sanchez et al-2014-biofuels, bioproducts and biorefining

Correspondence to: Arturo Sanchez

E-mail address: [email protected]

670 2014 Society of Chemical Industry and John Wiley & Sons, Ltd

Modeling and Analysis

Bidimensional sustainability analysis of lignocellulosic ethanol production processes. Method and case study Arturo Sanchez, Gabriela Magaa, Diego Gomez, Mario Sols, Centro de Investigacion y de Estudios Avanzados del IPN, Unidad Guadalajara de Ingenieria Avanzada, Zapopan 45019, Jalisco, MexicoRene Banares-Alc antara, Dept. of Engineering Science (Parks Road), University of Oxford, Oxford OX1 3PJ, United Kingdom

Received October 2, 2013; revised June 12, 2014; accepted June 13, 2014View online August 1, 2014 at Wiley Online Library (wileyonlinelibrary.com); DOI: 10.1002/bbb.1512; Biofuels, Bioprod. Bioref. 8:670685 (2014)

Abstract: A method is proposed to analyze the environmental and economic sustainability of biore-fi neries producing lignocellulosic ethanol. The bidimensional method builds on the conceptual design of the production facility (mathematical models of mass and energy balances of each process stage, equipment sizing, and capital costing models) thus enabling the analysis of prospective technologies. The conceptual design articulates the relations among process stages as impact generators (IIG), stakeholders (and their concerns) as impact receivers (EIR), and indicators to measure the environ-mental and economic impacts of the process facility. Indicators are formulated as functions of process variables and parameters. Overall impacts for each domain are calculated by weighting the indicators using appropriate dimensional functions and scaling factors. Using the proposed method, the con-ceptual design of a standard biochemical biorefi nery provides the basis for building a sustainability framework comprising 15 indicators. While some of the obtained indicators are ubiquitous in the lit-erature, other less common yet important are identifi ed. A case study illustrates the use of the sustain-ability framework. A single-product biorefi nery is compared against a multi-product scheme, showing that single-product schemes may prove, under certain conditions, more sustainable than their multi- product counterparts. The sustainability framework provides the rationale to clearly identify the causal-ity from IIG to metric values. This information may be used to support the decisions regarding possible improvements of IIG or modifi cations of weighting parameters in the dimensional functions in order improve the sustainability of the processes under consideration. 2014 Society of Chemical Industry and John Wiley & Sons, Ltd

Keywords: lignocellulosic bioethanol production; sustainability analysis; process modeling; biorefi neries

2014 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 8:670685 (2014); DOI: 10.1002/bbb 671

Modeling and Analysis: Bidimensional sustainability analysis of lignocellulosic ethanol production processes A. Sanchez

ReCiPe, OpenLCA). However, LCA studies usually focus on greenhouse gas (GHG) and net-energy balances impact on the environment and results are highly dependent on the available information, the assumptions made (e.g. for emission or co-product credits) and the type of framework employed for their execution.10,11,16,23 Th erefore, although the outcomes of LCA provide valuable insights into potential environmental eff ects, the underlying uncer-tainties prevent the production of defi nitive quantitative results.18,20,24

With respect to the impact of biofuels on society, other factors related to economic, social, and geo-political issues have also been considered, to a lesser extent, in sustainabil-ity analyses as part of the decision-making process in the implementation of biofuels projects or programs.12,14,16,20 With regard to lignocellulosic bioethanol, since there are no commercial production plants currently in operation, only pilot and demonstration facilities, the sustainability analyses of the production stage in most published studies rely on data diffi cult to trace or that may not be consistent with large-scale production conditions.16,17

Th is work provides a method and its associated tools for the prospective analysis of environmental and economic impacts of lignocellulosic bioethanol production. Based on a process engineering approach2527 and employing math-ematical models, this method encompasses in a systematic and consistent manner, the environmental and economic domains. Th e mathematical models of processes provide the theoretical platform to consider specifi c production technologies, even those not yet commercially available (e.g. lignocellulosic biorefi neries). Th e method uses the conceptual design27 of the process facility being analyzed (process fl ow diagrams, equipment information, eco-nomic data, mass and energy balances of process stages) along with the process analysis method (PAM),28 a solid and comprehensive method that provides a conceptual framework for impact analysis, to obtain a suitable set of quantitative indicators measuring the impacts of the proc-ess facility. Such indicators are formulated as functions of process (i.e., input, output, and state) variables and proc-ess (i.e., design, physical, and chemical) parameters. Th e process mathematical model provides the machinery for calculating indicator values. Th ese values can be normal-ized with respect to a base case to perform an indicator-by-indicator analysis. An overall impact is also calculated by weighting these indicators using dimensional functions and scaling factors that refl ect specifi c criteria.

