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Sustainability Indicators for Chemical Processes: III. Biodiesel Case Study Gerardo J. Ruiz-Mercado,* Michael A. Gonzalez, and Raymond L. Smith U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States S Supporting Information *

ABSTRACT: The chemical industry is one of the most important business sectors, not only economically, but also societally; as it allows humanity to attain higher standards and quality of life. Simultaneously, chemical products and processes can be the origin of potential human health and environmental issues, incurring economic costs, and raising social concerns. An alternative to addressing these challenges after the fact is the implementation and development of more sustainable products and processes. To guide decision-makers, designers, and stakeholders in developing more sustainable chemical processes, the sustainability evaluation and design tool Gauging Reaction Effectiveness for the Environmental Sustainability of Chemistries with a Multiobjective Process Evaluator (GREENSCOPE) has been proposed and developed. Two previous contributions presented a holistic set of sustainability indicators as well as their definition, a sustainability measurement scale, and data requirements. This contribution demonstrates the successful implementation and use of GREENSCOPE for a sustainability performance assessment. In this evaluation, the manufacture of biodiesel is undertaken as a case study to demonstrate and describe this achievement. Results from this study show the positive features of this process and identify opportunities for sustainability improvement in the areas of material and energy use, environmental impacts, and economics during the production of this important biofuel. Therefore, GREENSCOPE is proposed as a fundamental tool for evaluating and designing more sustainable chemical processes.

1. INTRODUCTION Sustainable development, in the chemical industry, provides guidelines for the design of new manufacturing processes as well as establishes new objectives for existing ones to achieve quality of life improvements without placing an undue risk on the continuous availability of ecological goods and services. These guidelines are focused on addressing environmental, social, and economic aspects that may be affected by industry. An important issue that must be solved for addressing these system perturbations is how to accurately assess them, identifying which system components are affected, localize process and product aspects which generate them, redesign relevant processes and products, and demonstrate system improvements that (will) occur after implementing these process and product changes. In addition, a correct implementation of sustainable development has the potential to simultaneously minimize or eliminate the environmental risks and maximize the social and economic benefits of protecting the environment.1 Sustainability should be incorporated into industry, society, and government decision-making. Therefore, the U.S. Environmental Protection Agency (EPA) has recognized the importance of integrating sustainability concepts into the Agency’s mission.2 The Chemical Safety for Sustainability (CSS) National Research Program is one of the EPA’s responses to meeting its goals for protecting human health and safeguarding the environment. This is achieved to ensure safety during the design, production, and use of existing and new chemical products, and to meet the social, economic, and environmental needs of future generations.3 Research within the CSS program includes efforts to develop approaches for This article not subject to U.S. Copyright. Published XXXX by the American Chemical Society

chemical evaluations based on a life cycle perspective, which addresses and incorporates sustainability as described in Figure 1. The process and product stages (shaded in Figure 1) are key parts of a holistic sustainability analysis where the designers and decision-makers can implement changes to directly and indirectly modify and improve the entire life cycle sustainability. The implementation of GREENSCOPE evaluations can influence sustainability by directly leading to process and product improvements. Similarly, a GREENSCOPE with life cycle approach can dramatically (and indirectly) influence the combined supply chain/product-network sustainability.4 In an effort to advance the development of tools for the sustainable design of chemical processes, GREENSCOPE5 implements a methodology for the evaluation and modeling of chemical processes for their sustainability. This evaluation of sustainability is assessed by employing a set of indicators6 capable of transmitting and translating process performance, feedstocks, utilities, equipment, and output information into a sustainability measurement scale. These process characteristics (indicators) are categorized in the areas of environment, efficiency (material), energy, and economics, known as the 4 E’s. Therefore, this sustainability assessment describes how well the system under consideration makes use of mass and energy inputs to manufacture a valuable product, but meets social and environmental needs, all the while maximizing its economic benefits. To perform this sustainability approach, diverse types Received: October 14, 2012 Revised: January 25, 2013 Accepted: April 4, 2013

A

dx.doi.org/10.1021/ie302804x | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

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Figure 1. Sustainable development characteristics during all life cycle stages of chemical products (adapted from EPA’s CSS Strategic Research Action Plan3). The shaded area reflects the stages where designs based on GREENSCOPE evaluations have a direct influence. Other stages are affected indirectly and benefit from an additional life cycle perspective.

For example, the waste reduction algorithm (WAR)10−12 employs eight potential environmental impact indexes to evaluate the environmental performance of a process. Designers or operators can employ WAR to compare different process design or operational alternatives to identify which alternative generates the lowest impact scores across each category. Gani et al.13−15 developed a methodology for sustainable process design that evaluates process alternatives through a set of sustainability indicators, flowsheet decomposition techniques, sensitivity analysis, and profit optimization. This methodology concludes with a profit-optimized process alternative by considering constraints to restrict reductions in the relevant sustainability metric scores. Tugnoli et al.16,17 have proposed a set of indicators for quantitative assessment of sustainability during the early stages of a process. The obtained results are then normalized per unit of land area, which accounts for the impacts of the manufacturing facility within that region. In general, different indicator results are aggregated into three main sustainability areas (environmental, economic, and societal). The modular-based sustainability assessment and selection (m-SAS)18 approach is applied for the design of chemical processes by combining process synthesis (modeling and simulation), sustainability indicators, the analytical hierarchy process (AHP) methodology (indicator weight setting for aggregation), and normalized scores. Sugiyama et al.19 have developed a sustainability assessment approach for process design and decision-making that involves a set of well-defined economic, energy, and environmental, health, and safety indicators (EHS). Some of the indicators account for life cycle impacts (e.g., cumulative energy demand) and other indicators can be calculated with methods that depend on the process design and modeling stage level of complexity. As in the previous method, aggregation and normalization approaches are employed to unify the indicator scores. Additional contributions20−23 have proposed different methodologies to evaluate and incorporate sustainability assessment into conceptual process design. Despite all these efforts to incorporate sustainability assessment into process design and evaluation, challenges still exist in the definition of sustainability measurement scales, the process aspects that must be accounted for during sustainability assessment, needed sustainability approaches that are required for decision-making, and in the development of a realistic and practical sustainability framework. Therefore, this contribution

