Biomass Conversion into Fuels, Chemicals, or Electricity? A

Research Article Cite This: ACS Sustainable Chem. Eng. 2019, 7, 10570−10582

pubs.acs.org/journal/ascecg

Biomass Conversion into Fuels, Chemicals, or Electricity? A NetworkBased Life Cycle Optimization Approach Applied to the European Union Raul Calvo-Serrano,† Miao Guo,† Carlos Pozo,† Á ngel Galán-Martín,‡,§ and Gonzalo Guillén-Gosálbez*,‡

Downloaded via NOTTINGHAM TRENT UNIV on August 8, 2019 at 16:35:56 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.

†

Centre for Process Systems Engineering, Imperial College of Science, Technology and Medicine, South Kensington Campus, Roderic Hill Building, London SW7 2BY, United Kingdom ‡ ICB Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland § Departament d’Enginyeria Quı ́mica, Universitat Rovira i Virgili, Av. Paı̈sos Catalans 26, 43007 Tarragona, Spain S Supporting Information *

ABSTRACT: Biomass resources offer a very promising alternative to fossil fuels for the sustainable production of electricity, fuels, and chemicals. Nevertheless, the problem of deciding on the best use of biomass is highly complex, as many potential feedstocks and conversion pathways exist, each one displaying different economic and environmental performances. In this context, here we propose a network approach that combines economic and several environmental criteria together with resource availability and demand constraints to identify optimal biomass feedstocks and their conversion pathways into fuels, chemicals, and electricity. We apply this methodology to the European Union (EU) considering the biomass resources currently available and the main technologies for their conversion into valuable products. Our results show that annual savings of as much as 1.81 Gt CO2eq could be attained by exploiting biomass resources, although biomass cannot fully cover the total EU demand of ethylene, transport fuel, and electricity. The optimal environmental plan makes use of various biomass resources for power generation and biofuel production producing ethylene exclusively from naphtha. The approach presented generates valuable insight to aid policymakers on how to sustainably use biomass resources. KEYWORDS: Life cycle assessment, Network optimization, Biomass, Sustainability, Multiobjective optimization

■

INTRODUCTION

Biomass-based products often display better environmental prospects than their fossil-derived counterparts. This is particularly true when analyzing greenhouse gas (GHG) emissions, as biogenic carbon sequestration during plant growth can potentially achieve carbon neutral life cycle emissions, or even carbon negative, when coupled with carbon capture and storage8. Such advantages make bioproducts promising alternatives to conventional fossil-derived energy and products. Moreover, biomass resources are more reliable than other renewable energy sources (e.g., wind and solar power), which makes them appealing in the transition toward a sustainable energy mix. These advantages, however, can be overshadowed by production and management issues together with sustainability aspects in the biomass life cycle. First, different sectors (e.g., food and energy industries) compete for biomass resources,

Chemicals and energy production are at present predominantly sourced from fossil fuels (i.e., oil, gas, and coal),1 thereby worsening environmental degradation and resource depletion. Following the need to transition toward a more sustainable economy, as stated in the United Nations’ 2030 Agenda for Sustainable Development,2 there is an ongoing growing trend toward the development of more sustainable production systems based on reliable and sustainable raw materials. One of the most promising alternatives is to use biomass feedstocks obtained from crops, residues from agriculture, forestry, and other industry activities to produce a myriad of different products that could potentially replace those based on fossil fuels. For example, biomass can be used to produce transport fuels3 (e.g., biogasoline or biodiesel) as well as polymers precursors (e.g., bioethylene4,5). Similarly, electricity can be obtained from direct combustion of biomass, with the option of using carbon capture technologies to further reduce carbon emissions and attain better environmental performance.6,7 © 2019 American Chemical Society

Received: February 25, 2019 Revised: April 18, 2019 Published: April 29, 2019 10570

DOI: 10.1021/acssuschemeng.9b01115 ACS Sustainable Chem. Eng. 2019, 7, 10570−10582

Research Article

ACS Sustainable Chemistry & Engineering

covered by biofuel products) obtained from generic biomassderived products (i.e., lignocellulosic material and vegetable oil), considering the total cost and global warming potential to identify the best conversion route. In contrast, the approach here proposed considers a diverse set of demands including chemicals (i.e., ethylene) and energy (i.e., electricity and transport fuels) to be derived from different types of biomass resources such as cultivated crops or forestry residues, enabling the characterization of biomass utilization strategies for particular regions through specific demand and biomass availability constraints. Moreover, the consideration of biomass types allows the identification of individual promising crops or residues in the transition toward more sustainable production patterns. The approach presented by König et al. (2019)20 only considers a single environmental criterion (i.e., global warming potential), thereby failing to provide a full picture of the total effects of biomass production and utilization on the environment. On the other hand, the approach here presented adopts the ReCiPe2008 environmental methodology,24,25 evaluating 18 midpoint damage indicators (i.e., climate change, ozone depletion, terrestrial acidification, freshwater eutrophication, marine eutrophication, human toxicity, photochemical oxidant formation, particulate matter formation, terrestrial ecotoxicity, freshwater ecotoxicity, marine ecotoxicity, ionizing radiation, agricultural land occupation, urban land occupation, natural land transformation, water depletion, mineral resource depletion, and fossil fuel depletion) and three endpoint indicators (i.e., damage to human health, ecosystem diversity, and resources availability). Both midpoint and endpoint indicators provide a comprehensive coverage of environmental aspects, allowing for the exhaustive exploration of trade-offs between environmental objectives and highlighting the potential occurrence of burden shifting between environmental impacts. We apply this approach to the European Union (EU), one of the major consumers of fossil resources26 with a clear commitment and well-defined goals on the utilization and management of biomass.27,28 In particular, here we study the potential to replace conventional products based on fossil fuels by their biomass-based counterparts: polymers, transport fuels, and electricity (representing 6%, 45%, and 42%29 of the total EU consumption of fossil resources, respectively). We note that chemical and plastic industries are anticipated to grow significantly and compete with transport fuel and electricity for fossil fuels, which justifies their inclusion in this study.1 Although this work focuses on the EU scenario, the proposed approach could be easily adapted to any region by adequately defining its specific biomass and production requirements and limitations (e.g., product demands and resources availabilities). The key optimization problems and research questions to be addressed are defined in the Problem Statement section. The Methods section provides a detailed description of the mathematical model and the solution strategies used. The EU scenario here considered is properly defined in the Case Study section before the results and conclusions of the work are presented.