Th e proposed method is presented, where the rela-tions among the mathematical models, the conceptual design and PAM, as well as the indicator analysis and the

Introductio n

Liquid biofuels are currently playing a very impor-tant role in the transportation sector worldwide since they are considered short-term substitutes for

fossil fuels. Over the past decade, research and commer-cial development of fi rst-generation liquid biofuels were strongly promoted by government mandates and public subsidies, until 2008, when the alimentary-energy crisis emerged. Th e feedstock shift to produce fi rst-generation bioethanol was identifi ed as an important cause of that crisis.1 Th enceforth, new government directives and strat-egies were developed promoting the use of second- and third-generation liquid biofuels (e.g. European Directive 2009/28/EC and a modifi cation proposal have suggested a 5% contribution from second-generation and third-generation biofuels to the transport sector by 20202). Some other factors, such as the impact of land-use change and the carbon debt associated with fi rst-generation biofuels production have encouraged the creation of market incen-tives to stimulate the production of second-generation and third-generation biofuels, aiming to achieve the cov-enanted percentage for 2020. Lignocellulosic materials as feedstock for biofuels production may play an important role in achieving these objetives.26

Th erefore, the understanding and assessment of the impact of liquid biofuels on the environment and soci-ety is receiving great attention. In particular, the need of well-structured techniques to assess those impacts has promoted the development of frameworks, methods, and tools capable of combining the environmental, economic, and social assessments. Most of the existing methods were created to support decision-making for macroeconomic policies, to consider production processes in very broad terms and to focus only on the environmental impacts.711 Just a few of the current methods consider the economic and social domains from an unifi ed perspective that can be applied for the evaluation of products or processes.1215 Furthermore, most methods are based on measurements with high levels of uncertainty due to the lack of agree-ment on data measurement and processing methods. Also, the signifi cant investment associated with soft ware development and data collection have proved to hamper the implementation of soft ware tools.12,1619 For an exten-sive review of the existing methods and tools for impacts evaluation see, for example, the Calcas Project.13

Regarding environmental analysis, life cycle assessment (LCA) is a well established method that has been applied to well-to-wheel analysis of liquid biofuels14,16,17,2022 using existing techniques and tools (e.g. SimaPro, Biograce,

672 2014 Society of Chemical Industry and John Wiley & Sons, Ltd | Biofuels, Bioprod. Bioref. 8:670685 (2014); DOI: 10.1002/bbb

A Sanchez et al. Modeling and Analysis: Bidimensional sustainability analysis of lignocellulosic ethanol production processes

Conceptual plant design

Th e conceptual design of a production plant comprises a process fl owsheet (a graphic representation of the process topology) and its corresponding mathematical models (mass and energy balances describing the phenomena that take place in and among process stages). An eco-nomic analysis considering equipment and operation costs is also an important element of a plants conceptual design.27

For the analysis method presented in this paper, most of the required information is provided by the conceptual design (additional necessary information may include economic, social and geo-political data; Fig. 1). For exam-ple, the defi nition of the process stages and the specifi c activities they perform will serve to identify the sources of the plants impacts. Also, every material and energy fl ow entering or leaving the system is calculated solving the mass and energy balances. Data regarding process (input, output, state) variables and design (physical chemical, operational) parameters are used to determine the magni-tude of the impacts.

weighting technique are explained. Th e analysis method and tools are then shaped to deal with biochemical biore-fi neries.29 In order to illustrate the methods application, a case study consisting of the comparison between two bio-chemical platforms for lignocellulosic ethanol production is presented. Finally, the highlighted aspects of the method and its application to the case study are discussed.

Sustainabil ity analysis method

Th e sustainability analysis method is composed of four main elements:

A conceptual plant design Application of the process analysis method (PAM)28 An indicator analysis A weighting process

Figure 1 shows a graphic representation of the sustain-ability analysis method. Rectangles represent stages to be executed, while rounded rectangles represent (required or generated) information.

Figure 1. Graphical representation of the sustainability analysis method.



in the fl owsheet. Inputs and outputs are paired with input or output process streams or process state vari-ables. Th eir values are calculated by solving the math-ematical models corresponding to the mass an energy balances.

Th e process stages constitute the process activities, and hence the IIG.

Mathematical models establish relations among the process stages (IIG) and third parties that can be iden-tifi ed as EIR. Some examples that apply to a biorefi nery are:

- Th e economic analysis considers investment capital for the calculation of total production costs (TPC). Th erefore, investors can be identifi ed as EIR since their economic profi t depends on the plant performance.

- TPC models also relate production costs to the prod-ucts sale price, which directly impacts on fuel con-sumers. Hence, consumers are identifi ed as EIR.

- From the transformation processes considered in the models, the composition of all output fl ows can be established. In the event of identifying any sub-stance over which specifi c disposal policies exist, both government and local population are consid-ered as EIR.

Process (i.e., input, output, and state) variables and process (design, operation, chemical, physical) param-eters considered in the models (such as fl ows, tempera-tures, concentrations, costs, etc.) serve to defi ne and calculate metrics. If required, complementary data from external sources (such as public databases, gov-ernment regulations, etc.) can be used.

Th e section on Steering the sustainability framework for evaluating biochemical biorefi neries presents the con-struction of the SF for a general biochemical biorefi nery.

Indicator an alysis

Th e indicator analysis consists of examining the results (metric values) in order to extract conclusions, and proves particularly useful when comparing two or more systems. In such cases, one of the systems being analyzed is chosen as a base case (on the basis of criteria such as its simplicity, its condition as a standard technology, or any other char-acteristic considered relevant for comparison purposes). Metric results are then normalized with respect to the base case and compared (see, for instance, Figs 5 and 6) in order to exhibit the relative impacts of the systems under analysis.

By considering both environmental and economic aspects, the analysis acquires a bidimensional nature and enables a broader understanding of the plants potential eff ects.

