Synthesis of an Environmentally Friendly Water Network System

Seong-Rin Lim† and Jong Moon Park*,‡

Department of Bioproducts and Biosystems Engineering, UniVersity of Minnesota, 1390 Eckles AVenue, St.Paul, Minnesota 55108, and AdVanced EnVironmental Biotechnology Research Center, Department of ChemicalEngineering, School of EnVironmental Science and Engineering, Pohang UniVersity of Science and Technology,San 31, Hyoja-dong, Pohang 790-784, South Korea

Water network synthesis contributes to cost reduction and the conservation of water resources by reducingwater consumption. A mathematical optimization model is developed in this study to synthesize anenvironmentally friendly water network system (WNS) by minimizing environmental impacts of a WNS.The concept of life cycle assessment (LCA) is integrated into the objective function of the model to evaluatethe environmental effect scores (EESs) of principal contributors to the environmental impacts of a WNS andto optimize tradeoffs among their EESs. The mass balances are formulated from the superstructure model,and the constraints are formulated to take into account the real situation in industrial plants. A case studydemonstrates the effect of the objective function on the configuration and environmental performance of aWNS and validates the mathematical optimization model. This model can be used for the design for environment(DfE) of WNSs in the context of sustainable development.

1. Introduction

Industrial plants have made much effort in reducing economiccosts from processes and systems to enhance their competitive-ness and profitability. Water supply systems are common targetsfor cost reduction in various industrial sectors. Many technolo-gies have been developed to reduce costs incurred from watersupply, water treatment, and wastewater treatment. Waternetwork synthesis has also been employed as a way to reducecosts because the synthesis decreases the flowrates of freshwaterconsumption and wastewater generation; as a result, thesynthesis contributes to the conservation of water resources. Theconcept of water network synthesis is to optimize a water supplysystem by maximizing water reuse: water sources (e.g.,wastewater, and/or treated wastewater) are used for water sinks(e.g., systems and processes) in case the properties of watersources meet the water quality requirement of water sinks.1

Most previous studies have focused on solving mathematicaloptimization models, such as nonlinear programming (NLP) andmixed-integer nonlinear programming (MINLP), to find globaloptima.1-6 The first water network optimization was performedfor a petroleum refinery plant in 1980.2 A genetic algorithmhas also been developed to solve MINLP used for wastewaterminimization and search for global optima.7 These studies arenecessary because of nonconvexities from bilinear variables inthe mass balances of the models.

The objective functions in previous studies have beenformulated to minimize economic costs rather than environ-mental impacts. Simple formulas for investment and operatingcosts have been used to represent a total annual cost.2 Moststudies have minimized a single contributor, such as a totalfreshwater flowrate, the number of interconnections, or fixedcosts.1,7-10 Some objective functions have been formulated withthe sum of operating costs for freshwater and capital costs forpipes and wastewater treatment, or with the sum of a few capitaland operating costs6,11or with the sum of the costs of freshwater

supply, water and wastewater treatment, pipes, and sewers.12

The concept of life cycle costing has been employed forevaluating the economic feasibility and profitability of a waternetwork system (WNS),13 and for developing the mathematicaloptimization model to synthesize an economically friendlyWNS.14

Pinch analysis technologies have been studied to graphicallyanalyze the process limiting data of water-using operations.1,15-19

These graphical targeting methods suggest the minimumfreshwater consumption flowrate of a water system and identifyany bottlenecks that affect freshwater consumption. The resultsof pinch analysis have been used to heuristically generate aWNS. However, these methods do not take into account theeconomic or environmental aspect of their designs.

Environmentally friendly design approaches to water networksynthesis have become more important to improve the envi-ronmental performance of a WNS as much effort has been madeto reduce environmental impacts of systems, processes, products,and services from the life cycle perspective. Life cycle assess-ment (LCA) is regarded as to a good tool to be applied to thedesign for environment (DfE): LCA is a systematic methodol-ogy to evaluate the significance of potential environmentalimpacts incurred during the life cycle.20,21The LCA of a systemquantifies all inputs and outputs which are associated with theacquisition of raw materials, production, use, and disposal overall the supply chains of the system during the life cycle, andthen evaluates the environmental effect scores (EESs) ofenvironmental impacts which are classified into various envi-ronmental impact categories such as emissions to air, water, orsoil.

