Optimal design for sustainable development of a

Waste Management 25 (2005) 833–846 www.elsevier.com/locate/wasman

Optimal design for sustainable development of a material recovery facility in a fast-growing urban setting Ni-Bin Chang *, Eric Davila, Brian Dyson, Ron Brown Department of Environmental Engineering, Texas A&M University – Kingsville, Kingsville, TX 78363, USA Accepted 14 December 2004 Available online 17 February 2005

Abstract Installing material recovery facilities (MRFs) in a solid waste management system could be a feasible alternative to achieve sustainable development goals in urban areas if current household and curbside recycling cannot prove successful in the long run. This paper addresses the optimal site selection and capacity planning for a MRF in conjunction with an optimal shipping strategy of solid waste streams in a multi-district urban region. Screening of material recovery and disposal capacity alternatives can be achieved in terms of economic feasibility, technology limitation, recycling potential, and site availability. The optimization objectives include economic impacts characterized by recycling income and cost components for waste management, while the constraint set consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening constraints. A case study for the City of San Antonio, Texas (USA) presents a vivid example where scenario planning demonstrates the robustness and flexibility of this modeling analysis. It proves especially useful when determining MRF ownership structure. Each scenario experiences two case settings: (1) two MRF sites are proposed for selection and (2) a single MRF site is sought. Cost analysis confirms processing fees are not the driving force in the CityÕs operation, but rather shipping cost. Sensitivity analysis solidifies the notion that significant public participation plays the most important role in minimizing solid waste management expenses. Ó 2005 Elsevier Ltd. All rights reserved.

1. Introduction There are a number of models available to exclusively address municipal solid waste management issues for regional decision-makers (Everett and Shahi, 1996a,b; Anex et al., 1996; Eisenstein and Iyer, 1997; Everett and Riley, 1997; Everett and Shahi, 1997; Everett et al., 1998a,b; Timms and Baetz, 1998; Wilson and Baetz, 2001a,b; Solano et al., 2002a,b). In order to reach mass throughput requirements and material/energy conservation goals, the application of systems engineering models with respect to a cost-effectiveness approach received extensive attention in the last three decades (Marks et al., 1970; Helms and Clark, 1974; Fuertes et al., 1974; Walker et al., 1974; Ku¨hner and *

Corresponding author. Tel.: +361 593 3898/+361 455 3179. E-mail address: [email protected] (N.-B. Chang).

0956-053X/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.wasman.2004.12.017

Harrington, 1975; Male and Liebman, 1978; Hasit and Warner, 1981; Jenkins, 1982; Chiplunkar et al., 1982; Gottinger, 1986; Kirca and Erkip, 1988; Zhu and ReVelle, 1990; Lund, 1990; Huang et al., 1993; Huang et al., 1994; Lund et al., 1994; Chang and Wang, 1996a,b; Huang et al., 1996; and Chang et al., 1997). The spectrum of solid waste management systems planning covers a variety of complex issues, including the assessment of workload balancing, vehicle routing, site selection of transfer station, recycling drop-off station, presorting facility, incinerators and landfills, and comparative risk assessment for proposed planning alternatives (Chang and Wang, 1996a,b; Chang and Lin, 1997; Chang et al., 1997; Kirca and Erkip, 1988; and Chang and Wei, 2000). Models for parts of the system as well as models covering the overall system with respect to various temporal and spatial criteria are of interest for system analysts and planners (Yeomans and Huang, 2003;

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N.-B. Chang et al. / Waste Management 25 (2005) 833–846

Chang and Chang, 2003). In both instances, optimization approaches demonstrating a synergistic concern for economic, ecological, and environmental factors substantially improve the quality of decision-making for waste collection, separation, recycling, waste treatment, and disposal. It is during the era of building intelligent and sustainable infrastructure systems in urban settings that recycling raw materials and energy recovery become essential in solid waste management systems in the 21st century. Recent studies show that recycling and waste-to-energy appear to work well together when integrating incineration and recycling facilities in a common framework to conserve landfill space in a solid waste management system (Chang and Chang, 1998, 2001, 2003; Lucas et al., 2004; Fang et al., 2004). Those analyses emphasized the synthesis and analysis of intelligent and sustainable levels of smart infrastructure systems for solid waste management (Chang and Chang, 2003). The main findings within this series of companion studies may prove useful to facilitate the evaluation of new managerial strategies for various types of solid waste management scenarios in terms of cost effectiveness, environmental impacts, and material/energy recovery. Many cities in North America, however, rely on landfills as their main option for waste disposal in connection with a voluntary or mandated curbside recycling program. Recent experience in San Antonio, Texas, indicates that budget cuts, population growth, rising waste streams, low curbside recycling participation, and taxpayer demands for lower taxes collide with a need for a quality curbside garbage pickup service for commingled recyclables. These pressures motivate possible installation of material recovery facilities (MRFs) in urban regions. The way to appropriately link those MRFs with existing waste collection program and landfills becomes of interest in decision-making. This paper is designed to address the optimal site selection and capacity planning of MRFs in conjunction with an optimal shipping strategy of solid waste streams in a multi-district urban region. Screening of material recovery and disposal capacity utilization alternatives can be achieved in terms of economic feasibility, technology limitation, recycling potential, and site availability. A case study for the city of San Antonio, Texas, presents its strength as a total solution in which four scenario settings demonstrate the robustness and flexibility of this modeling analysis.