Application of the process analysis method

PAM, proposed by Tahir and Darton28 articulates the defi -nition of suitable indicators for a multidimensional sus-tainability analysis. PAM provides guidelines to identify process activities, inputs, and outputs as well as to estab-lish the impacts of the system in three domains: environ-mental, economic, and social. Th e social domain, however, is not considered in this work.

PAM steps (represented in Fig. 1 by the PAM rectangle) are summarized as follows:

1. Establish the boundaries of the system under consid-eration. Identify inputs, outputs and activities.

2. Screen the inputs and outputs to detect those related to potential impacts in any domain (e.g. fresh water extraction, release of combustion gases, electricity con-sumption). Trace the identifi ed fl ows back to the proc-ess activities generating the impacts. Th ese activities are termed internal impact generators (IIG).

3. Identify social groups or entities being aff ected by the IIG as external impact receivers (EIR). For instance, stakeholders receiving dividends, or a social group not being able to benefi t from a natural resource due to production activities.

4. Establish the possible concerns of the EIR regarding the impacts (e.g. air pollution, process performance, or resource usage). Th ese concerns, referred to as issues, constitute the dimensional groups with which the indi-cators will be classifi ed.

5. For each issue, create indicators to represent its associ-ated impacts (e.g. amount of GHG gases released to the atmosphere).

6. For each indicator, defi ne one or more quantifi ers (e.g. equivalent mass of CO2 released). Quantifi ers are called metrics.

Th e set constituted by the IIG, EIR, issues, indicators, and metrics is referred to as the sustainability framework (SF). Figure 1 shows the SF elements enclosed by a dashed box.

Th e conceptual plant design is used to identify several SF elements and to formalize relations among them employ-ing the following guidelines:

Th e system delimitation and the identifi cation of proc-ess activities follow the process topology as shown



the impact, in economic terms, caused by each megajoule that is produced by the process plant.

Th e main information needed to build the dimen-sional functions may fall into one of the three following categories:

Relates the metric to some product or service with economic value. For instance, GHG emissions can be expressed in terms of CO2-mass equivalents, which can be related to currency by means of carbon bonds, assigning a cost for each emitted mass unit.

Costs imposed by regulating bodies for resources exploitation or residues discharge. For example, the payment of duties may be required in order to extract fresh water from a certain water body.

Sometimes it may result as convenient (or necessary) to combine elements from both categories. In the case of a metric measuring the temperature of water discharges, for instance, a suitable dimensional function could calculate the price (market value) of the cooling water needed to lower the temperature of the discharges below the permitted value (norm). If the discharged water was already within the permitted temperature range, the function would yield a value of zero, mean-ing that no impact is caused in relation to this particu-lar metric.

Given the diversity of units in which the indicators are expressed, some of them may prove specially diffi cult to translate to common units (thus raising the need for dimensionless indicators, examples of which will be pre-sented later in this paper). For these cases, production costs, which are already expressed in the desired com-mon units, constitute a useful resource for defi ning the dimensional functions. If concerned with the validity or signifi cance of using production costs to transform a (not necessarily related) indicator, one should recall that the goal of the dimensional functions is to change all indica-tors to common units so they can be integrated into a sin-gle result. Th erefore, in cases in which a better alternative is not available, using production costs is not an arbitrary choice, but a logical and useful one.

Each indicator must be analyzed in light of these three categories to determine which of them may serve for defi ning the corresponding dimensional function. Th e information obtained for all indicators may be organized in a dimensional criteria matrix (Table 4) which is a n 3 matrix (where n denotes the number of metrics obtained using PAM, and the three columns correspond to the categories) that shows which categories apply to each spe-cifi c metric. Such an array constitutes a useful visual and

Indicator we ighting

Th e indicator analysis caters for a detailed evaluation of the results. However, in order to establish the sustainabil-ity of a specifi c scheme considering domains of a diff erent nature under incomplete information, a weighting process is necessary to integrate and unify the indicator results.30

Th e execution of the weighting process requires the defi nition of dimensional functions and scaling factors. As their name suggests, dimensional functions serve to translate all indicators into the same units so they can be added up later. Scaling factors are dimensionless numbers that multiply the indicators (already translated to com-mon units) and assign them diff erent relative importance. Th e result of the weighting process is highly dependent on the values of these functions and factors. Th erefore, their selection criteria should be carefully defi ned according to the specifi c objectives of the analysis being carried out.

Selection of dimensional functions

Th e fi rst step for defi ning the dimensional functions con-sists in the selection of the common units in which all indicators are to be expressed. Th is is not a trivial issue given the bidimensional character of the present method. Both the environmental and economic domains should be considered, and their associated impacts are of very diff er-ent nature.

Environmental indicators may be expressed in a variety of units, several of them related to the diff erent emissions and discharges of the system. For the economic indica-tors, currency functions seem the natural choice for the domains common units. Moreover, experience has shown that relating economic indicators to environmental units is a diffi cult and unclear task. Th e inverse process (i.e., expressing environmental impacts in economic terms), however, is somewhat easier. Several policies, regulations, and incentives associate an economic value (usually a cost) to the environmental performance of production facilities. Th us, currency-related units may be suitable to measure the global impact of the process. Nonetheless, since large capacity plants produce greater amounts of emissions and discharges and have greater production costs than small capacity counterparts, currency units by themselves may prove misleading as global impact quantifi ers. Th us, cur-rency per unit of produced energy (USD/MJout) are chosen in this paper as common units. Th e term MJout refers to the energy available in the products, i.e., the lower heating value (LHV) of the produced fuels plus the energy deliv-ered by any other generated service, such as steam or elec-tricity. Th e global sustainability indicator will thus refl ect



to identify the process stages with the largest impacts. Specifi c strategies to improve the performance of these particular stages can be devised in order to improve the sustainability of the process.

