Design and Planning of Sustainable Industrial Networks: Application

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Ind. Eng. Chem. Res. 2010, 49, 4230–4248

Design and Planning of Sustainable Industrial Networks: Application to a Recovery Network of Residual Products Joaquim Duque,*,† Ana Paula F. D. Barbosa-Po´voa,‡ and Augusto Q. Novais† DMS, Instituto Nacional de Engenharia, Tecnologia e InoVaçaõ, Est. do Paço do Lumiar, 1649-038 Lisboa, Portugal, and CEG-IST, Instituto Superior Tećnico, UniV. Tećnica de Lisboa, AV. RoVisco Pais, 1049-101 Lisboa, Portugal

The present work describes the integration of environmental impact/damage evaluation into an optimization model for management of industrial networks. The selected methodology of environmental evaluation, the Eco-indicator 99, is based on the life cycle impact assessment. Its implementation and suitability is studied with an emphasis being placed on the strengths and limits of the methodology. The final model, derived from the application of process system engineering methodologies, is described as a mixed-integer linear program, which, once solved, is able to suggest the optimal processing and transportation routes, while optimizing a given objective function that either meets the design and environmental constraints or minimizes the ecoindicator. Whenever the impacts/damages costs are quantifiable, the calculation may also contemplate the inclusion of the environmental costs into the economic function that evaluates the network characteristic data and costs. An example based on the implementation of an innovative network for the recovery of the sludge obtained from aluminum surface finishing plants is presented. This illustrates the importance of including environmental impact/damage methodologies, explores their possible uses and analyzes obtained results. It is also used to perform a multiobjective analysis through an approximation to the Pareto curve for an economicenvironmental trade-off. This curve is obtained through the application of a ε-constraint method, by plotting a set of successive optimized solutions given by the maximization of an economic function that reflects the costs of disposal, processing, transport, and materials storage versus an impact indicator obtained from the environment pollutants emitted. This analysis is complemented with the minimization of the eco-indicator value (EI99), along with an estimate of the corresponding amount of sludge recovery. 2. Introduction Increasing awareness over the effects of industrial activities on the environment is leading, along with an ever more demanding legal enforcement, to the need of providing alternative ways to reduce negative environmental impacts.1 In the process industry, this problem is highly complex and the potential environmental risks compel process manufacturers not only to look after their production impacts, but also after waste disposal, steeping costs, soil occupation, and resource availability. Most of the literature addresses the case of designing a plant in order to minimize waste.2,3 These developments, reinforced by sustainability demands, lead to the reuse of waste materials, after total or partial pollutant content removal.4,5 That goes along with the transition from traditional supply chains to closed loop supply chains that include all reverse flows. However, and in order to address a limitation of the supply chain models identified in the literature,6,7 a considerable emphasis is placed in this work on process and transport modeling details.8 Further effort is placed on evaluating the importance of including a set of environmental impacts/damages in the model proposed for the synthesis and optimization of a general industrial network with uncertain demands.8 The modeling of the general network route and the environmental methodologies is based on the mSTN representation.7,9,10 The environmental methodology, the eco-indicator 9911 (EI99), hereby included in the general model, is based on the Life Cycle Impact Assessment. It explicitly extends the analysis of pollutant * To whom correspondence should be addressed. E-mail: [email protected]. Tel.: + 351 210 924 600. † Instituto Nacional de Engenharia, Tecnologia e Inovaçaõ. ‡ Instituto Superior Tećnico, UniV. Tećnica de Lisboa.

emissions to soil occupation/transformation and mineral/fossil resource consumption. The implementation used allows for the comparison of the emitted pollutants with those computed by the authors in a previous generalization12,13 of the minimum environment impact methodology (MEIM), along Stefanis guidelines.3 From the EI99 methodology11 a set of normalized incremental damages to human health, eco-system, and resources are obtained, using the actual values for those environmental effects applicable to the European space. Finally, after suitable weighting of those damages, a single indicator is generated, that is, the EI99. In this analysis, subjectivity issues are approached on the basis of three distinct cultural perspectives, that is, the equalitarian, the hierarchical, and the individualist, as proposed by Hofstetter14 and derived from the Cultural Theory.15 The model is generic in scope and leads both to the optimal network structure and to the associated operation. The former results from the synthesis of the processing steps, while the latter is described by the complete resource time allocation (i.e., scheduling of processing, transport, and storage of materials and utilities). According to Cano-Ruiz,16 as referred by Hugo,17 the use of the environment as a design objective instead of mere operational constraints may unveil novel process (network) alternatives achieving improved economic and environmental performance, as will be shown in this work. An example taken from the literature (Stefanis,3 after BarbosaPo´voa9) was included to validate and demonstrate the contribution of the model to decision-making. The inclusion of environmental methodologies gave impact values that were coherent and showed the importance of environmental aspects when trying to define friendlier industrial networks, thus proving the model’s usefulness as a decision aid tool.

10.1021/ie900940h  2010 American Chemical Society Published on Web 04/08/2010

Ind. Eng. Chem. Res., Vol. 49, No. 9, 2010

A motivating example based on the sludge recovery from aluminum surface finishing plants is presented for the proposed network over a given time horizon, with several possible objective functions having been employed: maximization of profit, minimization of investment, or minimization of the EI99. The first, maximization of profit, tends to maximize simultaneously the quantity of sludge processed and reflects the tradeoff between the cost for its disposal as is and its value after processing, while accounting for production, transport, and environment impacts, by guaranteeing the limits imposed on the pollutants emitted, the soil transformation/occupation and the resource consumption. The second, minimization of investment, envisages a network design suitable for processing a given quantity of sludge or product demand while reflecting the same trade-off. Finally, the third points to a network design that minimizes the EI99 indicator, while also reflecting the tradeoff between disposal, transformation costs, and a set of economic constraints that may be imposed. A multiobjective analysis is undertaken by solving the example for a succession of economic or environmental target values, using as the objective function, respectively, profit or the EI99 indicator, thus leading to an approximation to the Pareto surface that can be used to assert the economic and environmental optimal trade-off solution. 3. Representation Methodology A general representation methodology in process design is known as the State Task Network18,19 (STN), where processes are defined as a network of two types of nodes: state nodes (representing the feeds, intermediate and final products) and task nodes (representing the elementary processing operations transforming material between the different states). The STN identifies the input states used to produce each output state. Together with additional conditions (e.g., the proportion of material states to be used, processing task times, etc.) it defines the production recipes. A recipe, even if completed with a plant description of the available processing equipments and their suitability to carry out tasks, while sufficient to consider broad scheduling problems still contains a number of ambiguities. For example, it is implicitly assumed that transfers of materials between vessels are always possible; it remains unclear where a state produced at the end of a task is actually residing in the vessel allocated to the task, in a separate storage unit, or in a downstream vessel carrying out the subsequent task; it remains unclear the exact location of different quantities of materials of the same state. To overcome these ambiguities an extended representation was proposed,20 the maximal state task network (mSTN), using a complete plant network representation (the usual flowsheet) with explicit consideration of connections between processing units and of all important transfer operations, to unambiguously represent the combined recipe/flowsheet/transfer information and use it for the detailed scheduling of batch plant operations. The mSTN representation specifies an eState for each storable product, an iState and an oState, respectively, for the input and output state of each processing task eTask, and a tTask for each transfer task. The mSTN representation allocates suitable equipment units to the storage and to the processing and transportation tasks. All the possible processing, storage, and transfer operations are considered. Its mathematical formulation relies on a discretization of time, so that all processing and transportation times are integer and multiple of an elementary time interval ∆t, as presented by Barbosa-Po´voa.9

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The transfer tasks tTask use connections and are assumed to instantaneously deliver the product. To model the transfer of products between units installed in distant locations, the original model was initially extended to deal with a finite time transit for the connections. These types of material transfers require the use of transport vehicles and the need to account for the polluted waste they originate. Since the selection of a particular type of transport also depends on its load, the modeling of transport was initially considered as a transformation task, eTask. However, it was subsequently concluded that for the sake of model simplicity and tractability, transports could, within a reasonable approximation (less than 1%), still be modeled as a connection with an associated transit time.21,22 4. Problem Definition and Characteristics The problem of environmental impact integration into a network optimization model, as addressed in this work, can be defined as follows: GiVen is an industrial network superstructure (STN/flowsheet) characterized by (1) all the possible transformations, their process duration, suitable units’ location, capacities, utilities consumption, materials involved and wastes generated; (2) all waste producers, their location, and the quantity of wastes produced along with their pollutants content; (3) all the reuses and landfill disposals, their location, utility consumption, capacities, and, for the reuse, the wastes generated; (4) all possible transport routes, their associated suitable transports and duration. GiVen is the cost data for (5) equipment (processing, transport and storage units); (6) reuses and landfill disposal; (7) operations and utilities; (8) products values and raw materials costs. GiVen is the operational data for (9) type of operation (cyclic single campaign mode or short-term operation); (10) time horizon/cyclic time. GiVen is the environmental data (11) maximum acceptable concentration limits; (120 long-term effect potentials; (13) unitary marginal damages associated to each pollutant involved. Determine (14) the optimal network structure (processing operations, storage locations, and transfer routes of materials); (15) the optimal operating strategies (scheduling of operations, storage profiles, and transfer occurrences). So as to optimize an economic or environmental performance criterion. The former can be defined as a maximum plant profit or a minimum capital expenditure, while accounting for the environmental impacts involved and their imposed limits; the latter can be the minimization of the environmental impacts, where all operational and structural network restrictions, as well as cost limits are considered (Table 2). 5. Mathematical Model for MEI Methodology As mentioned before, the mSTN representation10 is used to model the general industrial network superstructure which leads to a generic formulation presented in the Appendix. This model incorporates a generalization of the MEI methodology so as to account for the waste generation at utility production and transportation levels.22 For the transport task the environmental impact is calculated based on the fuel oil consumption, therefore at the utility level. In addition, the model also includes the possibility of accounting for the environmental impact caused by repairing or replacing the transport units. Because of the characteristics of the proposed model, which can be applied to the production of a set of products and/or to

