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USING GEOGRAPHIC INFORMATION SYSTEMS (GIS)

IN INDUSTRIAL WATER REUSE MODELLING

C. E. NOBEL1

and D. T. ALLEN2

1Environmental and Wa ter Resources Engineering, The University of Texas at Austin, USA2Department of Chemical Engineering, The University of Texas at Austin, USA

This paper presents a model that identies cost-optimal water reuse scenarios. Themodel utilizes a linear programming algorithm within a Geographic InformationSystem (GIS). Specically, the model integrates database operations and optimization

methods with the visualization benets and geographic analysis offered by maps. The modeldetermines the feasible reuse opportunities based on water quality and nds the optimalmaterial exchange scenario based on product purchase, treatment, and transportation costs.The results are displayed on a map of the region along with accompanying data tables. The useof the model is illustrated by identifying cost and water savings associated with reuse inthe Bayport Industrial Complex in Pasadena, Texas. This model has applicability to waterreclamation project planning as well as water management in water-poor regions. Additionally,with minor modications, the water reuse model presented here may be used to quantitativelyanalyse the use and reuse of other materials. Thus, this model provides a quantitative tool topromote more efcient system-based material cycles.

Keywords: Geographic Information Systems (GIS); water reuse; Eco-Industrial Parks (EIP);modelling; linear programming.

BACKGROUND

Rising water costs, limited water supplies, waste minimi-zation, and pollution control issues are compelling indus-trial users of water to consider water reclamation, reuseand recycling. Currently, most wastewater is treated andreleased into receiving waters. However, in many cases it isfeasible for treated wastewater to be reused because certainwater uses (e.g., irrigation, manufacturing and sanitation)do not require the high-quality water they now receive. Ifwastewater is reused, then total water demand and efuenttreatment load can be lowered.

Despite their potential, water reclamation, reuse andrecycling technologies remain greatly underused1. Further-

more, most industrial water reuse focuses on recyclingand process modications within one facility2. An exten-sive amount of research has been conducted on industrialwater reuse and wastewater minimization and optimi-zation3. However, very little attention has been given tothe possibility of water exchange among industries, eventhough integrated water reuse management has beenrecommended as an effective means of water conservation4.Integrating water reuse throughout a region, rather thanmerely within a single facility, provides economies ofscale and more reuse opportunities. Regional integrationalso provides a systematic framework in which to overcome

the legal and public perception impediments to water reuse.However, previous regional reclamation projects havefaced difculty in identifying users for reclaimed water.These shortfalls have been attributed to insufcientplanning and design.

Planning and design of water reuse programmes at a

regional level will require not only traditional information

about the quantity and quality of water supply and demand,but also information about the geographical location wherethe supply and demand occur. Traditional approaches towater reuse have not included explicit quantitative geo-graphical data, even though conveyance and distributionsystems make up the principal costs of water exchangeprojects, and these costs depend primarily on geographicconsiderations such as distance between distributor andreceiver, and elevation differences (for pumping). In thispaper, a Geographical Information System (GIS) will beused to incorporate spatial information into a water reuseanalysis.

GIS is dened as `an organized collection of computer

hardware, software, geographic data, and personneldesigned to efciently capture, store, update, manipulate,analyse, and display all forms of geographically referencedinformation5. GIS integrates database operations withthe unique visualization benets and geographic analysisoffered by maps. Thus, GIS is an excellent frameworkin which to combine industry water characteristics andgeographic planning considerations to effectively modelwater exchange between industries. GIS is not simply acomputer system for making maps; rather, GIS is ananalysis tool that, unlike any other information system,discerns relative location by dening the spatial relation-

ships among all map elements. GIS contains map features(nodes, lines and areas), spatial information in topologi-cal data tables, and descriptive information in attributetables; the power of GIS lies in its link between thisspatial and descriptive data.

The goal of this paper is to describe the development of

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a GIS-based water reuse model and its application to acase study of the Bayport Industrial Complex in Pasadena,Texas. Test applications are used to analyse the modelpotential and limitations and application results are analysedto determine their sensitivity to the model parameters.