of data from the process (e.g., operating conditions) and external sources (e.g., thermodynamic properties) must be available or introduced into the tool.7 The concept for GREENSCOPE is based on the thought process and holistic approach that is needed for considering the sustainability of a system. In this case study, it centers on the evaluation of chemical processes at the conceptual design stages.8,9 Conceptual design comprises substages, beginning with an input/output structure on a process flow diagram (PFD) and ending with a fully described and detailed PFD of an actual potential design. It is then followed by an engineering economic analysis and iterative steps of process synthesis and optimization. As discussed in the authors’ previous publication,6 modifications at early stages of a project life are more effective for influencing the performance of the process during operation. Therefore, the introduction of sustainable development concepts during these early stages will have a greater influence on the sustainability of a process during operation. This is in contrast to performing this optimization within the physical constraints of the current existing system, which typically involves high implementation costs and has a low influence on the overall performance of the process. This contribution demonstrates and details the successful implementation of a sustainability assessment during the conceptual design phase for chemical processes (biodiesel production). First, a brief overview of previous efforts to introduce and apply sustainability into process development is given. Since the synthesis of biodiesel is employed as this case study, a section outlining the biodiesel production process, synthesis, and its relevancy as a key biofuel will be described. In addition, this work describes the sustainability assessment of a simulated biodiesel manufacturing process in accordance with the GREENSCOPE methodology. The results obtained illustrate the current state of sustainability for this simulated biodiesel process and identify possible process improvement opportunities, which can lead to increased sustainability. With this case study, GREENSCOPE is introduced as a mechanism to assist decision-makers in evaluating sustainability for achieving a more sustainable chemical process.

2. OVERVIEW ON EVALUATION OF SUSTAINABLE PROCESSES Varying methodologies for the quantification of potential environmental impacts (PEI) and their inclusion as decision factors for process evaluation and design have been reported. B



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Figure 2. CHEMCAD flowsheet for the simulated alkali-catalyzed manufacture of biodiesel.

aims to address and help overcome these important concerns by proposing GREENSCOPE as a practical and realistic tool for developing and evaluating sustainable chemical processes.

C3H5(OOCR)3 + 3CH3OH (triglyceride)

(methanol)

(e.g.,NaOH)

⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯→ 3RCOOCH3 + C3H5(OH)3 (FAME)

3. BIODIESEL AND THE NEED FOR RENEWABLE FUELS

(glycerol)

(1)

It has been reported that biodiesel has at least 50% lower lifecycle greenhouse gas emissions when compared to the baseline lifecycle greenhouse gas emissions of a petroleum based diesel fuel.24,34 Additionally, biodiesel offers benefits in several criteria emission categories when compared to fossil derived diesel. Such benefits include a decrease in carbon monoxide (CO) emissions, particulate matter (PM), and the quantity of sulfates generated, as well as decreased hydrocarbon and air toxic emissions.33 The Energy Independence and Security Act requires the United States to have a minimum annual use of 1.0 billion gallons of biomass-based diesel fuel for the period of 2011 to 2022, as well as a minimum requirement of 21 billion gallons of advanced biofuels consumed by 2022. Biodiesel is included in these two Renewable Fuel Standard mandatory demands. These requirements demonstrate the strong demand for this biobased fuel. With its importance as a manufactured good, and the requirements of EISA, it is important that any biofuel produced be done so in a sustainable manner. For these reasons, a biodiesel manufacturing process was selected as a case study for implementing the GREENSCOPE sustainability evaluation and design tool.

24

As defined by the Energy Independence and Security Act (EISA), biodiesel is a grouping of monoalkyl esters of long chain fatty acids which are derived from a plant or animal and meets the registration requirements for fuels and fuel additives under EISA and the requirements of ASTM standard D6751.25 The U.S. EPA has registered biodiesel as a motor vehicle diesel fuel and additive that can be used at any blend level with conventional fossil-fuel derived diesel up to B100 (100% of biodiesel content) in diesel vehicles. In addition, the U.S. EPA provides guidance for biodiesel producers and blenders/ users.26,27 In the United States, 1.01 billion gallons of biodiesel were produced in 2011, with the majority derived from soybean oil.28 However, biodiesel can be synthesized from other feedstocks such as vegetable oils (e.g., canola oil, sunflower oil, and palm oil), recycled cooking oils, and animal fats. Currently, biodiesel is produced through a transesterification reaction29−31 as described in eq 1. This process uses a simple alcohol (e.g., methanol) reacted with an oil or fat (e.g., soybean oil) under alkali-, acid-, or enzymatic-catalyzed conditions. However, the bulk of commercial biodiesel plants operate under alkalicatalyzed conditions.32 The feedstock oil or fat is converted into a fatty-acid monoalkyl ester (FAME) fuel (biodiesel), with glycerin as a byproduct (a ratio of one part of glycerin is produced per nine parts of FAME). The biodiesel (B100) can then be blended with conventional diesel fuel to generate a variety of blends. The most popular blends are B5 and B20 (5% and 20% biodiesel, respectively).33

4. PROBLEM DESCRIPTION (PROCESS MODELING AND SIMULATION) An alkali-catalyzed transesterification reaction was considered for the manufacturing of FAME. A vegetable oil with high oleic acid content was chosen as the oil feedstock to react with methanol (MeOH) in the presence of sodium hydroxide (NaOH). To obtain relevant process data, a complete steady state process simulation was performed using CHEMCAD35 software. The operating data for a biodiesel production process is based upon information and models included in contribuC



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Table 1. Feed stream Information for the Alkali-Catalyzed Biodiesel Manufacturing Process stream no.