Th e indicators of a given domain can be added up, obtaining a net impact for the whole domain. At this level, it is possible to compare the contribution of each domain to the systems sustainability.

Th e net impacts of both domains can be summed to yield a global result for the entire process. On the basis of this global fi gure, it is possible to rank the systems under study according to their sustainability.

Steering t he sustainability framework for evaluating biochemical biorefi neries

In this section, PAM is employed for building an SF for a general biochemical biorefi nery. Figure 2 shows a block diagram of a biochemical-scheme biorefi nery for lignocel-lulosic biofuels production. Th e system is composed of six stages: In the pre-treatment stage, feedstock is conditioned

synthesizing tool to decide which (combination of) criteria to use for defi ning each dimensional function. Remember, however, that the common units are referred to one unit of produced energy. Th e additional necessary data (such as, evidently, the total produced energy) are provided by the mathematical models of the conceptual design.

Selection of scal ing factors

Th e selection of scaling factors is a complex task which is related to specifi c geopolitical and economic context of the production facility. Th is task is usually carried out by ad hoc expert panels.30 Being an important source of subjectivity, the selection criteria and the coeffi cient values employed for each particular analysis should be clearly stated.

Aft er the dimensional functions have been constructed and applied to the proposed indicators along with the scaling factors, important information can be extracted at three diff erent levels:

Since all indicators at this point are expressed in the same units, their magnitudes can be compared in order

SEPARATIONPRE-TREATMENT

Gases

Solid waste

SACCHARIFICATION FERMENTATION

COGENERATIONWASTE-WATER

TREATMENT

CO2

EtOH

H2O

Solid waste

Electricity

Gases

CO2

H2O

H2O

Feedstock

H2O

H2O

Figure 2. Block diagram of the general biochemical biorefi nery scheme.



Tables 1 and 2 show the SF for the general biorefi nery scheme, which comprises 15 indicators (six and nine for the environmental and economic domains, respectively). Some of the proposed indicators are widely used in environmental and techno-economical analyses, such as emitted gases, water consumption, end use energy ratio (EER) defi ned as the ratio between the energy production and the energy demand,31 production cost, return on investment (ROI) and internal rate of return (IRR). In addition, other important albeit less widespread indicators (e.g. income due to co-product sales and amount of produced solid wastes) were also included due to the methods process engineering approach. Th e values of metrics related to extensive properties are divided by the plants total energy output. Hence, all metrics become inten-sive and subject to comparison against other schemes.

Th e development of SF and its elements (i.e., IIG, EIR, issues, indicators, and metrics) is illustrated with the construction of three environmental and three economic indicators. Th e remaining indicators and their associated elements are obtained following the same rationale, yet they are not discussed due to space restrictions.

Emitted GHG

Typical fermentation and combustion reactions generate CO2 and other GHG as products (e.g. CH4). Th ese reactions

in order to either solubilize sacarides or to render poly-mers as accessible as possible for further treatments. In the saccharifi cation stage sugars are depolymerized for their subsequent conversion to alcohol in the fermentation stage. Th e separation stage then purifi es the product(s) to the required concentration(s). A waste-water treatment stage is considered for reducing the organic content of water discharges, as well as a cogeneration stage for elec-tricity co-production. Th e inputs of the system consist of feedstock, other raw materials, water, electricity, and utilities (heating and cooling). Th e outputs include the produced biofuel, water discharges, solid wastes, carbon dioxide, electricity, and combustion gases.

Th is general biorefi nery scheme is not applicable to all existing or potential biorefi neries (e.g systems that gener-ate other products in addition to fuels). However, it may be compatible with a broad range of biofuels and electric-ity co-production systems, such as lignocellulosic ethanol production processes.

Applicatio n of PAM

As mentioned earlier, the analysis method proposed in this paper focuses on the production stages of biofuel pro-duction processes. Th erefore, the system boundaries are defi ned by the topology of the system as shown in Fig. 2.

Table 1. Sustainability framework (SF) f or the environmental domain. MJout: energy produced in the plant (EtOH LHV plus Electricity produced), MJin: heating, cooling and electricity required by the system.

Environmental Internal Impact Generators

External Impact Receivers

Issues Indicators Metrics

Fermentation Population Global warming due to GHG

Emitted GHG MCO2 =

gCO2eq _______ MJout

.Waste water treatment

Pretreatment and cogeneration

Cogeneration Population Air pollution from non GHG

Emitted non GHG MSO2 = gSO2eq _______ MJout

.

