumping was excluded from the system boundary of the LCA because its environmental impacts were assumed to be negligible considering the small proportion of its weight to the total of a TWTNS (Lim et al., 2008). 3.2. Operations and maintenance stage Environmental impacts in the O&M stage are incurred from electricity consumption for pumping and for wastewater treatment and from M&R. The system and its function for the LCA of electricity consumption were defined as the electricity required to operate equipment. The reference flow was set to 1 kWh of electricity to obtain its unit EES, which was used to estimate the total environmental impacts from electricity consumption for pumping and for wastewater treatment. Germany electricity mix data was used to evaluate the unit EES of the electricity. The environmental impacts of M&R for the pipeline were assumed to be linearly proportional to those of piping in the construction stage, taking into account that the M&R cost for the pipeline is assumed to be 3% of the construction cost for piping (Peters and Timmerhaus, 1991). 3.3. Disposal stage Environmental impacts incurred from recycling of pipe scrap were evaluated repeatedly for the various diameters of pipes in order to obtain a linear regression equation. The system and its function for the LCA of pipe scrap recycling were defined as the recycling of carbon steel pipe scrap into a part of a steel billet. The reference flow was set to the pipeline of 1 m. The system boundary included the processes required to recycle the pipe scrap, as shown in Fig. 1(b). The pipe scrap is transported to steelmaking facilities operating an electric arc furnace where scrap is charged, melted, refined, and tapped. The molten steel is converted into a semifinished product such as a steel billet that is suitable for further processing. The LCA results were used to generate a linear regression equation which enables prediction of the unit EES of pipe scrap recycling with the cross-sectional area of the pipeline. 4. Case study A case study was analyzed to demonstrate that the strategy presented in this study is suitable for synthesizing an environmentally friendly TWTNS and to estimate the effect of the model on the configuration and EES of a TWTNS. Two types of TWTNSs were synthesized independently using their own objective functions: minimization of the total flowrate of wastewater treated in distributed WTPs, and minimization of the total EES of a TWTNS. The EES-minimized TWTNS (ENS) was compared to the flowrateminimized TWTNS (FNS). The EESs of the principal contributors in each system were also estimated and compared. 4.1. Methods
Fig. 1. System boundaries for LCA. The LCA evaluated the environmental impacts from the processes and their supply chains: (a) construction of the pipeline and (b) recycling of pipe scrap.
Wastewater sources and existing distributed and terminal WTPs in an iron and steel plant were used to generate the FNS and ENS. Characteristics of the four wastewater sources are described in Supplementary Material (Table S-1), and those of the four distributed and one terminal WTPs are shown in Supplementary Material (Table S-2). The distributed WTPs were used to treat the wastewater sources one-to-one. The terminal WTP was used to polish the
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effluents of the distributed WTPs, in order to consistently meet discharge limits. All the WTPs consisted of physical and chemical treatment processes, such as coagulation, flocculation and sedimentation. A distance matrix for interconnections between the wastewater sources and sinks is shown in Supplementary Material (Table S-3). The required discharge quality of the terminal WTP was set at 20 mg/L for CODcr, 5 mg/L for SS, and 8 mg/L for F to stably meet environmental regulations. The FNS and ENS were independently generated using their own objective functions subjected to the same mass balances and constraints. For the objective function used for the FNS, t Minimize Ftpin ¼
X
Ftpin
(33)
tpin˛TP
The FNS was generated with Eqs. (14)–(33), and the ENS with Eqs. (1)–(32). All parameters were set to optimize the mathematical model for the ENS. The service life (t) was assumed to be 15 years, based on the lifetime of the pipes. The regression parameters for the EES of piping were obtained as follows:
ape ¼ 66:457; bpe ¼ 0:921; R2 ¼ 0:974 The optimal velocity required to determine the cross-sectional area of the pipeline in the pumping flow was obtained from its relationship with the flowrate. The field data gathered in the plant was used to obtain this regression equation. The parameters used to determine the optimal velocity are as follows:
aop ¼ 0:0297; bop ¼ 0:6173; R2 ¼ 0:849 The distances between the wastewater sources and sinks were used as pipe lengths. The unit EES of electricity consumption was set to 0.255 ELU/kWh according to the LCA results. The additional head (Ha) was assumed to be 10 m H2O to take into account available waster pressure at the end of the pipeline. The efficiencies of pump and motor were assumed to be 0.60 and 0.85, respectively. The annual operating time (top) of the WTPs was set to 8760 h. The parameter used to calculate the EES of M&R was assumed as follows:
d ¼ 0:3 The regression parameters used to estimate the unit EES of pipe scrap recycling were obtained as follows:
ade ¼ 96:955; bde ¼ 1:342; R2 ¼ 0:974 The FNS and ENS were generated from the optimal solutions to each model, which were found by using GAMS (GAMS Development Corporation, 2005): CPLEX for linear programming, MINOS for nonlinear programming, and DICOPT for MINLP. GAMS was iteratively run by designating the values of variables from the outcomes of a run as the initial values of variables for the next run. The best solutions were regarded as optimal solutions, which were employed to embody the configurations of each system. The best local solution is useful for industrial applications in the absence of a global optimum, which is difficult to be attained. This is because global optima cannot easily be obtained because of the nonconvexities derived from bilinear variables in the mass balances of contaminants. The CPU times were 3.547 s for the FNS and 3.875 s for the ENS on Pentium 4, 3.0 GHz PC. The EESs of the principal contributors in the FNS and ENS were estimated using Eqs. (1)–(13). The total EESs over the life cycle of the ENS were compared to those of the FNS. 4.2. Results and discussion The FNS and ENS were embodied from the optimal solutions to their models, as shown in Fig. 2. Characteristics of the FNS and ENS are summarized in Table 1. The total pipe length of the ENS was 1.2% less than that of the FNS, and the number of pumps required in the ENS was 8.3% less than that in the FNS. The power requirement for pumping of the ENS was 0.6% less than that of the FNS. These results show that the objective function for the ENS decreased its power requirement for pumping by reducing the pipe length and the number of pumps. The number of the distributed WTPs used in the FNS and ENS were the same; however, TP4 was closed in the FNS, while TP2 in the ENS. The power requirement for wastewater treatment of the ENS was 17.9% less than that of the FNS because
Fig. 2. Comparison of the total wastewater treatment network systems (TWTNSs): (a) flowrate-minimized TWTNS (FNS); (b) EES-minimized TWTNS (ENS) (WW: wastewater, TP: distributed wastewater treatment plant, TTP: terminal wastewater treatment plant).
