Spatially Explicit Multiobjective Optimization for the Strategic Design of

Article pubs.acs.org/IECR

Spatially Explicit Multiobjective Optimization for the Strategic Design of First and Second Generation Biorefineries Including Carbon and Water Footprints Andrea Bernardi,† Sara Giarola,†,‡ and Fabrizio Bezzo*,† †

CAPE-Lab − Computer-Aided Process Engineering Laboratory, Department of Industrial Engineering, Università di Padova, via Marzolo 9, 35131, Padova, Italy ‡ CPSE − Centre for Process System Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom S Supporting Information *

ABSTRACT: Biofuels production has been promoted in the attempt to address global warming and oil dependence concerns. However, the environmental impact of biofuels is a very complex issue and cannot be represented by GHG (greenhouse gas) emissions only (carbon footprint). In particular, water consumption (water footprint) has been recognized as a key issue in renewable fuels production. This paper proposes a multiobjective Mixed Integer Linear Programming modeling framework to optimize the environmental (i.e., the carbon and water footprints) and economic performances of bioethanol supply chains. Multiechelon, multiperiod, and spatially explicit features are embedded within the formulation to steer decisions and investments through a global approach. The strategic design and planning of corn- and stover-based bioethanol production networks is taken into account. A case study is presented referring to the emerging Italian ethanol production. Results show the effectiveness of mathematical programming-based tools to provide decision makers with a quantitative analysis assessing the economic and environmental performances of different design configurations.

1. INTRODUCTION Bioethanol promotion has been acknowledged as one of the most viable solutions for a sustainable transportation system. Although starch-based ethanol (first generation) has greater economic profitability, cellulose-based (second generation) ethanol seems to represent the technology that may lead to substantial greenhouse gas (GHG) emissions reduction (carbon footprint, CF) and mitigate other key issues such as competition with food crops.1 In fact, in order to represent an effective transition toward a sustainable energy system, renewable fuels production network should also comply with adequate environmental requirements. For instance, the EU (European Union) commission established mandatory sustainability targets: e.g., the reduction on GHG emissions should reach a minimum threshold of 50% from 2017 and 60% from 2018 onward.2 However, the quest for a paradigm shift in the current transport energy provision needs accounting for different issues in the multifaceted environmental problem. In particular, water consumption has recently become a major concern:3,4 current state of supply vulnerability and local scarcity of this resource4 has been advocating for a general reduction of humanity’s water consumption.5,6 Among the economic production sectors, agriculture is in charge of about 70% of freshwater withdrawals and future interests in bioenergy promotion make water demand projection increase even further.7,8 The Water Footprint (WF) concept9 is an indicator of the amount of freshwater consumed or polluted during the whole production process of a commodity. A general production system WF is assigned three main contributions, depending on © 2013 American Chemical Society

the nature of the freshwater lost during its operations: the green WF (i.e., due to evaporation of rainwater withdrawn by the system); the blue WF (i.e., due to evaporation of surface and groundwater withdrawn by the system); the gray WF (i.e., accounting for water polluted by the system). In this study we will consider the blue WF only. Quite recently, the assessment of WF on bioenergy production has also started being carried out,8 and in particular, several works have recently appeared dealing with the WF of biofuel production systems.10−13 A general finding is that a large-scale biofuels production can affect the overall water footprint significantly, although the actual stress of water resources depends on the feedstock type and cultivation practice and on the local hydrological background. In this complex context, mathematical programming techniques might provide decision makers with the necessary quantitative tools to optimize the future energy systems. In fact, the dramatic growth of the renewable fuels industry advocates for optimization strategies to help in exploring and analyzing diverse process alternatives along the entire supply chain (SC), in order to find optimal trade-offs between conflicting targets14 and to guarantee an efficient use of the limited resources. Over the last years a number of contributions have appeared where mathematical programming techniques have been Special Issue: PSE-2012 Received: Revised: Accepted: Published: 7170

September 11, 2012 December 23, 2012 January 4, 2013 January 4, 2013 dx.doi.org/10.1021/ie302442j | Ind. Eng. Chem. Res. 2013, 52, 7170−7180

Industrial & Engineering Chemistry Research

Article

Figure 1. Multiechelon supply chain configuration.

To the authors’ knowledge, the issue of WF in the design and planning of biofuel SCs has never been dealt with explicitly through a MoMILP formulation embedding each stage of the production network within the optimization framework (i.e., including biomass cropping, transportation, biofuels production, and transportation to demand centers). In fact, Bernardi et al.36 recently proposed a general MoMILP framework for the design of first and second generation ethanol SCs where carbon and water footprints are simultaneously addressed. However, that work was limited to the upstream supply chain (i.e., involving biomass production, transportation, and conversion into fuels), without addressing the environmental issues related to a demand-driven production network. Moreover, it did not deal with the combined effects of CF and WF issues in an explicit geographical context, and therefore it could not describe the relevant local effects on the SC design due to the territorial specificity on water availability and the resulting stress on local sources. This is a key issue for future bioenergy systems design, as discussed in some recent contributions37,38 showing how water embodied in corn-based ethanol is highly sensitive to regional characteristics and irrigation practice. This contribution aims at bridging this gap and showing how the supply chain design may be affected by the prioritization of the different objectives in a spatially explicit context. Northern Italy is taken as a real world case study to demonstrate the effectiveness of the approach. Following the framework developed in Giarola et al.,27 the mathematical formulation is based on a MoMILP modeling framework addressing multiperiod multiechelon biofuels SC optimization embodying features for spatially explicit siting of networks nodes. The economics has been assessed by means of Supply Chain Analysis (SCA) techniques, focusing on biomass cultivation, ethanol production capacity planning and technology selection, and logistics optimization. The environmental performance is evaluated in terms of both carbon and water footprint by adopting LCA (Life Cycle Assessment) techniques. This paper is organized as follows. After a general description of biofuels SC key issues, the mathematical formulation of the model is described. The optimization results are then presented and discussed. Finally, some general comments will conclude the work.