An attempt has been made in water network synthesis to takeinto account environmental impacts: multiobjective optimizationhas been studied by Erol and Thoming to minimize a totalannualized cost and environmental impacts.22 However, theobjective function had some limitations as follows: (1) Themultiobjective function has not taken into account the environ-mental impacts from electricity consumption, which is requiredto pump water from a water source to a sink. The electricityconsumption in the operation and maintenance (O&M) stage isone of the principal contributors to the environmental impacts

* To whom all correspondence should be addressed. Tel.:+82-54-279-2275. Fax:+82-54-279-2699. E-mail: jmpark@postech.ac.kr.

† University of Minnesota.‡ Pohang University of Science and Technology.

1988 Ind. Eng. Chem. Res.2008,47, 1988-1994

10.1021/ie071302d CCC: $40.75 © 2008 American Chemical SocietyPublished on Web 02/23/2008

of a WNS from the life cycle perspective.23 It should beemphasized that the total power requirement of pumps in a WNSis changed depending on its configuration, which is embodiedfrom an optimal solution to a mathematical optimization model.(2) The EES of only one environmental impact category wasused to formulate the objective function by selecting themaximum EES among the EESs in the various categories. Inother words, the configuration of the WNS from the objectivefunction was not optimized by minimizing overall environmentalimpacts.

Principal contributors to environmental impacts should beformulated in the objective function of the mathematicaloptimization model needed to generate an environmentallyfriendly WNS. Principal contributors, so-called hot spots, arethe primary targets on which to focus to effectively reduce totalenvironmental impacts. The employment of the principalcontributor simplifies the model by excluding minor contribu-tors, which makes it easy to apply the model to the real situationsof industrial plants.

This study developed a mathematical optimization model tosynthesize an environmentally friendly WNS from the life cycleperspective. The unit EESs of each principal contributor werefirst evaluated on the basis of LCA databases. The objectivefunction was formulated from the sum of the EESs of principal

contributors to environmental impacts throughout the life cycle.The mass balances were formulated on the basis of thesuperstructure model, and the constraints were formulated toreflect real situations in industrial plants. A case study wasperformed to validate the mathematical optimization model bydemonstrating the environmental performance of an EES-minimized WNS (EWNS). The EWNS was compared to (1)the WNS optimized by minimizing a total freshwater flowrate(FWNS), and (2) the WNS optimized by minimizing a totalfreshwater cost (CWNS). The EESs of principal contributorsin each system were estimated and compared to examine theeffects of the objective function minimizing a total EES on theconfiguration and characteristics of a WNS.

2. Life Cycle Assessment

An LCA database is used in quantitatively evaluating the unitEES of each principal contributor to be employed for theobjective function of the model used to synthesize an environ-mentally friendly WNS. The life cycle inventory analysis (LCI)of principal contributors is performed with the Ecoinvent v1.2database.24 The life cycle impact assessment (LCIA) employsthe EPS 2000 methodology to classify and characterize the unitEESs of principal contributors.25 This methodology evaluates

Figure 1. Generalized superstructure model used to generate a WNS.

Table 1. Limiting Process Data for Water Network Synthesisa

operation contaminant Cc,opinmax (mg/L) Cc,opout

max (mg/L) Mop(kg/h) FL,op (m3/h) Fopinmin (m3/h) Fopin

max (m3/h)

CODcr 50 600 6.5OP1 SS 20 200 2.0 70.7 90 150

Cl- 90 1100 12.9CODcr 30 500 3.3

OP2 SS 5 100 0.5 49.7 60 90Cl- 120 2300 16.4CODcr 39 500 3.5

OP3 SS 2 50 0.3 38.8 50 90Cl- 50 750 6.2CODcr 20 300 2.8

OP4 SS 4 60 0.5 25.3 40 80Cl- 20 300 2.8CODcr 23 400 3.2

OP5 SS 5 80 0.6 8.3 20 70Cl- 10 200 1.5CODcr 30 250 3.8

OP6 SS 20 100 2.0 24.3 50 200Cl- 1 10 0.1

a CODcr ) chemical oxygen demand by dichromate. SS) suspended solid. Cl- ) chloride ion.

Ind. Eng. Chem. Res., Vol. 47, No. 6, 20081989

environmental impacts on the basis of the willing-to-pay methodused in environmental economics and uses ELU (environmentalload unit) as a single unit for the evaluation of variousenvironmental impacts, regardless of environmental impactcategories such as emissions to air, water, or soil, and impactson humans. The single unit enables optimizing the tradeoffsamong the environmental impacts in different environmentimpact categories.