S.-R. Lim, J.M. Park / Journal of Environmental Management 90 (2009) 1454–1462
1461
Unit
FNS
ENS
Length
m
2440
2410
Pump
Number
–
12
11
for pumping accounted for 69.6% and 73.4% of the total EESs of the FNS and ENS. The EESs of electricity consumption for wastewater treatment accounted for 30.0% and 26.2% of the total EESs of FNS and ENS. Therefore, the electricity consumption for both pumping and wastewater treatment can be employed as a simplified objective function for the practical retrofitting of an existing wastewater treatment system.
Distributed wastewater treatment plants
Number (in use) Flowrate of treated wastewater Flowrate of bypassing wastewater
– m3/h
3 19.6
3 20.0
5. Conclusions
m3/h
14.6
14.2
Terminal wastewater treatment plant
Flowrate of treated wastewater
m3/h
34.2
34.2
Power requirement
Electricity (pumping) Electricity (wastewater treatment)
kW kW
Table 1 Characteristics of the FNS and ENS (FNS: flowrate-minimized total wastewater treatment network system, ENS: EES-minimized total wastewater treatment network system). Item Pipe
6.49 2.80
6.45 2.30
A strategy to synthesize an environmentally friendly TWTNS was developed from a life cycle perspective. A case study was performed to illustrate the model and to demonstrate the effect of the objective function on the configuration and environmental performance of a TWTNS. The major conclusions from this study are as follows: -
the power requirement of TP2 was greater than that of TP4. The flowrate of the wastewater treated through distributed WTPs in the ENS was 2.0% greater than that in the FNS; the flowrate of the wastewater bypassing distributed WTPs in the ENS was 2.7% less than that in the FNS. These were because the FNS was synthesized to minimize the flowrate of the wastewater treated in the distributed WTPs, while the ENS was generated by optimizing the tradeoffs between the decrease of the power requirement and the increase of the flowrate treated through the distributed WTPs. All the EESs of each principal contributor in the ENS were less than those of the FNS, and the total EES during the life cycle of the ENS was less than that of the FNS, as shown in Table 2. The EES of piping in the ENS was 1.0% less than that in the FNS. The EESs of electricity consumption for pumping and for wastewater treatment in the ENS were 0.5% and 21.7% less than those in the FNS, respectively. The EES of M&R in the ENS was 1.0% less than that in the FNS. The EES of pipe scrap recycling in the ENS was 1.0% less than that in the FNS. The total EES during the life cycle of the ENS was 6.0% less than that of the FNS. Decreasing the EES of electricity consumption for wastewater treatment was found to be a key factor to greatly reduce the environmental impacts of the TWTNS. Thus, the proposed model optimized the ENS by excluding the distributed WTP (TP2), which required the highest power for wastewater treatment. The electricity consumption for pumping and for wastewater treatment can be selected to simplify the model further. The EES in the O&M stage was the greatest component among the EESs in the life cycle stages, accounting for 98.0% and 97.9% of the total EESs of the FNS and ENS, respectively. The EESs of electricity consumption
Table 2 Environmental effect scores of the total wastewater treatment network systems (FNS: flowrate-minimized total wastewater treatment network system, ENS: EESminimized total wastewater treatment network system, Unit: ELU). FNS Construction (A)
Piping
Operations & maintenance (B)
Electricity for pumping during a year Electricity for wastewater treatment during a year Maintenance & repair (M&R) during a year Sub-total (during the service time, i.e., 15 years)
Disposal (C)
Pipe recycling
Total EES during the life cycle (A þ B þ C)
ENS
2604
2579
14,489
14,411
6255
5138
78
77
312,331
294,397
3795
3759
318,730
300,735
-
-
-
The mathematical optimization model can be used to effectively improve the environmental performance of a wastewater treatment system by retrofitting existing WTPs which treat lower wastewater flowrates than their design flowrates. The LCA results were integrated into the mathematical model to minimize the total environmental impacts of a TWTNS and to optimize tradeoffs among the environmental impacts of principal contributors. This methodology can be applied to improve the environmental performance of various processes and systems. The model can easily be applied to practically implement an environmentally friendly TWTNS in industrial plants, because the model can be simplified by using only the electricity consumption for pumping and wastewater treatment in the objective function of the model. The model for an environmentally friendly TWTNS can contribute to cleaner production and sustainable development. This is because minimizing the environmental impacts of a TWTNS induces its cost reduction: electricity consumption for pumping and for wastewater treatment is a common contributor to environmental impacts and economic costs.
Acknowledgements This work was financially supported in part by the Korean Science and Engineering Foundation (R11-2003-006) through the Advanced Environmental Biotechnology Research Center at Pohang University of Science and Technology and in part by the Korean Ministry of Commerce, Industry, and Energy through the Korean National Cleaner Production Center. This work was also supported by the program for advanced education of chemical engineers (2nd stage of BK21). Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi: 10.1016/j.jenvman.2008.09.005. References Carpentieri, M., Corti, A., Lombardi, L., 2005. Life cycle assessment (LCA) of an integrated biomass gasification combined cycle (IBGCC) with CO2 removal. Energy Conversion and Management 46, 1790–1808. Dewulf, W., Duflou, J., Ander, A., 2004. Toward a sectorwide design for environment support system for the rail industry. Environmental Management 34, 181–190. Donnelly, K., Beckett-Furnell, Z., Traeger, S., Okrasinski, T., Holman, S., 2006. Ecodesign implemented through a product-based environmental management system. Journal of Cleaner Production 14, 1357–1367. Friedrich, E., 2002. Life-cycle assessment as an environmental management tool in the production of potable water. Water Science and Technology 20, 29–36.
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Galan, B., Grossmann, I.E., 1998. Optimal design of distributed wastewater treatment networks. Industrial and Engineering Chemistry Research 37, 4036–4048. GAMS Development Corporation, 2005. GAMS, A User Guide. GAMS Development Corporation, Washington, DC. Hernandez-Suarez, R., Castellanos-Fernandez, J., Zamora, J., 2004. Superstructure decomposition and parametric optimization approach for the synthesis of distributed wastewater treatment networks. Industrial and Engineering Chemistry Research 43, 2175–2191. Huang, C., Chang, C.T., Ling, H., Chang, C.C., 1999. A mathematical programming model for water usage and treatment network design. Industrial and Engineering Chemistry Research 38, 2666–2679. IKP, PE Europe GMBH, 2004. Gabi 4.0 Software. ISO, 1997. Environmental Management – Life Cycle Assessment – Principles and Framework. ISO. ISO 14040. Kjaerheim, G., 2005. Cleaner production and sustainability. Journal of Cleaner Production 13, 329–339.