exploited to analyze and optimize several biofuel SCs. Most of the first contributions15−19 focused on the economic optimal configuration of biofuels (in particular, ethanol) taking into account both first and second generation technologies. Typically, agricultural practice, biomass supplier allocation, production sites location and capacity assignment, logistics distribution and transport system are all optimized in a territorial context. Other contributions20−24 addressed the presence of uncertainties in biomass supply amounts, biofuel market demands, biomass and biofuel market prices, and processing technologies. Mathematical programming techniques may be coupled to the concept of Green Supply Chain Management (GrSCM). In particular, Multi-Objective mathematical programming (MoMP) offers one of the most suitable tools enabling the exploration of balanced trade-offs between conflicting objectives,25 as GrSCM goals likely are. The GrSCM of a corn-based bioethanol network was first addressed by Zamboni et al.26 through a spatially explicit MoMILP modeling framework minimizing both SC operating costs and GHG emissions. Giarola et al.27 extended the approach to a multiperiod MoMILP model (accounting for Net Present Value, NPV, and GHG emission) of hybrid first and second generation ethanol SCs taking into account corn and corn stover. You and Wang28 proposed a spatially explicit multiperiod MoMILP model for biomass-to-liquid supply chains. Mele et al.29 provided a spatially explicit bicriterion MoMILP framework where environmental and financial criteria were both addressed in the ethanol production from sugar cane. They considered the Eco-indicator 99 and Global Warming Potential (GWP) as environmental impact metrics to be minimized. Zamboni et al.30 developed a whole systems multiobjective optimization of a first generation ethanol SC showing how efficient crop management may contribute significantly to mitigate GHG emissions. Giarola et al.31 proposed to incorporate the environmental impact within a MILP model by including carbon trading effects in the economic optimization. Akgul et al.32 presented a static MoMILP approach for the optimization of hybrid first and second generations SCs in the UK. You et al.33 developed a MoMILP model to address the optimal design and planning of cellulosic ethanol supply chains under economic, environmental, and social objectives. Kostin et al.34 presented a multiobjective approach to the design of bioethanol SCs considering several life cycle assessment impacts. Several environmental metrics are taken into account by Č uček et al.35 where different footprints are assessed against the environmental performance within a MINLP framework representing biomass SCs for energy (and fuel) production in a hypothetical region. Interestingly, they consider the water footprint, too.

2. ASSUMPTION AND METHODS The aim of the work is to develop a multicriteria decision making supporting tool to steer investments on biofuels SCs at strategic level. The three objective functions to optimize are the following: the financial performance, expressed in terms of NPV; the GHG emissions, i.e. the system CF; and the consumption of water along the entire SC, i.e. the WF. 7171

dx.doi.org/10.1021/ie302442j | Ind. Eng. Chem. Res. 2013, 52, 7170−7180


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The biofuels SC design problem can be formulated as follows. Given the following inputs: − Geographical distribution of demand centers; − Fuel demand over the entire time horizon (based on energy quotas imposed by EU legislation); − Biomass geographical availability; − Geographical location of biomass production sites; − Biomass potential for each site; − Biomass production cost as a function of geographical region; − Technical (yield) and economic (capital and operating costs) parameters as a function of biomass type, production technology, and plant scale; − Environmental burdens of biomass production as a function of biomass type and geographical region; − Environmental burdens of biofuel production as a function of biomass type and production technology; − Transport logistics (modes, capacities, distances, availability, environmental burdens, and costs); − Energy market prices and existing subsidies (green credits) the objective is to determine the optimal system configuration which maximizes the financial profitability while minimizing the GHG emissions as well as the consumption of water resources. Accordingly, strategic design involves decisions dealing with biomass selection and geographical location of biomass cultivation sites, technology selection for biomass to be converted to liquid fuels, byproducts valorization strategy, and, eventually, the logistic definition of transport system as well as supply chain nodes location. On the other hand, planning decisions regard capacity assignment of production facilities along the steps composing the time horizon. Therefore, the key variables to be optimized are − Geographical location of biomass cultivation sites; − Biomass production rate and feedstock mix to the plant; − Bioethanol production technology selection, location, and scale; − Characterization of transport logistics; − Financial performance of the system over the time horizon; − Environmental performance of the system over the time horizon (i.e., WF and CF). Bioethanol demand is set to vary along the 15-year time horizon, starting from 2010 to 2024. In accordance with the EU directive, the bioethanol quota is set equal to 5.75% for 2010, and from 2011 to 2024 the bioethanol blending quota is gradually increased until reaching the 2020 EU target 10% (on energetic basis). The overall time horizon has been divided into five time intervals (each three years long) to reduce computational burden and RAM requirements.