Most environmental impacts of a WNS are incurred fromthe consumption of freshwater and electricity in the O&Mstage: the principal contributors of a WNS are industrial anddeionized water, and electricity.23 The reference flows of theprincipal contributors are set to 1 m3 of industrial and deionizedwater, and 1 kWh of electricity, respectively. The unit EESs ofeach principal contributor are evaluated with the genericdatabase mentioned above. The environmental impacts ofwastewater from a WNS are not taken into account in themathematical optimization model for an environmentally friendlyWNS because the total contaminant loads in wastewater arenot changed through water network synthesis. In other words,the environmental impacts of wastewater are regarded as abaseline for the model.

3. Mathematical Optimization Model

3.1. Superstructure Model. A generalized superstructuremodel is used to develop the mathematical optimization modelfor an environmentally friendly WNS, as shown in Figure 1.This superstructure model includes all possible interconnectionsbetween water sources and sinks, such as those from the outletof an operation to the inlet of the others, as well as betweenfreshwater sources and water-using operations, to fully utilizeopportunities for water reuse and ultimately reduce the totalfreshwater consumption rate. However, local recycling from theoutlet to the inlet within an operation is not allowed to avoidexcessive costs derived from pumping with a high flowrate.13

It is assumed that a mixer combines many streams into a singlestream and that a splitter divides one stream into all possiblestreams to water sinks.

3.2. Mathematical Formulation. A mathematical optimiza-tion model for an environmentally friendly WNS consists ofan objective function formulating the total EES of a WNS, aswell as mass balances and constraints required to represent thesuperstructure model and real situations in industrial plants. Allsymbols are explained in the Nomenclature section.

3.2.1. Objective Function. The sum of the EESs of theprincipal contributors is used as the objective function, whichis minimized to optimize an environmentally friendly WNS.The total EES (TEES) is the amount of all the environmentalimpacts of the principal contributors generated per hour, eventhough an environmentally friendly WNS is to be synthesizedfrom the viewpoint of life cycle perspective. This is becauseall the environmental impacts of the principal contributors aresimultaneously incurred in the O&M stage. The objectivefunction is as follows:

The EESs of the consumption of industrial and deionizedwater are calculated from their flowrates and unit EESs obtainedfrom the LCA results. The EES of electricity consumption forpumping is estimated from its unit EES and the power

requirement which is calculated from the freshwater flowrateand pressure requirement. The pressure is the sum of the headloss through the pipeline and the additional head required tomeet water pressure at the end of the pipeline. The head loss iscalculated by using the Darcy-Weisbach equation.26 Theoptimal velocity in the pipeline is assumed to be proportionalto the flowrate.14 Equations used to estimate the power require-ment are as follows:

3.2.2. Mass Balances and Constraints.The formulation ofmass balances and constraints is based on the superstructuremodel described above. Equations used for the mass balancesand constraints follow.

For the overall mass balance of the entire water networksystem

For the mass balances of the mixers

For the mass balances of the operations

For the mass balances of the splitters

For the constraints of the flowrates and concentrations onthe operations

Minimize TEES) ∑w∈W

∑opin∈OP

Fw,opinUEw +

(∑w∈W

∑opin∈OP

Pw,opin + ∑opin∈OP

∑opout∈OP

Popout,opin)UEe (1)

Vw,opin ) aopFw,opin + bop (2)

Vopout,opin) aopFopout,opin+ bop (3)

Dw,opin ) x4Fw,opin

π‚Vw,opin(4)

Dopout,opin) x4Fopout,opin

π‚Vopout,opin(5)

HLw,opin ) flw,opin

Dw,opin

Vw,opin2

2(6)

HLopout,opin) flopout,opin

Dopout,opin

Vopout,opin2

2(7)

Pw,opin )FFw,oping(HLw,opin + Ha)

ηpumpηmotor

11000

(8)

Popout,opin)FFopout,oping(HLopout,opin+ Ha)

ηpumpηmotor

11000

(9)

∑w∈W

∑opin∈OP

Fw,opin - ∑ww∈WW

Fopout,ww- ∑op∈OP

FL,op ) 0 (10)

∑w∈W

Fw,opin+ ∑opout∈OP

Fopout,opin- Fopin) 0 (11)

∑w∈W

Fw,opinCc,w +

∑opout∈OP

Fopout,opinCc,opout- FopinCc,opin) 0 (12)

Fopin- FL,op - Fopout) 0 (13)

FopinCc,opin+ Mc,op - FopoutCc,opout) 0 (14)

Fopout- ∑opin∈OP

Fopout,opin- Fopout,ww) 0 (15)

1990 Ind. Eng. Chem. Res., Vol. 47, No. 6, 2008

For the constraints on the prevention of local recycling

where the value of opout is the same as that of opin.