Production process plant operation)

Local population Water usage Water consumption MWf = LFresh water _________

MJout

MWd = LDischarged water _____________

MJout

Other industries using the same water source

Water quality MCOD = COD _____ MJout

MWp = kgDisolved pollutants _______________

MJout

MWT = Water temperature

MpH = pH

Production process (plant operation)

Local population Disposal of wastes Amount of produced solid wastes

MS = kgDisposable wastes _______________

MJout

Government


Plant owners Process performance and resources usage

End use energy ratio (EER)

MEER = MJout ______ MJin

Investors



IIG. When analyzing a specifi c system, the stages that do not consume water should be excluded. Water extraction by industries may compromise its availability for human use. If more than one production facility shares the same water source, the diff erent facilities might also compete for water. Th erefore, both local population and industries are identifi ed as EIR. Water usage is recognized as a general-ized concern and thus chosen as an issue. Th e amount of consumed water is directly related to the magnitude of the impacts, so water consumption is chosen as an indicator. Th e volume of water that is employed by each energy unit generated is a suitable metric. However, the impact caused by water usage can be partially mitigated if a fraction of the water is returned to the water source. Th erefore, a sec-ond metric considering the volume of discharged water per energy unit generated, is also included.

Amount of produced solid wastes

Most production processes, including biorefi neries, generate solid wastes, for example solids produced by

occur within the fermentation, waste-water treatment, and cogeneration stages, which are therefore identifi ed as IIG. Population in general is aff ected by the eff ects of these gases on climate change, specially those inhabiting the region where the plant is located due to their proximity to the emis-sions source. Hence, population is selected as EIR. A gener-alized concern regarding GHG is global warming, which is identifi ed as an issue. Th e magnitude of the impacts depends on the amount of GHG emitted by the system, so it is chosen as an indicator. Finally, since the global warming potential (GWP) can be used to relate the warming eff ects of the dif-ferent GHGs to those of CO2, the metric is defi ned as mass of CO2 equivalent emitted per energy unit produced.

Water consumption

Water is required in the cogeneration stage for the pro-duction of steam. Depending on the specifi c conceptual design, water may also be required by other process stages (e.g. for hydrating or diluting streams). Hence, for the gen-eral scheme, the whole production process is identifi ed as

Table 2. Sustainability framework (SF) for the economical domain. MJtransport : total countrys energy requirements for transport, MJbioenergy: total countrys bioenergy production, Electricityout and Electricityin represent, respectively, the total electricity produced in the cogeneration stage and the total electricity consumed by the process. Values of pH, temperature and COD of discharges (Water quality indicator) are calculated averaging the values of all discharged water streams.

Economic Internal Impact Generators

External Impact Receivers

Issues Indicators Metrics


Fuel consumers Process performance and resources usage

Yield MY =

MJout _____________ kgpolysaccharides

Plant owners

Investors Production cost MTPC = USD ______ MJout


Investors Plant profi tability

Return on investment (ROI) MROI = ROI

Internal rate of return (IRR) MIRR = IRR

Plant owners Added value Mv =

USDsales USDpurchases _____________________ MJout


Nationall population Contribution to national economy

Reduction of fossil fuel imports

Mi = USDimport ____________

USDtotal import

Government


Fuel consumers Energy security Plants contribution to the countrys energy requirements

Mt = MJout __________

MJtransport

Government

MB = MJout __________

MJbioenergy


Investors Recovery of residues with economic value

Income due to co-product sales Mc =

USDCoproducts _____________ MJout

plant owners


Plant owners Electricity suffi ciency of the plant

Plants electrical productivity

ME = Electricityout ____________ Electricityin

investors

National energy company



( increasing their revenue), which constitute the EIR. Th e recovery of residues with economic value is the general concern (issue) related to this matter, and the revenue due to these sales is an adequate indicator. Impacts can be quantifi ed in terms of the economic profi t obtained by selling secondary products per energy unit generated.

Case study

In thi s section, the execution of the indicator analysis and the weighting process is illustrated by comparing two biochemical platforms for the co-production of lignocellu-losic ethanol and electricity. Both systems closely resemble the general biorefi nery scheme outlined earlier and thus Tables 1 and 2 can be used to select the appropriate indica-tors for the comparison.

Plants description

Th e process schemes to be evaluated are termed PETA and BIOREF, respectively. Both systems are based on a previously published conceptual design31 and produce lignocellulosic bioethanol, biogas, and electricity using wheat straw (70% polysaccharides content) as feedstock with an installed capacity of 2000 tonDB/day. BIOREF additionally produces biohydrogen which is fed to the cogeneration stage to increase electricity production. Energy integration is contemplated for both systems. Th e block diagram for PETA is presented in Fig. 3. Seven main processing stages are considered: pre-treatment (size reduction and thermochemical treatment), overlim-ing (neutralization), enzymatic saccharifi cation, alcoholic fermentation, separation (azeotropic distillation and molecular sieving), waste-water treatment (anaerobic treatment, aerobic treatment and clarifi cation), and cogeneration (electricity generation). Figure 4 shows the block diagram for BIOREF. It comprises the same process stages as PETA, except for the hydrogen production stage substituting the overliming. C5-hydrolizates are derived from the pre-treatment stage to a dark fermentation reactor. Since a waste-water treatment stage is considered, a fraction of the treated water can be recirculated back to the process. In order to simplify the analysis, full water recirculation (thus zero water discharges) is assumed for both schemes. Th is consideration, yet unrealistic in prac-tice, still allows for an adequate illustration of the method application. Th e process inputs for both schemes are raw materials (H2SO4, Ca(OH)2, enzymes and yeasts, bacteria, micro-organisms, fl occulants, and antifoamers), utilities (fresh water, pressurized air, electricity, steam-generator

mechanical or chemical unit operations and ashes gener-ated in the cogeneration stage. As above, the entire pro-duction process is identifi ed as IIG for this general scheme and must be refi ned when analyzing a specifi c system. An inadequate management of solid wastes may impact directly on the local population. Local authorities also constitute interested parties in case of hazardous wastes or if specifi c disposal policies exist. Th e local population and the government are thus recognized as EIR, being the disposal of solid wastes their general concern (issue). Th e amount of produced solid wastes is directly related to impacts intensity, and it is straightforward to calculate this amount from the mathematical models (mass balances). Hence, produced solid wastes is chosen as an indicator, quantifi ed as mass of disposable material produced per energy unit generated.