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Table 1. Time-Dependent Environment Impact Indicators CTAM (critical air mass, [kg air/h])

CTAM )

pollutant emission mass at interval t (kg pollutant/h) standard limit value (kg pollutant/kg air)

CTWM (critical water mass, [kg water/h])

CTWM )

pollutant emission mass at interval t (kg pollutant/h) standard limit value (kg pollutant/kg air)

SMD (solid mass disposal, [kg/h])

SMD ) mass of solids disposed at interval t (kg/h)

GWI (global warming impact, CO2 [kg/h])

GWI ) mass of pollutant at interval t (kg/h) × GWP (kg CO2/kg pollutant)

POI (photochemical oxidation impact, C2H4 [kg/h])

POI ) mass of pollutant at interval t (kg poll/h) × POCP (kg ethylene/kg poll)

SODI (stratospheric ozone depletion impact [kg/h])

SODI ) mass of poll at interval t (kg poll/h/h) × SODP (kg CFC11/kg poll)

Table 2. Equipment and Characteristics capacity (tonnes) units

suitability

minimal

maximal

unit R1a (1a) unit R1b (1b) unit R2a (2a) tank (V1) tank (V2) tank (V4) tank (V5) tank (V6)

T1 e T2 T1 e T2 T3 e T4 S1 S2 S4 S5 S6

50 50 50

150 150 200 unlimited unlimited 100 unlimited unlimited

10

Wwt )

costs

∑ ∑ (R

wijWijt

i

fixed (103 €)

variable (€/kg)

20 20 30

0.5 0.5 1

1

0.1

the recovery of pollutant products, the system frontier for the environmental impacts is defined at the raw materials level, including any type of utilities used. The model has the particularity of considering all possible concurrent transportations and transformations for the same operation (different instances within the superstructure), as well as all raw material consumers/producers and reusers. The pollutant production is added up for all the different types of waste. The limits on the total waste production and global environment impacts are introduced in the form of waste and pollution vectors, added to the model as additional restrictions. Those limits derive directly from legal regulations for the pollutants considered. The model also considers the possibility of imposing limits on the amount of final product required as well as on the amount of processed pollutant materials (raw materials), due to environmental impacts. To compute the indexes for the global environmental impacts specified, the model begins by computing the polluted wastes produced during each time interval. To achieve that, two sets of variable coefficients are defined, as proposed by Stefanis: 3 βwij, the quantity of waste w produced per mass unit of the batch transformed by task i taking place at unit j; βwu, the quantity of waste w produced at the utility generation level, per mass unit of utility u consumed. A third set of fixed coefficients is hereby proposed: Rwij, the quantity of waste w produced by the use of transport unit j at transportation task i, due to repair or replacement of this particular transportation unit. And the model variables are Wwt, a continuous variable to specify the quantity of waste w produced at time interval t; Wijt ) 1, a binary variable to specify the beginning, at time interval t, of task i on entity j; Bijt, a continuous variable specifying the batch size transformed by task i on entity j during time interval t; Uut, a continuous variable representing the quantity of utility u used during time interval t. The quantities of wastes w produced during the interval t are given by

+ βwijBijt) +

j∈Ki

∑β

∀w, t ) 1, ..., T (1)

wuUut

u∈U

The term RwijWijt is added to Stefanis’ generalization to account for pollution generated by the repair/replacement of transportation units (or more generally any processing unit used). Quantities of wastes w produced during each cycle, T

∑W

TWw )

∀w

wt

(2)

t)1

are subject to upper limits: ∀w

TWw e εw

(3)

If we are interested only in global quantities of wastes w produced, the three previous equations can be compacted into a single constraining equation, T

∑(∑ ∑β t)1

i

wijBijt

+

j∈Ki

∑β

wuUut)

∀w (3.a)

e εw

u∈U

The environmental impact m associated to the waste w produced at time t, is given by EIwmt )

∑ ∑γ

wmijBijt

i

+

j∈Ki

∑γ

wmuUut

∀w, m, t )

u∈U

1, ..., T (4) The corresponding constraint equation on m, can thus be written as T

GEIm )

∑ ∑ EI

wmt

t)1

e εm

∀m

(5)

w

Once again, if the values for the individual impact indexes EIwmt are of no interest, the previous two equations can be written as a single constraint equation, T

∑(∑ ∑ ∑γ t)1

w

i

j∈Ki

wmijBijt

+

∑γ

wmuUut)

e εm

∀m

u∈U

(5a) 6. Mathematical Model for Eco-indicator 99 Methodology The computation of the eco-indicator 99 effects can be developed in order to make it formally equivalent to the environmental impacts computed with MEIM. Nevertheless, there is a substantial difference in the final results: while MEIM


computes a quantity of a reference pollutant type as an environmental impact indicator, the EI99 computes the effect originated by the quantity of that very pollutant. For example, when considering stratospheric ozone depletion, the EI99 methodology computes the percentage of ozone depletion instead of an associated quantity of CFC11 emitted to the atmosphere. To model eco-indicator 99, several sets, parameters, and variables are defined and are presented along with all other variables in an annexed Glossary in the Appendix. 6.1. Damage as a Function of an Environmental Impact Indicator. The model developed with the eco-indicator 99, evaluates the damage produced by mimicking the mathematical approach used by MEIM methodology, thus assuring a formal relationship of the environmental indicators used on both methods. Furthermore and in order to grant the possibility of any viable comparison of results obtained for several cases of the same example, this work uses exclusively the hierarchical cultural perspective. Only in this way, by reducing all subjectivities to their bare minimum, may those comparisons be considered a measure of the relative environmental impacts or damages. In the EI 99 manual, a set of tables is presented for the damage values originated by the emission of several pollutants, which may be related to the corresponding environmental impact indicators. The damage of a given type d is given by damagedm ) kmp Mp

[DALY]

(6)

where the index d refers to the type of damage, m is the type of environmental impact, and p is the emitted pollutant; MP represents the emitted mass of pollutant p and km p is the corresponding unitary damage. (It is assumed here that damages vary linearly with the corresponding environmental impacts.) It should be noted that the units used for damages to the human health are expressed in Disabled Adjusted Life Years (DALY), as defined by the World Health Organization (WHO). Each environmental impact is usually associated to a given pollutant, here referred as the pollutant type, suitable for the study of that particular environmental impact. Given the unitary damage, km T, for the pollutant type T, eq 6 may be written as damagedm ) kTm

kmp kTm

Mp

[DALY]

(7)

where the fraction of unitary damages, known as the environmental impact potential of pollutant p for environmental impact m, PEIm P , is given by PEImp )

kmp

(8)

kTm

Equation 7 can then be modified to give damagedm ) kTm(PEImp Mp)

[DALY]

EIm PEImp Mp

It is important to remark here that this approach may be used directly to obtain sets of environmental impact potentials usable by MEIM for a considerable number of substances. 6.2. The Normalization of Damage for Europe. As explained by the authors of the eco-indicator 99, when computing the damage associated to a particular environmental impact as the effect of a given quantity of pollutant, this means the socalled marginal damage and not the total damage generated in the reference area (i.e., the entire European region) during the reference time period, normally one year. The relative contribution of the computed marginal damage to the total damage is obtained by normalization. The values of the total individual damage (DIDType), aggregated by damage type, as proposed by the EI99 manual for Europe, assuming a population of 3.8 × 108, are 1.54 × 10-2 [DALY/yr], 5.13 × 103 [PDF m2 yr/yr] and 8.41 × 103 [MJ/ yr], respectively, for damage on the Human Health, the EcoSystem Quality, and the Natural Resources. The normalized damage value for each pollutant p contributing to an effect EIm of type m is computed by using the normalization factor DId ) ∑mDIdm, where DIdm corresponds to the type of damage, d, to which the effect of the environmental impact m contributes. Thus:

damagedm

damagedm )

[kg]

(10)

kTm ) (PEImp Mp) DId

(11)

[yr]

where the fraction kTm/(DId) represents the normalized unitary damage associated to the pollutant type T, generating the indicator EIm, of environmental impact type m. 6.3. Weighting Damage Types for Europe. A set of weights is used to obtain a single nondimensional indicator, usually noted by Pt (it reads Points), from the contributions of all types of damage. Those weights result from a social study (performed by the authors of eco-indicator 99) measuring social sensibility to each type of damage for population samples representative of the three cultural perspectives, equalitarian, hierarchical, and individualist. Furthermore, due to the small dimension of the social panel used in that particular study, all subsequent work uses the set of weights mean values instead of individual weights associated to any one of the cultural perspectives. The factors used attribute damage weights of 40% to both human health and the eco-system quality, and the remaining 20% to natural resources. Results are normally presented in milli-Points (mPt) and thus the weighting factors used are, respectively, 400, 400, and 200. 6.4. The Importance of the Contamination Vector Way. On the eco-indicator 99 methodology the human health damage values associated, for example, to cancer incidence in target population depends on the pollutant’s contamination incidence, which in turn depends on the way it occurs, namely by food, water ingestion or inhalation. These ways are very much dependent on the natural emission vector used, V, that is,airborne, effluent, or soil emissions release. Thus, eq 11 has to be split to account for the emission vector used:

(9)

The value in brackets is here referred to as the pollutant type equivalent mass, and it is formally identical to the MEIM’s environmental impact indicator, (previously referred to as EIm); that is,

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∑k

m m TV(PEIpVMpV)

[DALY]

(12)

[yr]

(13)

V

which after normalization becomes damagedm )

1 DId

∑k

m m TV(PEIpVMpV)

V

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Thus, to maintain a formal equivalence with MEIM, it would be required to decouple each one of the effects for all the emission vectors used. This additional complexity may be avoided if, in such cases, the mathematical model considers a damage indicator, DEIm, instead of the effect to which it is associated, by writing damagedm

1 ) DEIm DId

[yr]

(14)

QOVp )

H T

T

∑ ( ∑ ∑ (λ

1 VpwBijt

t)1

i

j∈Ki

0 + λVpw Wijt) +

∑λ

∀V, p ∈ o (21)

puUut)

u∈U

Equation 16 remains unchanged, and eq 17 is then replaced by QRp )

H T

where

T

∑ ∑ λU ∑ λ (S pu

ut

t)1 u∈U∧u∈r

ps

- Ss,0)

s,tLast

∀V, p ∈ r (22)

s∈SF∪SP

DEIm )

∑k

EIm m TV (PEIpVMpV)

[DALY]

(15)

V

6.5. Modeling the Pollutant Emissions. Considering a “pollutant” index p to represent all the substances emitted, o, all soil occupations and transformations, f, and all the natural resources used, r, either mineral or fossil, the quantity of “pollutant” p generated over the production horizon, H, is thus given by three equations. This is usually known as the occurrence analysis. The first equation refers to all the substances emitted to vector V, it is to be used in fate analysis and is written as H

QOVp

)

∑∑∑ t)1

i

1 (λVpw Bijt

j∈Ki

+

0 λVpw Wijt

+

∑λ

VpuijUuijt)

∀V, p ∈ o (16)

u∈U

The second equation refers to all the soil occupations and transformations, it is used in land-use analysis and is written as

∑λ

QFp )

pjEj

∀p ∈ f

(17)

j

The third equation refers to fossil and mineral resource consumption/production, it is to be used in resource analysis and is written as H

QRp )

∑ ∑

λpuUut -

t)1 u∈U∧u∈r

∑

λps(Ss,tLast - Ss,0)

∀V, p ∈ r (18)

s∈SF∪SP

All those equations may be aggregated together to give a “pollutant” inventory: Qp )

∑ QO

V p

+ QFp + QRp

(19)

V

Using a set of given limits, εp, the pollutant quantities emitted (e.g., carbon dioxide emissions), may be constrained by Qp e ε p

∀p ∈ o ∪ r ∪ f

(20)

On a cyclic production mode (see annex I) and to avoid additional elaboration, derived from breaking down the production horizon into time cycles, the parameters deduced for the horizon time interval H are maintained throughout the entire cycle time T and eq 16 becomes simply

6.6. Computing the Environmental Impact Indicators. The environmental impact indicators are then given by the following equation: EIm )

∑ ∑δ V

V mVpQOp

+

p∈o

∑δ

mpQFp

p∈f

+

∑δ

∀m (23)

δmVp ) (kTmVPEImV p )

(23b)

mpQRp

p∈r

where parameter δmVp, given by

represents the unitary marginal damage from environmental impact, m, generated by “pollutant”, p, emitted to the environment, V, and kTmV is the unitary damage factor for the pollutant V type and PEImV p , a dimensionless potential. The variable QOp represents the quantity of “pollutant” p released to the environment V, which equals the pollutant mass, Mp, emitted to that environment. The δmp represents the impact generated by “pollutant” p, responsible for effect m, produced by soil occupation/conversion and the use of mineral and fossil natural resources. 6.7. Computing the Normalized Damages to Human Health, Eco-system and Resources. The individual unitary marginal damage is then given by the following equation: Dd ) η d

∑ EI

∀d

m

(24)

m

where the parameter ηd corresponds to an “individual normalization damage factor” of type d, given by ηd ) 1/(DId) where the global damage, DId, aggregates all the “individual global damages”, DIdm, associated to the effects of all the environmental impacts, m, that contribute to that particular type of damage: DId ) ∑mDIdm. This normalization factor represents our best estimate of the individual damage of type d already inflicted to each European inhabitant as a consequence of the actual quantity of “pollutants” globally emitted in Europe. 6.8. Computing the EI99. Using the weighting, χd, obtained from a social study, as reported in Pre Consultants (2001), on the “cultural” perception of the relative importance for the diverse types of damage, d, a damage indicatorsthe EI99sis obtained: EI99 )

∑χ D d

d

(25)

d

6.9. Costs for the Excess of Pollutant Emissions (e.g., CO2). It is sometimes important for the user to have available a model that accounts for the costs of acquiring an additional emission permit on the respective market, for the quantity of pollutant emitted above the legal limit initially granted. To


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Figure 1. STN flowsheet. Table 3. Equipment Connections, Allocations, And Costs units

costs

connection no.

from

to

allocated to

fixed (103 €)

variable (€/kg)

1 2 3 4 (5) 6

tank V1 tank V2 tank V1 tank V2 unit 1a unit 1a

unit R1a unit R1a unit R1b unit R1b tank V4 unit R2a

1 1 1 1 1 1

0.1 0.1 0.1 0.1 0.1 0.1

7 (8)

unit 1b unit 1b

tank V4 unit R2a

1 1

0.1 0.1

9 10 11 12

unit 2a unit 2a tank V5 tank V4

tank V5 tank V6 unit R2a unit R2a

state S1 state S2 state S1 state S2 state S4 state S3 state S4 state S4 state S3 state S4 state S5 state S6 state S5 state S4

1 1 1 1

0.1 0.1 0.1 0.1

achieve this with the present model, the corresponding pollutant is first excluded from the equation constraining all pollutant emissions. For example, in the case of carbon dioxide emissions, eq 20 is then replaced by ∀p|p * CO2

Qp e εp

(26)

and the excess quantity of carbon dioxide emitted, CO2_exc, is then given by CO2_exc )

∑ QO

V p

V

- εp |p ) CO2

(27)

The limit εp stands for the legal emission limit values, granted to the recovery network as a global entity. If, instead of that, legal permits are granted to each one of the individual entities for transport and transformation, the previous equation is to be replaced by T

CO2_exc )

∑ ∑ ∑ (λ

1 VpijBijt

(

j∈Ki

t)1

i

0 + λVpij Wijt +

∑λ

puUuijt)

- εpj)|p ) CO2(28)

u∈U

Now the variable CO2_exc quantifies the carbon dioxide that entities j produced in excess of their granted emission limit εpj.

Figure 2. Industrial superstructure.

By multiplying this excess value by the cost of the unitary permit per pollutant mass unit, (CO2Cost), an additional penalty term is generated, given by the equation below, which is to be added to the economic evaluation objective function. CLic ) CO2_exc*CO2Cost

(29)

7. Example To show the added value that the discussed approach adds to the design of industrial networks, a published example by Stefanis3 based on the work of Barbosa-Po´voa,9 is now analyzed. The following industrial plant recipe is considered: two products, S5 and S6, with a fixed demand of 8000 tonnes, are to be produced from two stored feedstocks, S1 and S2, in a time horizon of 800 h using a cyclic periodic operation of 8 h: (Task 1, T1) Heat raw material S1 for 2 h to produce intermediate state S3. S3 is unstable and therefore cannot be stored. Proportions are 1 mass unit S1 to 1 mass unit S3. (Task 2, T2) Process material S2 for 2 h to produce intermediate state S4. S4 is unstable and therefore cannot be stored. Proportions are 1 mass unit S2 to 1 mass unit S3. (Task 3, T3) Mix intermediate material S3 with material S4 and let them react for 4 h to form the final product P1, described as S5. Proportions are 1 mass unit P1 from 0.6 mass units S3 and 0.4 mass units S4..(Task 4, T4) Mix S3 with S5 and let them react for 2 h to produce the second final product P2, represented by state S6. Proportions are 1 mass unit P2 from 0.6 mass units S3 and 0.4 mass units S5. The corresponding State Task Network (STN) is shown in Figure 1, and the equipment suitability, allocation, connections and costs are characterized in Table 3. The raw material costs are 0.014 k€ for S1 and 0.001 k€ for S2, while the products values are of 195 k€ for P1 and 227 k€ for P2. The superstructure installation is shown in Figure 2, and the corresponding maximal-state-task-network (mSTN) is presented in Figure 3. The problem was solved using GAMS/CPLEX (v22.7.2 for WINDOWS) software running in an Intel Core 2 Duo 6600 at 2.4 GHz. Two case studies are explored: (case 1) The model corresponding to the superstructure presented in Figure 2 is optimized considering no installation costs for storage vessel V4; (case 2) the same as the previous, but now with the installation costs of vessel V4 being accounted for.

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Figure 3. mSTN representation.

Figure 4. Optimal operation for cases 1 and 2.