200

201

202

203

204

stream name temperature, °C pressure, kPa mass flow, kg/h average MW, kg/kmol enthalpy, MJ/h entropy, MJ/h/K

oil 25.0 100 1050.0 885.43 −2944.0 −76.42

methanol 26.7 100 117.2 32.04 −874.0 −99.48 × 10−3

NaOH 25.0 100 10.0 40.00 −201.9 −58.14 × 10−3

H2O 25.0 100 11.0 18.02 −174.4 −92.63 × 10−2

H3PO4 25.0 100 10.0 98.00 −137.0 −15.44 × 10−2

component mass fraction MeOH oil FAME glycerol NaOH H2O H3PO4 Na3PO4

0.000 1.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 1.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 1.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000 0.000 1.000 0.000

Table 2. Output Stream Information for the Alkali-Catalyzed Biodiesel Manufacturing Process stream no.

300

301

302

303

304

305

stream name temperature, °C pressure, kPa Mass flow, kg/h average MW, kg/kmol enthalpy, MJ/h entropy, MJ/h/K

FAME 208.60 10 990.00 297.04 −2007.0 −5.56

glycerol 112.00 50 104.14 91.00 −733.1 −62.45 × 10−2

H2O and MeOH 38.75 40 15.49 22.34 −187.4 −8.32 × 10−2

NaOH removal 60.00 100 11.98 117.43 −155.6 −3.45

waste oil 541.85 20 55.50 256.31 −191.6 −12.94 × 10−2

air rel. 208.58 10 21.11 66.57 −86.9 −70.46 × 10−3

component mass fraction MeOH oil FAME glycerol NaOH H2O H3PO4 Na3PO4

0.0000 0.0040 0.9960 0.0000 0.0000 0.0000 0.0000 0.0000

0.0020 0.0000 0.0000 0.9960 0.0000 0.0020 0.0000 0.0000

0.4405 0.0000 0.0001 0.0007 0.0000 0.5587 0.0000 0.0000

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5890 0.4110

0.0000 0.8750 0.0100 0.0000 0.1150 0.0000 0.0000 0.0000

0.0890 0.0020 0.7330 0.0000 0.0000 0.1760 0.0000 0.0000

tions by Zhang et al.,36−38 CHEMCAD biodiesel model,39 the U.S. Department of Agriculture (USDA) and U.S. Department of Energy (DOE) biodiesel model,34 and the ProSim biofuel production model.40 Since the biodiesel process modeling and product design are not the main objectives of this contribution and vegetable oils are comprised of several fatty acid chains, oleic acid, as a pure substance, was assumed as the compound that best represents this mixture of several oils. However, there are other approaches to handle the oil feedstocks as homogeneous mixtures of several oils and fats whose compositions differ with oil sources and crop growing conditions.41 The CHEMCAD biodiesel manufacturing flowsheet describing this simulated process is shown in Figure 2, and Table 1 provides a summary and description of the input streams and their specifications. To begin, pure oil, fresh makeup methanol, and NaOH are introduced and mixed with the recovered unreacted MeOH. Then, the entire mixture, having a MeOH to oil molar ratio of 6:1 is introduced to the reactor R-101, where a homogeneous alkali-catalyzed transesterification reaction at 60 °C is performed. A first order kinetic reaction and CSTR

model are employed to describe this important step. It is assumed that a 95% conversion of oil is achieved after 60 min of residence time. The products, FAME and glycerol, as well as the unreacted oil and excess alcohol are then moved to the distillation column T-101. The excess methanol is recovered as the top product stream with 99% purity and recirculated into reactor R-101. In addition, a water-washing column (unit W-101) was employed to remove the produced biodiesel from the distillation column T-101 bottom stream mixture. The biodiesel rich content output stream from unit W-101 is then fed into another distillation column (unit T-102), to obtain a biodiesel product with 99.6% purity (stream 300). The other two output streams from T-102 are releases containing unconverted oil (stream 304) and gas-phase FAME (stream 305). The bottom output stream from the unit W-101 (stream 14) contains almost all of the produced glycerol and traces of water, methanol, and NaOH. The catalyst must be neutralized prior to disposal. Therefore, fresh phosphoric acid (H3PO4) and stream 14 are mixed and fed into a neutralization reactor (unit R-102). This stoichioD



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Table 3. Summary of Operating Conditions, Design Specifications, Purchase Costs, and Total Module Costs of Main Processing Unitsa process equipment

operating conditions

pumps P-101/102/103/104

Pout: 400/400/400/120 kPa efficiency: 0.75 3550 rpm power: 0.463/0.062/0.067/0.169 MJ/h Ptop: 20/10/40 kPa Pdrop: 10/10/10 kPa Qcondenser: 369.1/528.0/88.0 MJ/h Qreboiler: 500.0/970.1/101.7 MJ/h heat duty: +64.31/−139.77 MJ/h

columns T-101/102/103

heat exchangers H-101/102 washing column W-101

reactors R-101/102

vertical vessels S-101/102

a

design specifications

T: 60 °C Ptop: 110 kPa Pdrop: 5 kPa

total purchase cost/total capital cost, 1 × 103 $

centrifugal

29.7/83

tray columns, valve tray 5/10/4 stages diameter: 0.6/1.2/0.5 m length: 10/12/10 m shell and tube area: 0.6/1.3 m2 packed column, 6 stages

340.5/1021.5

5.8/11.6 144.7/434.1

random packing, 3.5 m3 diameter: 0.8 m length: 10.0 m volume: 10.50/0.11 m3 diameter: 1.6/0.4 m height: 4.8/1.2 m volume: 1.25/0.11 m3 diameter: 0.9/0.4 m height: 1.8/0.8 m

T: 60/60 °C heat duty: 428.3/34.8 MJ/h residence time: 1.0/0.5 h T: 60/60 °C heat duty: +1.6/-6.6 × 10−5 MJ/h residence time: 0.5/0.5 h

67.1/169.2

7.2/49.2

Carbon steel is the construction material for most of the process equipment, except the pumps (cast iron).