Yield

Feedstock yield is a customary parameter of techno-economical analysis. Th e biorefi nery as a whole infl uences the amount of products that can be obtained per feedstock unit (hence considered the IIG). Changes in yield directly aff ect the economic profi t of the process. Th erefore, plant owners and investors constitute the EIR, the plants eco-nomic profi t being their main concern (issue). An ade-quate quantifi er for this indicator is the produced energy (i.e., useful energy available in products) per unit mass of feedstock.

Reduction of fossil fuel imports

Th e biofuels generated by the production process (identi-fi ed as IIG) may be used to cover a fraction of the countrys fuel demand. If this demand is (partially) satisfi ed by imports (as is the case for several countries32), the produc-tion of the system under study would reduce such imports, hence contributing to the domestic economy (which is identifi ed as the issue). Th e EIR are obviously both the government and population. Using the reduction of imports as an indicator is the natural choice. An appropri-ate metric to quantify the impact is the amount of money fed into the domestic economy by purchasing the plants fuel instead of importing it (USDimport), divided by the total fuel imports (USDtotal import).

Income due to coproducts sales

Besides fuels and electricity, biorefi ning processes gen-erate secondary products (e.g. CO2) that may have eco-nomic value. Recovering and selling those products may impact positively on both the plant owners and investors



(TPC) were calculated by setting NPV = 0, IRR = 4% and equity = 70%.31 Energy integration was carried out using the Pinch Method.26 All calculation stages were automated.32,34

Indicator analysis

Gi ven the specifi c topology of the schemes under consid-eration, a subset of the metrics presented in Table 3 was enough to account for the systems impacts. Th e reasons for excluding certain metrics from the current analysis are discussed below.

Th e process schemes under analysis consider full water recirculation. Th erefore, the metrics corresponding to water discharges do not apply.

Th e economic analysis is carried out for NPV = 0 (i.e., ROI = 027). Hence, the ROI metric provides no relevant information for this case study.

fuel) and the feedstock (wheat straw). Th e outputs are energy (electricity), steam, wastes (water, CO2, ashes, gyp-sum, other solid wastes) and ethanol as product. PETA and BIOREF are similar to the general biorefi nery scheme mentioned earlier, diff ering only in an additional stage (overliming and hydrogen production, respectively). Th e output of these stages (i.e., gypsum) constitutes a solid waste, whose impacts are considered in the SF of the general scheme. Th erefore, the set of indicators provided by the aforementioned SF is suitable for carrying on the impact evaluation of the present case study. Th e plants life cycle is estimated to be 18 years, initiating construc-tion in 2012 at a Mexican location with suffi cient wheat straw availability.31

Mass and energy balances were calculated using the SuperPro Designer (SPD v8.5) simulator. For the techno-economical analysis, the Net Present Value technique (NPV) was applied; Total Production Costs

SEPARATIONACID

PRE-TREATMENT

Gases



TREATMENTOVERLIMING

CO2

EtOH

H2O

Ashes

Electricity

H2O

Steam

Gases

CO2

Gypsum

H2O

Yeast

Enzymes

H2O

H2SO4

Feedstock

Ca(OH)2

Bacteria

Polymer

H2O

Figure 3. Block diagram of PETA.



Figs 5 and 6 aft er normalization with respect to PETA. In contrast to what may be expected from the comparison of a single product plant against a biorefi nery, PETA shows better performance than BIOREF in nine of the eleven metrics. Th e reasons of this behavior are the following:

Th e hydrogen production stage generates a signifi cant amount of carbon dioxide. Also, it enhances biogas production, which further increases CO2 production when burned in the co-generation stage. Th erefore, BIOREF emits considerably more greenhouse gases than PETA (around 32%).

Although BIOREF generates more electricity by fueling biohydrogen and biogas to the cogeneration stage, these gases are produced at the expense of diminishing the ethanol production (the feedstock fl owrate is the same for both plants). Moreover, since the CO2 present in the

IRR is considered an input parameter (i.e., set to a fi xed value of 4%) and therefore provides no useful informa-tion as an indicator.

Due to the use of diff erent technologies, the current selling price for ethanol ($0.65/L, 2012 average34) is signifi cantly lower than the TPCs calculated for both schemes ($0.93/L for PETA and $1.31/L for BIOREF). Th e added value indicator is meaningless since no competitive selling price can be fi xed.

Since no co-product recovery is considered for the present case study, this indicator does not apply either.

