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Life cycle energy assessment of a standby diesel generator set
Journal of Cleaner Production 149 (2017) 265e274
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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Life cycle energy assessment of a standby diesel generator set Kelly Benton a, Xufei Yang a, *, Zhichao Wang b a b
Department of Environmental Engineering, Montana Tech of the University of Montana, Butte, MT 59701, USA EcoEngineers, Des Moines, IA 50309, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 August 2016 Received in revised form 4 January 2017 Accepted 11 February 2017 Available online 16 February 2017
The global demand for emergency standby power (ESP) diesel generators continues to grow because of increasing population and urbanization in developing countries. In order to better understand and further reduce the environmental impact of these products, the life cycle assessment (LCA) methodology was applied to an 455 kW ESP diesel generator set to quantify the energy demands of each life cycle stage: materials, manufacturing, transportation, use, and end-of-life disposal. The life cycle inventory (LCI) was completed based on the information acquired from the manufacturing company and its suppliers, and the impact assessment, i.e., energy demand calculation was done using the data from the Ecoinvent and the Inventory of Carbon and Energy (ICE) databases. The results revealed that, similar to on-highway engines, diesel generators consumed most energy (>95% of the entire life cycle) during the use phase, followed by materials, transportation, and then manufacturing. Therefore, increasing fuel efficiency will have the largest energy and potentially environmental benefits. Printed circuit boards (PCBs), although of small mass, accounted for ~35% of energy demands during the materials stage. The materials-related energy demands can be considerably reduced by increasing remanufacturing and recycling rates. Results from this study are expected to help the genset manufacturers to optimize their product design, supply chain, and service so as to minimize the lifetime environmental impact of the product. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Life cycle assessment Standby generator set End of life Recycle Remanufacture
1. Introduction Increased environmental awareness among the public urges industries to proactively evaluate the impact of their operations on the environment. Industries are now moving beyond environmental compliance by incorporating sustainability in the list of company values, which sends a message to the public that their employees are taking actions to protect the environment and conduct business in a sustainable manner. These actions prompt environmental managers and decision makers to look at their products and services from cradle to grave. As a result, the need for Life Cycle Assessment (LCA) continues to grow. LCA is a method for evaluating the cumulative environmental impacts resulting from all stages in the product life cycle (Environmental Protection Agency, 2006). It started as a tool to evaluate individual products but has now developed into a standardized method for providing a scientific basis for environmental sustainability in various
* Corresponding author. E-mail address: [email protected] (X. Yang). http://dx.doi.org/10.1016/j.jclepro.2017.02.082 0959-6526/© 2017 Elsevier Ltd. All rights reserved.
industries (Curran, 2013; Kouchaki-Penchah et al., 2016a, 2016b). This study describes a life cycle assessment (LCA) performed on a standby diesel generator set in cooperation with a large diesel engine manufacturing company in the United States, which also produces power generation products. A standby diesel generator set, hereafter referred to as a genset, is a combination of a diesel engine with an alternator to convert chemical energy in diesel fuels to electricity (Fig. 1). Emergency standby power (ESP) gensets are used to supply power to electrical appliances during the power interruption of the utility source. ESP gensets are essential for applications that require an uninterrupted power supply. Today, nearly every industry needs an ESP genset, as economic loss can be far more expensive than the capital expenditure for the backup power equipment (RNCOS Market Research, 2014). The genset market is driven by the rapidly expanding global population and urbanization of cities throughout the world (Diesel Service and Supply, 2016). The genset demand will continue to increase as industries such as oil and gas, electronics, semiconductors, textiles, food processing units, automotive, shopping malls, and data centers turn to diesel generators to deal with unexpected power outages (Sverdlik, 2013). This demand is especially
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Fig. 1. A typical diesel generator set and its major components. Air filter, turbo charger, and connecting hoses were excluded for assessment due to limited data availability. Note the picture is for demonstration only and may not represent the actual unit investigated in this study.