limiting their availability for alternative products. Furthermore, biomass generation is also constrained by the availability of other resources (e.g., water, land, and agrochemicals), which can lead to burden shifting by which the use of biomass improves some environmental categories while worsening others.9 Lastly, the lack of maturity of some biomass transformation technologies still constitutes a major obstacle to overcome. Indeed, from a pure economic viewpoint, biomass-based processes are often less cost-effective than their business-as-usual counterparts, which hampers their wide deployment in the market place. To evaluate the merits of biomass use, previous works considered only limited key performance indicators such as efficiency or economic metrics, while focusing on single processes or conversion routes in isolation from each other.10,11 In practice, however, potential interactions and synergies between technologies need to be taken into consideration for a proper assessment of the alternative uses of biomass. The so-called biorefinery concept was developed to consider the interactions between production routes based on biomass,12−16 aiming to identify the optimal combination of technologies for the conversion of biomass feedstocks into valuable products17−20 often in terms of efficiency and economic performance. An overall comparative assessment of the main technologies for biomass use considering economic and life cycle environmental concerns and hundreds of alternative uses of biomass is, therefore, still lacking. As summarized by Daoutidis et al. (2013),21 to adequately address biomass utilization strategies and to explore their capabilities, it is necessary to simultaneously consider chemical and process design criteria (e.g., biorefinery approaches) together with availability, distribution, and requirement factors accounting for the potential interactions and limitations between levels of design. Furthermore, such a combined approach enhances the versatility and representativeness of the generated biomass utilization model, enabling the identification of optimal biomass utilization strategies taking into account technical as well as region-specific constraints. Hence, we here apply optimization methods to optimize both economic and life cycle environmental criteria in the selection of biomass conversion routes considering technical as well as resource availability constraints. In particular, the approach here proposed is based on the biomass process network presented by Kim et al. (2013),19 which is here enlarged in scope by (1) incorporating environmental aspects for both processes and products; (2) adding promising biomass species (e.g., Miscanthus22,23); (3) introducing regional limitations on biomass; and (4) including additional biofuel, bioenergy, and biochemical technologies and products. Although the approach here presented is based on the work by Kim et al. (2013),19 other works in the literature partially resemble ours. As an example, König et al. (2019)20 introduced a network approach to identify the economically and environmentally optimal utilization strategy of biomass resources to meet a given energy demand. This approach, however, uses a reaction network flux analysis instead of process networks for the characterization of biomass conversion routes, hence neglecting additional efficiency constraints and scale-up constraints, ultimately simplifying the potential transformation of biomass into valuable products. In addition to this methodological difference, the main goal of that work is the satisfaction of a single energy demand (only

■

PROBLEM STATEMENT We focus on identifying local biomass production and utilization strategies to satisfy a given demand of ethylene, transport fuels, and electricity in the EU considering 10571


Research Article

ACS Sustainable Chemistry & Engineering simultaneously economic and environmental criteria. The questions we aim to answer include the following: (1) Which products (i.e., ethylene, transport fuels, or electricity) should be obtained from biomass? (2) To what extent can local biomass resources cover the chemicals (mainly ethylene), transport fuels, and electricity demanded in the EU? (3) Which conversion pathways and processing technologies should be deployed to convert biomass into valuable products in a cost-effective and environmentally friendly manner? These questions address the most significant challenges biomass utilization in the EU faces. The following section describes all the assumptions, methods, and strategies used to answer them.

■

METHODS The methodology followed in this work comprises three different steps (see Figure 1): (1) the development of a biomass process network; (2) the derivation of a mathematical model based on this network; and (3) a solution procedure to identify optimal feedstocks and technologies. Development of a Biomass Process Network. Our approach relies on a biomass process network (BPN) linking biomass to intermediate materials and final products via available technologies. This network, based on the work by Kim et al. (2013),19 includes a wide range of biomass processing technologies (e.g., pretreatments and thermal, chemical, and biochemical processes) and their respective feedstocks, intermediates, and final products. Note that this approach considers several echelons in the transformation of biomass but does not take the form of a standard supply chain model, as for simplicity, it omits essential supply chain entities such as distribution networks or storage facilities. Figure 2 provides a sketch of the network, which covers several routes converting biomass into fuels (i.e., biogasoline, biodiesel, and ethanol blended with conventional gasoline and diesel), electricity (through technologies with and without carbon capture and storage), and chemicals. Although different chemicals could be considered, in this work we focus on ethylene only, as it can be produced from biomass resources and is the precursor of the most produced polymer (i.e., polyethylene), hence being in turn the most demanded chemical in the EU and the world. Note that although the network here presented focuses on the satisfaction of ethylene demand as the only chemical, other platform (e.g., methanol)30 and fine chemicals31 could be derived from the considered biomass resources. Furthermore, the BPN here considered also includes processes that generate high-value intermediates and byproducts such as γ-valerolactone and levulinic acid, whose demand was set to zero due to data-gaps and also to focus on ethylene for the reasons discussed above. Ethylene is, therefore, considered as a reference chemical due to its prominent demand levels compared to other bulk chemicals. In particular, for the year 2016 in the EU, ethylene demand (i.e., 20.5 MMT, million metric tonnes)32 surpassed the demand of both propylene (15 MMT) and methanol (9.5 MMT). Our BPN expands on the previous network by adding 22 technologies and 55 materials missing in the original work, all together covering 224 technologies and 175 materials (see Section S1 in the Supporting Information). The products

Figure 1. Overview of the framework, describing the elements of the biomass process network (BPN), the derived mathematical model, and the optimization strategy.

added to the network include electricity from the average local mix, conventional transport fuels (i.e., diesel and gasoline), and ethylene from naphtha, which allow the total EU demands to be met with business-as-usual technologies. Furthermore, Miscanthus, regarded as one of the most promising crops for energy generation,22 was also included in the network. Altogether, our network includes eight biomass feedstocks covering food crops (i.e., soybean, corn, and sugar cane), lignocellulosic resources (i.e., Miscanthus and switchgrass), and vegetal wastes (i.e., corn stover and hard and soft wood residues). In terms of technologies, we added the following ones: (1) bioenergy production with carbon capture and storage (BECCS); (2) bioethanol blending and combustion of transport fuels; and (3) dehydration of bioethanol to ethylene (see Section S2 in the Supporting Information). BECCS technologies generate electricity from the direct combustion of biomass, using a monoethanol amine absorption to capture up to 90% of the carbon dioxide emissions generated.33 To satisfy the demand of transport fuels, we consider pure fuels (i.e., gasoline and diesel) derived from both fossil and biomass resources, together with their blends with bioethanol: i.e., four blends of bioethanol with 10572


Research Article


Figure 2. Simplified representation of the biomass process network (BPN) considered in this work. The processes, products, and connections missing in the work by Kim et al. (2013)19 are highlighted in red. The full nomenclature used in this figure can be found in Table S1 in the Supporting Information.

Derivation of the Mathematical Model. To identify optimal biomass conversion routes, we develop a networkbased mathematical model following a similar approach as in Kim et al. (2013),19 originally introduced by Stadtherr and Rudd (1976).39 Here, each process/technology is described using a simplified set of parameters. More precisely, for each technology j we define the following parameters: mass yields for each material or product i (μij, kg of i/kg main input in j), technology conversion cost (cj, $/kg main input in j, which combines the relative annualized capital investment cost with other fixed production costs such as maintenance and labor), energy consumption (uj, W h/kg main input in j), and its associated cost (ucj, $/W h). For those technologies originally present in the work by Kim et al. (2013),19 their respective characterization parameters were directly obtained from that source. To be consistent with the process characterization provided therein, the 22 technologies here added to the biomass process network have been modeled applying the same assumptions as in Kim et al. (2013)19 (i.e., 10% interest rate and 20 year plant lifespan), and using additional data from the literature33,36,40 (see Section S2 of the Supporting Information for more details). For example, the cost parameters cj combine fixed production costs such as labor or maintenance together with relative investment capital costs as done by Kim et al. (2013).19

gasoline and biogasoline (10%, 25%, 40%, and 85% in volume, % v/v) as well as a 10% v/v blend with diesel and biodiesel. The fuels demands are expressed on a common basis using the total distance traveled, assuming an average load of 1000 tonnes for freight transport and 13 000 km per person for passenger transport.34,35 Thus, each fuel (either pure or blend) is converted into the equivalent distance traveled per unit of fuel consumed using appropriate conversion factors (km/kg of fuel) available in GREET.36 The BPN also models fossil-based polymers. A wide assortment of polymers can be found in the market, yet polyethylene, polypropylene, and polyvinyl chloride,29 all potentially derived from ethylene, show the largest demand. Hence, here we focus on covering the EU demand of ethylene through the bioethanol dehydration to ethylene route.37,38 We further modify and expand the network proposed by Kim et al. (2013)19 in several ways. First, due to the current marginal production of sugar cane in the EU, bagasse is here considered as byproduct of sugar cane consumption instead of as a feedstock, becoming available only when sugar cane is consumed. Additionally, energy utilities are classified into three categories: electric power, steam, and furnace heating. Finally, auxiliary feedstocks missing in the original network (i.e., hydrogen, catalysts, enzymes, nutrients, etc.) were also included in the network. 10573


Research Article


kg of biomass i/kg of biomass i′). We also define set ABP(i), which contains all crops i′ that generate waste i. The exogenous waste is limited by its maximum availability βi (in kg of biomass i). The agricultural waste availability constraint is provided in eq 5. In this work, this equation only applies to corn stover (the only element in set BT3), which can be generated alongside endogenous and exogenous corn.