2. Case study: overview of the study area The city of San Antonio previously maintained its own landfills until the Nelson Gardens landfill site reached its permitted limit in the early 1990s. Prior to that point, the city had routinely applied for variances from the state regulating authority to continue using

the landfill site. Increasing environmental legislation led to the promulgation of regulations that required increasing accountability to the regulatory agencies. Also, the cost of employee benefits and the liability posed by potential accidents, along with other hazards and the increasing cost of insuring city personnel and payments of workers compensation made it unsavory for the City to continue operating a landfill. Other cities across the nation were facing the same crisis and had sought relief through the privatizing the operation of their landfills. Anticipating the closure of the Nelson Gardens landfill, Waste Management, Inc (WMI) purchased land adjacent to the extant landfill and began development of a new site for the disposal of the cityÕs refuse. The City then moved to fully privatize all of its landfill operations. The City of San Antonio, through its method of competitive bidding, awarded the contract to WMI. After the introduction of the privatized operation, the City solicited bids for another landfill site to be located on the east side of the city. A long-term lease was awarded to Browning-Ferris Industries, Inc (BFI) for the operation of this landfill site, including the work of excavating the ground, placing impermeable liners, and installing a drainage network to remove leachate. Eventually the City let long-term, 30-year contracts to both landfill contractors for landfill services. Each contract had a guaranteed minimum of 45,454 tons per year and a gradual reduction in the fee rate for exceeding the minimum with no maximum limit. Another contract was awarded to Transfer Disposal Systems (TDS) for disposal of more refuse. The transfer station is located north of the San Antonio International Airport. TDS transfers the garbage approximately 112 km to a site near Buda, Texas, just outside of Austin, Texas. The contract guarantees a minimum of 90,909 tons per year.The San Antonio Department of Environmental Services maintains its four service centers, including Northeast, Northwest, Southwest and Southeast, in charge of routine solid waste collection. Fig. 1 shows the operational pattern in this solid waste management system. In addition to routine solid waste collection, Environmental Services performs the following services: (1) brush collection; (2) household hazardous waste center for the drop-off of household hazardous materials; (3) dead animal collection (road kill); and (4) recyclable pickup of paper by Abitibi. Tables 1 and 2 are operational records for the 2001–2002 fiscal year. In 1995, the City of San Antonio implemented a voluntary recycling program to help reduce the amount of waste being landfilled, thus extending the life of the landfills. Revenue generated from the sale of recycled material was expected to offset the operating costs. Unfortunately, poor citizen participation with curbside recycling results in insufficient income to justify the continued operation of the recycling program. The City


835

Fig. 1. San Antonio solid waste management service area and disposal options (Perez, 2004).

Table 1 Environmental services disposal cost in 2003 (US$) Company

Tonnage

Amount

BFI WMI TDS Total

66,893 158,306 91,598 316,797

$1,210,670 $2,641,665 $2,257,995 $6,110,330

Table 2 Environmental services revenue in 2003 (US$) Company

Tonnage

Amount

Abitibi Paper Bitters Brush Site Total

13,167 8,409 21,576

$490,730 $213,761 $704,491

administration recently proposed to rule out the recycling portion of its collection operation due to a lack of participation by its citizenry. However, the recyclable material is still present in the waste stream. This leaves recyclable material in the landfills to pose a major problem that shortens landfill life expectancy due to the displacement of space occupied by these materials. Therefore, the City is contemplating installation of a MRF together with waste routing optimization to continue the recycling program while requiring less curbside sorting from the general public. For solid waste collection purposes, the City is divided into four service areas where its service center houses trash and recyclables collection vehicles, shown

in Fig. 1. Recycling routes would first ensue from a service center into the service area where commingled recyclables mixed with solid waste are collected. At the MRF, the waste would undergo mechanical separation for recyclable material. Unrecoverable material is finally routed to the landfills. The ‘‘trash only’’ routes follow a similar course except the final destination is a transfer station or landfill. Fig. 1 also marks two potential MRF locations. The first (MRF1) is located on the Northwest area and the second (MRF2) is located near the BFI landfill in the Southeast service area.

3. Design for the environment The City of San Antonio has two options: to become the primary stakeholder, or to outsource the MRF installation. From this vantage point, the City must carefully evaluate system boundaries and enumerate MSW components in order to minimize its costs. This effort might bear a Decision Circle that graphically represents objectives, waste flows, and system pressures resembling Fig. 2. Three concentric circles give a general impression of the interplay between cost minimization and system realities. The nucleus represents material flows that emerge from a fundamental need for waste disposal. Given the CityÕs drive to minimize MSW costs, the middle ring forms an economic gauge that responds to system pres-

836


In order to test system planning robustness and flexibility, both scenarios are run two different case settings: 1. In Case 1, both MRF candidate locations compete for solid waste processing using integer programming (i.e., Scenarios A1 and B1 denote this practice hereafter). 2. In Case 2, the models are slightly modified to enforce selection of one MRF using integer programming (i.e., Scenarios A2 and B2 denote this practice hereafter).

Fig. 2. Decision Circle outlining the system model and components.

sures and waste routing needs. The outer ring imposes a system boundary from a material and decision standpoint. For example, system planners need to assure new system designs honor disposal obligations if a MRF is selected. Waste flows take on significant decision importance as they simultaneously impart influence on costs and constraints. Forming a Decision Circle may help municipal planners focus on key targets to complete conceptual planning. The subsequent stage involves intricate mathematical modeling to achieve material recovery that optimizes environmental quality and urban development.