Kuo, W.-C., Smith, R., 1997. Effluent treatment system design. Chemical Engineering Science 52 (23), 4273–4290. Lim, S.-R., Park, D., Park, J.M., 2008. Environmental and economic feasibility study of a total wastewater treatment network system. Journal of Environmental Management 88 (3), 564–575. Mann, J.G., Liu, Y.A., 1999. Industrial Water Reuse and Wastewater Minimization. McGraw-Hill, New York. McGhee, T.J., 1991. Water Supply and Sewerage. McGraw-Hill, New York. Peters, M.S., Timmerhaus, K.D., 1991. Plant Design and Economics for Chemical Engineers. McGraw-Hill, New York. Steen, B., 1999. A Systematic Approach to Environmental Priority Strategies in Product Development (EPS). Version 2000 – Models and Data of the Default Method. CPM Report No. 5. Center for Environmental Assessment of Products and Material Systems, Stockholm. Swiss Center for Life Cycle Inventories, 2005. Ecoinvent data version 1.2. Wang, Y.P., Smith, R., 1994. Design of distributed effluent treatment systems. Chemical Engineering Science 49 (18), 3127–3145.
Contents lists available at ScienceDirect
Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Environmental impact minimization of a total wastewater treatment network system from a life cycle perspective Seong-Rin Lim a, Jong Moon Park b, * a
Department of Bioproducts and Biosystems Engineering, University of Minnesota, 1390 Eckles Avenue, St. Paul, MN 55108, USA Advanced Environmental Biotechnology Research Center, Department of Chemical Engineering, School of Environmental Science and Engineering, Pohang University of Science and Technology, San 31, Hyoja-dong, Pohang 790-784, South Korea b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 November 2007 Received in revised form 18 August 2008 Accepted 19 September 2008 Available online 12 November 2008
Synthesis of distributed wastewater treatment plants (WTPs) has focused on cost reduction, but never on the reduction of environmental impacts. A mathematical optimization model was developed in this study to synthesize existing distributed and terminal WTPs into an environmentally friendly total wastewater treatment network system (TWTNS) from a life cycle perspective. Life cycle assessment (LCA) was performed to evaluate the environmental impacts of principal contributors in a TWTNS. The LCA results were integrated into the objective function of the model. The mass balances were formulated from the superstructure model, and the constraints were formulated to reflect real wastewater treatment situations in industrial plants. A case study validated the model and demonstrated the effect of the objective function on the configuration and environmental performance of a TWTNS. This model can be used to minimize environmental impacts of a TWTNS in retrofitting existing WTPs in line with cleaner production and sustainable development. 2008 Elsevier Ltd. All rights reserved.
Keywords: Environmental impact Life cycle assessment (LCA) Mathematical optimization model Wastewater treatment
1. Introduction Distributed wastewater treatment plants (WTPs) have been networked to reduce initial capital investment and operating costs (Wang and Smith, 1994). The main concepts of a wastewater treatment network system are to minimize the flowrate of wastewater treated in distributed WTPs and to maximize the flowrate of wastewater bypassing distributed WTPs, with the quality of the final effluent complying with discharge permits. This is based on the assumption that total capital investment and operating costs are proportional to the flowrate of wastewater treated in distributed WTPs (Galan and Grossmann, 1998; Huang et al., 1999; Mann and Liu, 1999; Hernandez-Suarez et al., 2004). A terminal WTP has been added to the distributed WTPs to polish the wastewater treated in the distributed WTPs and to consistently meet discharge permits (Lim et al., 2008). This system consisting of distributed and terminal WTPs is named a total wastewater treatment network system (TWTNS), which is more realistic because, in the distributed WTPs, the direct discharge of untreated and diluted wastewater into waters has negative impacts on the aquatic environment, and can be prohibited by regulation (as in Korea), even though the discharged water quality meets discharge permits.
* Corresponding author. Tel.: þ82 54 279 2275; fax: þ82 54 279 8299. E-mail address: [email protected] (J.M. Park). 0301-4797/$ – see front matter 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.jenvman.2008.09.005
Heuristical graphical approaches have been proposed to optimize distributed wastewater treatment network systems (Wang and Smith, 1994; Kuo and Smith, 1997). These graphical methods use a concentration-composite curve to obtain a minimum wastewater flowrate to be treated, and then heuristic design methodology is used to generate and merge the independent wastewater treatment subnetworks required to remove each contaminant in wastewater streams. Mathematical optimization models have also been applied in synthesizing distributed WTPs. A search procedure has been used to yield a global or near global optimum solution on the basis of the successive solution of a relaxed linear model and an original nonconvex nonlinear problem (Galan and Grossmann, 1998). A mathematical model has been proposed to determine the optimal water use and treatment network system with the lowest freshwater consumption rate and minimum wastewater treatment capacity (Huang et al., 1999). A superstructure decomposition and parametric optimization approach has been developed to obtain a global optimal network system (Hernandez-Suarez et al., 2004). All these studies have minimized the flowrate of wastewater treated in distributed WTPs as an objective function on the basis of the assumption mentioned earlier, in order to reduce economic costs. Synthesis of WTPs is a good strategy to cost-effectively improve the performance of existing distributed and terminal WTPs. Many WTPs have been treating lower wastewater flowrates than their
S.-R. Lim, J.M. Park / Journal of Environmental Management 90 (2009) 1454–1462
Nomenclature
Sets C ¼ {cjc is a contaminant in wastewater}, c ¼ 1, 2, ., Nc WW ¼ {wwjww is a wastewater source}, ww ¼ 1, 2, ., Nm TP ¼ {tpjtp is a distributed WTP}, tp ¼ 1, 2, ., Nn TP ¼ {tpinjtpin is a distributed WTP}, tpin ¼ 1, 2, ., Nn TP ¼ {tpoutjtpout is a distributed WTP}, tpout ¼ 1, 2, ., Nn TTP ¼ {ttpjttp is a terminal WTP}, ttp ¼ 1, 2, ., Nk TTP ¼ {ttpinjttpin is a terminal WTP}, ttpin ¼ 1, 2, ., Nk TTP ¼ {ttpoutjttpout is a terminal WTP}, ttpout ¼ 1, 2, ., Nk Variables Atpout,tpin cross-sectional area of a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP Atpout,ttpin cross-sectional area of a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP Aww,tpin cross-sectional area of a pipe from a wastewater source to the inlet of a distributed WTP Aww,ttpin cross-sectional area of a pipe from a wastewater source to the inlet of a terminal WTP binary variable for the existence of a distributed WTP Btpin Btpout,tpin binary variable for the existence of a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP Btpout,ttpin binary variable for the existence of a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP binary variable for the existence of a terminal WTP Bttpin Bww,tpin binary variable for the existence of a pipe from a wastewater source to the inlet of a distributed WTP Bww,ttpin binary variable for the existence of a pipe from a wastewater source to the inlet of a terminal WTP Cc,tpin concentration at the inlet of a distributed WTP Cc,tpout concentration at the outlet of a distributed WTP Cc,ttpin concentration at the inlet of a terminal WTP Cc,ttpout concentration at the outlet of a terminal WTP EDtpout,tpin environmental effect score of pipe scrap recycling from the outlet of a distributed WTP to the inlet of a distributed WTP EDtpout,ttpin environmental effect score of pipe scrap recycling from the outlet of a distributed WTP to the inlet of a terminal WTP EDww,tpin environmental effect score of pipe scrap recycling from a wastewater source to a distributed WTP EDww,ttpin environmental effect score of pipe scrap recycling from a wastewater source to a terminal WTP environmental effect score of electricity consumption EEpt for pumping environmental effect score of electricity consumption EEwtt for wastewater treatment environmental effect score in the construction stage EEScon EESO&Mt environmental effect score in the O&M stage environmental effect score in the disposal stage EESdis EMRCt environmental effect score of M&R EPtpout,tpin environmental effect score of piping from the outlet of a distributed WTP to the inlet of a distributed WTP EPtpout,ttpin environmental effect score of piping from the outlet of a distributed WTP to the inlet of a terminal WTP EPww,tpin environmental effect score of piping from a wastewater source to a distributed WTP EPww,ttpin environmental effect score of piping from a wastewater source to a terminal WTP flowrate at the inlet of a distributed WTP Ftpin