4. Case Study

A case study was performed to demonstrate the environmentalperformance of the WNS generated from the above mathemati-cal optimization model and to analyze the effects of the modelon the configuration and characteristics of the WNS. Three typesof WNSs were synthesized on the basis of their own objectivefunction: the total freshwater flowrate-minimized WNS (FWNS),total freshwater cost-minimized WNS (CWNS), and total EES-minimized WNS (EWNS). The configuration and characteristicsof the EWNS were compared to those of the FWNS and CWNS.The EESs of the principal contributors incurred during the lifecycle were estimated and compared to one another. And thetotal EESs of the three WNSs incurred during the life cyclewere obtained to compare their overall environmental perfor-mance.

4.1. Methods.Six water-using operations in an iron and steelplant were selected as the water sources and sinks for the casestudy. Table 1 presents their limiting process data for waternetwork synthesis. Table 2 is the distance matrix for theinterconnections between the water sources and sinks. Table 3presents the concentrations of the industrial and deionized waterused as the freshwater sources.

4.1.1. Water Network Synthesis.The three WNSs weregenerated by their own objective functions subjected to the massbalances and constraints. The objective functions for the FWNSand CWNS follow.

For the objective function to minimize the total freshwaterflowrate

For the objective function to minimize the total freshwatercost

The FWNS was generated using eqs 10-20, and the CWNSusing eqs 10-19 and 21. The unit costs of industrial anddeionized water were set at $0.60 U.S. and $0.85 U.S. per m3,respectively. The EWNS was generated using eqs 1-19.

All the parameters used in the objective functions were setto obtain optimal solutions. The unit EESs of the consumptionof industrial and deionized water were 0.155 and 0.454 ELU/m3, respectively, according to the LCA results using the EPS2000 methodology. The unit EES of electricity consumptionwas 0.255 ELU/kWh. The optimal velocity in the pumping flowwas obtained from its correlation with the flowrate. Parametersused to determine the optimal velocity in eqs 2-3 are as follows:

The distances between the water sources and sinks were usedas the pipe lengths. The additional head (Ha) was set at 25 mH2O. The efficiencies of pumps and motors were set to 0.60and 0.85, respectively.

The three WNSs were generated from the optimal solutionsto each mathematical optimization model. GAMS/MINOS27 wasused as an NLP solver to find the optimal solutions. Theconfiguration of each WNS was embodied from its optimalsolution.

4.1.2. Estimation of Environmental Effect Scores.TheEESs of the principal contributors incurred during the life cyclewere evaluated using eqs 1-9 and compared to one another inthe three WNSs in order to estimate the effect of the objectivefunction used for the EWNS on the EESs of its principalcontributors. The total EES of the EWNS was also comparedto those of the FWNS and CWNS to estimate the environmentalperformance of the EWNS from the viewpoint of the life cycleperspective. The life cycle was assumed to be 15 years withrespect to the lifetime of pipelines.

4.2. Results and Discussion.The EWNS was synthesizedby optimizing the tradeoffs among the environmental impactsfrom the consumption of industrial and deionized water and ofelectricity. The configurations of the FWNS, CWNS, and EWNSare shown in Figure 2. The characteristics of the three WNSsare summarized in Table 4. The total pipe length of the EWNSwas 42.6% and 38.9% less than those of the FWNS and CWNS,respectively. The number of interconnections for water reusein the EWNS was 45.5% and 33.3% less than those in theFWNS and CWNS, respectively. The total number of pumpsin the EWNS was 22.2% and 17.6% less than those of theFWNS and CWNS, respectively. The power requirement forpumping of the EWNS was 7.5% and 6.0% less than those ofthe FWNS and CWNS, respectively. These results were becausethe objective function for the EWNS drove to the decrease inthe total pipe length, and the numbers of interconnections andpumps in order to reduce electricity consumption for pumping.The total flowrate of industrial water in the EWNS was 81.9%and 0.5% greater than those of the FWNS and CWNS,respectively; the total flowrate of deionized water in the EWNSwas 60.1% less than that in the FWNS, but was 13.7% greaterthan that in the CWNS. In other words, the total freshwaterconsumption was minimized in the FWNS by maximizing theconsumption of deionized water; the consumption of industrialwater was maximized in the CWNS because of its lower unitcost, to minimize the total freshwater cost. When the EWNSwas compared to the CWNS, the flowrate of deionized waterin the EWNS was increased to reduce its total pipe length, whichwas because of the objective function used for the EWNS.