MODEL DEVELOPMENT

The water reuse model identies and displays bothfeasible and optimal reuse water exchange scenarios forregions containing many water users. It can be used as aquantitative and visual tool to help planners prepare effec-tive water reuse schemes among a network of co-locatedfacilities by allowing the user to systematically create cost-

justiable scenarios for water exchange among industries.Additionally, by enhancing water reuse, the model can beused as a tool to promote the goals of reducing raw waterconsumption, lowering operating costs and reducingenvironmental impact.

The water reuse model was designed using the Environ-

mental Systems Research Institute (ESRI) GIS softwarepackage ArcView version 3.0a on a personal computer.Avenue, the programming language associated with Arc-View, was used to write the program scripts that run themodel. The model was designed to use a GUI (Graphic UserInterface)-based framework; thus, the model tasks areassigned to buttons. The user need only `point-and-clickand respond to the message box prompts in order to runthe model. The only input required is a facility character-istics data le. The GIS framework allows for spatialanalysis and provides output maps that can be used aseffective communication tools. Additionally, the model was

designed to be exible to accommodate varying scenariosand incorporate more data sets, as they become available.The creation of the map framework and the subsequentdata manipulation and analysis can be broken into threesteps: basemap construction, water exchange identication,and exchange optimization.

Basemap Construction

The rst step in any GIS project is to create a basemapof the area of interest. The basemap provides thegeographic framework for the model and contains thedata important to the situation. Each data layer included in

the basemap is called a `theme. Each theme hasa corresponding feature attribute table that containsdescriptive information about each element within thetheme (e.g., individual facility address, water quantity andwater quality.) The themes important to a water reuse

modelling scenario include information about facilitylocation (to determine distance), relative elevation, andfacility water characteristics. Thus, this model includesthe following themes:

Street Mapprovides the reference for facility location. Digital Elevation Model (DEM)a grid theme thatconsists of a sampled array of elevations for ground

positions. Facility Networka point theme containing data forwater source and destination (i.e., sink) facilities in aregion. Facility characteristic data include facility address,water quantity and quality information. The facilities arelocated on the map by `geocoding (the equivalent ofpushing a pin into a street map on the wall). These data areimported from a user text le.

The GIS framework allows the model the exibilityto expand to easily incorporate other types of data such asland use or utility themes. Figure 1 shows an example ofa basemap for a small network of facilities including a

water treatment plant (WTP), a wastewater treatmentplant (WWTP) and three manufacturing facilities (CHM,GAS and CYC). The corresponding facility feature attri-butes are shown in Table 1. Each point `contains a rowof data in the table that includes address information,used for geocoding, as well as water quantity and qualitydata important for determining exchange feasibility andoptimization.

Water Exchange Identication

After a basemap of the network is created, the model

determines the feasible options for water exchange in thenetwork using the water quality inuent and efuentparameters. To determine feasible water exchanges, themodel tests each possible pair of sources and sinks to seeif the source facilitys water is clean enough for thedestination facility. The model can test any number ofquality parameters, as long as both inuent and efuentrequirements are imported in the facility characteristictable. When a match is identied, a feasible exchangepathway (arc) is created on the basemap. Along withidentifying feasible exchange pathways, the modelcalculates the distance and elevation change for eacharc. Additionally, the model identies the type of water

(e.g., fresh, reused) based on source and destinationfacilities. This information is used to determine thetransportation and water costs in the optimization phaseof the model.

Figure 2 shows feasible water exchanges for the net-

296 NOBEL and ALLEN


Table 1. Feature attribute table for the small network of facilities; data include location and water quality and quantity information. Each row of datacorresponds to a facility point feature.

Efuent Characteristics, mg/l Inuent Requirements, mg/lQin,

Name Address 1000 gpd TOC TSS TDS TOC TSS TDS

CHEM 9640 Bayport Bvd. 91 675 106 556 50 100 500GAS 9950 Chemical Rd. 86 18 72 284 5 10 250CYC 12222 Port Rd. 300 22 66 488 20 50 450WTP 11400 Bay Area Bvd. n/a 2 0 50 0 0 0WWTP 10800 Bay Area Bvd. n/a 7 20 450 2000 2000 2000

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work presented in Figure 1. Feasible exchange pathwaysare displayed as arrows on the basemap. Not surprisingly,traditional water exchange routes (supply from the watertreatment plant, discharge to the wastewater treatmentplant) are identied as feasible exchanges. However, themodel also identies new reuse options for water exchangebetween facilities and from the treated wastewater.Although the pathways identied by the model are theo-

retical `straight line representations between feasiblefacility pairs for this illustrative analysis, the GIS frame-work allows the exibility of including existing pipenetworks. Each feasible exchange arc is represented by arow of data in the feasible exchange feature attributeshown in Table 2. Beyond the source and sink information,this table includes distance and elevation data calculatedby the model using attribute information from the basemapthemes.