1455
Ftpin,ttpin flowrate from the inlet of a distributed WTP to the inlet of a terminal WTP flowrate at the outlet of a distributed WTP Ftpout Ftpout,tpin flowrate from the outlet of a distributed WTP to the inlet of a distributed WTP Ftpout,ttpin flowrate from the outlet of a distributed WTP to the inlet of a terminal WTP flowrate at the inlet of a terminal WTP Fttpin Fttpout,dis flowrate at the outlet of a terminal WTP Fww,tpin flowrate from a wastewater source to the inlet of a distributed WTP total flowrate of wastewater treated in the distributed Ftpint WTPs HLtpout,tpin head loss through a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP HLtpout,ttpin head loss through a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP HLww,tpin head loss through a pipe from a wastewater source to the inlet of a distributed WTP HLww,ttpin head loss through a pipe from a wastewater source to the inlet of a terminal WTP power requirement for wastewater treatment in Ptpin a distributed WTP Ptpout,tpin power requirement for pumping wastewater from the outlet of a distributed WTP to the inlet of a distributed WTP Ptpout,ttpin power requirement for pumping wastewater from the outlet of a distributed WTP to the inlet of a terminal WTP power requirement for wastewater treatment in Pttpin a terminal WTP Pww,tpin power requirement for pumping wastewater from a wastewater source to the inlet of a distributed WTP Pww,ttpin power requirement for pumping wastewater from a wastewater source to the inlet of a terminal WTP TEES total environmental effect score during the life cycle vtpout,tpin optimal velocity through a pipe from the outlet of a distributed WTP to the inlet of a distributed WTP vtpout,ttpin optimal velocity through a pipe from the outlet of a distributed WTP to the inlet of a terminal WTP vww,tpin optimal velocity through a pipe from a wastewater source to the inlet of a distributed WTP vww,ttpin optimal velocity through a pipe from a wastewater source to the inlet of a terminal WTP Parameters regression parameter for an environmental effect score ade of pipe scrap recycling regression parameter for an optimal velocity aop regression parameter for an environmental effect score ape of piping regression parameter for an environmental effect score bde of pipe scrap recycling regression parameter for an optimal velocity bop regression parameter for an environmental effect score bpe of piping concentration of a wastewater source Cc,ww d coefficient for an M&R cost f friction factor g acceleration of gravity additional head required to take into account the Ha elevation of WTPs hmotor motor efficiency hpump pump efficiency
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ltpout,tpin Pipe length from the outlet of a distributed WTP to the inlet of a distributed WTP ltpout,ttpin Pipe length from the outlet of a distributed WTP to the inlet of a terminal WTP lww,tpin pipe length from a wastewater source to the inlet of a distributed WTP lww,ttpin pipe length from a wastewater source to the inlet of a terminal WTP contaminant load of a distributed WTP Ltpin contaminant load of a terminal WTP Lttpin r density of wastewater removal efficiency of a distributed WTP Rc,tp removal efficiency of a terminal WTP Rc,ttp t service lifetime operating hours per annum top unit environmental effect score of electricity UEe consumption Superscript max maximum
design capacities and have superfluous treatment capacities. This is because wastewater generation rates have been decreased by water reuse, recycling and recirculation which are used to reduce freshwater consumption rates. Therefore, the synthesis can be employed to effectively manage the resources and costs associated with wastewater treatment by fully utilizing the superfluous capacities. However, it should be mentioned that the synthesis should also take into account the environmental performance of a wastewater treatment system in order to be in line with cleaner production and sustainable development. The environmental and economic feasibility of a TWTNS was evaluated and analyzed to show the effect of the wastewater treatment network synthesis performed to reduce economic costs (Lim et al., 2008). The result showed that the environmental impacts of the TWTNS were greater than those of the conventional wastewater treatment system. Therefore, a new mathematical optimization model is required to synthesize the most environmentally friendly wastewater treatment network system. Much effort has been made to reduce environmental impacts of systems, processes, products and services over their life cycle. Design for environment (DfE), eco-design, and cleaner production have become more important concepts in many industries to enhance their environmental and economic performance (Dewulf et al., 2004; Donnelly et al., 2006; Kjaerheim, 2005). A life cycle perspective is required to take into account tradeoffs among the environmental impacts incurred throughout all life cycle stages. This is because improving the tradeoffs of one stage can worsen the tradeoffs of other stages. Therefore, tradeoffs among environmental impacts should be optimized to minimize the total environmental impacts associated with systems, processes, products and services. Life cycle assessment (LCA) is a systematic methodology to evaluate environmental impacts incurred during the life cycle (Friedrich, 2002; Carpentieri et al., 2005). LCA quantifies environmental impacts from all inputs and outputs through the system boundary, which enables to identify principal contributors to the total environmental impacts of the system. These principal contributors, so-called hot spots, are primary targets to focus on to effectively reduce total environmental impacts. DfE, eco-design and cleaner production have been employed to decrease the environmental impacts of principal contributors. This study developed a mathematical optimization model to retrofit existing distributed and terminal WTPs into an environmentally friendly TWTNS from the life cycle perspective. The
min
minimum
Acronyms DfE design for environment EES environmental effect score ELU environmental load unit ENS environmental effect score-minimized total wastewater treatment network system FNS flowrate-minimized total wastewater treatment network system LCA life cycle assessment LCI life cycle inventory analysis LCIA life cycle impact assessment M&R maintenance and repair MINLP mixed integer nonlinear programming O&M operations and maintenance TEES total environmental effect score TWTNS total wastewater treatment network system WTP wastewater treatment plant
objective function of the model was formulated with principal contributors to environmental impacts in all life cycle stages. The mass balances were formulated on the basis of the superstructure model, and the constraints were formulated to reflect real wastewater treatment situations in industrial plants. LCA was used to obtain linear regression equations from the relationship between the specifications of contributors and their environmental effect scores (EESs) indicating the amount of environmental impacts: the linear relationship is employed to simplify the mathematical model. A case study was performed to validate the mathematical optimization model by demonstrating the environmental performance of an EES-minimized TWTNS (ENS). The ENS was compared to the TWTNS (FNS) optimized by minimizing a total flowrate of wastewater treated in distributed WTPs. The EESs of principal contributors in each system were estimated and compared to examine the effects of the proposed model used to minimize a total EES on the configuration and characteristics of a TWTNS.