The general biofuel supply network (Figure 1) can be divided into two main substructures. The first one concerns the upstream fuel production and involves biomass cultivation, biomass transport, and fuel production sites; the second one is related to the product distribution to demand centers. Between the end nodes of the network there can be a broad variety of production technologies, transportation modes, and logistics choices. The design outcomes are not unique and they strongly depend on production technology and even more on specific geographical context in which the system is going to be operating. SCA and LCA are needed to compare alternative topologies of the same production network on economic and environmental terms. The methodology proposed to assess the impact on global warming refers to the standard LCA approach as laid out by the ISO guidelines.39 In determining the life cycle assessment of biofuels GHG emissions, conversion factors are needed to derive the impact on global warming. The GHG contribution to global warming is first captured by inventorying a particular set of burdens (CO2, CH4, N2O), which have been grouped together in a single indicator expressed in terms of carbon dioxide equivalent emissions (CO2-eq), according to the concept of 100 year global warming potentials.40 The environmental performance in terms of stress on water resources consumption is evaluated extending the methodology proposed in Hoekstra et al.41 to a LCA approach, where the indirect effect due to farm and processing inputs is embedded within the WF assessment. The amount of energy provided by fuels fed to an internal combustion engine is chosen as the functional unit for the system environmental assessment. The operating impact of the system is evaluated from biomass cultivation up to fuel distribution. The set of LCA stages s considered in the evaluation are: biomass production (bp), biomass pretreatment (bpt), biomass transport (bt), fuel production (f p), and fuel transport ( ft). Accordingly, s ∈ S = {bp , bpt , bt , fp , ft }

Byproducts end use effect on WF and CF is accounted for according to a substitution method involving credits derived from the displacement of alternative goods with byproducts. This determines GHG emissions/water consumption savings, which are subtracted from the primary product (ethanol) overall impact. Credits and the subsequent emissions/water consumption discount are a result of goods or energy displacement by process byproducts end-uses. A brief discussion is required with concern to the so-called indirect land use change (iLUC). The iLUC effect should quantify the fact that the assignment of evermore cultivated lands to intensive energy crops would increase the overall GHG emission and water consumption, because new land needs exploiting for growing food crops displaced by energy crops.42,43 Although this is clearly a critical issue, the iLUC effect has not been included here, as it represents a highly controversial subject for many reasons44 and it is denoted by large uncertainties on preliminary assumptions and approach. In starch-based processes, the main byproduct, DDGS (distiller’s dried grains with solubles), could be used either as a substitute for cattle feed or as a fuel for CHP (combined heat and power) generation. In cellulose-based processes, lignin is exploited to produce electricity and a power excess can be sold to the national grid.

3. MATHEMATICAL FORMULATION The modeling framework has been formulated as a multiperiod MoMILP problem according to the common approaches adopted in the design of bioethanol multiechelon SCs at a strategic level.45,46 It embodies different features for spatially explicit siting of supply networks nodes16 as well as facility capacity planning and technology selection of strategic fuel systems.47 The MoMILP solution algorithm relies on a commonly adopted approach 14,48 based on the linear combination of the objective functions. The solution is a set of Pareto-optimal solutions weighting the three conflicting 7172



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Impsl, t =

objectives (economic, impact on global warming, and impact on water resources). In the following, the main mathematical model is presented, proposing the objective functions definition and then discussing the environmental framework. The economic model refers to the work by Giarola et al.27 and is summarized in the Supporting Information. 3.1. Objective Function. The economic objective function (Objeco [€]), is estimated in terms of the NPV of the system and needs to be maximized in configuring the production network to optimize business profitability. It is calculated by summing the discounted annual cash flows (CFt [€/y]) for each time period t minus the capital investment (TCIt [€]) when a production facility is established. Accordingly Objeco = NPV =

∑ 3CFt ·dfCFt − TCIt ·dfTCIt

Impsl, t =

Imp′lbt ′ , t = +

(3)

Imp′lft ′ , t =

2

3

4

5

0.329 0.432

0.216 0.284

0.142 0.187

∑ TItl t

∑ Impsl,t ,

f l′smalltruck′

The impact factors related to transportation and f l′ft′,m) are either measured as [kg CO2-eq/(t·km)] or [m3H2O/(t·km)] depending on the nature of the environmental impact. The reference flow is represented by the delivery distance times the freight amount. The delivery distance is determined as the straight route between the centers of each network element g, LDg,g′ [km], increased by applying a tortuosity factor specific for transport mode m between g and g′, τg,m,g′, to reflect the actual geographical distances between cells. The load of goods transported can be either expressed as the flow rate of biomass i to be transferred via mode m between two elements g and g′ at time period t, Qbi,g,m,g′,t [t/y] or the flow rate of bioethanol to be delivered via mode m between two elements g and g′ at time period t, Qfg,m,g′,t [t/y]. When the impact is due to the transportation of biomass i within the same cell g where it is produced at time t, then the reference flow is defined as Pbi,g,t [t/y], i.e., the local biomass production. The impact due to the fuel production stage ( f p) is represented as

(4)