Once all processes are properly characterized, the network performance can be directly estimated from the material flows within the network. These flows are modeled using three variables: the amount of feedstock i purchased (Pi, in kg), the amount of product i sold to exogenous markets (Si, in kg), and the amount of main raw material consumed by each technology j (Wj, in kg). For clarity, model variables are italics, while parameters (except Greek letters), sets, and indexes are given in normal font. Sets IN(i) and OUT(i) are introduced to model the mass balance for every material in the network model. The former set includes all the technologies j that consume material i, while the latter defines the technologies j that produce material i. The mass balance is given in eq 1, where the amount of material purchased plus the amount produced equals the amount consumed plus the sales. Pi +

∑

∑

μijWj =

j ∈ OUT(i)

j ∈ IN(i)

∑

μijWj + Si ∀i (1)

i ∈ BT1

Pi ≤ aml bpyi

(6)

Si = 0 ∀i ∉ SP

(7)

TC =

∑ Pi pci + ∑ Wj(c j + u juc j) i

j

(8)

Note that, to realistically compare alternative biomass-derived products against their conventional counterparts, the production costs of the latter were modeled exclusively through the purchasing cost parameter pci. We, therefore, assume that all the costs associated with the production of the conventional product are lumped into the purchasing cost (i.e., both fixed and variable costs). Environmental Assessment. With regards to the environmental assessment, we apply the life cycle assessment (LCA) framework described in the ISO 1404041 to our BPN. In terms of scope, we follow a cradle-to-gate analysis, which accounts for the environmental burdens embodied in the inputs entering the network (i.e., biomass and auxiliary feedstocks together with conventional products) as well as those generated in the processes of the network (direct emissions as well as burdens embodied in the energy utilities consumed). The goal of the environmental assessment is to evaluate 18 midpoint and 3 endpoint indicators42 following the ReCiPe2008 methodology.24 Due to the multiproduct nature of the BPN, we define as functional unit a total amount of chemicals, electricity, and fuels demanded (i.e., kg of ethylene, kW h of electricity, and transport km driven), where the driven distance is expressed as total amount of kilometers traveled to enable a fair comparison between different fuel types (e.g., bioethanol with diesel). This multiproduct functional unit indirectly expands the system boundaries beyond the network itself, thereby avoiding the need to apply allocation procedures to estimate the individual impact of each product generated in the network and simplifying the analysis. Although system boundary expansions often arise in consequential LCA approaches,43 the LCA approach here followed is fundamentally attributional. Nevertheless, it would be fairly simple to adapt the presented approach to perform consequential LCA studies by simply modeling the relationship between market prices and the considered product demands. Figure 3 presents the underpinning life cycle systems approach. Note that by including the option of using

(2)

(3)

Equation 4 limits the consumption of forestry wastes in set BT2 (i.e., hard and soft wood residues) below the corresponding maximum availability, here defined by parameter pMaxi. Pi ≤ pMax i ∀i ∈ BT2

Pi = 0 ∀i ∉ PF

The objective function seeks to minimize both the total costs and life cycle impacts. The total cost (TC) takes into account the technology conversion costs (cj) and energy utility consumption costs (ucj) as well as the purchasing cost of feedstocks and conventional products (pci) as shown in eq 8.

Unlike previous works, ours includes regional biomass availability constraints, accounting for the competition between technologies for biomass feedstocks. In particular we consider three different biomass availability constraints: (1) cultivation availability constraint, limited by the amount of usable marginal land; (2) forestry waste availability constraint, bounded by the maximum availability of wastes; and (3) agricultural waste availability, subject to two simultaneous sources, as it can be obtained from cultivated biomass in marginal land and as a waste from exogenous sources. Sets BT1, BT2, and BT3 are defined to model the aforementioned availability constraints for each biomass feedstock i. As formulated in eq 3, the availability of cultivated biomass included in the set BT1 (i.e., soybean, corn, sugar cane, switchgrass, and Miscanthus) is limited by the amount of locally available marginal land (aml). Parameter bpyi (in kg of biomass i per hectare), denotes the biomass production yield for each biomass type.

∑

(5)

Additional constraints (eqs 6 and 7) are included to enforce that only biomass feedstocks, energy utilities, and conventional products, in set PF, can be purchased, while only final products and byproducts, in set SP, can be sold.

Si′ ≥ d i ∀i

i ′∈ AD(i)

αii′Pi′ + βi ∀i ∈ BT3

i ′∈ ABP(i)

The satisfaction of the product demands di is enforced by eq 2, either using biomass-derived or conventional materials. The latter are included in the set AD(i), which contains all conventional materials i′ (i.e., fossil-derived) that can satisfy the demand of material i (e.g., use of conventional gasoline to satisfy the demand of transport fuels). Si +

∑

Pi ≤

(4)

The agricultural waste availability constraint considers endogenous biomass sources (i.e., wastes generated alongside crops produced for our process network) as well as exogenous sources (i.e., wastes generated from crops for other uses). The endogenous waste is considered to be proportional to the crop being produced in the network, where the amount of waste biomass i generated from crop i′ is given by parameter αii′ (in 10574


Research Article


Solution Procedure. The overall model seeks to simultaneously minimize the total cost (TC) and environmental impacts (TIk) as follows: Model M1; Min{TC, TI1, ..., TIk}; s.t. eqs 1−9; Pi, Si, Wj ≥ 0 ∀i,j. Model M1 takes the form of a multiobjective linear programming optimization problem, which we solved using the ε-constraint method.48 This method minimizes a single objective (e.g., TC) while enforcing the remaining objectives (TIk) to be lower than a defined maximum value εk, as expressed in eq 10. TIk ≤ εk ∀k

The single-objective problems solved are, therefore, as follows: Model M1-ε; Min TC; s.t. eqs 1−10; Pi, Si, Wj ≥ 0 ∀i,j. Model M1-ε is calculated for several sets of these ε-values, obtaining a different optimal solution for each of them. Together, these solutions define the Pareto set, describing the trade-off between multiple objectives (i.e., an objective can be improved by necessarily worsening at least another). Following this methodology, here we solve two different types of problems: (1) individual separate optimizations of the 18 midpoint ReCiPe2008 indicators against the total cost (TC); and (2) simultaneous optimization of the total cost and the three endpoint ReCiPe2008 indicators. The first case deals with 18 two-objective optimization problems. The second case solves only one single optimization problem with four objectives, therefore requiring three independent sets of εvalues. For this second case, we apply Monte Carlo sampling for the generation of the ε-values, solving M1-ε for each generated sample.49

Figure 3. Graphical representation of the life cycle stages considered in the environmental assessment of the network. The conventional products include all the feedstocks and technologies involved in their production.