4. Model formulation The following integer programming model formulation details the methods used to choose the best location and optimal design capacity for the MRF with respect to associated cost and income components. Two types of management models include: (1) the City is the investor and will be in charge of construction and operation during the facility lifecycle, while (2) the City chooses to privatize the MRF so that they will have to pay a processing fee on a per-ton basis. Both models provide MRF site selection and capacity planning integration with the existing collection program and its disposal capability. Overall, the model settings give the City decision makers the following scenarios: Scenario A – City of San Antonio constructs and operates the material recycling facility. Scenario B – The MRF is privatized, and thus a private company constructs and operates the facility while the City pays a processing fee on a per-ton basis.

The formulation outcome is a nonlinear integer programming model in both scenarios. The information incorporated into the optimization objectives includes economic impacts characterized by recycling income and cost components for waste management. These components remain steady over time. The constraint set thereby consists of mass balance, capacity limitation, recycling limitation, scale economy, conditionality, and relevant screening constraints. The nomenclature is summarized in Appendix I. 4.1. Scenario A1 In this scenario, the City is the investor and in charge of construction and operation during the lifecycle. Integer programming allows both MRF candidate sites to be considered simultaneously or independently in order to give decision making the greatest flexibility. 4.1.1. Objective function Minimize net system cost z = total shipping cost (C1) + total disposal cost (C2) income from recycling (C3) + total operation cost of MRF (C4) + total construction cost of MRF (C5) + registration cost (C6): XX XX C1 ¼ X ij D1ij P 1 þ Y ik P 1 D2ik i

þ

j

XX j

C2 ¼

X i

þ

i

Z jk P 1 D3jk ;

Y i1 P 2 þ

X

X j

Z j2 P 3 þ

C4 ¼

X

Z j1 P 2 þ

X

X i

Y i3 P 4 þ

i

XX j

ð1Þ

k

j

C3 ¼

k

l

Rjl I jl

Y i2 P 3

X

Z j3 P 4 ;

ð2Þ

j

X

X ij ;

ð3Þ

i

a DCj þ b I j UCF;

ð4Þ

j

C5 ¼

X

c DCj þ d I j UCF CPIR CRF ;

j

ð5Þ


C6 ¼

X

e I j CRF ;

ð6Þ

j

capital recovery factor " nm ! # 1 þ my ð1 þ yÞn nm ; ¼ mi y þ y n ð1 þ yÞ 1 1 þ my 1

where C1 is total shipping cost ($/year); C2 is total disposal cost ($/year); C3 is the income from recycling ($/year); C4 is the annual total MRF operating cost ($/year); C5 is amortized MRF construction cost; C6 is amortized MRF registration cost, which is lumped with the construction cost in the cost/benefit analysis; CRF is a capital recovery factor with i interest rate, m number of intervals the interest is compounded, y is yield to maturity on the bond, n as the number of years to repay the municipal bond; P1 is unitary shipping cost ($/ton/km); P2 is unitary disposal cost at Landfill 1 (WMI) ($/ton); P3 is unitary disposal cost at Landfill 2 (BFI) ($/ton); P4 is unitary disposal cost at Transfer Station (TDS) ($/ ton); Xij is commingled recyclable waste stream shipped from i to j in tons per year (TPY) (i = 1, 2, 3, 4 Service centers) and (j = 1, 2 MRFs); Yik is waste stream shipped from i to k (TPY) (k = 1, 2, 3 Landfills and Transfer Station); Zjk is the residual waste stream shipped from j to k for disposal (TPY); and DCj is design capacity of the MRF in tons per day (TPD) (j = 1, 2, MRFs); Ijl is average income from selling recyclable l ($/ton) (l = 1 for paper; l = 2 for glass, l = 3 for plastics, and l = 4 for metal); Rjl is the recycling ratio of recyclable l in the waste stream at MRF site j (%) (l = 1, 2, 3, 4); UCF is a unit conversion factor that makes the unit of cost in the derived cost functions to be consistent with those in the other cost components; a, b, c, and d are regression coefficients in the cost functions; e is the cost the City incurs registering a MRF with the Texas Commission on Environmental Quality that is amortized (Texas Administrative Code, TAC Rule Log No. 2003-028-330-WS); D1ij is the average shipping distance from i to j (km); D2ik is the average shipping distance from i to k (km); D3jk is the average shipping distance from j to k (km); and CPIR is a consumer price index ratio that updates the construction an operation cost information from the baseline in the past to the present along the timeline (unitless). Constraints: s.t. 1. Mass balance constraint for source location: propels all solid waste generated in a service area ships to other treatment or disposal components in the network. X X Gi ¼ X ij þ Y ik 8i: ð7Þ j

k

837

2. Commingled recyclable waste stream: delineates recyclable waste stream as a product of waste generation in a service area and overall citizen participation. X X ij 6 ðC p P r Þ Gi 8i: ð8Þ j