Imp′lfp ′ , t =

∑ ∑ ∑ f′lfp ′ ,i ·Pfi ,k ,g ,t , i

g

f l′f p′,i

∀t (10)

k

m3H2O/t],

where the factor, [kg CO2-eq/t or is multiplied by the ethanol production from the conversion of biomass i, Pf i,k,t [t/y]. The effect of byproducts is essential to allocate the total impact associated with a particular production chain. Currently there is no universally accepted method to cope with this issue, particularly with concern to WF (a discussion on this issue can be found in Bernardi et al.36). Here the substitution method is chosen, as suggested by the ISO standards:39 the system needs

∀ t , l ∈ {CF, WF}

s

∀t

g ,g′

(9)

where l might be set either to CF or WF to represent the carbon and water footprints, respectively. In both cases, the total impact over time (TIOTl) is estimated by summing the SC annual impact over time TIlt either on global warming [kg CO2-eq/y] or on water resource depletion [m3H2O/y]. The definition of the annual impact, TIlt, needs to consider the effect of the SC operation on environment per each LCA stage s (Impls,t [kg CO2-eq/y or m3H2O/y]). Accordingly TItl =

∑ f′lft′ ,m ·(∑ Qfg ,m,g ′ ,t ·LDg ,g ′·τg ,m,g ′), (f l′bt′,m,

Concerning the environmental framework, two environmental objective functions (Objl) are proposed, stated as Objl = TIOT l =

∀t (8)

m

period 0.5 0.658

∑

g ,g′

f ′lsmalltruck ′ ·Pbi , g , t ·LDg , g ′ ,

i,g

Table 1. Time Dependent Discount Factors for Cash Flows, df CFt,, and Capital Investments, df TCIt 1

(7)

∑ f′lbt′ ,m ·(∑ Qbi ,g ,m,g ′ ,t ·LDg ,g ′·τg ,m,g ′) i,m

0.761 1

∀ t , s = bp , bpt

g

Water consumption and GHG emissions related to biomass transport (bt) and fuel transport (f t) depend on both the distance traveled and on the means of transport used m. Accordingly

Setting the interest rate (ζ) to 15%,27 the resulting discount factors have been calculated and reported in Table 1. Profitsrelated variables descending from eq 1 are discussed in the Supporting Information.

df CFt df TCIt

∑ ∑ f il,s ,g ·Pbi ,g ,t , i

(2)

3 + 3ζ + ζ 2 3(1 + ζ )3t

dfCFt =

(6)

The impact factor used to assess the environmental effects associated with biomass production (bp) depends on cultivation practice as well as on the geographical region in which the biomass crop has been established. The impact factor for biomass pretreatment (bpt) depends on biomass i as well as on processing technology k. The total water consumption of both biomass production and biomass pretreatment (biomass pretreatment does not depend on spatial location, but for sake of simplicity we retain eq 7 for both biomass production and pretreatment) is obtained by multiplying the impact factor ( f li,s,g [kg CO2-eq/t or m3H2O/t]) for the amount of biomass produced (Pbi,g,t [t/y]):36

where df CFt and df TCIt are the discount factors related to time period t specific for CFt and TCIt, respectively. They are defined as reported in the following equations:49 ⎛ 1 ⎞3(t − 1) dfTCIt = ⎜ ⎟ ⎝1 + ζ ⎠

∀ s , t , l ∈ {CF, WF}

i

(1)

t

∑ f il,s ·Fs ,t ,

(5)

Impls,t

The impact rate is determined by applying an impact factor, f li,s, [kg CO2-eq/unit or m3H2O/unit] for biomass i at stage s, to a reference flow, Fs,t [units/y], which is specific to LCA stage s at time t: 7173



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impacts. The overall impact in terms of water consumption for the agricultural stage is obtained by summing the direct and indirect contributions. The novelty of the work is the direct contribution assessment, which differs from the one performed by Bernardi et al.,36 because the amount of water consumed for cropping has a geographical dependence and it is estimated according to a spatially explicit approach. In fact, the direct water consumption due to biomass crops is rather sensitive to the specificity of local data and its estimation may lead to different results than the one obtained by using some average water consumption information. According to Allen et al.,51 the direct impact for crop cultivation is the difference between the water loss because of crop evapotranspiration and the amount of water provided by rainfall. The rainfall contribution needs to be diminished by a factor representing the recharge rate for ground and surface water (about 30% of the irrigation rate52). The average rainfall on each cell was estimated by actual meteorological data, while data for estimation of evapotranspiration were taken from Todorovic et al.53 The resulting set of WF , is reported in Table S1 in the impact factors, f ′corn′,′bp′ Supporting Information. Note that no direct contribution is attributed to stover. This is in agreement with the substitution method since stover is given a residual value. In other words, it is assumed that stover is never cultivated per se, but is a residue from corn cultivation. The indirect contribution to WF from biomass growth is due to the production of farm inputs (fertilizers and pesticides)54,50 as well as diesel usages for corn and stover collection55,56 and is evaluated according to Bernardi et al.36 In fact, the indirect impact from the agricultural phase on water resources is not influenced by the geographical localization of the crop cultivation. In this work this impact factor is set equal to 1.56 m3/t for corn and 0.45 m3/t for stover. For imported corn, both direct and indirect effects are determined by averaging values from the literature.57 4.4. Biomass Pretreatment. Biomass pretreatment usually involves feedstock drying operations. Water embedded within the biomass is lost due to either natural (for stover) or mechanical evapotranspiration (for corn). This amount of (direct) water consumption, however, has been already accounted for within crop irrigation water rates. In addition, the amount of indirect water consumption due to corn thermal drying has been neglected, according to the literature.50 4.5. Transport Systems. The distribution infrastructure includes trucks, rail, barges, and ships as possible delivery means. Trans-shipping was also included as a viable transport option for biomass importation. The biomass transfer within each production element g was described assuming the employ of small road tankers (small trucks). All transport related parameters have been defined according to Zamboni et al.16 The transportation impact on water resources only depends on indirect effects due to fuel production.55 The overall impact on water resources characterizing the transport means are summarized in Table S2 in the Supporting Information. 4.6. Ethanol Production. A set of ten alternative ethanol production technologies has been considered and investigated (Table 3). Among the available technical alternatives, three main process design configurations can be identified: (i) the dry grind process (DGP), i.e. the standard corn-based ethanol process;58 (ii) the dilute acid hydrolysis (DAP), where stover only is converted into ethanol;59 and (iii) the hybrid process