evaluation. The input and output stream data provided in Tables 1 and 2, and other stream data such as renewability index, entropy flow, volumetric flow, and stream vapor fraction, are introduced into the GREENSCOPE evaluation and design tool. In addition, data from each reaction representing temperature and pressure, operating conditions, conversion, and stoichiometric coefficients were collected and imported into GREENSCOPE. Additionally, substance property data as described in the authors’ previous publication7 must be available to the assessment tool. Tables 3 and 4 feature the process equipment specifications and the process utility demand inventories, respectively. Upon collection of all the required data and

metric neutralization reaction which has 100% conversion of the NaOH is described by eq 2. The unit R-102 output stream is then sent to a gravity separator (unit S-102) to remove the generated trisodium phosphate (Na3PO4). Finally, the glycerol product with 99.6% purity is obtained from the bottom of distillation column T-103 (stream 301). The waste stream at the top of the unit T-103 is rich in water and methanol. 3NaOH + H3PO4 → Na3PO4 + 3H 2O

(2)

Relevant specifications for this process, such as physical and thermodynamic property values and compositions for all process output streams are shown in Table 2. Now that the process case study operating conditions and reaction stoichiometries have been modeled and a simulation performed, the data for a sustainability assessment to this current biodiesel manufacturing process using GREENSCOPE are available. Other process examples may obtain the necessary data for a sustainability assessment from experiments, an existing process, etc. The next section will describe the general procedure employed to calculate all of the sustainability indicators.

Table 4. Summary of Heating, Cooling, Power, and Waste Treatment Demands during the Production of Biodiesela utility type medium pressure steam @10 barg, 184 °C; steam is produced from saturated water @ P = 1.7 bar and 115 °C using a natural gas-fired industrial boiler with 90% efficiency moderately low T refrigerated water, Tin = 5 °C, Tout = 15 °C; moderately low temperature refrigerated water in at 5 °C and returned at 15 °C is supplied by a combined cooling water loop/refrigeration cycle as described in Turton et al.9(pages 232−238) electricity

5. GREENSCOPE IMPLEMENTATION FOR SUSTAINABILITY ASSESSMENT At the time of publication, the GREENSCOPE process evaluation and design tool is operational within Microsoft Excel 2007; however, the authors are working to develop a standalone software version. This tool comprises nearly 140 indicators, which to be calculated, require process data (i.e., process equipment, operating conditions, streams, utilities) as well as physicochemical and thermodynamic properties of substances involved in the process under evaluation. The first task consists in defining the system under study and collecting data from all of the streams entering or leaving the studied system. In this work, the entire biodiesel manufacturing process constitutes the system (gate-to-gate) under sustainability

waste disposal nonhazardous, solid waste disposal nonhazardous, liquid waste disposal hazardous, solid waste disposal hazardous, liquid air purification

utility flow rate needs 1051.90 kg/h 26752.84 kg/h

0.62 kWh/h 64.64 kg/h 21.86 kg/h 4.93 kg/h 7.05 kg/h 126.81 m3/h

a

These values are represented by the amounts of utilities required to cover the needs of energy and waste treatment from the manufacturing process.

E



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introduction into the GREENSCOPE evaluation tool, the indicator scores can be calculated. However, decision-makers (or the user) must identify and select the best-case (100%) and worst-case (0%) scenarios to establish the sustainability scale for each indicator. In general, GREENSCOPE provides clear guidelines and default values for these two scenarios6 for each indicator. The next subsections will describe and discuss the evaluation and results of indicators according to each category of the 4 E’s for this simulated biodiesel processing facility. In addition, particular issues (i.e., assumptions, reference states) regarding some indicators (e.g., exergy indicators) will be explained in detail. However, some procedures and data aspects such as calculation of indicators, data sources, and the selection of besttarget (100%) and worst-case (0%) scenarios will not be described in detail as these aspects and procedures were implemented as described in the authors’ previous publications.6,7 5.1. Environmental Base. Selected environmental indicators are based on process input data, other indicators are based on operating conditions, and the remaining are vital in assessing the impacts of chemicals utilized in the system and the potential impacts of their release. This classification aids with the formation of subcategories of indicators and with their calculation according to which process area (inputs, process, and outputs) with which they are related. An example of this application of subcategorization is during the production of biodiesel. Five streams (oil, MeOH, water, NaOH, H3PO4) are introduced into the system (streams 200−204), and the corresponding hazardous material lists were reviewed to determine if these input substances are classified as hazardous chemicals. This is the beginning step in the assignment of needed data for the sustainability evaluation. For this process, three of the five compounds, MeOH, NaOH, and H3PO4, were categorized as hazardous chemicals. The input mass flows of these compounds (streams 201, 202, and 204) were then summed to determine the total mass of hazardous substances fed into the process. The corresponding aggregate amount of produced biodiesel and glycerol (streams 300 and 301, respectively) represents the total mass of product. Thus, any indicator representing a ratio of material input or released material per unit of product (e.g., HHirritation) can now be calculated. The environmental process indicators that are based upon operating conditions (e.g., process temperature for computing the CEI (chemical exposure index indicator) and a portion of EHS indicators (e.g., SHmobility, SHacute/tox.) were calculated by collecting physicochemical properties (i.e., density, heat capacity, enthalpy of combustion, etc.), classification list codes (e.g., ECclass, GK, Rcode) of particular substances associated with potential hazards, threshold values (e.g., ERPG, IDLH, NFPA), and standard toxicity test results (e.g., LD50, LC50) of all of the chemicals either entering (inputs) or leaving (outputs) the process. In addition to the databases and tools provided in Table 5 of Ruiz-Mercado et al.7 which can be employed to supply the needed data inputs for calculating the EHS based sustainability indicators, the ProSim Simulis42 tool was utilized to obtain additional thermophysical property data of all eight substances utilized in the simulated process. The substance potency factor (e.g., PFCO2, PFCFC‑11, PFethylene) contributions that are employed to evaluate atmospheric and aquatic effect indicators (e.g., GWP, ODP, PCOP), were collected from the IChemE report43 and the