3. Mass balance constraint for MRFs: confirms that rate of incoming waste equals the rate of outgoing waste plus the amount removed in the material recovery process. X X X X X ij ¼ Z jk þ Rjl X ij 8j: ð9Þ i

k

l

i

4. Capacity limitation constraint for landfill/transfer station: guarantees waste inflow destined for final disposal matches or exceeds contract-based minimums. X X Y ik þ Z jk P CAPk 8k: ð10Þ i

k

5. Capacity limitation constraint for MRFs: maintains the waste inflow destined for material recovery at or below the design capacity. ! X X ij =365 6 DCj 8j: ð11Þ i

6. Scale economy constraint: ensures the MRF investment abide economies of scale (Chang and Wang, 1995). It also justifies the use of linear construction and operation cost functions. DCj 6 DCmax I j

8j;

ð12aÞ

DCj P DCmin I j

8j:

ð12bÞ

7. Recycling limitation constraint: characterizes the recyclables in the commingled waste stream destined for the MRF. Ljl 6 Rjl 6 U jl

8jl:

ð13Þ

8. Non-negativity constraint: eliminates infeasibilities by filtering only positive waste streams for consideration in the optimal solution. I j 2 f0; 1g 8j; X ij ; Y ik ; Z jk P 0

8i; j; k;

ð14Þ

where Cp is the percentage of citizen participation in curbside collection; Pr is the percentage of the overall waste stream that is comprised of commingled recyclables; CAPk is the minimum of waste destined for disposal site k (TPY); Ij is a binary integer variable; it is equal one when the facility is included in the system, otherwise zero (unitless); and DCmin and DCmax are correspondingly the minimum and maximum MRF capacity justified by economies of scale (TPD).

838


4.2. Scenario A2

P MRF j 6 PUBj I j

8j;

ð19aÞ

In this scenario, only one of two MRF candidates is chosen in any alternative. It requires Eqs. (1)–(14) with an additional constraint: 9. Conditionality constraint: assures the one-time initialization of a new MRF site in the system. X I j ¼ 1: ð15Þ

P MRF j P PLBj I j

8j:

ð19bÞ

j

4.3. Scenario B1 In this scenario, the City chooses to privatize the MRF so that the City will have to pay a processing fee on a per-ton basis. Both candidate sites can be included in the system optimization alternative at the same time. Information incorporated into the objective functions includes economic impacts characterized by all relevant cost components for waste management. The constraint set contains mass balance, capacity limitation, recycling limitation, recycling summary, and related screening constraints. All constraints, except for the MRF tipping fee screening, are similar to those in Scenario A1. Objective function: Minimize total cost z = total shipping cost (C1) + total disposal cost (C2) + total processing cost at MRF (C3): XX XX C1 ¼ X ij D1ij P 1 þ Y ik P 1 i

j

D2ik þ

XX j

C2 ¼

X i

þ

Y i1 P 2 þ

X j

C3 ¼

X

i

k

Z jk P 1 D3jk ;

ð16Þ

k

X j

Z j2 P 3 þ

Z j1 P 2 þ

X

X i

Y i3 P 4 þ

i

DCj P MRF j 365;

Y i2 P 3

X

Z j3 P 4 ; ð17Þ

j

ð18Þ

j

where C1 is total shipping cost ($/year); C2 is total disposal cost ($/year); C3 is redefined as MRF processing fee ($/year); and PMRF is the processing fee governmental agency needs to pay for recycling at the MRF ($/ton). Constraints: s.t. 1. Eqs. (7)–(14): classify waste streams, enforce mass balance across MSW components, meet disposal obligations, and purport economies of scale that also define Scenario B1. 2. Screening constraint: screens possible processing fees in the nonlinear domain.

PLBj and PUBj are the lowest and highest MRF tipping fees ($/ton) in Texas, respectively. 4.4. Scenario B2 In this scenario, a private entity invests, constructs, and operates the MRF. A single MRF candidate site is selected in any solution. This requires amending Scenario B1 to include Eq. (15). 3. Eqs. (7)–(15) and Eq. (19): allow decision makers and planners to model the optimal cost of privatizing a single MRF operation. 5. Data collection and simulation The selection of data analysis methods may affect the quality and comparability of optimal solutions produced in different scenarios. Given the wide array data available in solid waste management site-specific information in the form of locations, waste generation, and waste composition were first investigated, for instance. From a simulation standpoint, there is a need to understand the role of monetary flows in waste management and municipal finance. Political and demographic boundaries undergo frequent changes in fast growing urban areas, and San Antonio is no exception to that phenomena. While the current administrative map might have slight changes to service area borders, the overall effect on the macro analysis of the solid waste analysis will be negligible and the global shipping patterns will remain. The service areas for waste collection were analyzed from select Environmental Services GIS layers. Once a service area centroid is calculated, this allows for simulation of the distance traveled in the service area during solid waste pickup in the absence of micro routing distances (Perez, 2004). The total shipping distance includes distance traveled from the service center to the community plus that traveled from the community to the MRF or the landfills/transfer station. Fig. 4 demonstrates the typical composition of the Texas waste stream. Waste characterization in concert with the publicÕs recycling participation rate defines the maximum waste stream eligible for commingled recyclable pickup with the city. The average tipping fee for a processing facility ranges from $20.07 to $103.17 per ton placing the average around $41.21 per ton (Chartwell, 2001). Waste generation rate in the target year of 2010 was applied directly according to the results in a companion study (Dyson and Chang, 2004). Table 3 lists the projected values of waste generation rate by service centers. Cost–benefit components,