Although not included in this analysis, additional feasiblepathways could be identied if efuents were blendedtogether to create a new source with different water quality

characteristics as demonstrated for the Bayport industrialfacility by Keckler and Allen6. The model is exibleenough to incorporate blending into the feasible exchangeidentication phase. Additionally, partial treatment at thefacility could also be included in the model to createnew pathways with cleaner efuent. The treatment costsand blending costs would be added to the optimizationformulation.

Optimization

Given all the possible options from the feasible exchange,

the model determines which arcs present the optimalsolution for the network. This is accomplished using alinear program. The objectives of the problem are tominimize the cost to purchase, treat and transport waterthrough the arcs and/or to maximize fresh water conserva-tion by minimizing the ow out of the water treatment plantnode:

Minimize cost=X

j[J

X

i[I

(Ci,j + Ti,j) Xi,j

Minimize water=X

j[J

Xwtp,j

The decision variables for the water reuse linear programscenario are represented by Xi,j, the ow rate (in 1000s ofgallons per day) of water from source facility i to destina-tion facility j. Minimizing the objective functions shownabove determine the optimal set of Xi,j, (i.e., which of thearcs to use in the network and how much water totransport through each arc.) The rst objective functionminimizes cost by minimizing the water (Ci,j) andtransportation (Ti,j) costs per volume times the volumeper day of all of the arcs in the system; the secondequation minimizes the ow of water from the watertreatment plant (WTP) (i.e., the quantity of water used in

the network.)The water cost values (Ci,j) depend on the type ofwater (i.e., fresh, reclaimed, reused, disposed) and canvary depending on location and situation. Thus, users canuse default values or choose to enter their own cost data.This model feature makes it possible to easily test the

same scenario for varying water cost options. The cost oftransporting water (Ti,j) is primarily due to pumping (i.e.,energy costs). At this point, the model does not includecapital costs, but these could be integrated into the model

as a function of distance. The model calculates the trans-port costs as a function of the distance and elevationderived from pump power and energy loss equations:

Ti,j($/1000gal) = 7.9 104Dz + 4.1 10

4DL

One meter of elevation gain is approximately twice asexpensive as one additional meter of distance. The detailsof the transport cost calculations are given in the thesisdescribing this research7.

Each facility has a demand for water and a quantity itdischarges imported from the facility characteristics datale. These ow rate values provide the supply and

demand constraints for the network. The objectivefunction and constraints form the linear program thatdetermines the optimal solution for the network. Thelinear program in the model was written in the GeneralAlgebraic Modelling System (GAMS) language. Giventhe parameter inputs for a specic water reuse scenariofrom the reuse model, a solver within the GAMS softwarepackage determines the optimal solution to the linearprogram. These results are then integrated and displayedon the model basemap.

Figure 3 shows the optimal water exchange scenario forthe small network in which the facilities supply anddemand constraints are met at minimum cost and water use.

The corresponding feature attributes are given in Table 3.The optimal scenario includes two reuse pathways: from theWWTP to the CYC facility, and from the CYC facilityto the CHM facility. The optimal path feature attributetable includes source, sink and ow rate information. Thewidth of the optimal path features on the basemap isgraduated in size depending on the ow rate value (i.e.,larger ow rates result in thicker lines). Table 4 shows aquantitative summary of the results for the small network.In this scenario, the optimal-cost and the optimal-waterconservation solutions are the same.

MODEL APPLICATION

In order to test the water reuse model, it was appliedto various scenarios for selected facilities in the BayportIndustrial Complex in Pasadena, Texas. The Bayport com-plex basemap for these facilities is shown in Figure 4. The

297USING GIS IN INDUSTRIAL WATER REUSE MODELLING


Table 2. Feasible exchange are feature attribute table including distanceand change in elevation between facilities. Bold rows represent reuse

opportunities.