Table 5. Transformity, Standard Chemical Exergy Reference,48 and Specific Physical (Chemical) Exergy Values of the Substances and Energy Utilities Entering or Leaving the Biodiesel Manufacturing Process substance or type of energy utility MeOH oil FAME glycerol NaOH H2O H3PO4 Na3PO4 electricity combined cycle cooling water natural gas a

transformity value 1.76 × 106 kgb 5.18 × 107 kgb 5.24 × 107 kgb 4.49 × 108 kgb 2.69 × 106 kga 6.64 × 102 kgb 2.65 × 106 kga 2.65 × 106 kga 5.76 × 105 kWhb 6.90 × 104 MJa 1.71 × 106 m3b

exergy reference value

MseJ/

718.0 MJ/kmol

MseJ/

35354.6 MJ/kmol

MseJ/

12282.9 MJ/kmol

MseJ/

1640.6 MJ/kmol

MseJ/

74.9 MJ/kmol

MseJ/

9.5 MJ/kmol

MseJ/

104.0 MJ/kmol

MseJ/

518.1MJ/kmol

MseJ/

3.6 MJ/kWh

MseJ/

3.8 × 10−3 (5.1 × 10−4) MJ/kg 0.84 (0.53) MJ/kg

MseJ/

Data from Zhang and Long.45 bData from Cao and Feng.46

TRACI44 database. Figure 3 depicts on a radar diagram the individual scores of all environmental based sustainability indicators. The center of the radar graph represents a zero sustainability value (i.e., worst-case scenario) and the external boundary of the graph represents a 100% sustainability value (i.e., best-case scenario). The worst-case reference potency factor values assigned to environmental indicators no. 23 to no. 44 (see Supporting Information, Table S1) were different than the default GREENSCOPE proposed values.6 For this case study, all output materials have at least a potency factor equal to 1 for the calculation of the worst-case reference values, whereas, the default worst-case values for those substances with a potency factor different from zero were the only ones, which were accounted. Finally, for those environmental indicators based on emergy calculations (see Supporting Information, Table S1, indicators no. 45 to no. 51), the energy and material flow data (provided in Tables 1, 2, and 4) for all streams either entering or leaving the process were converted to their respective emergy flows by multiplying each energy/material value with their corresponding transformity parameter value. These transformity values in terms of solar equivalent joules (seJ) are provided in Table 5. 5.2. Efficiency (Material) Base. The material efficiency indicators are employed to represent the number and magnitude of feedstocks and other input materials, which are required to obtain the volume of desired valuable product(s). Ideally, to produce one unit of mass of product a corresponding unit of mass of feedstock is required. However, this is rarely the case and larger quantities of feedstock(s) are required, thus, emphasizing the need for material efficiency. The majority of the efficiency indicators employ input/output process material flows and their respective chemical reaction data. For this case study, all global input/output material flows and average molecular weights were used as described in Tables 1 and 2. The net transesterification reaction as described by eq 1 is the F



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Figure 3. GREENSCOPE environmental indicator results for the simulated alkali-catalyzed manufacture of biodiesel.

primary reaction employed and was used to collect the needed stoichiometric coefficients to evaluate these material efficiency indicators (e.g., reaction yield, actual atom economy, stoichiometric factor). The oil feedstock (stream 200) is identified as the limiting reagent and is only 95% consumed after one hour of reaction in the CSTR reactor. Figure 4 shows the calculated efficiency indicator results. These results are represented in a dimensionless sustainability scale using the worst-case and best-case scenarios as described by Table 2 in Ruiz-Mercado et al.6 For the calculation of some efficiency indicators (see Supporting Information, Table S2 indicators no. 15 and no. 16), the amount of reaction and postreaction solvents, as well as the amount of catalyst recovered must be identified and collected. For this case study, water is the solvent and NaOH the catalyst, and their respective mass flows are used as described in Tables 1 and 2. In addition, FAME, glycerol, methanol, and oil are all categorized as renewable materials;

and the oil, glycerol, H3PO4, and Na3PO4 are categorized as recyclable valuable material. Finally, if an output substance is categorized as waste, a percentage of recovery of that output needs to be specified by the user, otherwise, a 0% recovery value (worst-case) is assumed by default. 5.3. Energy Base. This important sustainability aspect was calculated by performing a caloric energy balance and a process exergy analysis. Since the simulation results for this biodiesel process generated the corresponding enthalpy values of all process streams, the energy (i.e., caloric) and entropy content values of all streams either entering or leaving the systems were collected. Tables 1 and 2 provide these calculated values for all global input/output streams and their respective enthalpy and entropy values. However, it is not a requirement to have this direct assessment of these thermodynamic values. There are many correlations, equation of state models, and thermodynamic package tools that can calculate the stream enthalpy and entropy values by employing pure substance physical properties G



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Figure 4. GREENSCOPE material (efficiency) indicator results for the simulated alkali-catalyzed manufacture of biodiesel.

(e.g., standard phase change temperatures and enthalpies, substance heat capacity, heat of formation) and composition, temperature, and pressure conditions. A schematic representation for the calculation of the enthalpy and entropy of a component is described by Table 4 in Ruiz-Mercado et al.7 In addition, the energy needs for the main processing units were calculated during process modeling and simulation and are provided in Table 3. It is important and necessary to identify the source of energy being provided to the system, since these energy needs are being fulfilled by a variety of utilities such as electricity (e.g., power need in pump P-101), steam (e.g., heating need in heat exchanger H-101 and reboiler of distillation column T-101), and cooling water (e.g., heat removal need in the condenser of the distillation column T103). The computation of these total utility needs was performed by assuming all utilities are provided off-site and delivered to the battery limit of the modeled process. These assumptions include the following: steam is produced from saturated water at P = 1.7 bar and 115 °C using a natural gasfired industrial boiler with a 90% efficiency that employs 2.251 MJ as a primary energy equivalent (see p. 243 in Turton et al.9) per kilogram of produced steam. For process cooling needs, moderately low temperature refrigerated water is introduced at 5 °C and returned at 15 °C and is supplied by a combined cooling water loop/refrigeration cycle as described in Turton et al.9 (pp. 232−238). Additionally, the cooling water is provided off-site and delivered to the process. It is necessary to express these cooling needs in terms of primary energy equivalents (i.e., in this example as electricity). This value was calculated to be 71.42 kWh of electricity required per 1 GJ of heat removed from the process. In general, 1 MJ of electricity is the equivalent of 3 MJ of caloric energy from a primary energy source.47