to be expanded in order to account for byproducts displacing alternative goods in the market (e.g., credits). According to this approach, the biofuel SC credits on emission as well as on water usage (CRDlk,t [kg CO2-eq/y or m3H2O/y]) are detracted from the primary product (ethanol) total GHG emissions/water usage. For instance, in the corn-based bioethanol system, DDGS could be used either as a substitute for cattle feed or as a fuel for CHP generation.26 On the other hand, in stover-based processes, lignin is exploited to produce electricity, which may provide a power excess to be sold to the national grid.27 In the hybrid technology integrating corn- and stover-based bioethanol productions, two alternative options are investigated: (i) DDGS (as fodder) and electricity surplus are both sold to the market; (ii) both DDGS and lignin are fueled to a CHP system for power production (and electricity surplus is sold to the market). The biofuel SC credits need evaluating along the entire production network and are defined as a negative contribution to the overall bill. According to eq 11, a discount factor (eclk, [kg CO2-eq/t or m3H2O/t]) is applied to the biofuel production rate PT′ethanol′,k,t (t/y) through technology k at time t: CRDkl , t = −eckl ·P′Tethanol ′ , k , t ,

∀ k, t

(11)

4. CASE STUDY The emerging ethanol production of Northern Italy was chosen to illustrate the applicability of the proposed model. The SCA and GHG-related LCA issues are approached as in Giarola et al.27 The impact in terms of water consumption has been defined in terms of WF according to the methodology proposed by Hoekstra et al.41 The WF takes into account also indirect water consumption due to biomass cultivation and ethanol production plant inputs (fertilizers, chemicals, etc.).50 As outlined above, the substitution method is chosen to assess byproduct end-use effect on the overall impact bill. In the following, the main hypotheses regarding WF assessment are discussed per each LCA stage considered, according to current level of technology development (Instance I). Then, the effects concerning improved efficiency of agricultural irrigation and process water consumption reduction are dealt with (Instance II). 4.1. Spatially Explicit Features. An important step in implementing a spatially explicit modeling framework is the territorial characterization required to map all the possible SC configurations within the area of study. Northern Italy was discretized into 59 cells, also considering biomass importation from an additional hypothetical cell, representing foreign suppliers.16 4.2. Demand Centers. Ethanol demand was defined as in Giarola et al.27 and bioethanol was assumed to be sent to blending terminals located as in Zamboni et al.16 The blending quota (abiding by EU obligations) is reported in Table 2. 4.3. Biomass Production. The characterization of biomass production WF needs accounting for all direct and indirect Table 2. Ethanol Blending Percentage (etperct) on Mass Basis over Years (Percentages Correspond to the Energetic Quotas Imposed by EU Directive) period etperct

1

2

3

4

5

10.2

12.1

14.0

15.8

17.6 7174



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data for DGP-based technologies (6 m3/t of ethanol), and from the literature for DAP technologies (7.37 m3/t of ethanol).59 As regards the processes combining first and second generation technologies (hybrid and hybrid-CHP), their water consumption rates were analyzed through composing makeup rates of the two processes.27 The indirect stress on water resources due to biofuel production step is mainly due to the production of the chemical reagents, enzymes, and energy (e.g., natural gas, electricity) required for their realization. This contribution was determined equal to 1.78 and 0.68 m3/t of ethanol obtained from corn and stover respectively.56,54,60 Thus, the overall impact in terms of water consumption is 3 WF WF 7.78 ( f ′corn′,′f p′ ) and 8.05 ( f ′stover′,′f p′ ) m /t of ethanol, respectively, produced from corn and stover. 4.6. Credits. The credits on the overall impact are set up according to the substitution approach as discussed earlier. They are assigned to both DDGS and electric energy and reflect their potential end-uses. The global impact credit on water resource usage (ecWF k ), in particular, is expressed as water savings due to byproducts utilization. This is reported per each technology k in Table S3 in the Supporting Information. With concern to DDGS and electricity credits, it is assumed a DDGS-to-soy substitution ratio equal to 0.69, and a DDGS-toethanol ratio of 0.954 on a mass basis.27 Then, irrigation requirements for soy cropping (159 L/kg of soy) were determined according to the same approach used to estimate water consumption for corn growth. In this case, however, a spatially explicit approach was unnecessary. As regards electricity-derived credits, power generation yields for each technology, ωk, (Table S3 in the Supporting Information) were compared to the water requirements determined for power generation technologies currently used in Italy.60 4.7. Technological Improvements for Irrigation and Process Water Usage. A scenario (Instance II) is also considered where technological advancements are accounted for in water management. Improvements in the agricultural