2. Mathematical optimization model 2.1. Superstructure model A TWTNS is generated on the basis of the generalized superstructure model (Lim et al., 2008). The superstructure model is presented in Supplementary Material (Fig. S-1). This superstructure model includes all possible interconnections: (1) from the wastewater sources to the inlets of the distributed and terminal WTPs to allocate wastewater into all the WTPs; and (2) from the outlets of the distributed WTPs to their inlets to dilute the contaminant concentration of influents to the distributed WTPs, and (3) from the outlets of the distributed WTPs to the inlets of the terminal WTPs to polish the wastewater treated in the distributed WTPs. The existence of each interconnections in the superstructure model and the operation of each WTP in the distributed WTPs are expressed as binary variables in the mathematical optimization model: ‘‘0’’ means that the interconnection is not necessary or the WTP does not need to be operated; and ‘‘1’’ means that the interconnection is required to transfer wastewater or the WTP is required to treat wastewater. Wastewater in all the streams is transferred by pumps. It is assumed that the mixers combine all possible streams into one stream and that the splitters divide a given stream into all possible streams.
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2.2. Mathematical formulation The mathematical optimization model minimizing the environmental impacts of a TWTNS is a mixed integer nonlinear programming (MINLP) model because bilinear variables are included in the mass balances of contaminants and because binary variables are required to express whether the streams exist or whether the WTPs are operated. Sets are defined for this mathematical formulation as follows: C, a set of contaminants in wastewater; WW, wastewater sources; TP, distributed WTPs; and TTP, terminal WTPs. 2.2.1. Objective function The sum of EESs over the life cycle is minimized as the objective function. The total EES (TEES) is composed of the EESs of principal contributors to environmental impacts in the stages of construction, operations and maintenance (O&M), and disposal. The objective function is as follows:
Minimize TEES ¼ EEScon þ
t X
The principal contributors in the O&M stage are electricity consumption for pumping and for wastewater treatment, and maintenance and repair (M&R). The EES of electricity consumption for pumping is estimated from the power requirement which is calculated with the flowrate and pressure required for pumping. The pressure is the sum of the head loss through the pipeline and the additional head required to take into account the elevations of WTPs. The head loss is calculated by using the Darcy–Weisbach equation (McGhee, 1991). Electricity consumption for wastewater treatment is required to operate equipment such as agitators, flocculators and sludge scrapers in distributed and terminal WTPs. The EES of M&R is assumed to be proportional to the EES of piping in the construction stage. Equations used to estimate the EES in the O&M stage are as follows:
EEStO&M ¼ EEtp þ EEtwt þ EMRCt
þ EESdis
(1)
EEtp ¼ @
t¼1
X
EEScon ¼
X
þ
X
X
EPww;tpin þ
ww˛WW tpin˛TP
X
X
X
EPtpout;ttpin
ð2Þ
tpout˛TP ttpin˛TTP
EPww;tpin ¼ ape Aww;tpin þ bpe lww;tpin Bww;tpin EPww;ttpin ¼ ape Aww;ttpin þ bpe lww;ttpin Bww;ttpin EPtpout;tpin ¼ ape Atpout;tpin þ bpe ltpout;tpin Btpout;tpin EPtpout;ttpin ¼ ape Atpout;ttpin þ bpe ltpout;ttpin Btpout;ttpin
(3)
where B means the binary variable to formulate the existence of a pipe.
¼
Fww;tpin Fww;ttpin ; Aww;ttpin ¼ ; Atpout;tpin vww;tpin vww;ttpin Ftpout;tpin Ftpout;ttpin ; Atpout;ttpin ¼ vtpout;tpin vtpout;ttpin
Ptpout;tpin
tpout˛TP tpin˛TP
þ
X
X
1 Ptpout;ttpin AUEe $top
tpout˛TP ttpin˛TTP
HLww;ttpin ¼ f
!0:5
lww;ttpin p g Aww;ttpin
(4)
vww;tpin ¼ aop Fww;tpin þ bop ; vww;ttpin ¼ aop Fww;ttpin þ bop ; vtpout;tpin ¼ aop Ftpout;tpin þ bop ; vtpout;ttpin ¼ aop Ftpout;ttpin þ bop (5) The optimal velocity in the pipeline is assumed to be linear with the flowrate as a way to express their relationship, because designating the optimal velocity as a parameter cannot take into account the effect of pipe diameter on the variability of head loss and because an exact equation for their relationship cannot be obtained even from nonlinearity without the distance of the pipe. It should be noted that a regression equation for the optimal velocity from flowrate, pipe diameter and the distance of the pipe was not taken into account here because this makes the mathematical model more complicated and is not the scope of this study.
HLtpout;ttpin ¼ f
v2ww;tpin
!0:5
ltpout;tpin p HLtpout;tpin ¼ f g Atpout;tpin
where
Aww;tpin ¼
X
X
Pww;ttpin
ww˛WW ttpin˛TTP
lww;tpin p HLww;tpin ¼ f g Aww;tpin
EPtpout;tpin
X
þ
X
X
Pww;tpin þ
EPww;ttpin
ww˛WW ttpin˛TTP
tpout˛TP tpin˛TP
þ
X
X
ww˛WW tpin˛TP
The EES in the construction stage originates from piping for the streams shown in the superstructure model. The EES of piping is estimated using the linear regression equation obtained in the LCA (the R2 of this regression is 0.974, as shown in the case study). Equations used to estimate the EES of piping are as follows:
(6)
where
0 EEStO&M
1457
4
v2ww;ttpin
!0:5
ltpout;ttpin p g Atpout;ttpin
Bww;tpin
4
v2tpout;tpin
!0:5
Bww;ttpin
4
(8) Btpout;tpin
v2tpout;ttpin 4
Btpout;ttpin
rFww;tpin g HLww;tpin þ Ha 1 hpump h 1000 motor rFww;ttpin g HLww;ttpin þ Ha 1 Pww;ttpin ¼ hpump h 1000 motor rFtpout;tpin g HLtpout;tpin þ Ha 1 Ptpout;tpin ¼ hpump h 1000 motor rFtpout;ttpin g HLtpout;ttpin þ Ha 1 Ptpout;ttpin ¼ hpump hmotor 1000
Pww;tpin ¼
0 EEtwt ¼ @
X
Ptpin Btpin þ
tpin˛TP
X
(9)
1 Pttpin Bttpin AUEe $top
(10)
ttpin˛TTP
where B means the binary variable to formulate the existence of a WTP.