Table 6 describes the overall direct energy balance for this process in terms of direct caloric energy value of the feed and Table 6. Overall Direct Energy Balance of the Biodiesel Manufacturing Process in Terms of Direct Caloric Energy Value of the Feed and Output Streams, Total Heating, And Cooling Needs, And Power Needs input, MJ/h feed streams output streams total heating total cooling power added total

output, MJ/h

renewable, MJ/h

nonrenewable, MJ/h

−3361.59

−3825.00 −2740.10

−513.30 −621.49

−4338.30

2100.78 −1124.90 2.22 −3360.21

−3361.59

output streams, total heating, and cooling needs, and power needs. Note the enthalpy flow values for each stream are subcategorized depending on the renewability labeling of the material fed or leaving the system as described in the last paragraph of the previous subsection. Figure 5 shows the calculated values for the energy sustainability indicators for this simulated biodiesel manufacturing process evaluation. The specific caloric content of the total valuable products was selected as the best target (2.5 MJ/kg) and 10 times (10×) this value was chosen as the worst case reference value to establish the dimensionless sustainability scale. In addition, the net quantity of energy for waste treatment was calculated by adding the heating, cooling, and power needs (as primary energy equivalent units) from equipment R-102 and S-102. The energy requirements for H



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Figure 5. GREENSCOPE energy indicator results for the simulated alkali-catalyzed manufacture of biodiesel.

units T-101 and P-103 represent the process material recycling energy needs. And finally, the exergy values for this process were computed as described in Table 4 of Ruiz-Mercado et al.7 Table 5 lists the standard chemical exergy values for each substance, the exergy factor for electricity, and the specific chemical and physical exergy for steam and cooling water demands. For the computation of the physical exergy values, some modifications (changes to a unified reference state) to the process thermodynamic data values must be implemented since the process simulator results for enthalpy and entropy values had a different reference state (ideal vapor heat of formation at 25 °C) than the one employed for exergy calculations (liquid state enthalpy and entropy values at 25 °C). 5.4. Economic Base. For the evaluation of the economic profitability indicators (e.g., net present value indicator, NPV) during the manufacturing of biodiesel, the capital and manufacturing costs (CTM and COM) must be calculated. To achieve this the calculation procedure described in Table 3 of Ruiz-Mercado et al.7 was used. As with any process its cost is largely dependent on the price of raw material (CRM), cost of utilities (CUT), cost of operating labor (COL), purchased cost of equipment, and cost of waste treatment (CWT). Table 7 lists the specific substance costs, utility rates, and the average hourly wage that were used for calculating the above-mentioned costs by multiplying these cost rate values with the respective process material and utility flows as listed in Tables 1, 2, and 4. To provide the needed estimated purchase cost for the main processing equipment, the equipment list for this process was multiplied by their respective purchase cost to arrive at the needed values as listed in Table 3. The majority of the equipment costs were obtained directly from the CHEMCAD simulator. However, other equipment costs (the purchased cost of units S-101, S-102, R-101, and R-102) were calculated using the CAPCOST tool.9 To complete this cost inventory, other miscellaneous economic parameters must be known for

Table 7. Inventory of Feedstock and Product Costs, Utility Costs, And Average Hourly Wages. In addition, the Feedstock and Utility Annual Needs and Costs as Well as the Annual Product Generation and Price Are Described item hourly wages, $/h FAME, kg glycerol, kg MeOH, kg H3PO4, kg oil, kg water, kg NaOH, kg steam, kg cooling water, kg electricity, kWh waste disposal nonhazardous, solid, 1/kg waste disposal nonhazardous, liquid, 1/kg waste disposal hazard, solid, 1/kg waste disposal hazard, liquid, 1/kg air purification, 1/m3

price, $/unit

annual needs, 1/yr

total cost, $/yr

27.79 1.675 0.881 9 0.446 0.9 1.2 0.000 067 0.43 0.029 59 0.000 185 0.06 0.036

8 678 341.8 981 792.0 1 027 375.2 87 660.0 9 204 300.0 96 426.0 87 660.0 9 220 879 615.0 234 515 419.8 5 392.9 566 596.6

14,536,222 805,112 458,209 78,894 7,639,569 7 37,694 272,846 43,385 324 20,398

191 624.8

6,899

1.1

43 205.9

47,527

1.1

61 833.6

68,017

1 111 642.8

62,252

0.036

0.056

calculating any process cost (e.g., CTM, COM) and any profitability indicator (e.g., NPV) including land cost, life of the plant, depreciation method, discounted rate, etc. These assumed values are provided in Table 8. Upon collection of all necessary data inputs, the GREENSCOPE economic sustainability indicators were calculated, and their results were presented in Figure 6. The calculation of the I



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6. DISCUSSION ON SUSTAINABILITY ASSESSMENT AND PROCESS EVALUATION Since sustainability is a holistic concept, its implementation during the process development phase, or any phase, must be supported by a realistic, practical, and technical evaluation procedure. Currently, some sustainability assessment approaches have been proposed and applied during the design of chemical processes. For an existing process evaluation, current assessment results obtained are often compared with results from a previous test(s) or with results obtained from another process producing the same valuable product. However, many of the important aspects relative to sustainability such as having a reliable and practical measurement scale to track whether gains or improvements toward sustainability are actually being made at different process design scales, variation of process boundaries, or evaluation of novel processes are still lacking. To address this gap, the authors are presenting GREENSCOPE as a tool that can support and fulfill these needs for the sustainability evaluation of a chemical process. A core contribution of the GREENSCOPE process evaluation methodology and tool is the ability to define and calculate the best case and worst case limits for each of the 140 indicators. By setting these boundary limits for each indicator, the corresponding indicator value can be calculated as a percent sustainability value. This value now allows for a direct assessment of a chemical process for its own sustainability status and for assessing how far the process is from achieving a more desirable sustainable state. During this case study, the majority of the proposed best and worst case scenarios were implemented without any adaptation or modification from what was originally proposed in the