Th e metric values calculated for both systems are shown in Table 3. Data of the specifi c case study context (e.g. volume of gasoline imports and fossil fuel demand for transport) were obtained from Mexican government databases.35,36 Th ese results are graphically presented in

SEPARATIONACID

PRE-TREATMENT

Gases



TREATMENTHYDROGEN

PRODUCTION

CO2

EtOH

H2O

Ashes

Electricity

H2O

Steam

Gases

CO2

Gypsum

H2O

Yeast

Enzymes

H2O

H2SO4

Feedstock

Ca(OH)2

Microorg

Bacteria

Polymer

H2O

Figure 4. Block diagram of BIOREF.



Table 4. Case studys dimensional criteria matrix. The shaded areas are the chosen dimensional criteria.

Metric Market value

Taxes, duties and policies

Production cost

Environmental domain

MCO2

MSO2

MWf

MS

MEER

Economic domain

MY

MTPC

Mi

Mt

MB

ME

Table 3. Metric values for PETA and BIOREF schemes.

Indicators Metrics Value

PETA BIOREF


Emitted GHG MCO2 =

gCO2eq _______ MJout

2.21E+02 2.90E+02

Emitted non GHG

MSO2 = gSO2eq _______ MJout

6.21E+00 7.64E+00

Water consumption

MWf = LFresh water __________

MJout 5.37E01 9.48E01

Amount of produced solid wastes

Ms = kgDisposable wastes _______________

MJout

3.14E02 2.73E02

End use energy ratio (EER)

MEER = MJout ______ MJin

1.01E+00 8.96E01

Economic domain

Yield MY =

MJout _____________ kgpolysaccharides

8.61E+00 7.00E+00

Production cost MTPC =

USD ______ MJout

3.62E02 4.67E02

Reduction of fos-sil fuel imports

Mi = USDimport ____________

USDtotal import

3.76E03 2.80E03

Plants contri-bution to the countrys energy requirements

Mt = MJout __________

MJtransport

2.46E03 2.00E03

MB = MJout __________

MJbioenergy

3.92E02 3.21E02

Plants electrical productivity

ME = Electricityout ____________ Electricityin

1.81E+00 1.92E+00

biohydrogen and biogas streams is not recovered, it is fed to the cogeneration stage, acting as an energy car-rier and rendering the stage highly ineffi cient. Th erefore, BIOREF total energy production (ethanols LHV plus electricity) and the EER are lower than for PETA.

For this case study, the only non-greenhouse gas produced is SO2, of which both schemes generate the same amount since they have the same sulfur inputs. However, the corresponding metric value is higher for BIOREF due to its lower energy production.

0.00 0.50 1.00 1.50 2.00

MJout / MJin

Kg disposable material / MJout

LFresh water / MJout

g SO2 eq / MJout

g CO2 eq / MJout

PETA BIOREF

Figure 5. Indicator analysis for the environmental domain.

0 0.2 0.4 0.6 0.8 1 1.2 1.4

Electricityout / Electricityin

MJout / MJbioenergy

MJout / MJtransport

USD import / USD total import

USD / MJout

MJ out / kg polysaccharides

PETA BIOREF

Figure 6. Indicator analysis for the economic domain.



Table 5: Functions used for translating metric units to (USD/MJout). D: Dimensionalized metric, CCO2: cost of carbon dioxide bonds[37], CSO2: cost of sulfur dioxide bonds[38], CW: water extraction duty[39], CSW: Mexican solid disposal cost[40], CF: feedstock cost, m F: feedstock mass flow rate, m P: polysaccharides flow rate, CG: gasoline barrel cost[36], VG: gasoline barrel volume[41], HG: gasoline LHV[43], HEtOH: ethanol LHV[42], VEtOH: volumetric flow rate of EtOH, Eout: total produced energy flow rate, CE: electricity cost, EElec: total produced electric energy flow rate.

Indicator Dimensional function D(USD/MJout)

PETA BIOREF


Emitted GHG MCo2 CCo2 7.72E04 1.02E03

Emitted non GHG MSo2 CSo2 4.28E04 5.27E04

Water consumption MWdCW 9.07E05 1.60E04

Amount of produced solid wastes MSCS 6.00E04 4.72E04

End use energy ratio (EER) (MEER 1)MTPC 5.06E04 4.85E03

Economic domain

Yield M Y 1 CF m F m p

1 1.07E02 1.32E02

Production cost MTPC 3.62E02 4.67E02

Reduction of fossil fuel imports Mi CG V G 1 H G

1 VEtOH HEtOH E out 1

7.49E05 5.06E05

Fraction of the transport energy demand that the plant can cover MtMTPC 8.91E05 9.34E05

Fraction of the total national bioenergy that is produced in the plant

MBMTPC 1.51E03 1.92E03

Plants electrical productivity CE EElec E out 1

M E 2 ( M E

1 ) 2.21E03 3.33E03

Water consumption is larger for BIOREF due to an extra water input for rehydrating the input streams to the saccharifi cation stage (since the hydrolyzates are diverted to hydrogen production).

Since the pH required in the overliming stage is larger than the required for hydrogen production, a larger amount of (Ca(OH)2) is needed for PETA. Th is trans-lates into more solid wastes (gypsum).

PETAs higher yield is due to its higher energy production.

Due to the equipment capital cost of hydrogen produc-tion and the lower energy produced by the BIOREF scheme, its total production cost is higher.

BIOREFs lower bioethanol production translates into less imports reduction and a lower contribution to the countrys energy requirement.