Table 3. Ethanol Technologies Assessed in the Study process k

DGP

1 2 3 4 5, 6, 7 8, 9, 10

X X X

DAP

input hybrid

grain X X X

X X X

X X

output

stover

ethanol

CHP

X X X X

X X X X X X

X X X X X

DDGS X X X

(hybrid), where both corn grain and stover are processed to obtain ethanol.27 With concern to DGP, three options are analyzed according to how power is supplied to the plant: either by the grid (k = 1) or by using DDGS (k = 2) or stover (k = 3) to fuel a CHP generation system. Note that instance k = 3 implies that stover is an input to the process, but it is exploited in the CHP generation only. DAP is identified by k = 4. As usual, heat and power are provided by feeding lignin to a CHP station. Also in the hybrid process, lignin is always exploited to provide heat and power as a process output. As regards the hybrid technologies, a purpose-designed Aspen Plus model was set up to define three options representing specific (1:1, 1:2, 1:3) corn-to-stover ratios. In this case, too, two alternative DDGS end-uses are taken into account, i.e. either DDGS is sold in the animal feed market (k = 5, 6, 7, where the three indexes stand for the three decreasing corn to stover ratios) or it is fed along with lignin to the CHP station (k = 8, 9, 10). According to the environmental standpoint, water consumption was evaluated as a consequence of a facility being brought into operation, neglecting the manufacture of physical capital.56 The amount of water directly consumed by a biomassto-ethanol facility, given by the process and cooling towers makeup, was determined according to literature58 and industrial

Figure 2. Pareto surface: the optimal WF vs NPV curve (points A, B, C, and D) is projected on a WF-NPV plane (a) and a WF-CF plane (b). Point E is also a point laying on the 3D Pareto surface and represents the best CF solution with positive NPV. 7175



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Figure 3. SC economic optimum with an imposed GHG emission reduction of 60% respect to gasoline (Instance I).

5.1. Pareto Analysis. The Pareto curve of optimal solutions for Instance I in terms of WF vs NPV is reported in Figure 2 (Figure 2a shows the projection on the WF-NPV plane; Figure 2b shows the projection on the WF-CF plane). As expected, the results reveal a conflict between environmental and economic performance in dealing with biofuels production. Point A represents the economic optimum. It involves the establishment of a SC based on the standard DGP process with the DDGS sold as animal fodder (k = 1). Revenues coming from byproducts sale enhance the economic performance and the NPV of this configuration is 1.17 €/GJ of ethanol produced. The environmental impact of this configuration is high on WF (9.98 m3/GJ, corresponding to 212 L of water/L of fuel) as well as on CF (77.2 kg CO2/GJ, corresponding to a GHG reduction of about 10% compared to gasoline (GHG emissions factor for gasoline = 85.8 kgCO2-eq/GJ67)). Corn is also imported from abroad (70% of the total corn requirement is satisfied with imported corn at the end of time period 5), due to the lower price of imported corn, which is directly shipped to the plants located near the main ports. The main contribution to WF is due to biomass irrigation, which accounts for about 97% of total water consumption. The maximum value of WtAR (0.43 for underground water, corresponding to high stress, and 0.12 for surface water) is reached in the cell with largest corn production. Moving down toward better environmental performance, point B involves the establishment of both DGP and hybrid (k = 7) ethanol production plants. This configuration still has a positive NPV (0.23 €/GJ of ethanol produced) and, due to stover utilization, exhibits a significantly lower WF (1.9 m3/GJ or 40.4 L of water/L of fuel) and a lower CF as well (39.4 kg CO2/GJ, corresponding to a GHG reduction of about 54% compared to gasoline). The biomass is mainly produced near the plants (13% is imported). Point C involves the establishment of production plants based on hybrid technology only. The resulting SC has a better environmental performance (WF is 0.62 m3/GJ, corresponding to 13.1 L/L), but a negative NPV (−0.56€/GJ), which indicates that the configuration is economically unfeasible. The water

phase assumed to replace sprinkler with drip irrigation: water consumption was assumed to drop by 33%.61 The same performance improvement was assumed to calculate credits for soy cultivation. Improvements in bioethanol production were based on some energy and water flows optimization works: direct water requirements were assumed to drop to 1.17 L/L of ethanol for corn-based DGP62 and to 2.7 L/L of ethanol for second generation facilities.63 4.8. Water Stress Index. To investigate the impacts on water resources resulting from water consumption, the water withdrawals to availability ratio (WtAR) index has been evaluated. The index is determined as the ratio between the direct water withdrawals and the surface or underground water available in each cell.64 The water availability was estimated from a report of the Italian Water Research Institute.65 The threshold values for WtAR are 0.2, 0.4, and 0.8, representing a medium, high, and very high stress on water resources, respectively.