technological parameters, and economic index were assessed and reorganized for appropriate use in the model. Municipal projects of this magnitude are often financed by municipal bonds with an average annual interest rate of 5% (Merrill Lynch, 2003). To reflect that idea, part of the system cost goes into making amortized yearly payments on a 10-year bond that in this system is simulated 4.75% interest compounded semi-annually (Berk, 2001). The interest rate is simulated based on San AntonioÕs credit rating of ‘‘AA+’’ given by the largest bond rating agencies Fitch Ratings and Standard & PoorÕs (American City Business Journals Inc., 2002). An investigation into average operation and construction costs for publicly or privately owned MRFs concludes that design capacity exhibits an economy of scale (Chang and Wang, 1995). Concentrating on the linear section of the non-linear total construction cost and total operation cost functions allows one to stay within the range that exhibits the best scale economy (100–1000 TPD, tons per day) as shown in Fig. 3. Operating an MRF at 50 TPD will cost more on average compared to a facility at 500 TPD since the unit cost

839

Fig. 4. Composition of Texas waste stream (TNRCC, 2000).

of providing the service decreases with a rise in magnitude of the service. Consequently, the construction and operating costs in objective function in Scenario A include linearized cost curves built from the linear tail of the operation and construction cost curves over the best scale economy range. These are subsequently corrected by an Engineering News Record (ENR, 2003) building cost index (BCI) to adjust dollars to present value and location differences in applications. Scenarios A and B include the scale economy a constraint to assure the best return on the MRF investment. Thus, the adoption of linearized construction and operation cost functions of MRFs that were summarized and updated based on previous study eases the efforts of cost/benefit matrix (Chang and Wang, 1995). Parameter values applied in the model are listed in Appendix II.

6. Results and discussion

Fig. 3. Total construction cost and total operating cost in million US dollars vs. design capacity (Chang and Wang, 1995).

In order to give the decision maker best comparison, the scenario outcomes are compared in a manner that will make them cost competitive. Depending on what the City will find valuable and deem viable for a specific scenario, it is the analystÕs duty to clearly communicate

Table 3 Simulation results for solid waste generation in San Antonio in tons per year (TPY)a Model

Northeast

Southeast

Southcentral

Northwest

Total

Base case 1 2 3 4 5 Range Disparity

93,833 88,238 90,761 92,550 96,481 127,912 [88,238–127,912] 39,674

65,355 93,483 77,825 79,333 82,875 80,208 [77,825–93,483] 15,658

75,058 89,319 87,984 89,251 93,224 91,773 [75,058–93,224] 18,139

113,449 95,085 105,379 109,077 113,748 108,316 [95,085–113,748] 18,663

347,675 366,125 361,949 370,210 386,328 408,209 [361,949–408,209] 46,260

a

Dyson and Chang (2004).

840


the results. A cost–benefit analysis together with a sensitivity analysis and policy analysis will ensue to sort out the implications of the model simulation. The location of the better MRF and its optimal capacity design are determined by a series of LINGOÒ optimization programs (LINGO, 2003). The base case scenario optimizes waste shipment without a MRF present in the system. Northwest and Southwest service areas ship waste to the WMI landfill in the east while the Northeast region opts for the transfer station in the west. The Southeast corner should principally contract with the BFI landfill. This routing pattern adheres to current contract obligations. Table 4 outlines the optimal MRF capacities for scenarios with material recovery. Scenarios A1 and B1 allow both recycling facilities to compete, although alone A1 elects dual facilities. In the single MRF site selection scenarios, MRF2 is preferred. The recycling effort in Scenario A is 284 TPD. When the system is forced to choose one recovery facility, Fig. 5 establishes that Scenario A2 enjoys the effect of scale economy when it opts for MRF2. The 97 TPD of waste routed from the Northeast to MRF1 in Scenario A1 is simply shifted towards MRF2 in Scenario A2. There is no surplus waste routed to the transfer aside from capacity condition. This is likely due to a combination of a high capacity requirement, the largest tipping fees for final disposal, and distance from southern service areas. Fig. 6 applies for Scenario B in general, suggesting that a MRF does little to minimize the system costs. Table 4 Overview of recycling results per scenario in tons per day (TPD) Scenario

DC1

DC2

Total

A1 A2 B1 & B2

172 0 0

112 284 100

284 284 100

Fig. 5. Scenario A2 optimal waste routing within each route in tons per day (TPD).

Fig. 6. Scenario B1 & B2 optimal waste routing within each route in tons per day (TPD).

Having to pay a tipping fee for private processing proves marginally beneficial in terms of system costs than the base case, whilst achieving only half the material recovery of Scenario A. Scenario BÕs low material recovery level indicates a strain to meet the MRF requirement with an optimal design capacity of 100 TPD. Analogous to Scenario A, the transfer station receives minimal waste excess from its contract from the Northeast service area. 6.1. Direct cost–benefit analysis Table 5 summarizes the present value net cost breakdown for all scenarios under consideration. The base case ascertains that the shipping cost is the major contributor accounting for 86% of the total cost in the municipal solid waste system at a price of US$48.7 million/ year. If a publicly or privately owned MRF would alleviate MSW expenses, then the MRF system could usher cost savings for the City. Meeting recycling mandates can be difficult as a result of increased complexities in the shipping strategy. In A2 86% of system costs go into shipping. In contrast, A1 has site selection flexibility to distribute across 2 MRFs and 3 final disposal facilities. This clarifies the reason behind the decrease in shipping expenditures and a smaller system net cost. Scenario A1 is the first to illustrate the considerable cost for building and operating MRFs. Nevertheless, the investment is justifiable based on potential recycling revenue, which is estimated at US$11.1 million/year in Scenario A. Unfortunately, to maintain this level of recycling effort with a single MRF requires an additional US$3.5 million/year in shipping. Generally, the single public MRF results in a net savings around US$7 million/year when compared to the optimal base case. At a net cost of US$48.7 million/year, Scenario B recycles at a level of 100 TPD, meaning privatization