Table 8. Summary of Miscellaneous Economic Parameters That Must Be Collected for Calculating the Process Cost (e.g., CTM, COM) and Profitability (e.g., NPV) Indicators P, the number of processing steps involving the handling of particulate solids (e.g., transportation and distribution). In general, P = 0. land cost, Cland, $US life of the plant, n, yr fixed income tax rate given by the IRS, Φ, fraction depreciation method plant startup at end of year number year in which the first investment is made before the startup time, b, yr salvage-value, recm, $US discount rate, rd present worth factor, PWFcf. Single payment at the end of year m minimum acceptable rate of return, MARR

0

0 10 0.3 straightline 1 1 0 0.1 (1+i)−m 0.16

best-case and worst-case reference values for the economic indicators was performed based on the sustainability values described by Table 3 in Ruiz-Mercado et al.6 In general, the GREENSCOPE tool was developed to create the best and worst economic profiles along with their respective profitability and process cost assessment. This is done by assuming the maximum and minimum processing costs (e.g., direct and indirect costs, manufacturing costs, fixed charges, general expenses, working capital, CRM, CUT, COL, CWT) and the average values contained in current chemical company reports.49

Figure 6. GREENSCOPE economic indicator results for the simulated alkali-catalyzed manufacturing of biodiesel. J



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authors’ previous publication;6 however, for some indicators these sustainability boundary values were changed or adapted to maintain their practicability for being implemented. For example, the safety hazard reaction/decomposition II (SHreac/dec II) environmental indicator worst-case value was modified to capture the potential of an unexpected temperature rise and the resulting decomposition enthalpies of the process substances having a potential for an uncontrolled release. Thus, this new scenario was adopted for the calculation of a new worst-case limit for this environmental indicator. The results for many of the environmental sustainability indicators, as described in Figure 3, represent a high level of sustainability and are indicative of good process performance for many environmental aspects. However, there are some critical aspects related to solid releases as reflected in some of the indicator scores (see Supporting Information, Table S1 indicators no. 52−57, 59, 60). This is the result of the converted fats and oils being categorized as solid materials for landfill, and the release of unreacted oil (stream 304) representing a high volume of solid waste. By having this information from the sustainability evaluation, there exist opportunities for achieving improved sustainability for the process. These opportunities for improvement include the ability to capture the output waste stream, with a high content of valuable feedstock (oil) and recirculating it back into the system. In addition, the liquid waste related indicators (see Supporting Information, Table S1 indicators no. 65 and 66) exhibit low sustainability indicator scores. These scores may be improved if the amount of released liquid waste (stream 302), which is primarily a mixture of water and methanol can be reused by reintroducing it back into the process. The main reaction, a base-catalyzed transesterification of oil with an alcohol, is an inherently strong and selective reaction as exhibited by the high conversion of the oil to produce the desired biodiesel. This high conversion results in many of the efficiency sustainability indicators having high sustainability scores as shown in Figure 4. These high sustainability scores reflect high process efficiency achieved to convert these feedstocks into valuable products. However, to achieve this, the process requires large amounts of excess methanol and the homogeneous catalyst (NaOH, stream 202). Additionally, the large excess of methanol has an impact later in the process reflected by the need for separation units (T-101 and W-101) after the reaction is complete. These separation steps are energy intensive (heat addition and removal). Therefore, by altering the reaction conditions or employing new process chemistry a potential result could be the elimination or minimization of separation steps. This improvement may lead to an improvement in the sustainability performance across all four bases (e.g., less capital and manufacturing costs, less energy intensity, lower emissions). The energy sustainability indicator results (see Figure 5) describe a thermodynamic process analysis that is primarily based on conventional energy balances and exergy analysis. According to the results, this simulated biodiesel manufacturing process requires a small quantity of energy per unit mass of generated product (10 MJ/kg). These favorable energy intensity results are common in the production of fuels. Additionally, another important aspect that contributes a benefit to this type of manufacturing process is the high energy content of the main feedstock (oil) which is not lost during the chemical reaction and remains in the biodiesel product.

For this case study, it was assumed any energy supplied to the process such as electricity, steam, and cooling water was generated from nonrenewable energy sources. This explains the zero value obtained for the renewability-energy index indicator (RIE). As mentioned above, the required excess MeOH contributes to a high demand of energy. This contribution is further reflected in the energy for recycling indicator (Erecycl), which has a worst-case limit assumed as 10% of the total process energy demand. Under the current conditions, 30% of the total energy demand is consumed by the MeOH recycling units (units T-101 and P-103). Upon completing this sustainability evaluation, energy improvement opportunities have been identified in the output process streams (see Table 2). These streams exit the process at elevated temperatures. Such opportunities for experiencing energy gains include placing heat exchangers in the prior process steps (e.g., for preheating needs), or reevaluating the design of the product purification units (units T-102 and T103) which may be overdesigned, and reviewing operating conditions that may be changed to still perform the same task but requiring less energy to do so. In addition, the exergy related sustainability indicators (see Supporting Information, Table S3 indicators no. 10 to no. 14) exhibit high sustainability scores, which is the direct effect of minimum quantities of exergy being fed directly into the process that is lost or destroyed. The calculations performed demonstrate a 1.3% exergy loss as the direct result of entropy being generated. In comparison with the evaluation of the previous sustainability bases, the economic results detail both profitability and costs. In accordance with the required procedure for computing these economic sustainability indicators, their results show a strong dependency on costs that are associated to the process inputs such as feedstocks and energy utilities. While this not a new consideration, it does demonstrate the value of the GREENSCOPE methodology for its economic component. For this case study, the cost per unit for the oil feedstock is an important parameter and a dependent variable when considering the economic sustainability of this chemical process. According to the collected data and calculated indicators, CRM represents 58% of the total manufacturing cost (COM). However, it must be detailed the proposed sustainability scale for CRM has an established range value between 25% (best case) to 45% (worst case) of the COM. In addition, the COM has the same importance as the sales revenue when performing the profitability analysis. For this case study, a biodiesel sale price of 1.675 US $/kg was assumed for the calculation of sales revenue. Since the price for biodiesel changes as a function of time, the assumed value will be either above or below the current market price. Therefore, this price uncertainty introduces a high variability to the economic analysis for the current simulated biodiesel manufacturing process, resulting in a process that easily shifts from feasible to infeasible economic results. Furthermore, it was assumed that feedstock costs and product sale prices did not change during this economic sustainability evaluation. Upon comparison of the current results, for this evaluation, with the average values obtained from current chemical company reports,49 it appears this current process can be further improved to yield a more profitable business (e.g., higher NPV and present value ratio). In addition, other results have demonstrated the total capital investment (TCI) can be recovered after 2.9 years of the plant start-up, which is a good payback period since the average life K