chemicals, fuels, and electricity from either biomass or the business-as-usual technologies for the satisfaction of their respective demands, we can assess alternative uses of biomass considering different technical constraints and economic and environmental objectives. In terms of modeling, LCA principles are explicitly included in the mathematical formulation by linking some continuous variables denoting flows of materials and purchases of feedstocks and products to the corresponding impact. More precisely, the life cycle impact category k embodied in 1 kg of material, either a feedstock or a material produced following the business-as-usual technology, is given by pIik (impact units of k/kg of i). Following a similar modeling approach as in the economic objective function in eq 8, the cradle-to-gate LCA impacts associated with all the stages and echelons in the production of conventional products are here combined and represented by parameter pIik. Additionally, for each technology j, we consider two impact contributions: the direct impact from the emissions and waste generated during the process operation, defined by parameter eIjk (impact units of k/kg main input in j), and the impact embodied in the energy utilities consumed by the technology, denoted by parameter uIjk (impact units of k/W h). Impact parameters are based on full LCAs on the corresponding systems, which can be retrieved from LCA data repositories like Ecoinvent 3.344 and USLCI,45,46 accessed via SimaPro v847 or similar software packages, as well as technical sources including scientific articles and reports. The total life cycle impact in each category k (TIk) is, therefore, computed via eq 9. TIk =

∑ Pi pIik + ∑ Wj(eI jk + u juI jk) ∀k i

j

(10)

■

CASE STUDY We assess the best use of biomass in the EU considering the main region-specific limitations and constraints. Product demands (di), biomass availability factors (bpyi, aml, pMaxi, αii′, and βi), purchasing cost coefficients (pci), and environmental performance parameters (pIik, eIjk, and uIjk) are, therefore, contextualized for the EU territories and year 2016. Furthermore, all factors displaying seasonal behaviors (e.g., biomass availabilities) have been characterized using their respective average values. Note that the presented model is general enough to address other case studies and other regions by adequately characterizing the cost, environmental, and availability parameters. Demand values and their sources are given in Section S3 in the Supporting Information. The available marginal land of the EU is set as aml = 23 Mha (millions of hectares),50 while the biomass production yield values (bpyi) and their sources are provided in Section S4 of the Supporting Information. In this study, we only consider marginal land to avoid potential competition between the current and the novel potential uses of biomass proposed here. Nevertheless, it would be fairly simple to include factors to account for current biomass production practices and existing biomass requirement constraints to study, for example, the potential competition

(9)

The final stage of the LCA methodology aims to evaluate the results obtained, in this case by analyzing together environmental and economic results as described in the ensuing Results and Discussion section. 10575


Research Article


For comparison purposes, we also defined the business-asusual (BAU) scenario, in which all demands are satisfied by conventional means (i.e., ethylene from naphtha and electricity based on the EU averaged electricity mix56). The transport fuel requirements in this BAU scenario are assumed to be fully satisfied with conventionally produced diesel (76%) and gasoline (24%).57 Under these assumptions, it is possible to estimate the total cost (TC = $573 billion) and total environmental impacts in the BAU scenario (e.g., climate change impact of 3.48 Gt CO2eq, which corresponds to roughly 80% of the total GHG emissions in the EU58).

between biomass conversion into energy and chemicals and food products. For forestry residues, the maximum availability (pMaxi) for both hard and soft wood is assumed to be 8.82 and 9.92 MMT,51 respectively. Corn stover is assumed to be an agricultural waste, hence being produced by corn either cultivated in marginal land or for other uses. Only 50% of the corn stover generated in the harvest of corn from marginal land is usable (i.e., αii′ = 0.5 kg corn stover/kg corn), as this is the common practice to avoid soil carbon loss.51 Note that additional biomass residue constraints could be included to evaluate and minimize some indirect negative effects of agricultural residue utilization such as wind and water soil erosion, plant nutrient balance, soil water and temperature dynamics, soil compaction, and off-site environmental impacts.52 The maximum availability of corn stover from corn for other uses in the EU is set to βi = 28.9 MMT.53 The purchasing cost values (pci) are provided in Section S5 of the Supporting Information. Most feedstocks (i.e., auxiliary, conventional, and biomass) are given purchasing costs, while a zero cost is assumed for biomass residues (i.e., corn stover and hard and soft wood). Regarding the environmental assessment, the ReCiPe200824 values of the impacts embodied in the feedstocks (pIik) were retrieved from SimaPro v847 accessing the databases Ecoinvent 3.344 and USLCI45,46 (see Section S6 of the Supporting Information for further details). Similarly to the purchasing cost parameters, corn stover and hard and soft wood biomass residues were assumed to have no negative environmental impacts embodied, only accounting for the respective amount of carbon sequestrated during their growth. This modeling approach was adopted in other works that consider both cultivated and residue biomass resources,54 where all negative environmental impacts and costs were allocated to the main products (e.g., corn or marketable wood) instead of to their residues. The process impacts, on the other hand, were obtained as follows. For the processes modeled in the network in Kim et al. (2013),19 the impact generated directly from their operation is unknown. Nevertheless, energy consumption often represents the main contribution to this impact. Therefore, we assumed that the impacts of these processes can be approximated by the impact due to the consumption of energy utilities, setting the process operation impact contribution to zero (i.e., eIjk = 0) for them. The energy utilities consumed in these processes include steam heating, electricity, or furnace heating, for which the impact contributions (uIjk) were taken from SimaPro v847 (database Ecoinvent 3.344). The same environmental approach was used to characterize the production of ethylene from ethanol. The BECCS and fuel combustion technologies added to the network do not consume energy utilities (i.e., uj = 0; note that additional energy requirements in technologies like BECCS have been already discounted from their energy output), but their emissions play a significant role in the environmental profiles; these are modeled via the process operation impact contribution parameter (eIjk). BECCS impact contributions were estimated using SimaPro v8,47 calculating the characteristic emissions for all biomass types.55 On the other hand, the emission profiles of the combustion for the different fuels were obtained from the GREET software,36 using SimaPro v8 to calculate their environmental contribution.

■

RESULTS AND DISCUSSION The optimization model M1-ε was implemented in the General Algebraic Modeling System (GAMS)59 software version 24.9.2 and solved with the solver CPLEX 12.7.1.0 using an Intel Core i5-4570 3.20 GHz computer. The model features 393 equations and 5479 continuous variables, taking 0.07 CPU seconds to compute each Pareto solution. Optimal Strategies for Particular Midpoint Impacts. We start by solving M1-ε considering the total cost versus each of the 18 midpoint indicators at a time, generating 10 Pareto optimal solutions in each case. Due to space limitations, here we only display the results for climate change, while the remaining 17 midpoint results (including relevant indicators for biomass utilization such as water depletion) are analyzed in Section S7 in the Supporting Information. Figure 4a depicts the trade-off between total costs and climate change. Taking the BAU values as reference, in which ethylene, fuels, and electricity are generated with conventional technologies, we found three solutions S7, S8, and S9 (depicted in green) representing win−win scenarios in which both the economic and environmental performance are simultaneously improved. Overall, the climate change impact could be reduced up to 52% (from 3.48 Gt CO2eq in the BAU, to 1.68 Gt CO2eq in solution S1 in Figure 4a) at the expense of increasing cost by 49% (from $573 billion in BAU to $852 billion in solution S1). In contrast, costs could be reduced inasmuch as 9% (from $573 billion in the BAU, to $522 billion in solution S10 in Figure 3a), at the expense of marginally increasing the impact by 2% (from 3.48 Gt CO2eq in BAU to 3.54 Gt CO2eq in solution S10). To get further insight into the results, in Figure 4 we show a breakdown of the demands of ethylene, electricity, and transport fuels according to their source (see subplots in Figure 4b−d, respectively), using the allocation method described in Section S8 in the Supporting Information. As observed, ethylene demand is satisfied fully by conventional ethylene in all solutions. For electricity, lower impact values are achieved as the share of biomass in the mix grows, with solutions S1−S9 generating electricity mainly from Miscanthus via BECCS, and to a lower extent from other biomass types, i.e., sugar cane (solution S1), hard wood (solutions S1−S6), soft wood (solutions S1−S4), and corn stover (solutions S1−S10). Taking solution S2 as an example, BECCS from Miscanthus covers 45% of the total electricity demand (i.e., 1254 TW h, equivalent to the combined electricity demands of Germany, France, Italy, Poland, and Sweden), with an additional 3% provided by BECCS using other biomass feedstocks. On the other hand, the transport fuels demand is marginally covered by biomass (up to 10% in S1), using either sugar cane to produce ethanol to be blended with conventional gasoline 10576