841

Table 5 Cost distribution on a yearly basis for the base case, private, and public MRF ownership (US$) Scenario

Shipping cost

Disposal cost

Operation cost

Construction cost

Potential income

Net cost

A1

$41,636,899 83% $45,187,798 86%

$5,511,909 11% $5,523,689 10%

$2,161,355 4% $1,296,783 2%

$1,089,340 2% $700,230 1%

$11,139,864 – $11,139,864 –

$39,259,639 – $41,568,636 –

Scenario

Shipping cost

Disposal cost

Material recovery facility disposal cost

Net cost

B1 & B2

$40,676,615 83%

$6,508,993 13%

$1,540,665 3%

$48,726,272 –

Scenario

Shipping cost

Disposal cost

Net cost

Base

$41,661,544 86%

$7,065,061 14%

$48,726,605 –

A2

Note: Percentages in the table represent fractions of the total cost per scenario.

is comparable with the base case on a cost basis. Without a source for income in this scenario, privatization offers diminutive relief from the base costs regardless the MRF tipping fee. Sensitivity analysis serves as a imperative evaluation function for any silver lining that might exist for privatization. The intent is to test tipping fee sensitivity and citizen participation in order to identify a range of prices that may present privatization as cost-competitive, or bolster public ownership support. 6.2. Sensitivity analysis Testing the privatization scenario for sensitivity across the lower and upper average tipping fees in Texas did not produce compelling results until the fee fell below US$41.21 per ton. Although Scenario B lacks income to make the system cost competitive with Scenario A, privatization would lower shipping costs when compared to the base or the material recovery case. Examination of citizen participation (Cp) across a spectrum of values produces diverse consequences across scenarios. Varying participation from 32% to 90% and measuring MRF capacity and the net cost shows that as participation increases, the system cost in Scenario A2 decreases with an increase in design capacity. In A2, it is clear that a boost in citizen participation diminishes net costs with the increased potential for recycling income. Scenario B2, on the other hand, does not exhibit such a drastic cut in system cost. The difference between the lowest and highest simulated participation rate reveals a maximum US$2.4 million/year in savings in B2. According to Fig. 7, A2 holds a more pronounced US$9.2 million/year differential. For this reason participation plays a more pivotal role in cost reduction for public MRF ownership that is connected to larger income potential. It is apparent that A2 capitalizes from available recyclables in the waste stream. The same cannot be said about Scenario B2 where the design capacity remains stagnant despite an increase in recycling participation. A lack of income in this scenario

Fig. 7. Participation rate sensitivity as it impacts net cost in million dollars and design capacity of MRF2 in scenario A2.

provides no incentive for the formulation to increase MRF capacity above 100 TPD. Modeling robustness is prevalent in that design capacity grows with increased citizen participation. Scenario A2 is particularly affected by the participation range, and extraordinary decreases in system cost due accompany an increased potential to capitalize from recyclables. It leaves a possibility to advance household participation in curbside recycling by educating the public in order to achieve a high level of participation (70%), though one cannot easily quantify the expenses to achieve such a lofty public relations goal. 6.3. Indirect cost–benefit analysis Indirect costs and benefits allow closer scrutiny into what is ineffable in scenario planning. Although sensitivity analysis shows the importance of public participation of the recycling program, the cost associated with promotion and cultivation of public support cannot be easily modeled. If the promotion of recycling consists of a short-term investment, then cost savings from material recovery may leave capital to invest in an ambitious marketing strategy directed at increasing citizen participation and program sustainability.

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industry outreach capability. This presents a key advantage towards privatization if San Antonio management cannot presently engage the recyclable material markets in the near or long-term future.

Solid waste permits present an important hidden cost to San Antonio. Landfill permits for new construction or expansion present opportunities for landfill operators to pass increased surcharges. Although MRFs are not yet common in Texas, it leaves planners with an opportunity to explore future regulations at the State level. Material recovery facilities in Texas presently lie in a grey area, but begin to manifest themselves as ‘‘Type VI facilit[ies]. . . involving a new or unproven method of managing or utilizing municipal solid waste, including resource and energy recovery projects (Texas Administrative Code RULE §330.41)’’. MRFs undoubtedly qualify for that criterion, meaning a potential site must register rather than undergo expensive permitting (Bard, 2003). Pending changes to the Texas Administrative Code showcases how regulators plan to cope with municipalityÕs (and private industryÕs) demands for recycling permit flexibility. These changes are meant to eliminate cumbersome permit requirements that deter legitimate recycling (Bard, 2003). A Title V permit could range between US$225,000 to US$1,000,000, while registration costs range from US$215,000 to US$750,000 according to analysts with the TCEQ Strategic Planning and Appropriations. Facilities may register as MRFs if they recycle more than 10% of the incoming waste stream that would normally go to a landfill (Texas Administration Code, TAC Rule Log No. 2003-028-330-WS). By design, MRFs can easily meet that requirement by maintaining a minimum level of curbside sorting. As registration becomes viable, MSW permits will not present a tremendous hurdle to private industry or the City when pursuing construction and operation of a MRF. The need to find sustainable markets for the recycled material throughout the lifetime of the operation presents a critical hidden cost in material recovery. The costs of evaluating and sustaining these secondary material markets would be similar in each scenario, with the keen exception that private waste handlers have greater