prevalent in Asia-Pacific, where the data center industry is rapidly expanding, especially in Singapore, Malaysia, Philippines, Thailand, and Australia. Data centers require gensets with a capacity of up to 20 MW (MW) for ESP applications, and therefore, the demand for large diesel gensets with a power output capacity between 1 MW and 3 MW is on the rise (Frost and Sullivan, 2013). It was predicted that the market for 1e3 MW diesel gensets will grow from ~$590 million in 2012 to ~$800 million in 2017 (Sverdlik, 2013). Another study found that the Indian diesel generator market grew 9.5% between 2012 and 2013, and the market would grow at a compound average growth rate of around 11% in value terms during 2014e2018 (RNCOS Market Research, 2014). The global genset market will continue to be driven by the lack of grid infrastructure in remote locations and increasing industrialization in developing countries. Despite the rapid growth in market demand, only a few LCA studies have been done on diesel gensets. Most of them entailed a comparison of different energy production devices including gensets. Gmünder et al. (2010) compared jatropha oil fueled gensets with diesel gensets, photovoltaic panels, and power grids with respect to greenhouse gases (GHGs) emission and other environmental impacts such as acidification and eutrophication, and concluded that jatropha oil fueled gensets significantly reduced GHGs emission when compared to the other three systems. However, no information was provided regarding the consumption of energy and materials during the manufacturing, transportation, or disposal of the diesel genset. Fleck and Huot (2009) compared a small wind turbine with a diesel genset for residential off-grid use, and reported that although the wind turbine was slightly more expensive than the diesel genset over the entire life cycle, it delivered 93% reduction in GHGs emission. Numerous simplifications and assumptions were made during the assessment of the genset. For example, the material composition of the diesel genset was approximated to be 60% steel, 35% aluminum, and 5% copper. Obviously, this and similar simplifications may cause considerable uncertainties in the final LCA results. Pascale et al. (2011) compared a 3 kW community-scale hydroelectric system with a 7 kW diesel genset in rural Thailand, and found that the hydroelectric system offered better environmental and financial benefits than the genset. However, similar to the study by Gmünder et al. (2010), no information was provided regarding materials and energy consumption during the genset manufacturing or transportation. LCA studies have also been done on diesel engine and alternator,
the two major parts of a diesel genset. Li et al. (2013) investigated the energy consumption and environmental impacts of an on-road diesel engine over its entire life cycle, and reported that the use phase accounted for >99.0% of the total primary energy demand, 97.7% of the total GHGs emission, and 94.2% of the total acidification potential. Cooney et al. (2013) compared mass transit buses driven by diesel engines and electric motors, and concluded that the use phase was dominant in causing global warming, carcinogens and other environmental impacts for both diesel-powered and electric buses. Zhang et al. (2015) compared remanufactured diesel engines with newly built ones, and found that engine remanufacturing reduced the eutrophication potential by 79% and the GHGs emission by 67%. Schau et al. (2012) investigated the economic and environmental benefits from remanufacturing of alternators, and revealed that remanufactured products caused only 12% of the GHGs emission and costs when compared with new ones. Although efforts have been made to assess the life cycle of diesel gensets and their key components, a detailed systematic investigation is still lacking, especially for large-capacity gensets. To manufacturers, an LCA will allow decision makers to better report, understand, and interpret the environmental impact of their product in a manner that promotes sustainable product and process choices in the future (Curran, 1996). In 2013, the company who manufactured the diesel genset in this study partnered with a master student from the Massachusetts Institute of Technology (MIT) to perform an LCA on a 15 L displacement engine used in the on-highway application (Bolin, 2013). The primary focus of that study was to understand the energy demands of the life cycle stages prior to the use phase because it was well recognized that the use phase was the most energy intensive for on-highway applications. This genset study not only includes the engine information, but extends the analysis to the full life cycle of the engine as a part of the genset. 2. Methodology 2.1. Goal and scope definition The goal of this study is to perform an LCA on a standby genset in order to quantify the energy demand for each life cycle stage and identify which is the most energy intensive one. The life cycle stages of this analysis include materials, manufacturing, transportation, use, and end of life (EoL), making the study a “cradle-tograve” analysis. This LCA has been streamlined in order to align the results of the assessment with the goal of the study. A combination of the streamlining techniques described by Keith Weitz at the United States Environmental Protection Agency (USEPA) conference on streamlining LCA was used to perform this streamlined LCA (Weitz and Sharma, 1998, Weitz et al., 1999). 