Figure 5. Materials storage evolution. Table 4. Statistics for Capital Costs Minimization in Cases 1 and 2 variables

time to [s]

capital cost solution value [k€]

produced quantities [t]

case

total

binary

no. iterations

gen

exec

optim

best poss

S3

S4

1 2

503 503

122 122

215 262

0.016 0.016

0.144 0.150

241 246.48

241 246.48

8000 8000

8000 8000

For both cases the goal is to minimize the investment. The minimization of the objective function (OF) originates optimal solutions that correspond to the operations scheduling presented in Figure 4. Within the final plant structure unit R1b was not used (installed), while all the remaining equipment is installed. The storage capacities along the cycle time intervals, identical for case 1 and 2, are shown in Figure 5. The solutions statistics are presented in Table 4. The optimization results are global optima and coincide with those presented by Barbosa-Po´voa.9 Both solutions employ the same units, install equal capacities, and process similar batches.

They also store the same quantities of intermediate material S3 in V4, a 44.8 tonnes vessel. Environmental Considerations. To compute the environmental impact for the industrial network presented, indicators using either the MEIM or the eco-indicator methodology are used. Additional specifications are introduced to the process recipes. These involve accounting for utility consumptions, as presented in Table 5, and for the corresponding pollutants emitted per mass unit of utility used (see Table 6). The new optimizations lead, again for both cases, to the same optimized structure, capacities, storage policy, etc. Furthermore both cases use the

Ind. Eng. Chem. Res., Vol. 49, No. 9, 2010 Table 7. Utilities Consumed

Table 5. Task Consumption of Utilities fixed/variable (unit mass) units

task

electricity [kWh]

R1a, R1b R1a, R1b R2a R2a

T1 T2 T3 T4

0/4.0 0/8.0 0/2.0 0/4.0

4237

values (per/)

water [m3]

diesel [ m3]

0/0.05 0/0.03 0/0.004 0/0.02

0/0 0/0 0/0 0/0

utility

cycle

horizon

electricity [kWh] water [m3] diesel [m3]

3.1744 × 103 3.5328 × 101

3.17440 × 105 3.5328 × 103

Table 8. Pollutants Emitted, According to MEIMa emitted pollutants (per/)

same amount of utilities shown in Table 7, and the environmental indicators give similar information, as can be seen by comparing the values presented in Table 8 and Table 9, respectively, for the MEIM and the EI99 approaches. Once again the values found by minimizing investment for cases 1 and 2 are the same for utilities consumptions and quantities of pollutants emitted. Both environmental impact methodologies gave the same results shown in Table 8 and Table 9. Equipment Substitution. From the environmental results shown previously, units R1a and R1b were considered obsolete since they emit an excessive amount of CO2. To avoid legal penalties they are replaced by more efficient units, using diesel instead of electricity. In a first attempt, unit R1b was replaced by another type of unit having fixed installation costs of 25 k€ and variable costs of 0.75 €/kg and with utilities consumption presented in Table 10. Additionally, the upper limit of 1500 kg was imposed on CO2 emissions. This new problem has identical operating conditions and hence it can produce the same quantity of products. This explains why the minimization of investment leads now to operation schedules that are identical to those obtained previously, but having unit R1b instead of unit R1a due to the enforcement of the environmental constraints. No important changes were observed in the problem statistics, excepting the investment capital (see Table 11 by comparison with Table 4). The new optimization leads to a new pattern of utility consumption, see Table 12, which originates a new rate of pollutants emissions complying with the emission bounds imposed, Table 13 and Table 14. This example, frequently used in the specialized literature, enables the validation of the recovery network model presented above. Besides suggesting an optimized design and schedule, in total agreement with the solutions previously published, it also computes a set of environmental indicators that are proven to be very useful from the legal and sustainability points of view. The embedded environmental methodologies allow the attainment of constrained solutions complying with environmental upper bounds imposed on pollutant emissions, hereby illustrated by a CO2 upper limit. The present network model may use either methodology for environmental assessment, MEIM or EI99, depending on the simplicity or completeness of the intended environmental assessment. MEIM is relatively simple, but only quantifies a set of equivalent pollutant emissions and only handles a compliance fraction of legal limits for a very short list of impacts. On the other hand, EI99 besides broadening the scope of the environmental assessment also performs an analysis of

environmental indicator pollutant (units and type)

cycle

horizon

CTWM GWI [kg CO2] SODI [kg CFC11]

635.5866 2317.312 9.5232

63558.66 231731.2 952.32

a

All pollutants having a null value are from now on omitted.

Table 9. Pollutants Emitted, According to Eco-indicator 99 emitted pollutants (per/) pollutants emitted [kg]

cycle

horizon

SPM CO2 CO SO2 NOx

0.31744 2317.312 12.6976 12.38016 6.03136

31.744 231731.2 1269.76 1238.016 603.136

Table 10. Task Consumption of Utilities for Renewed Unit 1b fixed/variable (unit mass) unit

task

electricity [kWh]

water [m3]

diesel [m3]

R1b R1b

T1 T2

0/0 0/0

0/0.05 0/0.02

0/0.0003 0/0.0007

the pollutant effects from which it advances to a damage analysissto human health, eco-system quality, and mineral and fossil resource availability. The E99 methodology offers a single indicator that may constitute in itself an objective function to minimize or else to complement an economic objective in a biobjective problem. The examples presented next will illustrate in more detail some of the potential uses of the broader EI99 methodology. 8. Recovery Route Example 8.1. General Description and Implementation. To illustrate the use of the proposed model, an example based on the optimization of a recovery route for Al-rich sludge is presented. The anodization and lacquering of aluminum are processes that generate significant amounts of waste in the form of Al-rich sludge. As an economical alternative to disposal, this sludge can be treated and employed as coagulant and flocculant for the treatment of industrial and municipal effluents. As the surface treatment plant location does not coincide, in general, with the locations for industrial or municipal water treatment facilities, suitable transports are needed. On the basis of these characteristics, the current recovery route network problem can be described as follows: Given the raw materials (i.e., sludge) differences in pollutant content, two different general types, that is, S1 and S2 are

Table 6. Pollutants Emitted per Utility Consumptiona utility

CO

CO2

SO2

NOx

particles

electric water diesel

4.151 × 10-3

7.306 × 10-1

3.872 × 10-3

1.941 × 10-3

1.026 × 10-4

1.483 × 101

2.6095 × 103

3.460 × 101

1.98 × 10-1

a

Note: All values used were rounded up to 3 significant digits.

HC/NMHC

units

4.547 × 101

[kg/kWh] [kg/m3] [kg/m3]

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Table 11. Statistics for Investment Minimization of Cases 2 Using MEIM and EI99 variables

time to [s]

capital cost solution value [k€]

produced quantities [t]

case 2

total

binary

no. iterations

gen

exec

optim

best poss

S3

S4

MEIM EI99

503 503

122 116

53 32

0.015 0.015

0.107 0.094

268.28 268.28

268.28 268.28

8000 8000

8000 8000

Table 12. Utilities Consumed with Bounded Emission of CO2

Table 15. Typical Transport Container Monetary and Fuel Costs

values

units

utility

cycle

horizon

upper bound/cycle

electricity [kWh] water [m3] diesel [m3]

1536 35.328 0.13184

153600 3532.8 13.184

1536 35.328 0.13184

Table 13. Pollutants Emitted, According to MEIM, CO2 Limited emitted pollutants environmental indicator pollutant (units and type)

cycle

horizon

upper bound/cycle

CTAM CTWM GWI [kgCO2]

0.010547 307.9066 1464.064

1.05472 30790.66 146406.4

100000 100000 1500

Table 14. Pollutants Emitted, According to Eco-indicator 99, CO2 Limited emitted polutants pollutants emitted [kg]

cycle

horizon

upperbound/cycle

PAH SPM CO2 CO SO2 NOx

0.59328 0.179968 1464.064 8.1216 5.9904 7.5328

59.328 17.9968 146406.4 812.16 599.04 753.28

100000 100000 1500 100000 100000 100000

considered, which will be designated as states in what follows. Preliminary experimental studies15 verified the possibility of two different transformation processes for these two states (Figure 6): (1) a dilution process that uses S1 and S2 at relative proportions of 60% and 40%, which are processed by task T1 for a 1 day period, originating a 5% diluted material (S3) that is stable, in the proportion of 1 to 0.99, input and output mass units respectively, and producing a 1% mass units of waste (WT1); (2) a drying process that uses S1 and S2, at relative proportions of 25% and 75%, to be processed by task T2 during 2 days, originating a storable final state S4 with the proportions of 1-0.95 and originating a 1% mass units of waste (WT2). The S3 and S4 output states are suitable for the treatment of municipal and industrial effluents at different geographical locations, thus the material transfers require a transport, represented by a transportation task, which can be ensured by two alternative road transports (Tr1, Tr2), differing in costs and capacity. It has been verified experimentally that S4 presents better results for the industrial effluent treatment.

Figure 6. The dilution and drying STN recipes.

capacity rental costs diesel consumption

[m3] [€/month] [l/100 km]

values 6 40 30-35

10 40

20 85

These process recipes used in classical installations may alternatively be performed in modern, more expensive installations with an “ecological recipe” that improves the environmental performance. It is assumed that the ecological recipes counterparts achieve better yield of the dilution process, in the proportions of 1-0.999, and produce less waste, in the proportions of 1-0.001, for both processes. The example considers all the possible connections and the number of available trucks is limited to one for each transport type. The transport is undertaken in standard containers with available capacities of 6, 10, and 20 m3, corresponding to the costs presented in Table 15 that also shows the typical transports’ diesel consumption. The transport transit time is based on the distance between the entities connected, assuming an average speed of 80 and 67 km/h, respectively, for types Tr1 and Tr2. This example considers the possibility of using two different types of transformation entities at every Portuguese district, one based on a less advanced, more pollutant, and less costly technology (installation cost of 55 k€), and another employing more advanced, cleaner, and more costly and efficient technology (installation cost of 70 k€). The installable transformation capacity is assumed to vary continuously between 7 and 150 tonnes. Eight industrial entities (producers of sludge) were assumed installed in northern and central Portugal, as presented in Figure 7. Finally, it is also considered that there is one client

Figure 7. Producers installed. “Produtor”, “Transformador”, and “Cliente” stand, respectively, for producer, transformer, and client.