Source Sink Distance, m D Elevation, m

GAS CHEM 730 0

CYC CHEM 916 0WWTP CHEM 1389 1WWTP CYC 1712 1WTP CHEM 1786 2WTP GAS 1066 2WTP CYC 1625 2CHEM WWTP 1389

1

GAS WWTP 904

1CYC WWTP 1712

1

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efuent data used in the case study were provided bythe Bayport facility of the Gulf Coast Waste DisposalAuthority. The Bayport facility provided average 1996gures for ow rate, Total Organic Carbon (TOC), TotalSuspended Solids (TSS) and Total Dissolved Solids (TDS)for 21 process streams and the 5 utility streams. The inuentrequirement values were estimated to provide a range

of requirements for different types of facilities based onStandard Industrial Classication (SIC) Codes adaptedfrom the literature available for reuse inuent require-ments8,9. The water cost values used in the optimizationprogram are shown in Table 5. The costs of fresh waterand disposed water were obtained from the water andwastewater treatment plants in the Bayport IndustrialComplex. The values for reused and reclaimed water wereselected to make it economical for individual facilities topurchase these types of water in comparison to fresh

water.

Large Network

First, the model was applied in a manner similar to thatoutlined for the small network; the entire network wastested to determine feasible exchange pathways based onquality constraints and then optimized for maximum costand water savings. During the feasible exchange phase,the model identied 74 possible reuse pathways. Thesenumerous pathways are split among the sources with the

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Figure 3. Cost-optimal water use network for the small network reuse paths are highlighted, and the width of the line corresponds to ow rate (1000 gpd).

Table 3. Optimal small network feature attributetable including source, sink and ow rate.

Flow rate,Source Sink 1000 gal/day

CYC CHEM 91WWTP CYC 300WTP GAS 86CYC WWTP 209CHEM WWTP 91GAS WWTP 86

Figure 1. Layout of the basemap for the small network including a streetmap and digital elevation map (DEM). Facilities are geocoded by theiraddress.

Figure 2. Feasible exchanges output for the small network. Blue arrowsrepresent the traditional system, while green arrows show the feasiblereuse opportunities.

Table 4. Quantitative water and cost savings for the smallnetwork optimal water use scenarios.

Fresh water use, Cost,Scenario 1000 gal/day $/day

Without reuse 477 4540With reuse 86 3696Percentage reduction 82% 19%

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`cleanest efuent; the cleaner the efuent, the more poten-tial destinations. Likewise, facilities with lower inuent

standards have more potential sources.Given these feasible pathways, the network was rstoptimized to minimize cost. The resulting water use networkis shown in Figure 5. Of the 74 possible pathways,the optimal network included 30 reuse pathways in theminimum-cost solution. The majority of the recycled waterused in the system is reclaimed water from the WWTP.Next, the network was optimized for maximum waterconservation. Modelling this scenario would be applicablein a situation where a limited amount of fresh water is

available. The minimum water use optimal solution alsoused 30 reuse pathways. However, the optimal water

conservation optimization network had more industry-to-industry reuse pathways and fewer originating at thewater treatment plant (WTP). Again, the majority of therecycled water is reclaimed water from the wastewatertreatment plant (WWTP). The minimum cost and maximumwater conservation results are similar in this scenariobecause of the minimal elevation changes. Table 6 shows aquantitative summary of the optimal cost and waterconservation scenarios in comparison to the traditionalwater use network.



Figure 4. Facility basemap for the large Bayport network.

Table 5. Cost of water based on source and destination.

Type of water Source Sink Cost, $/1000 gal

Fresh Water treatment plant Industries 0.75Reclaimed Wastewater treatment plant Industries 0.50Reused Industries Industries 0.75Disposed Industries Wastewater treatment plant 7.50

Figure 5. Cost-optimal pathways for the large network graduated by ow rate (1000 gpd).

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Reclamation Plant Scenario

Many communities are beginning to recognize the valueof reusing the water treated at wastewater treatment plantsas `reclaimed water. In a reclamation plant scenario, thewastewater treatment plant could serve as a potentialsource for water in a region instead of just as adestination. Thus, in this scenario, the water treatmentplant and the wastewater treatment plant are the onlypossible water sources for the facilities in the Bayport

network. The model identied 24 possible pathways forreclaimed water use in the network based on water qualityrequirements.