BIOREFs electricity production is larger (by about 6%) due to the extra fuel (H2 and CH4) fed to the cogenera-tion stage. Note, from Table 3, that both systems are electrically self-suffi cient.

Weighting

Th e categories selec ted for the weighting process are shown in Table 4. In this work, one category was restricted to each metric. Th e dimensional function matrix for the

case study along with the selected category for each metric are shown in Table 4. Th e resulting dimensional func-tions are presented in Table 5, along with the values of the weighted metrics. In order to identify positive values with benefi cial impacts and vice versa, negative signs are assigned to those functions associated to negative impacts. For the current case study all scaling factors are set to 1, thus assigning the same relative importance to all metrics.

Comparing the magnitude of the weighted metrics shown in Table 5, GHG emissions, solid wastes and the EER were found to be the main contributors to the PETAs impacts on the environmental domain (with 32%, 18% and 21% of the total impact, respectively). By means of the SF, the process stages associated to these impacts can be identifi ed, in this case cogeneration, fermentative proc-esses and overliming. Th ese stages constitute the improve-ment areas for enhancing the process sustainability. For BIOREF, the EER contributes with 69% of the total envi-ronmental impact. Purifying the biohydrogen and biogas streams before feeding them to the burner, in order to increase its energy production, seems to be a feasible alter-native to increase the plants sustainability.

With respect to the economic domain, TPC is the most important contributor (above 70%) to both schemes impacts. A compromise can be identifi ed between



improving the environmental and economic sustainability of the system (e.g. reducing production costs vs including a purifi cation process for H2 and CH4 streams).

Figure 7 shows the aggregated metric values per domain and the global value for each scheme. Th e contribution of the economic domain to the global value in both schemes are one order of magnitude larger than the environmental counterparts.

From an overall point of view, PETA is more sustainable (both, environmentally and economically) than BIOREF. Th e GHG emissions, the bioethanol production cost and the EER are the main responsible metrics for BIOREFs low sustainability.

Conclusions

Th e sustainability a nalysis method and its associated calculation tools presented in this work provide a solid platform for establishing the environmental and economic impacts of prospective lignocellulosic ethanol production processes. Th ese impacts are quantifi ed using a well-sup-ported and case-specifi c set of indicators.

Th e application of the method was illustrated with a case study comparing two biochemical platforms for cellulosic ethanol production. Th e analysis revealed that multi-prod-uct plants are not necessarily better (in terms of sustainabil-ity) than single-product plants. Th e SF provides the ration-ale to clearly identify the causality from IIG to metric val-ues. Th is information may be used to support the decisions regarding possible modifi cations of the IIG. Furthermore, a compromise between economic and environmental sustain-ability was highlighted, in which weighting coeffi cients play

a very important role. Th e construction of weighted metrics with more than one category will also widen the scope of aspects (e.g. normative versus process-related issues) that could be considered in the sustainability analysis.

Acknowledgments

Partial fi nancial support from the Sustainability Energy Fund of the Secretary of Energy, Mexico (grant SENER 2010-150001) is kindly acknowledged. Th e authors extend their gratitude to the reviewers for their valuable feedback to improve this work work and to Mr Victor Sevilla for providing the VPN analysis tools.

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-7.00E-02

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-5.00E-02

-4.00E-02

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Arturo Sanchez

Arturo Sanchez, (B.Sc. Chem. Eng. ITESO, Mexico, 1985; M.I. Chem. Eng. UAM-I, Mexico, 1989; Ph.D. Imperial College, U.K., 1994). He is currently a Senior Research Scientist at Cinvestav-Gdl, Mexico. He has been a Visiting Research Scientist the Dept. of Computing, Imperial College;

the Group of Applied Mathematics, Mexican Petroleum Institute; and the Dept. of Eng. Sci., University of Oxford, U.K. He has authored more than 100 papers and a book and has graduated more than 30 M.Sc. and Ph.D. students. His research interests include the formal devel-opment of automation systems and advanced biofuels process engineering.



Mario Sols

Mario Sols is currently a B.Sc. student in chemical engineering at ITESO, Mexico and collaborates at the Biofu-els Laboratory in Cinvestav-Gdl on the sustainability analysis area.

Diego R. Gomez

Diego R. Gomez holds a B. Sc. in chemical engineering from ITESO (2012). He is currently research as-sistant at Cinvestav-Gdl. His current interests include the dynamic mod-eling and simulation of processes, the sustainability analysis of conceptual biorefining facilities and the develop-

ment of the corresponding data analysis tools.

Ren Banares-Alcantara

Ren Baares has a first degree in Chemical Engineering from UNAM (Mexico). He then studied in Carnegie Mellon University where he obtained a Master and a PhD degrees in Chemical Engineering. He has worked for the University of Oxford since 2003 where he is associated to the Department of

Engineering Science (as a Reader) and to New College (as a Tutorial Fellow).

Gabriela Magaa

Gabriela Magaa (B. Sc. Chem. Eng. ITESO, Mexico, 2009) is currently research assistant at Cinvestav-Gdl. She has been worked at the Biofuels Laboratory since 2009 in the conceptu-al design and the sustainability analysis of biorefineries. She has authored more than 10 research papers on biofuels

production, biorefineries and their sustainability analysis.

sanchez et al-2014-biofuels, bioproducts and biorefining

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