Ind. Eng. Chem. Res., Vol. 49, No. 9, 2010 Table 16. Proportional Demands [tonnes] district 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Aveiro Beja Braga Bragança C.Branco Coimbra E´vora Faro Guarda Leiria Lisboa Portalegre Porto Santare´m Setu´bal V. Castelo V. Real Viseu

state S3

state S4

110 25 128 23 32 68 27 61 28 71 330 20 275 70 122 39 35 61

90 20 105 19 26 56 22 50 21 58 270 16 225 57 100 32 28 50

entity (i.e., an effluent treatment plant) in every district, with demands that are proportional to the district population as presented in Table 16. Those nominal demands have upper and lower bound values that are roughly 25% above and under the nominal values, respectively. The boundary for the LCIA includes the raw materials (industrial sludges), but not the corresponding production processes, the utilities production, all network transformations and transports, and final products delivery, This delivery of final products replaces the use of customary wastewater treatment products. The pollution thus avoided, corresponding to aluminum production, is then included with negative values in the LCIA assessment. The superstructure for the recovery route example is depicted in Figure 8, while the transport’s environmental data are presented in Table 17. For optimization purposes it is considered a production time horizon of 30 days with a periodic operation of 5 days. The example which modeled transports as connections was

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solved using GAMS/CPLEX (v22.7.2 for WINDOWS) software running in an Intel Core 2 Duo 6600 at 2.4 GHz. In a cyclic single campaign operation mode, with the two types of transports, the model is characterized by 77318 single equations, 61867 variables, of which 31208 are discrete. The model solution takes a resource time varying between roughly 15 min and 6 h, with termination criteria of 2%, that is, the relative gap between the obtained and the best possible solutions, or with a resource time limit of 22000 s, where the gap is about 5% or less. 8.2. Optimized Case Results. To perform a multiobjective analysis on the economic (profit) and environmental (EI99 indicator) optimization results, the example is solved using the ε-constraint method, by executing, respectively, a succession of economic and of environmental optimization runs, as presented in Tables 18 and 19, leading to a common tradeoff surface, Figure 9, that can be used to assert the optimal joint solution. However, within the region where the tradeoff is tighter, the solutions do not exactly match, as a result on one hand of the discrete nature of the network configuration and the nonexistence, therefore, of a continuum of viable configurations, and also of the relative gap used to stop the optimization. If instead of imposing lower bounds on damage for the economic maximization, those bounds are imposed on the quantities of CO2 emitted, the optimization results are those presented in Table 20. The corresponding curve is juxtaposed to the economical and environmental ones shown on Figure 9. Apart from the outlier represented by point A6, a common trend is found for these two optimization criteria, which pinpoints to the environmental marked importance of CO2 for a network where fuel-operated transport is dominant. The sludge transformation (recovery), however, also contributes to this effect, which explains the occurrence of point A6, where the CO2 target decrease can only be achieved through reduction in the amount of sludge undergoing recovery. That reduction in transformed

Figure 8. Partial representation of the recovery route superstructure. Table 17. Pollutants Emitted by Utilities Consumption

3

diesel [kg/m ] electricity [kg/kWh]

CO

CO2

SOx

NOx

Partıćulas

HC/NMHC

14.828 4.151 × 10-3

2609.5 7.306 × 10-1

3.872 × 10-3

34.6 1.941 × 10-3

0.198 1.026 × 10-4

4.547

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Table 18. Profit Maximization with an Upper Bound on Damage model statistics time to [s] upper bound (case) [mPt] 2950 (I) 2925 (H) 2900 (G) 2850 (F) 2750 (E) 2600 (D) 2400 (C) 2200 (B) none (A)

no. iterations

gen

9355665 994 13667291 989 11519276 989 6139438 994 134366 987 55429 1007 43341 986 23885 975 7419 973

solution value [k€]

exec

optim

relax

22000 21999 22000 11421 652 244 201 153 20

908.2 925.41 935.4 944.2 950.1 951.0 958.2 959.0 953.1

955.3 958.2 958.9 963.1 969.0 969.0 970.0 970.0 971.0

produced quantities [t] relative gap 4.93 3.42 2.45 1.96 1.95 1.83 1.18 1.13 1.84

× × × × × × × × ×

10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2

utilities consumed

EI99 [mPt]

capital cost [k€]

S3

S4

electricity [kWh]

water [m3]

diesel [L]

-2950 -2925 -2900 -2850 -2764 -2610 -2439 -2242 -1989

704.8 576.0 632.2 664.3 652.9 652.9 638.0 551.0 605.6

1883 1892 1884 1892 1892 1892 1892 1892 1892

1422 1428 1424 1426 1428 1428 1428 1428 1428

511 2031 4225 4138 5887 1957 5654 5654 5906

94.305 94.949 94.848 95.556 95.252 95.521 95.505 95.506 95.556

1493 1447 1330 1564 1811 2933 3337 4243 5310

Table 19. Damage Minimization with a Lower Bound on Profit model statistics time to [s]

solution value [Pt]

lower bound (case) [k€]

no. iterations

gen

exec

optim

relax

none (J) 750 (K) 800 (L) 850 (M) 900 (N) 910 (O) 915 (P) 920 (Q) 925 (R) 935 (S) 950 (T) 955 (U)

4596 4842 9031 24411 22139 17708 127271 812325 295613 10001582 10035420 9067169

978 976 976 982 988 987 990 981 983 983 978 971

33 51 85 158 164 176 753 2660 1515 22000 22000 22000

-2.960 -2.983 -2.980 -2.965 -2.954 -2.944 -2.941 -2.930 -2.928 -2.898 -2.803 -2.738

-3.010 -3.015 -3.013 -3.009 -3.013 -3.001 -2.996 -2.989 -2.986 -2.968 -2.967 -2.899

produced quantities [t] relative gap 1.66 1.08 1.08 1.45 1.95 1.91 1.83 1.96 1.94 2.35 5.52 5.55

× × × × × × × × × × × ×

10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2

products corresponds to a solution that goes strongly apart of the general tendency, the quasi-Pareto front. A6 is therefore not an acceptable solution, as opposed to A5, where a satisfactory economic-environmental trade-off is achieved, while still ensuring the treatment of the available aluminum sludge. The optimized network configurations obtained vary from a centralized production (map A, in Figure 10) where less transformers (a minimum of four) are installed (i.e., the more

Figure 9. Profit versus damage trade-off curves.

utilities consumed

profit [kE]

capital cost [k€]

S3

S4

electricity [kWh]

water [m3]

diesel [L]

703.4 750.0 800.0 850.0 900.0 910.0 915.0 920.0 925.0 935.0 950.0 955.0

704.8 576.0 632.2 664.3 652.9 638.0 551.0 605.6 534.0 569.0 367.0 325.3

1431 1588 1777 1758 1874 1885 1883 1891 1892 1892 1886 1892

1180 1180 1180 1358 1416 1424 1417 1424 1427 1427 1424 1428

7 366 332 0 55 434 1308 1332 1742 3452 6006 5747

71.6 79.5 89.0 88.0 93.9 94.5 94.4 94.9 94.9 95.4 95.3 95.5

1479 1361 1356 1458 1500 1508 1450 1475 1463 1398 1621 1962

economically favorable solutions), to a local production (map J, in Figure 10) involving a larger number of transformers, eight on average. A high number of transformers is found for the more environmentally demanding solutions (cases S-J), where they are generally collocated with the industrial sludge producers, as shown in the examples of Figure 10, with their corresponding production schedules shown in Figure 11. In general, the economic maximization tends to use the most


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Table 20. Profit Maximization with an Upper Bound on CO2 Emissions model statistics time to [s]

solution value [k€]

CO2 limit (cycle) [t]

no. iterations

gen

exec

optim

relax

750 (A7) 800 (A6) 900 (A5) 1000 (A4) 1200 (A3) 1500 (A2) 2000 (A1) None (A)

6392662 1373799 5211076 1302604 493540 202892 31232 7419

979 9778 979 971 978 970 974 972

11000 2778 10003 3007 1652 803 129 20

905.9 920.6 940.1 946.0 949.0 957.0 952.4 953.1

956.5 938.9 958.9 964.4 967.0 968.4 969.7 971.0

produced quantities [t] relative gap 5.29 1.96 1.95 1.90 1.87 1.19 1.79 1.84

× × × × × × × ×

10-2 10-2 10-2 10-2 10-2 10-2 10-2 10-2

economical processes while the environmental minimization uses preferably the less pollutant processes, as it can be asserted from Figures 10 and 11, which is usually associated with higher costs. Nevertheless the unrestricted solutions (A and J) are both anchoring cases, as can be seen in Figure 9 (to be noted that solution J presents an apparent damage value 0.8% inferior to the one of solution K, while in fact they are numerically identical within the 2% accepted tolerance). Other solutions, with economic and environmental values both close to the best possible values, are found (Maps G and S, in Figure 10) either by compromising a small percentage of profit or of environmental performance, while, respectively, maximizing profit or minimizing damage. These compromise solutions overlap for both approaches, but are more demanding on computational resources (see Tables 18 and 19). The effect of the limits of CO2 emissions on the solutions was already analyzed earlier, and it should be noted that these solutions achieve poorer results on the damage indicator because the emission reduction contributes only to the human health damage and has a proportionally much lesser impact on reducing damage, when compared to the one avoided, through aluminum sludge recovery, by the savings on natural resources, that is, Al, whose ore extraction involves energy intensive processes. In Figure 11, describing the network operation, Trf# gives the installation location. It corresponds to the sequential numbering of both transformers (one of each type) installed in each district, alphabetically ordered. For example Lisbon is the 11th district and thus has an economical transformer Trf21 (assigned an odd number) and an ecological transformer Trf22