The optimal water conservation solution takes advan-tage of all of these possible reuse pathways, leaving onlythose facilities with stringent inuent requirements to besupplied with fresh water. The cost-optimal model resultsfor the Bayport reclamation plant scenario shown inFigure 6 uses 20 of the 24 possible reuse pathways. Ascan be seen from Figure 6, it costs less for the water treat-ment plant to supply some the facilities closer to it thanthe wastewater treatment plant due to reduced transpor-tation costs, even if the reclaimed water costs $0.25 lessthan fresh water (see Table 5). Obviously, the cost of freshand reclaimed water has a signicant effect on the cost-optimal solution for the network, and the solution couldchange depending on these water costs.

Table 7 contains a quantitative summary of the cost andwater savings found for the optimal cost and water conser-vation solutions. Although the water savings are comparableto the large network that included facility-to-facility reuse,the cost savings are much lower in the reclamation plantscenario. This difference is due to the fact that in thereclamation plant scenario, all efuent goes to the WWTP;thus, no treatment costs are avoided, so the cost savings

are lower than when water in the network can be reusedwithout treatment.

New Facility Scenario

In this model application, it was assumed that a newfacility (`NEWFAC) was planning to locate in theBayport Industrial Complex Area. Given this hypotheticalfacilitys water requirements and planned location, themodel was run to determine the most cost-effective sourceand destination to meet the facilitys water requirements.The feasible exchange phase of the model identied 5

sources and 12 destinations as NEWFACs feasible wateruse options; these sources and sinks are shown in Figure 7.The optimal water supply and destination solution isshown in Figure 8 and listed in Table 8. Because mostof the feasible pathways contain `reused water, (i.e., facilityto facility), the optimal solution was primarily a factor of

transportation cost; the optimal sources and sinks balancedistance and elevation energy costs. Because NEWFAC hada higher ow rate than the optimal source (CYC1) and sink(INO2), it received from and discharged water to twofacilities.

DISCUSSION

As demonstrated above, the reuse model was able toidentify feasible reuse pathways and optimal water usenetworks for several scenarios along with signicantpotential cost and water savings. However, this case studywas intended as a demonstration of the model applicationand potential, rather than to identify specic improvementsfor the Bayport Industrial Complex. As with all models,the results are highly dependent on input parametersand assumptions. The reuse model results are dependenton the facility characteristic data as well as water priceand transportation costs. This discussion not only evaluatesthe results of the Bayport case study, but also illustrateshow the water reuse model output can be analysed todetermine which parameters are the most critical in the

model application scenario, and thus lead to improvedmodelling and planning results.

Input Data Sensitivity Analysis

The ow rates in the system provide the supply anddemand constraints for the linear program. The effect ofeach supply and demand constraint can be quantied byits shadow price. The shadow price, pi, represents thevalue of one additional unit of resource i; therefore, theshadow price for the CYC1 inuent ow rate demand,pCYC1in, represents the cost savings or increase that would

occur if the ow rate demand increased by 1000gal/day.Table 9 gives the shadow prices, constraint values andranges for the ow rate constraints for the small networkoptimal solution discussed in the Model Formulationsection.

Table 9 shows that the CHM In (i.e., demand) constrainthas a negative shadow price while all the other constraints

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Table 6. Cost and water savings for the minimum cost and minimum wateruse network optimization results.


Traditional 8708 84336Minimum cost 850 90% 67196 20%Minimum water use 258 97% 67715 20%

Table 7. Quantitative cost and water savings for the minimum cost andminimum water use results for the Reclamation Plant Scenario.


Traditional 8708 84336Minimum cost 1105 87% 81262 4%Minimum water use 258 97% 81306 4%

Table 8. NEWFAC cost-optimal sources and sinks, includingow rate.