Research Article


Figure 5. Usage of the available crop and residue biomass feedstocks for the climate change Pareto solutions.

marginal land and selected residues are considered in the analysis. As can be observed in the subplot (Figure 5a), impact in climate change (e.g., solutions S1−S6) is reduced by increasing the marginal land dedicated to energy crops (i.e., Miscanthus) as well as other cultivated biomass feedstocks (i.e., soybean and sugar cane). Moreover, as we move toward low-cost solutions, more land is devoted to soybean, reaching 73% of the total available land in the minimum cost solution (S10), which highlights the economic potential of soybean in the EU scenario. Subplots in Figure 5b−d show that biomass residues are often used to their full extent, as they are assigned no cost and have zero impacts embodied. Only corn stover is not selected in the minimal cost solution (i.e., solution S10), indicating that the cost associated with its processing is not competitive against conventional alternatives. Nevertheless, biomass residues seem a good option to satisfy product and energy demands at lower costs and environmental impacts. Note that all solutions in Figure 4 are equally optimal, being equally valuable for policy-makers. Each Pareto point entails a particular biomass network configuration, representing an optimal alternative for the best utilization of biomass for a given unique combination of cost and environmental profile. The choice of the preferred solution should be made considering the decision-maker’s criteria and, preferably, using multicriteria decision-making tools to guide the selection.60 Figure 6 illustrates the optimal biomass network corresponding to the minimum climate change solution (solution S1), where sugar cane is employed to produce bioethanol, which is blended with conventional gasoline (25% v/v) to cover 38% of

Figure 4. Optimization results for the minimization of total cost (TC) and the climate change midpoint indicator for the satisfaction of 2016 set demands in the EU. (a) Pareto curve of the optimal values, where the current EU BAU is represented by a red circle. (b−d) Biomass feedstock contribution toward the satisfaction of ethylene, electricity, and transport fuel demands, respectively, for solutions S1−S10.

(25% v/v) (solution S1) or soybean and woody biomass residues to produce biodiesel (solutions S4−S10). Moving toward lower costs, we observe an increase in the use of soybean, which shows its economic potential to compete with conventional fuels; as an example, the minimum cost solution (S10) satisfies 9% of the transport demand from soybean. Figure 5 shows, for each Pareto solution, the potential use of marginal land availability for crop biomass (Figure 5a) as well as the use of the available waste biomass (i.e., corn stover, Figure 5b; hard wood, Figure 5c; and soft wood, Figure 5d). Note that in solutions S1−S6 (lower impact values), although all available marginal land and residues are exhausted, there is not enough biomass to meet the full demand of ethylene, electricity, and fuels in any single year. This highlights the shortage of local EU biomass to fully cover the ethylene, electricity, and transport fuel demands simultaneously. Furthermore, our results show that regional availability constraints for biomass feedstocks are critical for a proper modeling of biomass utilization, as otherwise unrealistic solutions that overestimate the true potential of biomass resources might be produced. We also stress that our estimates for the potential of biomass in EU are conservative, since only 10577


Research Article


Figure 6. Optimal biomass utilization for minimum climate change impact.

Figure 7. Multiobjective (3 endpoints and total cost) optimization results. (a) Pareto surface of the optimal points and the BAU point. Pareto surface projections on (b) the HH and RA dimensions, (c) the HH and ED dimensions, and (d) the ED and RA dimensions. Contribution of biomass to the satisfaction of (e) ethylene demand, (f) electricity demand, and (g) transport fuel demand in the reference Pareto points. Points A− D represent the extreme solutions for the TC, HH, ED, and RA objectives, respectively. Solution E represents the intermediate solution of the extreme points.

related with toxicity such as marine ecotoxicity. Miscanthus, on the other hand, appears as the most convenient biomass resource for the production of electricity, often appearing in low environmental impact solutions for most of the midpoint environmental indicators considered (e.g., climate change). One of the few consistent results for all the considered midpoints is the increasing utilization of soybean for the production of fuels for transport in the lower costs solutions, indicating the potential of this resource to generate economically viable fuel alternatives. This particular behavior is also responsible for generating practically flat Pareto regions for some midpoint indicators, e.g., agricultural land occupation or fresh water ecotoxicity, as it leads to marginal cost savings while significantly worsening the environmental performance. In a similar fashion to soybean, biomass residues (i.e., corn stover and hard and soft wood resources) are often exhausted due to their zero impact embodied. Nevertheless, corn stover is always disregarded in the minimum cost solution for all the midpoints results, indicating that it is not competitive against conventional products. The selection of technologies obtained for the different midpoints follows a similar behavior to that of the biomass

the transportation fuel demand. Furthermore, the bagasse from sugar cane together with Miscanthus and other biomass residues (corn stover, hard wood, and soft wood) are fed to the BECCS technology for electricity generation. In addition, the ethylene demand is completely satisfied by conventional steam-cracking of naphtha. While the results here displayed only consider the climate change midpoint indicator, the optimization solutions obtained for other midpoint indicators present similar behaviors regarding the types of biomass being selected or technologies used. Nevertheless, the cultivated biomass production and utilization show quite different profiles depending on the particular midpoint indicator analyzed, as some crops have significantly better environmental performance over others in either their production or utilization phases. For example, sugar cane and corn are rarely used consistently, the former being generally present only in the minimum environmental impact solutions (as can be seen in Figure 5 for the climate change midpoint) and the latter being used as a transition crop between environmentally and economically appealing crops. Switchgrass presents an intermediate behavior, with a more significant production when optimizing midpoints more 10578


Research Article


Figure 8. Optimal biomass utilization for the reference solution E.