Figure 10. Optimized network configuration for cases A, G, S, and J.

utilities consumed

EI99 [mPt]

capital cost [k€]

S3

S4

electricity [kWh]

water [m3]

diesel [kL]

-2682 -2791 -2844 -2796 -2769 -2667 -2534 -1989

382 383 414 367 411 337 314 307

1827 1862 1891 1890 1892 1892 1892 1892

1402 1418 1428 1428 1427 1428 1428 1428

0 0 106 209 2150 3043 2491 5906

91.42 93.19 94.91 95.05 95.53 95.56 95.10 95.56

1.73 1.85 2.05 2.25 2.17 2.56 3.24 5.31

(assigned an even number). On the operations schedule, the number on the first row represents the batch value for that specific operation (in tonnes), while the rectangles’ color identify the operation typeslighter for the dilution operation (noted Dilut) and darker for the drying operation (noted Secag); these are followed by tags Eco for the ecological and Pol for the economical types of transformation. 8.3. The Analysis of the Optimized Solutions. To further detail the analyses of the optimization results, a set of four solutions were chosen from Figure 4: two balanced solutions, G and S, and the unbounded solutions, A and J. For comparison purposes, their respective data values are presented in Table 21, while in Figures 10 and 11 are presented, respectively, their optimized networks and transformation Gantt charts. In general, the economical optimization tends to increase the recovery network net value by simultaneously (1) maximizing the revenue, producing the highest possible quantity of final products, (2) minimizing the installation costs, favoring the use of economical transformation processes and transports, (3) minimizing the operation costs, by centralizing the production and thus using fewer, but of larger capacity, transformer entities, installed at locations geographically closer to a position central to the clients. Similarly, the main factors on minimizing the environmental damage, by decreasing order of importance, are in general (4) using recovered products implies savings on natural resources, that is, Al, and therefore also savings on energy and its associated pollution. This has a direct negative (i.e., favorable)

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Figure 11. Optimized transformation schedules for cases A, G, S, and J.

contribution to the values for the damage indicator. (5) Favoring local production, thus minimizing the transport route lengths

and reducing transports’ pollutant emissions, (6) selecting the most environmentally friendlier transformation processes.

Table 21. Comparing Economically and Environmentally Optimized Solutionsa max Profit case no. iterations time to [s] profit [k€] EI99 [mPt] solution relative gap capital cost [k€] produced quantities [t] electric [kWh] water [m3] diesel [L] a

gen exec

S3 S4

min EI99

A

G

S

J

7419 972 20 953.1 (100%) -1989 (51%) 1.84 × 10-2 605.6 1892 1428 5906 95. 6 5310

11519276 989 22000 935.4 (98%) -2900 (98%) 2.45 × 10-2 632.2 1884 1424 4225 94.8 1330

10001582 9839 22000 935.0 (98%) -2898 (98%) 2.35 × 10-2 569.0 1892 1427 3452 95.4 1398

4596 9789 33 703.4 (65%) -2960 (100%) 1.66 × 10-2 704. 8 1431 1180 7 71.6 1479

The values in bold correspond to the limit values imposed.


From the detailed analysis of the previous table and figures some case distinct characteristics can, in addition, be identified: Case A (“Less Green”) small number of transformers (4 over a total of possible 18) selects average long routes, with a total of 44 freights mostly uses economical transports that are slower and more pollutant higher consumption of utilities: diesel (5310 L), electricity (5906 kWh), water (95.6 m3) total dependence on the least costly, but more pollutant, transformation process existence of a high capacity transformer-cluster installed in the Northern region (114.87 tonnes of capacity serving 72% of the clients) relatively high consumption of the transformed sludge (S3, 1892 tonnes; S4, 1428 tonnes) high profit: 953.1 k€ good results for the damage indicator: EI99 ) -1989 mPt Case J (“Green”) high number of less pollutant, more costly transformers (8 over a total of 18 possible) uses an additional low cost transformer (located in Guarda) favors the use of transformers close to the sludge producers uses a total of 55 freights, over average short routes favors the use of slower or faster transports for, respectively, short or long routes smaller consumption of utilities: diesel (1479 L), electricity (7 kWh), water (71.7 m3) prevalence of small/medium capacity transformer-cluster, located at the North and Centre regions relatively low consumption of the transformed sludge (S3, 1431 tonnes; S4, 1180 tonnes) low profit: 703.4 k€ excellent results for the damage indicator: EI99 ) -2960 mPt Case G uses a high number of transformers (8 over a total of 18 possible) favors the use of transformers close to the sludge producers uses short/medium routes favors the use of slower transports for shorter routes, in a total of 53 freights high consumption of utilities: diesel (1330 L), electricity (4225 kWh), water (94.9 m3) favors the use (7 out of 8) of the less expensive, more pollutant, transformation process mostly uses small/medium capacity transformer-clusters, located in the Northern and Centre regions, but incorporates one additional and more efficient transformer of high capacity (111.44 tonnes), located in Lisboa relatively high consumption, among these four cases, of the transformed sludge (S3, 1884 tonnes; S4, 1424 tonnes) high profit: 935.4 k€ very good results for the damage indicator: EI99 ) -2900 mPt Case S uses a high number of transformers (9 over a total of 18 possible) favors the use of transformers close to the sludge producers uses short/medium routes favors the use of slower transports for shorter routes, in a total of 54 freights high consumption of utilities: diesel (1398 L), electricity (3452 kWh), water (95.4 m3)

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favors the use (8 out of 9) of the less expensive, more pollutant, transformation process mostly uses small/medium capacity transformer-clusters, located in the Northern and Centre regions in Lisboa, it selects two transformers, one of each type relatively high consumption, among these four cases, of the transformed sludge (S3, 1892 tonnes; S4, 1427 tonnes) high profit: 935.4 k€ very good results for the damage indicator: EI99 ) -2898 mPt. Those observations allow the following general conclusions: (a) Solution A, obtained from a purely economic optimization (no bounds imposed on damage), uses only four transformers of the less expensive type, located in the Northern (Braga and Porto) and Central regions (Lisboa and Portalegre), where there are greater concentrations of producers. As it would be expected, this solution obtains a relatively higher profit value and consumes comparatively more electricity and also more diesel, because of the longer range of the transport routes. A higher capacity transformer was installed at a less central location (Braga), as intuitively it would be expected, because by collocating the transformation and the sludge producer entities, the overall transportation distances are greatly reduced. In fact solution B uses even less transformers of the economical type, one for each region (Viseu, Lisboa, and Beja) and obtains a better profit, precisely because the transformers are, besides being collocated with the producers, at more central locations relative to the client demands. Both solutions are well within the tolerance used for the optimization stopping criteria. (b) Solution J, by comparison with A, uses twice the number of transformers, still located at the Northern and Centre regions. Their majority is of the less pollutant type, collocated with the sludge producers at Braga, Porto, Aveiro, Coimbra, Leiria, Santare´m, and Lisboa, and one of the less expensive types (Guarda), not collocated with any sludge producer. Solutions K and L, where producers and transformers are collocated, that are still within the tolerance margin used for optimization, both present higher values for the optimized eco-indicator, respectively, of 2983 and 2980 mPts. The damage minimization imposes a substantial reduction (of about 32%) on the quantity of the final products, thus allowing for a lower consumption of utilities, along with the preferential use of the less pollutant transformation process, drastically reducing the electricity consumption. It also favors the shortening of the route lengths, thus reducing diesel consumption. (c) G and S are very similar solutions, despite the fact of having been obtained by different optimization criteria. They use eight transformers collocated with sludge producers (Braga, Porto, Aveiro, Viseu, Coimbra, Leiria, Santare´m, and Lisboa), and both favor the use of less expensive transformation processes. Through the use of only one or two less pollutant transformers, respectively, for cases S and G, a better economic performance is obtained. Diesel consumption is thus strongly reduced by the shorter transport routes, while the introduction of the less pollutant transformation processes slightly reduces the electricity consumption, thus contributing to lessen the quantity of pollutants emitted and consequently to a better environmental performance. The net result is that both solutions obtain excellent economic and environmental values, only 2% distant from the best optimized values, at the expense of an acceptable penalization of 2 h, on the required computational resources.