2.1.1. Functional unit The subject of this study is a standby diesel genset. This particular model is equipped with a heavy-duty 15 L engine with a 455 kW rating. As an emergency standby power supply, the primary function of the genset is to convert chemical energy in diesel fuels to mechanical energy (by a diesel engine) and then to electricity (by an AC alternator) during power grid disruptions. The functional unit of the genset is based on the amount of diesel fuels consumed, which is a function of fuel economy and operation time. Under normal conditions, the genset has a life expectancy of 20 years and an operation time of 50e100 h per year, according to the manufacturer. 2.1.2. Process description and system boundaries To conduct the LCA, the generator was divided into five main
K. Benton et al. / Journal of Cleaner Production 149 (2017) 265e274
components, which are the engine, alternator, radiator, electronic controls, and skid (Fig. 1). There are additional parts such as the air filter, turbo charger, and connecting hoses that were not considered due to limited data availability, and they account for 13% of the genset mass. Individual components are manufactured at different facilities of the company in the United States and other countries, or purchased from other providers before delivered to the company's central facility in the United States for assembly into gensets. The processes that are specific to the production and use of the genset are summarized as follow: The individual parts are made in their respective manufacturing facilities and then shipped to the assembly facility. Upon completion of assembly, the product is distributed to the customer, where it is used until it has reached its end of life. Then it can be recycled, remanufactured, or sent to a landfill. Most likely, the disposal route is a combination of these three options. This study assesses the entire life cycle of a genset with varying level of detail for each stage. The “materials” stage includes the raw material extraction and processing required to make the raw material into a usable form. The “manufacturing” stage for this LCA is defined as the stage in which each of the five main parts of the genset is built in its respective manufacturing facility. This stage also includes the step in which the individual parts are assembled into a genset because of the impact metric and similarity in data type. The “use” phase is fairly straight-forward and was taken as a category of its own. The “end of life” stage, however, is more complicated because of the variety of disposal routes, which will be explained in detail in a later section. While transportation occurs between each of these stages, the only piece considered is between the part manufacturing facilities and final assembly site. Downstream transportation that occurs after the product is built was not considered due to a lack of relevant data. This will lead to a significant underestimate of energy consumed during the “transportation” stage. This LCA considered the processes discussed above and the energy inputs associated with them (Fig. 2). Each process has outputs such as air and water emissions that have an impact on the environment but were not considered in this LCA because the goal
267
of the study is to quantify the energy demand for each stage.
2.1.3. Impact metric The impact metric used for this study is energy in units of megajoules (MJ). Energy will then serve as an indicator of the overall environmental impact. Energy is a simple metric and is generally understood by the public, and can be used as a proxy for CO2 if necessary (Ashby, 2013). The relationship between the two impact metrics for diesel engines was further validated by Bolin in the MIT study, in which a strong correlation between embodied energy and greenhouse gas emissions was presented. Energy is also most closely related to production costs more than any other environmental metrics (Bolin, 2013), so this metric has the benefit of relating to not only greenhouse gases, but cost as well. The purpose of this method is to quantify the total energy use throughout the life cycle of the genset, including the direct and indirect energy usage during the extraction of raw materials, manufacturing, transportation, use, and waste disposal. There are four different types of energy considered in this study: (1) embodied energy, primary production; (2) embodied energy, recycling; (3) electricity; and (4) fuel. The embodied energy (EE) is defined as the total primary energy consumed from direct and indirect processes relevant to the materials within the cradle-to-gate boundaries. This includes all activities from raw material extraction, manufacturing, transportation, and through fabrication until the product is ready to leave the factory gate. It is important to note that the energy consumption at each life cycle stage can be supplied by unsustainable (e.g., fossil fuels) and/or sustainable energy sources (e.g., wind power), and this has a significant impact on the overall environmental impact of the diesel genset. However, due to a lack of relevant data, this study does not further distinguish the type of energy sources. This simplification will not affect the life cycle energy balance within the system boundary given in Fig. 2, but will be influential if energy supplies are included in the boundary.