Fopinmin e Fopine Fopin

max (16)

Cc,opine Cc,opinmax (17)

Cc,opoute Cc,opoutmax (18)

Fopout,opin) 0 (19)

Minimize Fwt ) ∑

w∈W∑

opin∈OP

Fw,opin (20)

Minimize costwt ) ∑

w∈W∑

opin∈OP

Fw,opinUCw (21)

aop ) 0.0297,bop ) 0.6173

Table 2. Distance Matrixa

FW1 FW2 OP1 OP2 OP3 OP4 OP5

OP1 2250 280OP2 2060 1010 1010OP3 4960 4930 4980 4980OP4 920 1010 1030 140 4120OP5 980 1140 1200 220 4060 170OP6 4550 4580 4660 4390 380 3900 3850

a FW ) freshwater. OP) water-using operation. Unit: meter.

Table 3. Concentrations of Freshwater Sources

freshwater Cc,w (mg/L)

CODcr SS Cl-

FW 1 industrial water 0 0 15FW 2 deionized water 0 0 0

Ind. Eng. Chem. Res., Vol. 47, No. 6, 20081991

The EWNS incurred the lowest environmental impacts amongthe WNSs, which validated that the proposed model synthesizesan environmentally friendly WNS. Figure 3 shows the EESs of

the principal contributors and the total EESs of the three WNSsincurred during the life cycle. The EES of industrial water inthe EWNS was greater than those of the FWNS and CWNS,respectively. The EES of deionized water in the EWNS wasless than that in the FWNS, but was greater than that in theCWNS. The EES of electricity in the EWNS was less than thoseof the FWNS and CWNS, respectively. These results were inline with the results mentioned in Table 4. The total EES ofthe EWNS was lower than those of the FWNS and CWNS:the EWNS was the most environmentally friendly WNS. Thiswas because the objective function for the EWNS optimizedthe tradeoffs among the EESs of the principal contributors tominimize the total EES. Therefore, the results of this case studyvalidated that the mathematical optimization model proposedin this study can be used to synthesize an environmentallyfriendly WNS.

Figure 2. Configurations of the water systems: (a) total freshwater flowrate-minimized WNS (FWNS); (b) total freshwater cost-minimized WNS (CWNS);(c) EES-minimized WNS (EWNS) (FW, freshwater; OP, water-using operation).

Table 4. Characteristics of the Three WNSsa

unit FWNS CWNS EWNS

length m 57260 53750 32850pipe number of

interconnectionsfor water reuse

11 9 6

pump number 18 17 14industrial water flowrate m3/h 120.4 218.0 219.0deionized water flowrate m3/h 150.0 52.6 59.8electricity

consumptionpower requirementfor pumping

kW 244.0 240.2 225.7

a FWNS: Total Freshwater Flowrate-minimized WNS, CWNS: TotalFreshwater Cost-minimized WNS, EWNS: EES-minimized WNS)

1992 Ind. Eng. Chem. Res., Vol. 47, No. 6, 2008

5. Conclusions

A mathematical optimization model was developed to syn-thesize an environmentally friendly WNS by minimizing thesum of the EESs of the principal contributors to the environ-mental impacts of a WNS. The concept of LCA was integratedinto the model in order to formulate the environmental impactsof the principal contributors. The case study validated the modelby analyzing the effect of its objective function on theconfiguration and characteristics of a WNS and by demonstrat-ing the environmental performance of the WNS generated fromthe model.