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9. Conclusions This paper incorporates the eco-indicator 99 methodology into a model for the design and planning of industrial networks. This model uses process engineering mathematical representations and simplified transport modeling to simultaneously solve the design of the optimized network structure, define the transport and transformation schedules, and compute a damage indicator EI99 that may be regarded as an additional objective function of the model, besides the traditional economic functions. This eco-indicator 99 implementation also presents a way of obtaining, from its given parameters, estimates for the corresponding pollutants’ impact potential values used by MEIM environmental methodology. A Portuguese case-study associated with a new process, under development, and associated logistics network for the recovery of the sludge obtained from aluminum surface finishing plants is explored. The industrial waste recovery network model was able to cope with a large example involving 61867 variables (of which 31208 are discrete). It allowed obtaining an economicenvironmental trade-off curve, by imposing a succession of target values on the damage indicator and the profit to the economic maximization and the damage minimization, respectively. The model requires a variable, but acceptable, amount of computational resources (from 1/4 to 6 h) and is able to attain solutions that are relatively close to the best solution possible (less than 2%, for the majority of the cases, or less than 5% for the worst cases). The unrestricted optimization of both economic and environmental objectives gives solutions that represent the extreme cases presenting the higher economic and environmental values. More balanced solutions (economically and environmentally) are found by either compromising a small percentage of profit, while maximizing the economic function, or marginally increasing the damage value, when minimizing the damage indicator. Logistics structures varying from centralized production with less transformers installed and thus longer transport routes (for the more economically favorable solutions), to a local production, where more transformers and shorter transport routes are used (for the more environmentally demanding solutions), were obtained and describe the trade-off that must exist when balancing economic versus environmental objectives. Additionally the model developed allows the study of imposing strict limits on pollutant amounts versus the acquisitions of additional emission permits on the respective market (e.g., CO2 case). This provides the decision maker with the necessary information to make the right decision when trying to balance costs versus impact damage under specified legal bounds options. As a main conclusion, the present work sustains the need for including the environmental methodologies on the design and planning of industrial networks, since it is possible to achieve better solutions from the environmental point of view at the cost of small reductions in the economic revenues. A1. Mathematical Model The mSTN mathematical formulation relies on a discretization of time, so that all processing and transportation times are integer and multiple of an elementary time interval ∆t, as presented by (Barbosa-Po´voa, 1994a) and generalized by Duque et al. (2003, 2004, 2006, 2007a) and Duque (2007b) which also defines the sets and variables (discrete, continuous) defined in the annexed glossary. Wrap around Operator. A wrap around operator is needed for cyclic operation:

τ(t) )

{

t if t g 1 τ(t + T) if t e 0

(a)

The constraints and objective functions describing the design of batch plants to allow for environmental impact considerations are formulated as follows: Constraint Equations. Installed units:

∑E

jk

) Ej

∀j

(30)

k

Unit allocation constraints: pi-1

∑ ∑W

ijτ(t-θ)

i∈Ij θ)0

∀j, t ) 1, ..., T

e Ej

(31)

Unit capacity and batch size constraints: max Φmin ij Vj - Vj (1 - Wijt) e Bijt e

∀i, j ∈ Ki, t ) 1, ..., T(32)

Φmax ij Vj and

∀i, j ∈ Ki, t ) 1, ..., T

0 e Bijt e Vmax j Wijt

(33)

where Vjmax ) max kVmax the available capacity range for unit jk j:

∑V

min jk Ejk

e Vj e

k

∑V

max jk Ejk

∀j

(34)

k

Dedicated Storage Constraints. Constraints to the materials stored at state s, in storage tanks j, during time interval t: ∀s, j ∈ Ks, t ) 1, ..., T + 1 (35)

0 e Sst e VjΦsj

If intending to avoid material accumulation at intermediate states, the following conditions need to be verified: ∆s ) Ss,T+1 - Ss0 ) 0 ⇒ Ss,T+1 ) Ss0

(36)

Leading to the replacement of variable Ss,T+1 from the equations, Ss0 becomes the variable to optimize. Constraining the unit j available storage capacity:

∑V

min jk Ejk

e Vj e

k

∑V

max jk Ejk

∀j ∈ Ξ

(37)

k

Constraint equation for the type of units installed:

∑E

jk

) Ej

∀j ∈ Ξ

(38)

k

Connections Capacity Constraints. 0 e BTπnt e BTcΦπc

∀c ∉ cTrp, π ∈ Ic, t ) 1, ..., T + 1(39)

which for terrestrial transports: 0 e BTπnt e Vmax c Wπnt

∀π, n, t, c ∈ cTrp

(40)

and max Φmin π BTc - Vc (1 - Wπnt) e BTπnt e ΦπBTc ∀π, n, t, c ∈ cTrp(41)

where Vmax ) max (BTmax c ck ).


Available transport capacity constraint:

∑ BT

min ck Eck

∑ BT

∀c

max ck Eck

e BTc e

k

(42)

k

∑E

∀c

e1

ck

(43)

k

max where BTmin are, respectively, the minimum and ck and BTck maximum capacity of connection c of type k. Terrestrial transport allocation constraints;

2pπ

∑ ∑W

πnτ(t-(θ-1))

π∈Icπ θ)1

e

∑E kc

∀t, n, c ∈ cTrp

ckc

(44) where the factor 2, in the summation upper limit, imposes a return transit time and the summation on π constrains the transport types used. Utility Constraints. pi-1

Uut )

∑ ∑ ∑ (R

uijθWijτ(t-θ)

i

+ βuijθBijτ(t-θ)) +

j∈k θ)0 pπ-1

∑ ∑ (R

uπθWπnτ(t-θ)

π∈Iπc θ)0

+ βuπθBπnτ(t-θ))

Objective Functions. Three different design objectives are considered here. (1) The minimization of the capital cost of the plant, given the installation and auxiliary equipment cost. (2) The maximization of the net profit per cycle, defined as the capital costs of installed equipment, plus the utilities and other operating costs minus the raw material costs (that since the RM used avoids a landfill storage cost, the net contribution to the OF is positive) plus the income from selling the products (those products can have costs associated to transports and landfill disposal) plus the costs per unit of material processed. (3) The minimization of the environmental impact indicator, implemented according to the eco-indicator 99 methodology. The first can be used as a management decision support whenever the key decision for creating the recovery route is solely based on the investment needed to implement it. The second approach goes a little further, by considering the capital costs inherent to the investment needed to implement the recovery route, computing the profit obtainable by reutilization of the transformed, originally polluted, residual product instead of the use of dedicated storage that occupies soil. The third allows the estimation of a general damage indicator that includes the damage to human heath the eco-system and natural resources. Minimization of Capital Cost. minCC ) [

∀u, c ∈ cTrp, t ) 1, ..., T(45)

where the last summations on the right side of the equation refers to terrestrial transport consumptions. The constraint equation for the total utility available during time interval t: ∀u, t ) 1, ..., T

0 e Uut e Umax u

∑V

e Vj e

max jk Ejk

∑ ∑ ∑ (OC

∑ ∑ F¯ B

is ijτ(t-pjis)

Nπmax

∑( ∑ n)1

BTπnτ(t-pπs) -

π∈ΠsT

∑

π∈(Πs⊂eΠsT)

j

BTπt -

∑

∑

-

(48)

is ijτ(t-pis)

∑ ∑ (OC

0

π

π∈(Πs⊂eΠsT)

Wijt + OCij1Bijt) +

Wπnt + OCπ1BTπnt) +

∑ ∑ OC S

s st

s

+

t

∑ ∑ OC

utUut(52)

t

Raw material operation costs are defined as RMC )

∑ ((S

s0

- Ss,tLast)ps +

s∈SF

∑R

s,tps)

(53)

t

The product revenues are defined as

+

PR )

∑ ((S

s,tLast

- Ss0)Vs +

s∈SP

BTπnt) +

[$](51)

)]

0 ij

n)1 π∈IπT

∑D

s,tVs)

(54)

t

and the annualized net profit is

π∈ΠsT

BTπt - Dst + Rst

1

ck

u

∀j ∈ Γ

∑ ∑F B

i∈Ts j∈Ki

c

i∈Ii

k

i∈T¯s j∈Ki

ck

k

(47)

Mass Balances. The initial amount of each state Si is considered as a variable that depends on the design and is also optimized. Sst ) Sst-1 +

jk

Maximization of Annualized Net Profit. As part of the objective function the operational cost are defined as

t

min jk Ejk

j

0

Nmax π

k

1

jk

ck

If they are associated to a set of dedicated equipment a set of constraints needs to be defined to restrain the storage tank capacity dedicated to each utility u

∑V

0

jk

k

c

OC )

∀u, j ∈ Ku, t ) 1, ..., T

∑ ∑ E (CC + V CC ) + ∑ ∑ E (CC + BT CC j

(46)

0 e Uut e Vj

4245

∀s, t

[

max profit ) (PR - OC - RMC)

CC×CF

(49) Note: In eq 49, the symbols ⊂e mean “not a subset of”. Production Requirement Constraints. Production constraint max min for each product stored in state s, where QsSTN,t and QsSTN,t,

hours yr T [$/yr](55)

That, given a production horizon H * hours yr for cyclic operation leads to

[

hours yr T H CC×CCF × [$/H] hours yr

horizon max profit ) (PR - OC - RMC)

represent respectively the maximum and minimum production required for product in state s at the end of production horizon.