6.4. Policy analysis The MRF registration issue illuminates the need to ponder the ramifications of the decision to recover recyclables from a legal standpoint. One must realize that solid waste management can quickly galvanize strong social and political unrest. Policy analysis may help anticipate social dynamics and widen the system plannersÕ awareness for solid waste issues as they look to promote centralized material recovery options. It is in the CityÕs interest to save landfill space in order to avoid unpleasantness with the citizenry over complaints pertaining to landfill odor, sound, and health concerns (Fields, 2002). Amid global increases in landfill activity, relations between stakeholders and solid waste decision makers grow rifts around actual and perceived health risks (Ishizaka and Tanaka, 2003). Often, ‘‘top-down’’ decision making in the public or private sectors do not adequately engage community involvement. A lack of risk communication between local partners and service providers consequently exposes waste management projects to condemnation – irrespective of the underlying common good (Ishizaka and Tanaka, 2003). Case in point, San AntonioÕs southeastern residents perceive recent BFI permit amendments as undesirable. The changes would increase the facility capacity and extend landfill lifespan for an additional 50 years, and provide the City with a much-needed disposal option. Community members sued BFI in Texas courts, largely due to a perceived lack of environmental stewardship amid residentsÕ health and noise concerns (Sorg, 2003). The permit case is currently before the Texas Supreme

Table 6 Impacts of scenario A2 and scenario B2 compared to present landfill characteristics Landfill

Acres (km2)

Depth (m)

Remaining volume (m3)

Compaction rate (kg/m3)

Remaining tons (tons)

Landfilled (TPY)

Remaining years (Y)

Present consumptiona WMI 2.82 BFI 1.07

32 32

90,384,602 34,306,092

739 866

66,760,701 29,715,373

612,776 1,114,801

108.9 26.7

Scenario A2 WMI 2.82 BFI 1.07

32 32

90,384,602 34,306,092

739 866

66,760,701 29,715,373

508,982 1,011,007

131.2 29.4

Scenario B2 WMI 2.82 BFI 1.07

32 32

90,384,602 34,306,092

739 866

66,760,701 29,715,373

576,276 1,078,301

115.8 27.6

a

TCEQ (2004).


Court after BFI fought a lower appellate courtÕs decision to deny the new permit (Needham, 2003). The lesson from BFI is that thorny solid waste decisions in this urban area necessitate new forms of outreach and disposal options to balance environmental concerns with genuine public participation. Including a MRF option may help the City avoid potential debacles as they face impending disposal pressures with their rapidly increasing population. Table 6 expresses landfill space savings in terms of volume, compaction, and remaining years for the base case and the single public/private MRF options. Upon close inspection, Scenario A2 prolongs landfill life in San Antonio by 25 years, whereas Scenario B adds about a third of that quantity. Therefore, a publicly owned MRF will allow the City an integrated waste disposal network landfill that can prolong time to rethink complex siting and expansion plans with disposal contractors.

7. Conclusion Poor participation from the city residents in curbside recycling program results in a need to install MRFsin an urban setting. In this analysis, a series of optimization models address the optimal site selection and capacity planning for material recovery in conjunction with an optimal shipping strategy for waste streams in a multidistrict urban region. The macro analysis in this paper organizes two possible scenarios based on whether recovery facilities are run by a private or public agency. The modeling results provide decision makers opportunity to weigh MRF options alongside over important solid waste policies for the City of San Antonio. Cost–benefit analysis confirms that the processing fee is not the driving cost in the CityÕs operation, and rather the shipping cost is the most influential parameter. Sensitivity analysis solidifies the notion that public participation plays the most important role in minimizing MSW expenses when the system embraces recovery options. With limited municipal budgets in mind, this analysis points to environmental modeling that combines a public policy outlook to reason that a MRF may alleviate solid waste costs. The most favorable design of a material recovery facility complements system optimization techniques with cost–benefit, sensitivity, and policy analysis. The result is a holistic modeling approach that forms a nexus between environmental sustainability and cost minimization. Recent developments in State environmental policy, citizen unrest with landfill expansion, and City efforts to overhaul its waste routes compel system planners to examine MRFs as a viable alternative to improve the municipal bottom line and deliver quality environmental services. The next phase for San Antonio ought to rely on the microanalysis of collection routes to