5. RESULTS AND DISCUSSION The global framework was decomposed into two subproblems in order to downsize the problem.27 The first subproblem was obtained by dropping down the spatially explicit features and focusing on the mere selection of the most performing technologies. This allowed reducing the dimensions of the set of conversion technologies from ten to four technologies. The second subproblem was formulated implementing the entire model within the reduced space of technological option K′. K ′ = {1, 2, 4, 7}

The reduced problem was solved using the CPLEX solvers in the GAMS modeling tool66 in order to obtain the optimal system configuration according to the three objectives discussed in the above (about 307 000 continuous variables and 9300 discrete variables had to be optimized). Successively, a biobjective analysis was carried out considering only NPV and WF as objective functions, constrained to a reduction of emissions of 60% as imposed by the EU directive2 from 2018 onward. 7176



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stress index for underground water resources never reaches high stress values and only in two cells is greater than 0.2 (medium stress), with a maximum value of 0.26. The environmental optimum (point D) establishes second generation (k = 4) ethanol production plants only. The solution is economically unsustainable (NPV = −7.5 €/GJ), but a negative WF (−0.06 m3/GJ) is obtained. In this situation, water savings derived from the utilization of excess electric power are higher than the water used from the entire SC. The biomass for the plants is produced locally. Clearly, this SC configuration exhibits a low impact on water resources with a maximum value of WtAR equal to 0.012. Since three objectives were considered, the full Pareto set is in fact a surface and other solution points at lower CF were obtained (whose projection on the WF-NPV plane would lay on the left of the WF vs NPV Pareto curve). For sake of conciseness, we will discuss only one of such points, i.e. point E, also reported in Figure 2. It represents the best solution in terms of GHG emissions still resulting in a positive NPV (0.879 €/GJ). It involves the utilization of DGP technology, with the DDGS used to produce electricity in a CHP system (k = 6). The different end-use of DDGS lowers the CF of the supply chain, with a GHG emission reduction with respect to gasoline of 58% (nearly complying with the most restrictive EU limit). However, note that the WF deteriorates: five cells exhibit a value of WtAR for underground water ranging between 0.4 and 0.99, while the WtAR maximum value for surface water is 0.19. In the case of Instance II, i.e. assuming a technological improvement in water utilization, the Pareto surface exhibits the same technology selection (and thus is not reported). However, the WF values change considerably, dropping to 6.68 and 1.3 m3/GJ (corresponding to 142 and 27.7 L/L, respectively) for solutions A and B (with about 33% and 31% of reduction with respect to the base case). A complete cellulose-based facility (D) leads to a negative WF (−0.21 m3/ GJ, or −4.38 L/L). 5.2. CF-Constrained Optimization. The economic optimum obtained from the multiobjective optimization does not reach the target of GHG reduction required by EU directive from 2018 onward. To find the most profitable SC configuration also complying with EU regulations, an economic optimization was performed setting a constraint on the maximum level of GHG emissions assuming the same assumptions as in Instance I in terms of water usage factors. In Figure 3 the final SC design for the optimal constrained configuration is reported. It involves the establishment of a production system based on first generation technology (k = 3), where DDGS is used to produce electricity in a CHP station. The first generation plants located along the coastline are supplied by imported corn. Note that to reach the final goal of 60% GHG emission savings compared with a traditional fossil fuels SC, one hybrid technology supplied with local biomass needs establishing, too. The resulting NPV is equal to 0.71 €/GJ, while the WF is equal to 13.1 m3/GJ, corresponding to 279 L of water/L of fuel. Note that a higher WF is obtained than the economic optimum without limitations on GHG emissions (point A in Figure 2). This is due to the different end use of DDGS which is now burned in a CHP station, thus allowing for lower water credits than fodder production. Figure 4 illustrates the WtAR values (dark blue bars) corresponding to the cells where either biomass cultivation or bioethanol production takes place. The effect of technological improvement on water utilization (Instance II; light blue bars)

Figure 4. Water withdrawals to availability ratio (WtAR) of the SC configuration displayed in Figure 3 (showing economic optimum with an imposed GHG emissions reduction of 60% respect to gasoline). The bars represent the WtAR index (reported on the Y-axis) calculated as the ratio between the direct water withdrawals and either the surface (sw) or underground (uw) water available in each cell. The grid number refers to the grid reported in Figure 3. Both Instances I and II are illustrated.

is also reported. The SC design is likely to produce some stress on water resources in cells 25 and 27 where the major biomass cultivation sites are located (if only underground water were utilized the WtAR index increases to 0.4 and 0.69, respectively, thus indicating a high stress on water resources). Note that even if the supply chain is designed to fulfill the environmental constraints on GHG emissions, the local impact on water resources may be high, possibly even unacceptable. Also note that the effect of ethanol production facilities is nearly negligible on the resulting water stress. If water consumption factors account for the technological improvements as described in Instance II, the optimal ethanol SC configuration leads to a substantial reduction of the WtAR index. However, if only underground water were used, a high stress in cell 27, where the largest corn production land is allocated, would still be detected (WtAR = 0.45).

6. CONCLUDING REMARKS A spatially explicit and multiperiod MoMILP modeling framework for sustainable bioethanol supply chain design has been presented and discussed. A wider perspective of the environmental analysis embedding impacts on both global warming and on water resources has been embedded in the optimization framework. A spatially explicit approach has been considered in order to represent and evaluate the specificity of the geographical location, which has quite a significant impact on the WF assessment. The optimization results show that the strategic decisions not only need to rely on economic performance, but investors should also consider the specific trade-off between WF- and CF-related strategic solutions and need to account for the local resource availability to guarantee a sustainable biofuel design.