Flow rate,

Source Sink 1000 gal/day

CYC1 NEWFAC 300GAS2 NEWFAC 35NEWFAC INO2 99NEWFAC ORG3 236

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have positive shadow prices. This means that increasingthe demand for water at the CHM facility will decrease thewater-use costs for the network, while increasing the othersupplies or demands will increase the costs. The highervalues for the efuent constraints illustrate that, due tohigh treatment costs, in this scenario, it is more expensiveto dispose of water than to acquire it. A similar analysiscan be completed on the other facilities in the network as

well as other model applications to determine the scenariosensitivity to changes in supply and/or demand.The water efuent characteristics and inuent require-

ments determine the feasible exchange pathways in thenetwork. In each scenario the non-feasible pairs can beexamined to determine the limiting parameter(s) andextent of non-compliance to evaluate potential ways toimprove the number of feasible matches. For example, if afacility with a large amount of efuent had relativelyclean water except for a high level of TSS, it may beworth investing in on-site treatment (such as a settlingbasin) to lower the TSS level to ensure the efuent meetsthe requirements for reuse. However, it is important to

recognize that the inuent requirements used in the modelapplication are only estimations applied to wide groups offacilities that may have varying processes and require-ments. Therefore, before accurate results can be acquiredfrom the model, the inuent requirements for the facilitiesin the network need to be better characterized andquantied, preferably on the facility (or even process)level.

Water and Transportation Cost Sensitivity Analysis

The linear program used for the model cost optimization

is dependent on water and transportation (a function ofdistance and elevation) costs; thus, these factors greatlyaffect the model optimization solutions. The small networkcan be used to demonstrate how a sensitivity analysis ofthe model results can evaluate the model parameters. For

example, the optimal ranges for the water costs for eachpathway can be determined from the cost component ofthe objective coefcient (Ci,j). These water cost optimalranges are shown in Table 10.

The values of particular interest in Table 10 include theupper bound values for those pathways used in theoptimal network (represented in bold) and the lowervalues for the unused pathways. For example, the GAS

facility can send its water to the CHEM facility or theWWTP, but the most cost-effective route will be to theWWTP as long as it costs less than $7.76/1000gal.Likewise, the route from the GAS to the CHEM facilitybecomes the optimal choice only if the cost of water dropsbelow $0.49. Since these values are within $0.26/1000galof the water costs used in the model, the ranges indicatethat the optimal GAS!WWTP pathway is somewhatsensitive to water costs. A similar analysis can be carriedout for the other exchanges in this example and for thedistance and elevation parameters.

The reclamation plant scenario is unique because it hasonly two possible sources of water (the WTP and WWTP)

and only one possible sink (the WWTP) for each facility.Thus, each facility can be examined independentlyto determine the optimal water cost ranges for its potentialsupply pathways given the distance and elevation changeto the feasible sources. The Ti,j (transportation cost perow rate) values for each feasible supply pair can becompared to determine which `fresh and `reclaimedwater cost ranges would be optimal. In Table 11, Ti,jrepresents the WTP Ti,j value subtracted from the WWTPTi,j value for each facility:

Ti,j = Ti,j (WWTP) Ti,j(WTP)

Thus, given equal fresh and reclaimed water costs, if theTi,j value is positive, then the optimal water supply sourceis from the WTP, and if the value is negative, the optimalsource is from the WWTP. Figure 9 displays these supplynetwork results on the basemap for the region. As



Table 9. Small n etwork ow rate constraints (1000gpd) and shadow prices.

Name Optimal value Shadow price Allowable increase Allowable decrease

CHMIn 91

7.08 209 91CHMOut 91 8.07 1E+ 30 91GASIn 86 1.19 1E+ 30 86GASOut 86 7.87 1E+ 30 86CYCIn 300 1.21 1E+ 30 300CYCOut 300 8.21 1E+ 30 209

Table 10. Water cost values ($/1000 gal) and ranges for the small n etworks water-use solution. Bold rows represent optimal pathways.

Source Sink Water cost used in model Lower bound water cost Upper bound water cost

GAS CHEM 0.75 0.49 1E + 30GAS WWTP 7.50

0.37 7.76

CYC CHEM 0.75 1E + 30 1.01CYC WWTP 7.50 0.49 1E + 30WTP CHEM 0.75

7.81 1E + 30

WTP CYC 0.75 0.54 1E + 30WWTP CHEM 0.50

7.65 1E + 30

WTP CYC 0.50

0.71 0.71

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expected, given equal water cost values from eitherpotential source, the facilitys cost-optimal choice is fromthe closer option. Additionally, the absolute value of theTi,j values provides a quantitative representation of thesensitivity of the optimal pathway solution to variations inwater cost values. For example, low Ti,j values indicate thatslight variations in fresh or reclaimed water cost couldchange the optimal solution. In the case study application,because the WTP and WWTP have a small distance and noelevation change between them, the optimal network

solution is very sensitive to water cost.