EMRCt ¼ d$EEScon
(11)
The EES in the disposal stage is derived from the recycling of the pipeline built in the construction stage. This EES is estimated using the linear regression equation for the relationship between the unit EES of pipe scrap recycling and the cross-sectional area of the pipeline (the R2 of this regression is 0.974 as shown in the case
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study). Equations used to estimate the EES in the disposal stage are as follows:
X
EESdis ¼
X
ww˛WW tpin˛TP
þ
X
EDww;ttpin
ww˛WW ttpin˛TTP
X
X
X
EDww;tpin þ EDtpout;tpin þ
tpout˛TP tpin˛TP
X
X
EDtpout;ttpin
tpout˛TP ttpin˛TTP
(12)
where
EDww;tpin ¼ ade Aww;tpin þ bde lww;tpin Bww;tpin EDww;ttpin ¼ ade Aww;ttpin þ bde lww;ttpin Bww;ttpin EDtpout;tpin ¼ ade Atpout;tpin þ bde ltpout;tpin Btpout;tpin EDtpout;ttpin ¼ ade Atpout;ttpin þ bde ltpout;ttpin Btpout;ttpin
(13)
2.2.2. Mass balances and constraints The mass balances in this model are formulated on the basis of the superstructure model described above, and the constraints are formulated to reflect the real wastewater treatment situations in industrial plants. Equations for the mass balances and constraints are as follows. For the overall mass balance of the TWTNS,
X
X
X
Fww;tpin þ
ww˛WW tpin˛TP
X
X
Fttpout;dis ¼ 0
ð14Þ
ttpout˛TTP
For the mass balances of the distributed WTP inlet mixers,
X
X
Fww;tpin þ
ww˛WW
Ftpout;ttpin Ftpin ¼ 0
(15)
tpout˛TP
X
Fww;tpin Cc;ww þ
ww˛WW
X
Ftpout;ttpin Cc;tpout Ftpin Cc;tpin ¼ 0
tpout˛TP
(16) For the mass balances of the distributed WTPs,
Ftpin Ftpout ¼ 0
(17)
Ftpin Cc;tpin 1 Rc;tp Ftpout Cc;tpout ¼ 0
(18)
For the mass balances of the distributed WTP outlet splitters,
X
Ftpout
X
Ftpout;tpin
tpin˛TP
Ftpout;ttpin ¼ 0
(19)
ttpin˛TTP
For the mass balances of the terminal WTP inlet mixers,
X
Ftpout;ttpin þ
tpout˛TP
X tpout˛TP
X
Fww;ttpin Fttpin ¼ 0
(20)
ww˛WW
Ftpout;ttpin Cc;tpout þ
X
Fww;ttpin Cc;ww
ww˛WW
Fttpin Cc;ttpin ¼ 0
ð21Þ
For the mass balances of the terminal WTPs,
Fttpin Fttpout;dis ¼ 0 Fttpin Cc;ttpin 1 Rc;ttp Fttpout;dis Cc;ttpout ¼ 0
(24)
Ftpin 0
(25)
Ftpin Cc;tpin Lmax tpin 0
(26)
max Fttpin Fttpin Bttpin 0
(27)
Fttpin 0
(28)
Fttpin Cc;ttpin Lmax ttpin 0
(29)
max Fww;tpin Fww;tpin Bww;tpin 0 max Bww;ttpin 0 Fww;ttpin Fww;ttpin max Btpout;tpin 0 Ftpout;tpin Ftpout;tpin max Btpout;ttpin 0 Ftpout;ttpin Ftpout;ttpin
(30)
min Bww;tpin 0 Fww;tpin Fww;tpin min Fww;ttpin Fww;ttpin Bww;ttpin 0 min Ftpout;tpin Ftpout;tpin Btpout;tpin 0
(31)
min Ftpout;ttpin Ftpout;ttpin Btpout;ttpin 0
Fww;ttpin
ww˛WW ttpin˛TTP
max Ftpin Ftpin Btpin 0
(22) (23)
For the constraints of flowrates and loads on the distributed and terminal WTPs,
For the constraint of concentrations on the discharge quality of the terminal WTP required to consistently meet the discharge limits of environmental regulations, max Cc;ttpout Cc;ttpout 0
(32)
In this mathematical model, the number of the continuous variables is 7(mn þ mk þ n2 þ nk þ 1) þ (n þ k)(2c þ 3), the number of the binary variables is mn þ mk þ n2 þ nk þ n þ k, and the number of the constraints is 2(mn þ mk þ n2 þ nk) þ 3nc(n þ k) þ 5n þ 4k, where the number of the contaminants, wastewater sources, distributed WTPs, and terminal WTPs are c, m, n and k, respectively. 3. Life cycle assessment An LCA methodology was used to quantitatively evaluate the environmental impacts associated with principal contributors. The specifications of contributors and their LCA results were fitted to obtain linear regression equations. The principal contributors assessed in the LCA consisted of (1) piping in the construction stage, (2) electricity for pumping and for wastewater treatment and M&R in the O&M stage, and (3) pipe scrap recycling in the disposal stage (Lim et al., 2008). Existing distributed and terminal WTPs, and existing pump pits in a TWTNS were excluded from the system boundary for the LCA because the civil structures already existed. However, the system boundary included the electricity consumption required to operate mechanical equipment (agitators, flocculators and sludge scrapers) in the distributed and terminal WTPs. Chemicals consumption and sludge generation were excluded in the LCA because the dosages of coagulants and flocculants used and the quantity of sludge generated were assumed to be proportional to contaminant loads, which are not changed from wastewater treatment network synthesis. In other words, environmental impacts from chemicals consumption and sludge generation in a TWTNS are equal to those in other alternatives. This assumption could simplify the mathematical optimization model. It should be mentioned that it is difficult to accurately estimate the amounts of chemicals used and sludge generated because the properties of wastewater such as pH and
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alkalinity affect the dosage of coagulants and flocculants and the quantity of sludge generated. The LCA procedure was in accordance with the ISO 14040 series of standards (ISO, 1997). The life cycle inventory analysis (LCI) used the GaBi 4.0 (IKP and PE Europe GMBH, 2004) and Ecoinvent v1.2 (Swiss Center for Life Cycle Inventories, 2005) databases: the data of a process included all inputs and outputs associated with a process itself and its supply chains. The life cycle impact assessment (LCIA) evaluated the significance of potential environmental impacts on the basis of the LCI results. The EPS 2000 methodology (Steen, 1999) was employed for the classification and characterization in the LCIA to evaluate the EES of contributors. This methodology uses a single unit, i.e. ELU (Environmental Load Unit) regardless of environmental impact categories such as emissions to air, water, or soil, and impacts on humans. The single unit makes it easy to evaluate tradeoffs among the environmental impacts of different environment impact categories. The EESs of the contributors from the LCIA results and their specifications were fitted to generate linear regression equations. 3.1. Construction stage Environmental impacts incurred from piping were evaluated repeatedly for various diameters of pipes in order to obtain a linear regression equation. The system and its function for the LCA of the piping were defined as the carbon steel pipeline required to transfer wastewater. The reference flow was set to the pipeline of 1 m to obtain its unit EES, which was used to obtain its regression equation with its cross-sectional area and then this equation was integrated into the mathematical model to estimate the total environmental impacts of piping. The system boundary included the processes required to construct the pipeline, as shown in Fig. 1(a). Pipe is made from hot rolled strip with squared or slightly beveled edges, which has supply chains such as the extraction of iron ore, lime and coal as well as integrated steel processes. The strip is drawn through rolls forming a cylindrical shape. The edges