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ASSOCIATED CONTENT

S Supporting Information *

Economic part of the model, according to the paper by Giarola et al. (2011); values for water consumption (direct contribution) related to corn cultivation; water consumption related to biomass and ethanol transportation; parameters ωk and ecWF k representing the biomass-to-power conversion yields and the credits for avoided impact on water resources achieved by each technology k. This material is available free of charge via the Internet at http://pubs.acs.org. 7177



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AUTHOR INFORMATION

Corresponding Author

*Fax: +39.049.827.5461. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Fondazione Cariparo is gratefully acknowledged for Progetto Dottorati di Ricerca 2008 and 2011. ACRONYMS CF = Carbon Footprint CHP = Combined Heat and Power DAP = Dilute Acid Hydrolysis Process DDGS = Distiller’s Dried Grains with Solubles DGP = Dry-Grind Process EU = European Union GHG = Greenhouse Gas GrSCM = Green Supply Chain Management GWP = Global Warming Potential iLUC = indirect Land Use Change LCA = Life Cycle Assessment MILP = Mixed Integer Linear Programming MINLP = Mixed Integer Nonlinear Programming MoMILP = Multi-objective Mixed Integer linear Programming MoMP = Multi-objective Mathematical Programming NPV = Net Present Value SC = Supply Chain SCA = Supply Chain Analysis WF = Water Footprint WtAR = Water to Availability Ratio

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SETS g ∈ G = grid squares, G = {1,...,60} g′ ∈ G = set of square regions different from g i ∈ I = set of biomass typology, I = {corn, stover} j ∈ J = set of product, J = {ethanol, DDGS, power} k ∈ K = set of conversion technologies, K = {1,...,10} m ∈ M = set of means of transport, M = {truck, rail, barge, ship, trans-ship} l ∈ L = environmental objective functions, L = {CF, WF} s ∈ S = set of life cycle stages, S = {bp, bpt, bt, f p, f t} t ∈ T = set of time intervals (years), T = {1,...,20}

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SCALARS ζ = interest rate

f l′bt′,m = impact factors for biomass transport (s = bt) with mean of transport m on climate change [kg CO2-eq/(t*km)] (l = CF) or on water resources [mH2O3/(t*km)] (l = WF)] f l′smalltruck′ = impact factors for local biomass transport with small trucks on climate change [kg CO2-eq/(t*km)] (l = CF) or on water resources [mH2O3/(t*km)] (l = WF)] f l′ft′,m = impact factors for fuel transport (s = f t) with mean of transport m on climate change [kg CO2-eq/(t*km)] (l = CF) or on water resources [mH2O3/(t*km)] (l = WF)] f l′fp′,i = impact factors for fuel production from biomass i (s = f p) on climate change [kg CO2-eq/t] (l = CF) or on water resources [mH2O3/t] (l = WF)] LDg,g′ = local delivery distance between grids g and g′ [km] τg,m,g′ = tortuosity factor of transport mode m between g and g′ [-] ωk = electricity sold potential of technology k (kWh/Lethanol)

CONTINUOUS VARIABLES CFt = cash flow at time t [€/y] CRDlk,t = credits from avoided impacts related to conversion technology k at time t on climate change [kg CO2-eq/y] (l = CF) or on water resources [mH2O3/y] (l = WF) Fs,t = reference flow for life cycle stage s at time t [units/y] Impls,t = impact for life cycle stage s at time t on climate change [kg CO2-eq/y] (l = CF) or on water resources [mH2O3/y] (l = WF) NPV = net present value [€] Objeco = economic objective function expressed as NPV [€] Objl = environmental objective function expressed as overall impact on climate change [kg CO2-eq] (l = CF) or on water resources [mH2O3] (l = WF) Pbi,g,t = production rate of biomass i in cell g at time t [t/y] Pf i,k,g,t = ethanol production rate from biomass i through facility k at time t in grid g [t/y] T Pj,k,g,t = total production rate for product j through technology k at time t in grid g [t/y] Qbi,g,m,g′,t = flow rate of biomass i between g and g′ with transport mode m in time period t [t/y] Qfg,m,g′,t = ethanol flow rate between g and g′ with transport mode m in time period t [t/y] TCIt = total capital investment at time t [€] TIlt = total impact at time t on climate change [kg CO2-eq/y] (l = CF) or on water resources [mH2O3/y] (l = WF) TIOTl = total impact over time on climate change [kg CO2eq] (l = CF) or on water resources [mH2O3] (l = WF) WtAR = Water Withdrawals to Availability Ratio [-] REFERENCES

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PARAMETERS df CFt = discount factor at time period t, specific for CFt df TCIt = discount factor at time period t, specific for TCIt etperct = ethanol blending percentage at time t eclk = credits for avoided emissions of conversion technology k on climate change [kg CO2-eq/t] (l = CF) or on water resources [mH2O3/t] (l = WF)] f li,s = impact factors for biomass i and life cycle stage s on climate change [kg CO2-eq/t] (l = CF) or on water resources [mH2O3/t] (l = WF)] f li,s,g = impact factors for biomass i and cell g on climate change [kg CO2-eq/t] (l = CF) or on water resources [mH2O3/t] (l = WF)] for biomass production (s = bp) 7178



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