]

]

or

4246


[

H - CC × T H CCF × [$/H](55a) hours yr

horizon max profit ) (PR - OC - RMC)

(

)]

and finally the minimization of the eco-indicator 99 for the production horizon min EI99

(56)

[mPt/H]

AI.1. Environmental Modeling with MEIM. The methodology has been presented in the main core of this article, please see eqs 1-5a. AI.2. Modeling Transports as Connections. While considering land transports as model’s connections the allocation variables, Wπt, were introduced in order to account for the utilities consumed, wastes produced, and the operation costs. But the complexity of the model and the resources needed to solve it are, most of the times on MILP problems, directly related to the number of binary variables used. (The number of nodes of the branch and bound search tree grows exponentially with the number of binary variables.) Thus to reduce the solution times of complex problems an approximate modeling of transport as connections is used to avoid the allocation variables. This approach is valid when having one new truck available, at the beginning of each time interval, to either one of the transport types that may occur simultaneously. Consequently eqs 44, 40, and 41 are no longer needed; BTπnt collapses to BTπt and eq 39 becomes ∀c, π ∈ Ic, t ) 1, ..., T + 1 (39a)

0 e BTπt e BTcΦπc

Utilities Constraints. Case 1 when the project variables are needed. The utilities demand eq 45 is to be replaced by pi-1

∑ ∑ ∑ (R

Uut )

uijθWijτ(t-θ)

+ βuijθBijτ(t-θ)) +

∑V

+ βuπθ)Bπτ(t-θ)

i j∈k θ)0 pπ-1

∑ ∑ (CT R

max c

π uπθ /

∀t, u, π

c|π∈Ic

π∈IπT θ)0

(45a) where CTπ is defined as CπBπt /

∑V

∀t, π

max c SWπt

c|π∈Ic

so CTπ )

∑V

max c /Bπt

∀t, π|Bπt > 0

c|π∈Ic

that leads to

∑V

c|π∈Ic

max c /min(Bπt) t

g CTπ g 1

∀t, π|Bπt > 0 (57)

Thus given a specific example an educated guess for the Cπ value may be made obeying the constraints (57). By collapsing π a general coefficient CT is then given by

∑( ∑ V π

c|π∈Ic

max c /min(Bπt)) t

g CT g 1

∀t|Bπt > 0

(58)

This approach is still valid when using n simultaneous trucks available, at the beginning of each time interval, for each one of the transport types, but then the variables BTπnt do not collapse and eq 39 remain unchanged. 12. Glossary. Sets c ∈ C ) set of connections, c ) 1, 2, ..., Nconnections ck ) set of types ck for connections, c cTrp ) (subset of transport tasks treated as connections c) d ) set of types of damage to human health, eco-system quality, and natural resources (mineral and fo´ssil): DanoH ) human health damage, DanoE ) damage to the eco-system quality, DanoR ) damage to the mineral and fossil natural resources f ) set representing the diverse types of soil occupation/conversion: agr_urb ) converting agricultural soil for urban uses, agr_ind ) converting agricultural soil for industrial uses, urb_ind ) converting urban soil for industrial uses, ind_ind ) occupation of industrial type of soils I ∈ I ) set of processing tasks, i ) 1, 2 NTaskc Icπ ) (π: set of tasks that can be performed in connection c) Ij ) (i: set of tasks that can be performed in unit j) j ∈ J ) set of equipment units, j ) 1, 2, ..., NUnits jk ) set of types jk of equipment available for unit j Kj ) (j: set of units suitable for task i) Ks ) (j: set of storage tanks associated with state s) m ∈ M ) set of environmental impacts, for MEIM the m elements are CTAM ) critical air mass; CTWM ) critical water mass; SMD ) solid mass disposal; GWI ) global warming indicator; POI ) photochemical oxidation indicator; SODI ) stratospheric ozone depletion indicator for Eco-Indicator99 the m elements are EHcancer ) carcinogen indicator effecting human health; EHrespir ) respiratory indicator effecting human health; EHradia ) radiological indicator effecting human health; EHsodi ) stratospheric ozone depletion indicator effecting human health; EHmudcli ) climate change indicator effecting human health; EEtoxici ) ecosystem toxicity indicator effecting human health; EEacidif ) ecosystem acidification indicator effecting human health; EEeutrof ) ecosystem eutrophication indicator effecting human health; EElocal ) soil occupation/transformation local indicator effecting the ecosystem quality; EEregion ) soil occupation/transformation regional indicator effecting the ecosystem quality; ERmin ) mineral resource availability change indicator effecting the natural mineral resources consumption/availability; ERfo ) fossil resource availability change indicator effecting the natural mineral resources consumption/availability n ) set of trucks available to each transfer task n ) 1, ..., Ntrck o ) set of pollutants’ types emitted by the recovery network p ) set of the type of soil conversion/occupation/liberation and mineral and fossil resources consumed or recovered. r ) set of mineral and fossil resources used or recovered by the recovery network s ∈ S ) set of states Si ) (s: set of input states to task i) Sji ) (s: set of output states from task i) Ti ) (i: set of tasks requiring material from state s) Tj i ) (i: set of tasks producing material in state s) u ∈ U ) set of process utilities u ) 1, 2, ..., NUtiIs V ) set of vectors considered for pollutants’ emission: air, water, agricultural (agr) and industrial (ind) soil w ∈ W ) set of wastes, w ) 1,2, ..., NWastes π ∈ Π ) set of transfer tasks Ξ ) (j: set of dedicated storage vessels)

Ind. Eng. Chem. Res., Vol. 49, No. 9, 2010 Γ ) (j: set of vessels dedicated to store utilities) ψ ) set defining the types of stages used to compute the environmental impacts: Trf ) transformation stage of the recovery network; Trp ) transport stage of the recovery network; Util ) stage for the utilities used by the recovery network; Sol ) soil reallocation stage of the recovery network; Rec ) used natural resource stage of the recovery network Parameters CCjk0 ) fixed capital cost for unit j of type k CCjk1 ) size dependent capital cost for unit j of type k CCck0 ) fixed capital cost for connection c of type k CCck1 ) size dependent capital cost for connection c of type k CCF ) capital costs factor, the annualized coefficient proposed by Douglas Hr ) production horizon OCs ) operation cost associated to the dedicated storage of state s OCut ) cost associated to the use of utility u at time t pi ) max (pis: processing time for task i to produce state s ∈ S) ps ) price of raw materials s T ) cycle time, an integer multiple of ∆t Vs ) value of products s Rwij ) quantity of waste w produced by the use of transport unit j at transportation task i, due to repair or replacement of this particular transportation unit βwij ) quantity of waste w produced per unit mass of the batch transformed by task i taking place at unit j βwu ) quantity of waste w, produced at the utility generation level, per mass unit of utility u consumed δmp ) impacts generated by “pollutant” p, responsible for effect m, produced by soil occupation/conversion and the use of mineral and fossil natural resources δmVp ) impacts generated by a mass unit of pollutant p, produced by the network recovery occurrences p ∈ Oo, emitted to the environmental vector V and contributing to effect m εp ) The recovery network emission limit for pollutant p εpj ) emission limit for the emission of pollutant p by the entity j of the recovery network Φπc ) size factor of transfer task π in connection c λVpij0 ) quantity of type p pollutant emitted to the vector V by the occurrence of task i in the entity j λVpij1 ) quantity of type p pollutant emitted to the vector V per unit mass transformed by task i in the entity j λVpuij ) quantity of type p pollutant emitted to the vector V per unit of utility u consumed at entity j by task i λpu ) quantity of type p pollutant emitted to generate an unit of utility u consumed λpj ) surface area of type p “pollutant” to install the entity j (with p being a soil conversion f) λps ) quantity of type p pollutant not emitted per unit mass of recovered material s ηdm ) factor giving the contribution of effect m on damage d Fis ) proportion of input of task i ∈ Ti, from state s Fjis ) proportion of output of task i ∈ Tj i, in state s χd ) normalized factor to weight the importance of damage d according to the hierarchical perspective Variables Bijt ) the amount of material that starts undergoing task i in unit j at the beginning of period t BTc ) capacity of connection c BTπt ) quantity transported by task π at period t BTπnt ) quantity transported by task π by truck n at period t

4247

CLic ) penalization term, to include in the objective function, due to the additional costs for the purchased emission licenses for the quantity CO2_exc CO2_exc ) excess emission value of CO2, per cycle, in relation to the granted emission licenses CO2Cost ) unitary cost, per emitted kg, in the CO2 license emission market Dd ) damage value for type d damage Ds ) the amount of material delivered at state s at the end of the horizon Ej, ) binary equal to 1 if unit j is installed; 0 otherwise Ejk, ) binary equal to 1 if unit j of type jk is installed; 0 otherwise EIm ) type m environmental impact indicator value (formally equivalent to the variable defined for MEIM methodology, but defined here for a larger number of effects) EIwmt ) total environmental impact m for waste w generated over the time interval t EI99 ) eco-indicator 99 value. GEIm ) global environmental impact indicator m over one cycle Qp ) total amount of “pollutants” generated by the network. QFp ) total area of occupied/converted soil p,p ∈ f. QOVp ) total amount of pollutants of type p generated by the transports and transformations and Ss0 emitted to vector V QRp ) total amount of resources p used/produced, p ∈ r Ss0 (d) ) initial amount of material in state s as a function of the design Sst ) the amount of material stored in state s at the beginning of period t t ) absolute time (from the start of the cycle) TWw ) total amount of waste w generated over one cycle of operation Uut (d) ) total demand of utility u over interval t Vj ) capacity of unit j Wijt ) binary equal to 1 if task i starts in unit j at the beginning of period t; 0 otherwise Wwt (d) ) total amount of waste w generated over interval Wπt ) binary equal to 1 if task π starts at the beginning of period t; 0 otherwise Wπnt ) binary equal to 1 if truck n for task π starts at the beginning of period t; 0 otherwise

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ReceiVed for reView June 8, 2009 ReVised manuscript receiVed February 12, 2010 Accepted March 4, 2010 IE900940H

Design and Planning of Sustainable Industrial Networks: Application

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