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are pressed together forming a butt-weld with an electric welder. The pipe is transported from a pipe manufacturer to a wastewater treatment system and then is welded to construct the pipeline. The LCA results of piping were used to generate a linear regression equation which enables prediction of the unit EES of the pipeline with the cross-sectional area of the pipe. Equipment for pumping was excluded from the system boundary of the LCA because its environmental impacts were assumed to be negligible considering the small proportion of its weight to the total of a TWTNS (Lim et al., 2008). 3.2. Operations and maintenance stage Environmental impacts in the O&M stage are incurred from electricity consumption for pumping and for wastewater treatment and from M&R. The system and its function for the LCA of electricity consumption were defined as the electricity required to operate equipment. The reference flow was set to 1 kWh of electricity to obtain its unit EES, which was used to estimate the total environmental impacts from electricity consumption for pumping and for wastewater treatment. Germany electricity mix data was used to evaluate the unit EES of the electricity. The environmental impacts of M&R for the pipeline were assumed to be linearly proportional to those of piping in the construction stage, taking into account that the M&R cost for the pipeline is assumed to be 3% of the construction cost for piping (Peters and Timmerhaus, 1991). 3.3. Disposal stage Environmental impacts incurred from recycling of pipe scrap were evaluated repeatedly for the various diameters of pipes in order to obtain a linear regression equation. The system and its function for the LCA of pipe scrap recycling were defined as the recycling of carbon steel pipe scrap into a part of a steel billet. The reference flow was set to the pipeline of 1 m. The system boundary included the processes required to recycle the pipe scrap, as shown in Fig. 1(b). The pipe scrap is transported to steelmaking facilities operating an electric arc furnace where scrap is charged, melted, refined, and tapped. The molten steel is converted into a semifinished product such as a steel billet that is suitable for further processing. The LCA results were used to generate a linear regression equation which enables prediction of the unit EES of pipe scrap recycling with the cross-sectional area of the pipeline. 4. Case study A case study was analyzed to demonstrate that the strategy presented in this study is suitable for synthesizing an environmentally friendly TWTNS and to estimate the effect of the model on the configuration and EES of a TWTNS. Two types of TWTNSs were synthesized independently using their own objective functions: minimization of the total flowrate of wastewater treated in distributed WTPs, and minimization of the total EES of a TWTNS. The EES-minimized TWTNS (ENS) was compared to the flowrateminimized TWTNS (FNS). The EESs of the principal contributors in each system were also estimated and compared. 4.1. Methods
Fig. 1. System boundaries for LCA. The LCA evaluated the environmental impacts from the processes and their supply chains: (a) construction of the pipeline and (b) recycling of pipe scrap.
Wastewater sources and existing distributed and terminal WTPs in an iron and steel plant were used to generate the FNS and ENS. Characteristics of the four wastewater sources are described in Supplementary Material (Table S-1), and those of the four distributed and one terminal WTPs are shown in Supplementary Material (Table S-2). The distributed WTPs were used to treat the wastewater sources one-to-one. The terminal WTP was used to polish the
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effluents of the distributed WTPs, in order to consistently meet discharge limits. All the WTPs consisted of physical and chemical treatment processes, such as coagulation, flocculation and sedimentation. A distance matrix for interconnections between the wastewater sources and sinks is shown in Supplementary Material (Table S-3). The required discharge quality of the terminal WTP was set at 20 mg/L for CODcr, 5 mg/L for SS, and 8 mg/L for F to stably meet environmental regulations. The FNS and ENS were independently generated using their own objective functions subjected to the same mass balances and constraints. For the objective function used for the FNS, t Minimize Ftpin ¼
X
Ftpin
(33)
tpin˛TP
The FNS was generated with Eqs. (14)–(33), and the ENS with Eqs. (1)–(32). All parameters were set to optimize the mathematical model for the ENS. The service life (t) was assumed to be 15 years, based on the lifetime of the pipes. The regression parameters for the EES of piping were obtained as follows:
ape ¼ 66:457; bpe ¼ 0:921; R2 ¼ 0:974 The optimal velocity required to determine the cross-sectional area of the pipeline in the pumping flow was obtained from its relationship with the flowrate. The field data gathered in the plant was used to obtain this regression equation. The parameters used to determine the optimal velocity are as follows:
aop ¼ 0:0297; bop ¼ 0:6173; R2 ¼ 0:849 The distances between the wastewater sources and sinks were used as pipe lengths. The unit EES of electricity consumption was set to 0.255 ELU/kWh according to the LCA results. The additional head (Ha) was assumed to be 10 m H2O to take into account available waster pressure at the end of the pipeline. The efficiencies of pump and motor were assumed to be 0.60 and 0.85, respectively. The annual operating time (top) of the WTPs was set to 8760 h. The parameter used to calculate the EES of M&R was assumed as follows:
d ¼ 0:3 The regression parameters used to estimate the unit EES of pipe scrap recycling were obtained as follows:
ade ¼ 96:955; bde ¼ 1:342; R2 ¼ 0:974 The FNS and ENS were generated from the optimal solutions to each model, which were found by using GAMS (GAMS Development Corporation, 2005): CPLEX for linear programming, MINOS for nonlinear programming, and DICOPT for MINLP. GAMS was iteratively run by designating the values of variables from the outcomes of a run as the initial values of variables for the next run. The best solutions were regarded as optimal solutions, which were employed to embody the configurations of each system. The best local solution is useful for industrial applications in the absence of a global optimum, which is difficult to be attained. This is because global optima cannot easily be obtained because of the nonconvexities derived from bilinear variables in the mass balances of contaminants. The CPU times were 3.547 s for the FNS and 3.875 s for the ENS on Pentium 4, 3.0 GHz PC. The EESs of the principal contributors in the FNS and ENS were estimated using Eqs. (1)–(13). The total EESs over the life cycle of the ENS were compared to those of the FNS. 4.2. Results and discussion The FNS and ENS were embodied from the optimal solutions to their models, as shown in Fig. 2. Characteristics of the FNS and ENS are summarized in Table 1. The total pipe length of the ENS was 1.2% less than that of the FNS, and the number of pumps required in the ENS was 8.3% less than that in the FNS. The power requirement for pumping of the ENS was 0.6% less than that of the FNS. These results show that the objective function for the ENS decreased its power requirement for pumping by reducing the pipe length and the number of pumps. The number of the distributed WTPs used in the FNS and ENS were the same; however, TP4 was closed in the FNS, while TP2 in the ENS. The power requirement for wastewater treatment of the ENS was 17.9% less than that of the FNS because
Fig. 2. Comparison of the total wastewater treatment network systems (TWTNSs): (a) flowrate-minimized TWTNS (FNS); (b) EES-minimized TWTNS (ENS) (WW: wastewater, TP: distributed wastewater treatment plant, TTP: terminal wastewater treatment plant).