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achieve a more global cost minimization within an integrated disposal solution. Acknowledgements This material is based upon work supported by the National Science Foundation under Grant No. HRD0206259. The authors acknowledge the support of the data reports cited and used in this analysis. The authors are also thankful for technical and administrative support from many people in the Environmental Services Department of the City of San Antonio, including Daniel Cardenas, Arturo Alvarez, Pamela Bransford, David McDaniel, Juana Perez, and Rose Ryan. Appendix I. Nomenclature Decision variables C1 total shipping cost ($/year) C2 total disposal cost ($/year) C3 recycling income under public ownership, or privatized MRF processing fee ($/year) C4 total MRF operating cost under public ownership ($/year) C5 total MRF construction cost under public ownership ($/year) C6 MRF registration cost with the State under public ownership ($/year) Xij waste stream shipped from i to j (TPY) (i = 1, 2, 3, 4 (Service centers) and j = 1, 2, (MRFs)) Yik waste stream shipped from i to k (TPY) (i = 1, 2, 3, 4 (Service centers) and k = 1, 2, 3 (Landfills and Transfer Station)) Zjk waste stream shipped from j to k (TPY) (j = 1, 2, (MRFs) and k = 1, 2, 3 (Landfills and Transfer Station)) PMRF the processing fee governmental agency needs to pay for the recycling in MRF ($/ton) DCj design capacity of MRF (TPD) Ij a binary integer variable; it is equal one when the facility is included in the alternative, otherwise zero (unitless) Parameters P1 unitary shipping cost ($/ton/km) P2 unitary disposal cost at Landfill 1 (WMI) ($/ ton) P3 unitary disposal cost at Landfill 2 (BFI) ($/ton) P4 unitary disposal cost at Transfer Station (TDS) ($/ton) Rjl the recycling ratio of recyclable l in the waste stream at MRF site j (%) (l = 1 for paper, l = 2 for glass, l = 3 for plastics, and l = 4 for metal) Ijl average income from selling recyclable l ($/ton) (l = 1, 2, 3, 4)

844


CPIR

a consumer price index ratio that updates the construction an operation cost information from the baseline in the past to the present along the timeline (unitless) UCF a unit conversion factor that makes the unit of cost in the derived cost functions to be consistent with those in the other cost components ai, bi, ci, and di regression coefficients in the cost functions e MRF registration with the TCEQ ($) D1ij average shipping distance from i to j (km) D2ik average shipping distance from i to k (km) D3jk average shipping distance from j to k (km) CAPk minimum amount of waste that should be destined for disposal site k (ton/year) Gi waste generation rate at service area i (ton/year) (in this case: i = 1 Northeast Service Center; i = 2 Southeast Service Center; i = 3 South Central Service Center; i = 4 Northwest Service Center) Ujl and Ljl the upper and lower bounds of recyclables in the waste stream Cp percentage citizen participation in recycling venture (%) Pr percentage of the overall waste stream that is comprised of commingled recyclables (%) DCmin and DCmax the minimum and maximum capacity of MRF processing line above which the investment will be justified by economy of scale PLBj and PUBj the upper and lower bounds of the possible processing fee ($/ton) Appendix II. Parameter values used in LINGO program

P1 P2 P3 P4 Gi

Ij1

Rj1

Definition

Value

Unit shipping cost ($/ton/km) Tipping fee at BFI ($/ton) Tipping fee at WMI ($/ton) Tipping fee at TDS ($/ton) Waste generation rate i = 1: Northeast Service Area i = 2: Southeast Service Area i = 3: Southwest Service Area i = 4: Northwest Service Area Average recycling income l = 1 papera l = 2 glassb l = 3 plasticb l = 4 metalc Recyclables in waste stream l = 1 for paper l = 2 for glass l = 3 for plastic l = 4 for metal

4.81 18.22 16.72 24.89 (TPY) 92,550 79,333 89,251 109,077 ($/ton) 27.50 0.55 264.00 704.00 (%) [0.54, 0.58] [0.04, 0.08] [0.08, 0.13] [0.04, 0.08]

CAPk CAP1 CAP2 CAP3 D1ij D111 D112 D121 D122 D131 D132 D141 D142 D2ik D211 D212 D213 D221 D222 D223 D231 D232 D233 D241 D242 D243 D3jk D311 D312 D313 D321 D322 D323

Definition

Value

Minimum waste to BFI Minimum waste to WMI Minimum waste to TDS Average shipping distance Northeast SCd to MRF1 Northeast SC to MRF2 Southeast SC to MRF1 Southeast SC to MRF2 Southwest SC to MRF1 Southwest SC to MRF2 Northwest SC to MRF1 Northwest SC to MRF2 Average shipping distance Northeast SC to BFI Northeast SC to WMI Northeast SC to TDS Southeast SC to BFI Southeast SC to WMI Southeast SC to TDS Southwest SC to BFI Southwest SC to WMI Southwest SC to TDS Northwest SC to BFI Northwest SC to WMI Northwest SC to TDS Average shipping distance MRF1 to BFI MRF1 to WMI MRF1 to TDS MRF2 to BFI MRF2 to WMI MRF2 to TDS

(TPY) 45,454 45,454 90,909 (km) 23.75 33.60 45.90 16.95 32.25 16.65 26.15 40.70 (km) 43.40 45.55 13.05 28.65 45.20 32.85 27.05 18.45 28.80 50.55 32.40 32.10 (km) 43.35 41.10 17.00 11.35 32.30 18.00

Other parameters: CPIR Engineering News Record, 2003 a Variable construction cost ($/ton) b Fixed construction cost ($) c Variable operating cost ($/ton) d Fixed operating cost ($) e MRF registration cost ($) PMRF Average MRF tipping fee ($/ton) DCmin/max Scale Economy Constraint (TPD) a b c d

Acco-Bfi (2003). RecycleNet (2003). Newell (2003). SC: Service Center.

1.29 1519.90 864,572 7344.36 2,129,536 482,500 42.21 [100, 1000]


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