The major conclusions from this study are as follows: (1)the mathematical optimization model can be used to effectivelyimprove the environmental performance of a WNS in imple-menting a new water system or in retrofitting an existing watersystem; (2) the integration of LCA, i.e., the EPS 2000methodology, into the model enables the minimization of thetotal environmental impacts of a WNS and the optimization ofthe tradeoffs among the environmental impacts of principalcontributors; and (3) the model can be applied to other processintegration technologies, such as heat or hydrogen networksynthesis, in order to improve the environmental performanceof various processes and systems.

Nomenclature

SetsC ) {c|c is a contaminant in the water}, c ) 1, 2, ...,NcW ) {w|w is freshwater available}, m ) 1, 2, ...,NmWW ) {ww|ww is wastewater}, ww ) 1, 2, ...,NnOP ) {op|op is a water-using operation}, op ) 1, 2, ...,Nn) {opin|opin is a water-using operation}, opin ) 1, 2, ...,Nn) {opout|opout is a water-using operation}, opout) 1, 2, ...,

Nn

VariablesCc,opin ) concentration at the inlet of a water-using operationCc,opout) concentration at the outlet of a water-using operationcostw

t ) total freshwater costDw,opin ) diameter of a pipeline from a freshwater source to

the inlet of a water-using operationDopout,opin ) diameter of a pipeline from the outlet of water-

using operation to the inlet of anotherFopin ) flowrate at the inlet of a water-using operationFopout ) flowrate at the outlet of a water-using operationFopout,opin) flowrate from the outlet of a water-using operation

to the inlet of another

Fopout,ww ) flowrate from a water-using operation to a localwastewater treatment plant

Fw,opin ) flowrate from a freshwater source to a water-usingoperation

Fwt ) total freshwater flowrate

HLw,opin ) head loss through a pipeline from a freshwater sourceto a water-using operation

HLopout,opin) head loss through a pipeline from the outlet of awater-using operation to the inlet of another

Pw,opin ) power requirement for pumping from a freshwatersource to a water-using operation

Popout,opin) power requirement for pumping from the outlet ofa water-using operation to the inlet of another

Vw,opin ) optimal velocity through a pipeline from a freshwatersource to a water-using operation

Vopout,opin) optimal velocity through a pipeline from the outletof a water-using operation to the inlet of another

TEES) total environmental effect score of a WNS

Parametersaop ) regression parameter for an optimal velocitybop ) regression parameter for an optimal velocityCc,opin

max ) maximum concentration at the inlet of a water-usingoperation

Cc,opoutmax ) maximum concentration at the outlet of a water-using operation

Cc,w ) freshwater concentrationf ) friction factorFL,op ) water loss rate in a water-using operationFopin

min ) minimum flowrate at the inlet of a water-usingoperation

Fopinmax ) maximum flowrate at the inlet of a water-usingoperation

g ) acceleration of gravityHa ) additional head for pressure requirement at the end of a

pipelineηmotor ) motor efficiencyηpump ) pump efficiencylw,opin ) pipe length from a freshwater source to a water-using

operationlopout,opin) pipe length from the outlet of a water-using operation

to the inlet of anotherF ) density of waterMc,op ) mass load of a contaminantUCw ) unit cost of freshwaterUEe ) unit environmental effect score of electricity consumptionUEw ) unit environmental effect score of freshwater consump-

tion

AbbreViationsCWNS) total freshwater cost-minimized water network systemDfE ) design for environmentEES) environmental effect scoreELU ) environmental load unitEWNS ) total environmental effect score-minimized water

network systemFWNS ) total freshwater flowrate-minimized water network

systemLCA ) life cycle assessmentLCI ) life cycle inventory analysisLCIA ) life cycle impact assessmentMINLP ) mixed-integer nonlinear programmingNLP ) nonlinear programmingO&M ) operation and maintenance

Figure 3. Environmental effect scores of the three WNSs (FWNS, totalfreshwater flowrate-minimized WNS; CWNS, total freshwater cost-minimized WNS; EWNS, EES-minimized WNS).

Ind. Eng. Chem. Res., Vol. 47, No. 6, 20081993

TEES) total environmental effect scoreWNS ) water network system

Acknowledgment

This work was financially supported in part by the KoreanScience and Engineering Foundation (R11-2003-006) throughthe Advanced Environmental Biotechnology Research Centerat Pohang University of Science and Technology and in partby the program for advanced education of chemical engineers(second stage of BK21).

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AcceptedDecember 21, 2007

IE071302D

1994 Ind. Eng. Chem. Res., Vol. 47, No. 6, 2008