S.-R. Lim, J.M. Park / Journal of Environmental Management 90 (2009) 1454–1462
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Unit
FNS
ENS
Length
m
2440
2410
Pump
Number
–
12
11
for pumping accounted for 69.6% and 73.4% of the total EESs of the FNS and ENS. The EESs of electricity consumption for wastewater treatment accounted for 30.0% and 26.2% of the total EESs of FNS and ENS. Therefore, the electricity consumption for both pumping and wastewater treatment can be employed as a simplified objective function for the practical retrofitting of an existing wastewater treatment system.
Distributed wastewater treatment plants
Number (in use) Flowrate of treated wastewater Flowrate of bypassing wastewater
– m3/h
3 19.6
3 20.0
5. Conclusions
m3/h
14.6
14.2
Terminal wastewater treatment plant
Flowrate of treated wastewater
m3/h
34.2
34.2
Power requirement
Electricity (pumping) Electricity (wastewater treatment)
kW kW
Table 1 Characteristics of the FNS and ENS (FNS: flowrate-minimized total wastewater treatment network system, ENS: EES-minimized total wastewater treatment network system). Item Pipe
6.49 2.80
6.45 2.30
A strategy to synthesize an environmentally friendly TWTNS was developed from a life cycle perspective. A case study was performed to illustrate the model and to demonstrate the effect of the objective function on the configuration and environmental performance of a TWTNS. The major conclusions from this study are as follows: -
the power requirement of TP2 was greater than that of TP4. The flowrate of the wastewater treated through distributed WTPs in the ENS was 2.0% greater than that in the FNS; the flowrate of the wastewater bypassing distributed WTPs in the ENS was 2.7% less than that in the FNS. These were because the FNS was synthesized to minimize the flowrate of the wastewater treated in the distributed WTPs, while the ENS was generated by optimizing the tradeoffs between the decrease of the power requirement and the increase of the flowrate treated through the distributed WTPs. All the EESs of each principal contributor in the ENS were less than those of the FNS, and the total EES during the life cycle of the ENS was less than that of the FNS, as shown in Table 2. The EES of piping in the ENS was 1.0% less than that in the FNS. The EESs of electricity consumption for pumping and for wastewater treatment in the ENS were 0.5% and 21.7% less than those in the FNS, respectively. The EES of M&R in the ENS was 1.0% less than that in the FNS. The EES of pipe scrap recycling in the ENS was 1.0% less than that in the FNS. The total EES during the life cycle of the ENS was 6.0% less than that of the FNS. Decreasing the EES of electricity consumption for wastewater treatment was found to be a key factor to greatly reduce the environmental impacts of the TWTNS. Thus, the proposed model optimized the ENS by excluding the distributed WTP (TP2), which required the highest power for wastewater treatment. The electricity consumption for pumping and for wastewater treatment can be selected to simplify the model further. The EES in the O&M stage was the greatest component among the EESs in the life cycle stages, accounting for 98.0% and 97.9% of the total EESs of the FNS and ENS, respectively. The EESs of electricity consumption
Table 2 Environmental effect scores of the total wastewater treatment network systems (FNS: flowrate-minimized total wastewater treatment network system, ENS: EESminimized total wastewater treatment network system, Unit: ELU). FNS Construction (A)
Piping
Operations & maintenance (B)
Electricity for pumping during a year Electricity for wastewater treatment during a year Maintenance & repair (M&R) during a year Sub-total (during the service time, i.e., 15 years)
Disposal (C)
Pipe recycling
Total EES during the life cycle (A þ B þ C)
ENS
2604
2579
14,489
14,411
6255
5138
78
77
312,331
294,397
3795
3759
318,730
300,735
-
-
-
The mathematical optimization model can be used to effectively improve the environmental performance of a wastewater treatment system by retrofitting existing WTPs which treat lower wastewater flowrates than their design flowrates. The LCA results were integrated into the mathematical model to minimize the total environmental impacts of a TWTNS and to optimize tradeoffs among the environmental impacts of principal contributors. This methodology can be applied to improve the environmental performance of various processes and systems. The model can easily be applied to practically implement an environmentally friendly TWTNS in industrial plants, because the model can be simplified by using only the electricity consumption for pumping and wastewater treatment in the objective function of the model. The model for an environmentally friendly TWTNS can contribute to cleaner production and sustainable development. This is because minimizing the environmental impacts of a TWTNS induces its cost reduction: electricity consumption for pumping and for wastewater treatment is a common contributor to environmental impacts and economic costs.
Acknowledgements This work was financially supported in part by the Korean Science and Engineering Foundation (R11-2003-006) through the Advanced Environmental Biotechnology Research Center at Pohang University of Science and Technology and in part by the Korean Ministry of Commerce, Industry, and Energy through the Korean National Cleaner Production Center. This work was also supported by the program for advanced education of chemical engineers (2nd stage of BK21). Appendix. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi: 10.1016/j.jenvman.2008.09.005. References Carpentieri, M., Corti, A., Lombardi, L., 2005. Life cycle assessment (LCA) of an integrated biomass gasification combined cycle (IBGCC) with CO2 removal. Energy Conversion and Management 46, 1790–1808. Dewulf, W., Duflou, J., Ander, A., 2004. Toward a sectorwide design for environment support system for the rail industry. Environmental Management 34, 181–190. Donnelly, K., Beckett-Furnell, Z., Traeger, S., Okrasinski, T., Holman, S., 2006. Ecodesign implemented through a product-based environmental management system. Journal of Cleaner Production 14, 1357–1367. Friedrich, E., 2002. Life-cycle assessment as an environmental management tool in the production of potable water. Water Science and Technology 20, 29–36.
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