CONCLUSIONS AND RECOMMENDATIONS

This work has integrated water reuse modelling with aGeographic Information System to calculate and displayfeasible and optimal water exchange scenarios in a region.The concept of coupling reuse analyses with Geographical

Information Systems is not limited to water reuse, however.Future work could include modifying the model to analyseother types of material ows, such as solvents, cardboardor energy. Additionally, facilities need to better characterizetheir input requirements in order to provide more accurateexchange feasibility results. Adding new data themes tothe basemap, such as existing infrastructure and land useareas, could provide insight into specic piping networklocations. Finally, the model should include capital coststo make the cost-optimization more robust.

Although there are many opportunities to improve andexpand on this research, it is a rst step. This modelprovides a quantitative planning tool to promote moreefcient systems-based material cycles by incorporatinggeographic elements and providing a exible frameworkfor the systematic evaluation of various regionalexchange scenarios. By allowing users to test scenarioseasily and display results in a map-based format, this

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Figure 6. Reclamation scenario water supply for the cost-optimal water supply network.

Figure 7. Feasible sources and sinks for NEWFAC water use.

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model can demonstrate how materials reuse can lead tocompetitive advantage and improvements in resource andmaterial use.

REFERENCES

1. Postal, S., 1992, Last Oasis: Facing Water Scarcity (W.W. Norton,New York).

2. Bowman, J. A., 1994, Saving water in Texas Industries, TexasWater Resources, 20(1), http://twri.tamu.edu/twripubs/WtrResrc/v20n1/text.html.

3. Mann, J. G. and Liu, Y. A., 1999, Industrial Water Reuse andWastewater Minimization (McGraw-Hill, New York).

4. Oron, G., 1996, Management modeling of integrative wastewaterand reuse systems, Water Science and Technology, 33(1011): 95105.

5. Environmental Systems Research Institute (ESRI), 1995, Understand-ing GIS: The Arc/Info Method(ESRI, Redlands, CA).

6. Keckler, S. and Allen, D. T., 1998, Material reuse modeling: A casestudy of water reuse in an industrialpark,Journal of Indu strial Ecology,2(4): 7992.

7. Nobel, C. E., 1998, A Model for Industrial Water Reuse: AGeographic Information Systems Approach to Industrial Ecology,Thesis for a Masters of Science in Engineering (The University ofTexas at Austin).

8. Environmental Protection Agency, 1992, Guidelines for Water Reuse,publication no. EPA/625/R-92/004.

9. Environmental Studies Board Committee on Water Quality Criteria,1972, Water Criteria 1972 (National Academy of Sciences, NationalAcademy of Engineering, Washington, DC).

ADDRESS

Correspondence concerning this paper should be addressed toProfessor D. T. Allen, Center for Energy and Environmental Resources,The University of Texas at Austin, J.J. Pickle Research Campus,10100 Burnet Road, MS R7100, Austin, Texas 78758, USA.

The manuscript was received 23 February 1999 and accepted for

publicati on after revision 21 March 2000.



Table 11. Ti,j values for each facility. If Ti,j is lessthan zero, the facilitys optimalsource is the WWTP,

given equal water cost values.

Facili ty Op ti mal so ur ce Ti,j

AGR1 WWTP

.28CHM1 WWTP

0.17

CHM2 WTP 0.22CHM3 WTP 0.11CYC1 WTP 0.03INO1 WWTP

0.17

INO2 WTP 0.11ORG1 WTP 0.28ORG10 WTP 0.24ORG2 WWTP

0.36

ORG3 WWTP

0.20ORG4 WTP 0.19ORG5 WWTP

0.36

ORG7 WWTP

0.28ORG8 WTP 0.14ORG9 WTP 0.19PLA1 WTP 0.36PLA3 WWTP

0.20

PLA4 WTP 0.15UCYC1 WTP 0.04UGAS2 WWTP

0.07

UINO4 WWTP

0.17UORG1 WTP 0.28UORG2 WWTP

0.36

Figure 9. Optimal supply network for the reclamation p lant scenario given equal fresh and reclaimed water costs. Flow rates are g iven in 1000 gpd.

Figure 8. Optimal sources and destinations for NEWFAC water use.

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