Additionally, the BAU scenario is clearly dominated by all optimal solutions, with potential improvements of up to 9% in TC, 46% in HH, 52% in ED, and 22% in RA. Notably, the BAU scenario clearly shows the worst performance in damage to resources availability (see the subplot in Figure 7b), again highlighting the potential benefits of replacing conventional technologies by others based on biomass utilization. The Pareto points in Figure 7a can be roughly grouped into two subregions: a narrow region (R1) with lower TC values (between A and D) and a wider and more disperse region (R2) with higher TC values (between B, C, and D). These two regions differ in the use of biomass, with solutions in region R1 consuming soybean (alongside hard and soft woods and corn stover) to produce biogasoline and biodiesel without using any blend with bioethanol (see the breakdown of solutions A and E in Figure 7g). In contrast, the transport fuel demand for solutions in region R2 (see solutions B−D) is satisfied up to 11% through the blending of conventional gasoline and diesel with bioethanol from sugar cane, discarding the use of soybean entirely. In all solutions, ethylene is entirely produced from conventional petrochemical processes, while all the biomass is mainly employed to generate electricity. In particular, Miscanthus and biomass residues (hard wood, soft wood, and corn stover) are used to satisfy up to 48% of the total electricity demand in B. Notice how solutions C and D present a similar biomass contribution to all product demands while significantly differing in terms of endpoint indicators and cost (e.g., $852 billion versus $659 billion total cost, respectively). This discrepancy originates from the different ways in which they handle the fuel demand: solution C makes use of conventional gasoline and blending with bioethanol (25% v/v), while D makes use mostly of conventional diesel blend (10% v/v) and a minor fraction of conventional gasoline blend (85% v/v). The greater use of gasoline in solution C explains the higher TC and RA and lower HH and ED objective values compared to solution D, which mainly relies on bioethanol for blending and diesel. The selection of biomass feedstocks and processes for the EU varies across the optimal Pareto points. For illustrative purposes, Figure 8 displays the production strategy identified in the reference solution E. Due to its balanced performance, solution E includes technologies present in most solutions, ultimately providing a more versatile production strategy. In essence, solution E fully satisfies ethylene demand through conventional production (steam-cracking of naphtha) while the electricity demand is met by BECCS of Miscanthus and hard wood residues, as in extreme solutions B−D. The remaining hard wood and soft wood residues are converted

resources, some being consistently selected and the selection of others depending very much on the particular midpoint. For example, the pathway followed for the production of fuels for transport depends heavily on the midpoint indicator, displaying different combinations of conventional and biomass-based gasoline and diesel as well as their respective blends with ethanol. In contrast, the production of electricity is generally carried out by BECCS technologies (mostly using Miscanthus). On the opposite side, ethylene demand is exclusively met by conventional steam-cracking of naphtha for all midpoints, indicating that the dehydration of bioethanol is not economically (nor environmentally) competitive when compared to conventional ethylene production technologies and other biomass uses. While these are some general remarks, a more detailed description of the results for all the remaining 17 midpoint indicators can be found in Section S7 in the Supporting Information. Optimal Strategies for Endpoint Impacts. We simultaneously minimize next the total cost (TC) and the three endpoint categories: damage to human health (HH), to ecosystems diversity (ED), and to resources availability (RA). This is done by solving model M1-ε for 4000 Monte Carlo samples, each entailing a different combination of ε values within their lower and upper bounds, previously obtained from the calculation of the extreme solutions (i.e., optimization of the 4 individual objectives separately). Only 3520 of the 4000 samples generated were feasible (see Figure 7), where each of the axes of the 3D subplot (Figure 7a) corresponds to one environmental objective, while the solutions’ color indicates the total cost (i.e., darker color implies lower cost). To clearly display the inherent trade-offs between objectives, we show in subplots in Figure 7b−d the solutions projected onto different 2D spaces. To facilitate the discussion, we consider a set of reference extreme solutions A−E on the Pareto surface, corresponding to the minimum cost solution (A), minimum HH (B), minimum ED (C), minimum RA (D), and an intermediate solution (E) that corresponds to the solution with smallest Euclidean distance to the average of the extreme Pareto solutions A−D. Additionally, subplots in Figure 7e−g show the biomass resources allocated to ethylene, electricity, and transport fuels for reference solutions A−E. Figure 7 illustrates the trade-off between the economic and environmental objectives. As observed, burden shifting takes place in some points in the subplot in Figure 7b, with impact on human health (HH) decreasing at the expense of worsening resources availability (RA). This highlights the need for holistic environmental assessments covering more than one impact metric to adequately assess the sustainability level of biomass utilization. 10579


Research Article


impacts, biomass resources availability, and ethylene, electricity, and transportation fuels demands. Our results suggest that significant savings in both costs and environmental impacts can be attained by a proper utilization of the biomass resources available. In particular, we identified optimal strategies considering midpoint and endpoint environmental indicators. We found that climate change could be reduced by 52% with respect to 2016 levels at the expense of increasing the cost by 49%. These results highlight the key role that biomass can play to achieve the EU environmental commitments. In most of these strategies, the EU biomass resources were mainly used to cover a fraction of the fuels for transport and electricity demands, with ethylene demands being covered almost exclusively through conventional means (i.e., derived from naphtha). Our results also show that local EU biomass is insufficient to cover the ethylene, electricity, and transport fuels demands, requiring additional renewable resources or biomass imports for their full renewable production. Meanwhile, the environmental impact can be significantly reduced by allocating energy crops cultivated in marginal land and agroforestry residues to the production of electricity and biofuel rather than bioethylene. The technologies and production means necessary for these improvements, however, may not be mature enough, having to rely on more feasible but less advantageous solutions. For instance, electricity production from BECCS requires biomass supply chains that are not yet in place, while biofuel production is mature enough to be deployed at larger scales. In summary, our framework is intended to support decisionand policy-makers in the transition toward a more sustainable future by systematically selecting the best uses of biomass among competing routes. The proposed modeling framework can be applied to inform regional and national policy decisions by providing valuable insight into how to optimally use biomass.

into methanol via syngas from direct gasification. This methanol is then used in the transesterification of triglycerides from soybean to obtain both biogasoline and biodiesel, complemented with conventional diesel to cover the EU transport fuel demand. Uncertainty Analysis for Midpoint Impacts Optimal Strategies. The model here presented contains a large number of parameters needed to properly characterize regional requirements and limitations (e.g., product demands, biomass availabilities, purchasing prices, embodied impact values, etc.) as well as the biomass process network and its performance (e.g., mass yields, technology conversion costs, technology emission impacts, etc.). These parameters, unfortunately, often present some degree of uncertainty due to several factors (e.g., temporal variability, unknown behavior, incomplete information, etc.). Therefore, it is convenient to assess the effects of these uncertainties to evaluate the validity and robustness of the results. As in this work we focus on evaluating the environmental performance of biomass use, the emphasis is placed on the modeling of uncertainties associated with environmental parameters (i.e., pIik, eIjk, and uIjk). These were handled using SimaPro v8,47 assuming that the life cycle inventory entries could be modeled via the Pedigree matrix61 with the default values available in the aforementioned software. To compute confidence intervals for the midpoint indicators, we therefore fixed the optimal values of the flows in the biomass network found by the optimization algorithm (i.e., Pi and Wj), and recalculated the environmental performance (i.e., TIk) for a set of 1000 scenarios generated randomly in SimaPro. Note that each scenario entails a different value of the impact per unit of the reference system, e.g., amount of energy utility (uIjk), amount of raw material (pIik), etc. The specific uncertainty ranges calculated for the 18 midpoint indicators can be found in Section S9 in the Supporting Information. The effects of the environmental uncertainties vary quite significantly across midpoint indicators, with some indicators displaying low average standard deviations (e.g., 6.52% for the climate change indicator) and others presenting very large deviations (e.g., 124% for ionizing radiation indicator). Highly uncertain midpoint results (i.e., ionizing radiation or human toxicity) are often driven by emissions with high uncertainty (e.g., radioactive elements or heavy metals). On the other hand, the diversity of agricultural practices causes a notorious variability in the land use impact, thereby leading to large natural land transformation midpoint uncertainties

■

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssuschemeng.9b01115. Biomass process network products and processes, additional processes details, final products demands, biomass feedstocks production yields, feedstock prices, environmental data references, midpoint Pareto results, biomass allocation methodology, and midpoint Pareto uncertainty analysis (PDF)