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from other data-sources. Since an economic assessment is a portion of a process sustainability evaluation, one was performed to obtain all relevant process cost data from the feedstock and utility demands. Calculation of all of the GREENSCOPE indicators was performed after completion of all data requirements. The results obtained for this simulated biodiesel manufacturing process were illustrated on radar diagrams for each of the sustainability bases (environmental, efficiency, energy, and economic bases). The center of the radar graph represents a zero sustainability value (i.e., worst-case scenario) and the external boundary of the graph represents a 100% sustainability value (i.e., best-case scenario). This clear sustainability measurement scale allows users and designers to identify trends and process areas in need of sustainability improvement. In addition, a complete process sustainability evaluation provides results to aid in understanding the most relevant process aspects, which are significant in influencing the sustainability performance. This can be completed by analyzing and comparing indicator results when applied to the same aspect but describing the results under differing points of view or value judgments. Finally, this contribution demonstrated the practicability and robustness of the GREENSCOPE tool for process sustainability evaluation during the manufacturing of biodiesel. This, the authors’ third contribution in this series, is practical evidence the proposed set of indicators, and their realistic measurement scales can quantitatively measure the sustainability level for any chemical process without falling into ambiguous definitions or qualitative aspects of sustainability. Therefore, GREENSCOPE is proposed and demonstrated as a reliable and powerful tool for sustainability evaluations and has the potential to aid in the development and optimization of chemical processes to increase their sustainability.

of a biodiesel facility is 10 years. The cumulative cash ratio indicator (CCR) is a useful indicator when several projects with different capital costs have to be compared. By definition, if the CCR is greater than 1 the project is viewed as profitable, whereas, if the CCR value is less than 1, the project is deemed unprofitable. A CCR value of 4.7 was calculated for this simulated process, which indicates it is an economically feasible project (i.e., profitable). It should be noted when these results are compared with average values obtained from current chemical company reports using the proposed sustainability scale for this CCR indicator, the results obtained show a best scenario value equal to 56, which can be achieved for this type of business and conditions. Therefore, the relative sustainability is 8.35%. Finally, when reviewing the other economic sustainability indicators (e.g., process costs), the results describe a favorable process cost performance in the areas of energy, water, and waste treatment costs. This detailed information can be helpful to support decisions that may be made to provide process modifications that might involve additional expenses as reflected in increments of these manufacturing costs. Since these costs are below those of the chemical sector average, designers may be allowed to implement these process changes for achieving energy, environmental, and efficiency (material) improvements without creating a significant negative impact to the economic state of the process.

7. SUMMARY The successful implementation and use of the GREENSCOPE process sustainability evaluation and design tool for a simulated biodiesel manufacturing process has been demonstrated in this contribution. This chemical process case study evaluation was performed by following and utilizing the taxonomic classification and definition for all of the GREENSCOPE sustainability indicators as categorized in the environmental, efficiency (material), energy, and economic bases. By extending this evaluation and measuring the sustainability of this chemical process, this contribution has demonstrated the feasibility of the previously proposed GREENSCOPE methodology and tool. Additionally, this tool has successfully calculated and quantified sustainability indicators for a chemical process by identifying and developing a practical sustainability scale for each indicator as defined by the proposed two boundary value scenarios. These reference values represent a best possible target (100% of sustainability) and a worst case (0% of sustainability) on a sustainability measurement scale. Owing to the relevance of biodiesel supplying the current and future energy demands of the United States, as stated by the requirements of EISA, it is crucial that all biodiesel manufacturing processes perform under sustainable guidelines. Therefore, a biodiesel manufacturing process was selected as the case study for implementing the GREENSCOPE sustainability evaluation and design tool. An alkali-catalyzed transesterification reaction was considered for the manufacturing of FAME and a complete steady state process simulation was performed. Upon completion of the simulation, process data consisting of material input/output streams, energy requirements, process operating conditions, and equipment design specifications were collected from the process simulation results. Additional data pertaining to materials (feedstocks and products), thermophysical properties, toxicities, potential hazards, threshold values, standard toxicity test results, potency factors, etc. were collected

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ASSOCIATED CONTENT

S Supporting Information *

Listing of indicators for each basis, best, and worst case scenarios for each indicator, indicator-calculated and percent sustainability value: Table S1. Environmental Indicators; Table S2. Efficiency Indicators; Table S3. Energy Indicators; Table S4. Economic Indicators. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel.: 513-569-7030. Fax: 513-569-7111. Notes

The U.S. EPA through its Office of Research and Development (ORD) conducted the in-house research described here. It has not been subject to Agency review and therefore does not necessarily reflect the views of the Agency. No official endorsement should be inferred. The authors declare no competing financial interest.

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REFERENCES

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NOTE ADDED AFTER ASAP PUBLICATION Due to a production error, this paper was published on the Web on May 9, 2013, without the revised artwork for Figure 3 implemented. The corrected version was reposted on May 13, 2013.

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