■

CONCLUSION Biomass-derived products will play a major role in the transition toward a more sustainable economy by reducing our dependence on fossil resources. In this regard, prioritizing the best use of biomass is crucial so as to identify the most appropriate strategies to protect our planet while maintaining our economic growth. In this work, we presented a multiobjective optimization framework that identifies the best uses of biomass while accounting for both economic and environmental objectives simultaneously. The biomass network here optimized encompasses eight different biomass feedstocks, 224 conversion technologies, and 167 products, all together yielding thousands of potential alternative ways to meet the demand. This approach was applied to the European Union (EU), contextualized with region-specific costs, environmental

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Miao Guo: 0000-0001-7733-5077 Gonzalo Guillén-Gosálbez: 0000-0001-6074-8473 Author Contributions

All authors contributed equally. Notes

The authors declare no competing financial interest. 10580


Research Article


■

(17) You, F.; Tao, L.; Graziano, D. J.; Snyder, S. W. Optimal Design of Sustainable Cellulosic Biofuel Supply Chains: Multiobjective Optimization Coupled with Life Cycle Assessment and Input-Output Analysis. AIChE J. 2012, 58 (4), 1157−1180. (18) Gong, J.; Garcia, D. J.; You, F. Unraveling Optimal Biomass Processing Routes from Bioconversion Product and Process Networks under Uncertainty: An Adaptive Robust Optimization Approach. ACS Sustainable Chem. Eng. 2016, 4 (6), 3160−3173. (19) Kim, J.; Sen, S. M.; Maravelias, C. T. An Optimization-Based Assessment Framework for Biomass-to-Fuel Conversion Strategies. Energy Environ. Sci. 2013, 6 (4), 1093. (20) Kö nig, A.; Ulonska, K.; Mitsos, A.; Viell, J. Optimal Applications and Combinations of Renewable Fuel Production from Biomass and Electricity. Energy Fuels 2019, 33, 1659. (21) Daoutidis, P.; Marvin, W. A.; Rangarajan, S.; Torres, A. I. Engineering Biomass Conversion Processes: A Systems Perspective. AIChE J. 2013, 59 (1), 3−18. (22) Lewandowski, I.; Clifton-Brown, J.; Trindade, L. M.; Van der Linden, G. C.; Schwarz, K.-U.; Müller-Sämann, K.; Anisimov, A.; Chen, C.-L.; Dolstra, O.; Donnison, I. S.; et al. Progress on Optimizing Miscanthus Biomass Production for the European Bioeconomy: Results of the EU FP7 Project OPTIMISC. Front. Plant Sci. 2016, 7, 1620. (23) Attard, T. M.; Mcelroy, C. R.; Gammons, R. J.; Slattery, J. M.; Supanchaiyamat, N.; Lessa, C.; Kamei, A.; Dolstra, O.; Trindade, L. M.; Bruce, N. C. Supercritical CO 2 Extraction as an Effective Pretreatment Step for Wax Extraction in a Miscanthus Biorefinery. ACS Sustainable Chem. Eng. 2016, 4, 5979. (24) Goedkoop, M.; Heijungs, R.; Huijbregts, M.; De Schryver, A.; Struijs, J.; Van Zelm, R. ReCiPe 2008 First Edition (Version 1.08) Report I: Characterisation; Ruimte en Milieu, 2013. (25) Parvatker, A. G.; Eckelman, M. J. Comparative Evaluation of Chemical Life Cycle Inventory Generation Methods and Implications for Life Cycle Assessment Results. ACS Sustainable Chem. Eng. 2019, 7, 350. (26) BP Statistical Review of World Energy 2018; BP: London, 2018. (27) Bogaert, S.; Pelkmans, L.; Van den Heuvel, E.; Devriendt, N.; De Regel, S.; Hoefnagels, R.; Junginger, M.; Resch, G.; Liebmann, L.; Mantau, U.; et al. Sustainable and Optimal Use of Biomass for Energy in the EU beyond 2020; EC, 2017. (28) How Much Biomass Can. Europe Use without Harming the Environment?; EEA, 2006; Vol. 7, ISSN 1725-9177. (29) Plasticsthe Facts 2017. An Analysis of European Plastics Production, Demand and Waste Data.; Plastics Europe: Wemmel, 2018. (30) Demirbaş, A. Biomass Resource Facilities and Biomass Conversion Processing for Fuels and Chemicals. Energy Convers. Manage. 2001, 42 (11), 1357−1378. (31) Yan, K.; Luo, H. Production of γ-Valerolactone from Biomass. Production of Platform Chemicals from Sustainable Resources 2017, 413−436. (32) European Chemical Industry CouncilCefic. European Market OverviewPetrochemicals Europe. https://www. petrochemistry.eu/about-petrochemistry/petrochemicals-facts-andfigures/european-market-overview/ (accessed Feb 21, 2019). (33) Oreggioni, G. D.; Singh, B.; Cherubini, F.; Guest, G.; Lausselet, C.; Luberti, M.; Ahn, H.; Strømman, A. H. Environmental Assessment of Biomass Gasification Combined Heat and Power Plants with Absorptive and Adsorptive Carbon Capture Units in Norway. Int. J. Greenhouse Gas Control 2017, 57, 162−172. (34) EU Transport in Figures; Publications Office of the EU, 2018. DOI: 10.2832/05477. (35) EurostatSummary of annual road freight transport by type of operation and type of transport. http://appsso.eurostat.ec.europa.eu/ nui/submitViewTableAction.do (accessed Feb 21, 2019). (36) Wang, M.; Wu, Y.; Elgowainy, A. Operating Manual for GREET: Version 1.7; Argonne National Laboratory: Chicago, 2007; p 154. (37) Zhang, M.; Yu, Y. Dehydration of Ethanol to Ethylene. Ind. Eng. Chem. Res. 2013, 52, 9505−9514.

ACKNOWLEDGMENTS G.G.-G. would like to acknowledge the financial support received from the Spanish Government (CTQ2016-77968-C31-P, MINECO/FEDER, UE).

■

REFERENCES

(1) World Energy Balances 2018. International Energy Agency IEA, 2018. (2) United Nations Sustainable Development Knowledge Platform. https://sustainabledevelopment.un.org/sdgs# (accessed Nov 24, 2018). (3) Albert, J.; Jess, A.; Kern, C.; PoÖ , F.; Glowienka, K.; Wasserscheid, P. Formic Acid-Based Fischer−Tropsch Synthesis for Green Fuel Production from Wet Waste Biomass and Renewable Excess Energy 2016. DOI: 10.1021/acssuschemeng.6b01531. (4) Yang, M.; Tian, X.; You, F. Manufacturing Ethylene from Wet Shale Gas and Biomass: Comparative Technoeconomic Analysis and Environmental Life Cycle Assessment. Ind. Eng. Chem. Res. 2018, 57, 5980. (5) Van Uytvanck, P. P.; Hallmark, B.; Haire, G.; Marshall, P. J.; Dennis, J. S. Impact of Biomass on Industry: Using Ethylene Derived from Bioethanol within the Polyester Value Chain. ACS Sustainable Chem. Eng. 2014, 2 (5), 1098−1105. (6) Cormos, C.-C. Hydrogen and Power Co-Generation Based on Coal and Biomass/Solid Wastes Co-Gasification with Carbon Capture and Storage. Int. J. Hydrogen Energy 2012, 37, 5637−5648. (7) Tang, Y.; You, F. Multicriteria Environmental and Economic Analysis of Municipal Solid Waste Incineration Power Plant with Carbon Capture and Separation from the Life-Cycle Perspective. ACS Sustainable Chem. Eng. 2018, 16, 3. (8) Gutiérrez-Arriaga, C. G.; Serna-González, M.; Ponce-Ortega, J. M.; El-Halwagi, M. M. Sustainable Integration of Algal Biodiesel Production with Steam Electric Power Plants for Greenhouse Gas Mitigation. ACS Sustainable Chem. Eng. 2014, 2 (6), 1388−1403. (9) Bazilian, M.; Rogner, H.; Howells, M.; Hermann, S.; Arent, D.; Gielen, D.; Steduto, P.; Mueller, A.; Komor, P.; Tol, R. S. J.; et al. Considering the Energy, Water and Food Nexus: Towards an Integrated Modelling Approach. Energy Policy 2011, 39 (12), 7896− 7906. (10) Wright, M. M.; Daugaard, D. E.; Satrio, J. A.; Brown, R. C. Techno-Economic Analysis of Biomass Fast Pyrolysis to Transportation Fuels. Fuel 2010, 89, S2−S10. (11) Li, W.; Ghosh, A.; Bbosa, D.; Brown, R.; Wright, M. M. Comparative Techno-Economic, Uncertainty and Life Cycle Analysis of Lignocellulosic Biomass Solvent Liquefaction and Sugar Fermentation to Ethanol. ACS Sustainable Chem. Eng. 2018, 6, 16515. (12) Cherubini, F. The Biorefinery Concept: Using Biomass Instead of Oil for Producing Energy and Chemicals. Energy Convers. Manage. 2010, 51 (7), 1412−1421. (13) Villegas Calvo, M.; Colombo, B.; Corno, L.; Eisele, G.; Cosentino, C.; Papa, G.; Scaglia, B.; Pilu, R.; Simmons, B.; Adani, F. Bioconversion of Giant Cane for Integrated Production of Biohydrogen, Carboxylic Acids, and Polyhydroxyalkanoates (PHAs) in a Multistage Biorefinery Approach. ACS Sustainable Chem. Eng. 2018, 6, 15361. (14) Cheali, P.; Gernaey, K. V.; Sin, G. Toward a Computer-Aided Synthesis and Design of Biorefinery Networks: Data Collection and Management Using a Generic Modeling Approach. ACS Sustainable Chem. Eng. 2014, 2 (1), 19−29. (15) Tsakalova, M.; Lin, T. C.; Yang, A.; Kokossis, A. C. A Decision Support Environment for the High-Throughput Model-Based Screening and Integration of Biomass Processing Paths. Ind. Crops Prod. 2015, 75, 103−113. (16) Karka, P.; Papadokonstantakis, S.; Kokossis, A. Cradle-to-Gate Assessment of Environmental Impacts for a Broad Set of Biomass-toProduct Process Chains. Int. J. Life Cycle Assess. 2017, 22 (9), 1418− 1440. 10581


Research Article

ACS Sustainable Chemistry & Engineering (38) Zacharopoulou, V.; Lemonidou, A. Olefins from Biomass Intermediates: A Review. Catalysts 2018, 8 (1), 2. (39) Stadtherr, M. A.; Rudd, D. F. Systems Study of the Petrochemical Industry. Chem. Eng. Sci. 1976, 31 (11), 1019−1028. (40) Zhang, M.; Yu, Y. Dehydration of Ethanol to Ethylene. Ind. Eng. Chem. Res. 2013, 52, 9505−9514. (41) ISO. ISO 14040. Euro code SS-EN-1191−2, No. 148902, 2006. (42) Bare, J. C.; Hofstetter, P.; Pennington, D. W.; De Haes, H. A. U. Midpoints versus Endpoints: The Sacrifices and Benefits. Int. J. Life Cycle Assess. 2000, 5 (6), 319−326. (43) Earles, J. M.; Halog, A. Consequential Life Cycle Assessment: A Review. Int. J. Life Cycle Assess. 2011, 16 (5), 445−453. (44) Swiss Centre For Life Cycle Inventories, 2017. Ecoinvent Data V3.3. Ecoinvent Cent. Ecoinvent 3.3. https://www.ecoinvent.org/ home.html (accessed May 20, 2017). (45) The National Renewable Energy Laboratory; U.S. Department of Energy. U.S. Life Cycle Inventory Database: NREL. https://www. nrel.gov/lci/ (accessed Nov 24, 2018). (46) Cooper, J. S.; Noon, M.; Kahn, E. Parameterization in Life Cycle Assessment Inventory Data: Review of Current Use and the Representation of Uncertainty. Int. J. Life Cycle Assess. 2012, 17 (6), 689−695. (47) SimaPro Database Manual; Pre’ Consultants, 2014; pp 1−48. (48) Cohon, J. L.; Marks, D. H. Multiobjective Programming and Planning; Academic Press, 1978; Vol. 140. DOI: 10.1016/S00765392(08)60912-1. (49) Copado-Méndez, P. J.; Pozo, C.; Guillén-Gosálbez, G.; Jiménez, L. Enhancing the ϵ-Constraint Method through the Use of Objective Reduction and Random Sequences: Application to Environmental Problems. Comput. Chem. Eng. 2016, 87, 36−48. (50) Cai, X.; Zhang, X.; Wang, D. Land Availability for Biofuel Production. Environ. Sci. Technol. 2011, 45 (1), 334−339. (51) Elbersen, B.; Startisky, I.; Hengeveld, G.; Schelhaas, M.-J.; Naeff, H. Atlas of EU Biomass Potentials; Biomass Futures, 2012. (52) Wilhelm, W. W.; Hess, J. R.; Karlen, D. L.; Johnson, J. M. F.; Muth, D. J.; Baker, J. M.; Gollany, H. T.; Novak, J. M.; Stott, D. E.; Varvel, G. E. REVIEW: Balancing Limiting Factors & Economic Drivers for Sustainable Midwestern US Agricultural Residue Feedstock Supplies. Ind. Biotechnol. 2010, 6 (5), 271−287. (53) Monfreda, C.; Ramankutty, N.; Foley, J. A. Farming the Planet: 2. Geographic Distribution of Crop Areas, Yields, Physiological Types, and Net Primary Production in the Year 2000. Global Biogeochem. Cycles. 2008, 22 (1), 1−19. (54) Fajardy, M.; Mac Dowell, N. Can BECCS Deliver Sustainable and Resource Efficient Negative Emissions? Energy Environ. Sci. 2017, 10 (6), 1389−1426. (55) Oreggioni, G. D.; Brandani, S.; Luberti, M.; Baykan, Y.; Friedrich, D.; Ahn, H. CO2 Capture from Syngas by an Adsorption Process at a Biomass Gasification CHP Plant: Its Comparison with Amine-Based CO2 Capture. Int. J. Greenhouse Gas Control 2015, 35, 71−81. (56) Indicator Assessment: Data and Maps Overview of Electricity Production and Use in Europe; EPA, 2016. (57) Cooper, J. Statistical Report 2017; Fuels Europe: Brussels, 2017. (58) Total Greenhouse Gas Emission Trends and Projections; European Environment Agency: Copenhagen, 2018. (59) Rosenthal, R. GAMS-A User’s Guide; GAMS Development Corporation, 2017. (60) Figueira, J.; Greco, S.; Ehrgott, M. Multiple Criteria Decision Analysiss: State of the Art Surveys; Springer, 2005. (61) Lewandowska, A.; Foltynowicz, Z.; Podlesny, A. Comparative Lca of Industrial Objects Part 1: Lca Data Quality Assurance  Sensitivity Analysis and Pedigree Matrix. Int. J. Life Cycle Assess. 2004, 9 (2), 86−89.

10582


Biomass Conversion into Fuels, Chemicals, or Electricity? A

Recommend Documents