Fig. 2. System boundary of this study.
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2.2. Life cycle inventory analysis 2.2.1. Data requirements and data collection The data requirements for each life cycle stage are listed in Table 1. Engineers and specialists from a variety of departments within the manufacturing company provided information for this study. The multi-disciplinary involvement required for this study is a tribute to the complexity and broad nature of LCAs. In addition to the manufacturer, a variety of data sources were used to complete this study. Table 1 summarizes the data type and source used for each life cycle stage. The most challenging stage to collect data for was the materials stage because the genset is made of over 15,000 parts. This expansive list consists of pieces as small as nuts and bolts. To make the part list manageable and realistic, the list was grouped into five parts (engine, alternator, radiator, electronic controls, and skid) described in the scope of this study. A list of material types and masses for each part is not typically kept on record, so the level of accuracy was dependent on the data available. Part drawings, purchasing data, and general part knowledge shared by design engineers were all compiled to generate the data. It is important to note that these values are an estimate and are not exact. The EE values for calculating primary production of materials were acquired from a combination of Ecoinvent (Weidema et al., 2013), Materials and the Environment: Eco-informed Material Choice (Chapter 15) (Ashby, 2013), and the Inventory of Carbon and Energy (ICE) Version 2.0 (Hammond and Jones, 2011). The “manufacturing” data was collected from plant records of the manufacturer and suppliers. The data included facility energy usage and production data for the year of 2014. The “transportation” data was simply acquired by identifying the manufacturing and assembly location and calculating a travel distance between the two. The transportation mode was identified by the manufacturer, and the transportation energy intensities were acquired from Ecoinvent. Product warranty and life time data was collected from the manufacturer for the use phase assessment. The EoL disposal route is difficult to identify. The product is in the customer's hands when it has reached its EoL, so the customer is at liberty of choosing the disposal route, which is not usually reported back to the original manufacturer. Therefore, it is difficult to acquire this data. In order to explore the energy demands of different disposal routes, four scenarios were used in which different combinations of disposal Table 1 Data requirements and sources for each life cycle stage. Life Cycle Stage
Data Required
Units
Data Source
Materials
Material composition of parts Material mass Embodied energy, primary production values Facility energy usage Production volume Travel mode Travel distance Weight of shipment Energy intensity of transportation mode Lifetime Run time Fuel type Fuel efficiency Calorific value of fuel Disposal Route Material type Material mass Embodied energy, recycling values
e kg MJ/kg
Manufacturer and suppliers Ecoinvent, Ashby, and ICE Manufacturer and suppliers Manufacturer
Manufacturing Transportation
Use
End of Life
MJ e e km Ton MJ/t*km Ecoinvent yr hrs/yr e gal/hr BTU/gal e e kg MJ/kg
Manufacturer
routes were used. The recycling embodied energy used for these scenarios was acquired from Ashby (2013). For the few materials that lacked recycling data, it was assumed that the recycling energy was one-fifth of the embodied energy (Ashby, 2013). This assumption will inevitably cause uncertainties in calculated energy consumption (or saving) during the EoL stage. However, since only five materials (out of 19) used assumed recycling energy values, it is expected that the uncertainties caused would be relatively minor.
2.2.2. Data calculation procedures The data calculation procedures vary for each life cycle stage, so this section is broken down accordingly. Materials: The calculations for this stage are straightforward: the mass of material was multiplied by the embodied energy value (Tables S1 and S2). The sum of each material's energy is then the total energy for the material stage, as shown in equation (1).
EM ¼
X
mi *Hi
where M represents all of the materials in the genset and i2M represents the individual materials. E is energy (MJ), m is mass of a material (kg), and H is effective embodied energy of a material (MJ/ kg), which is defined in equation (7). Manufacturing: The energy for this stage was calculated by dividing the facility energy consumption by the number of parts produced. The sum of each facility's energy is then the total energy for the manufacturing stage, as shown in equation (2).
EF ¼
X f j2F
p
(2) j
where F represents all of the manufacturing facilities, j2F represents the individual facilities, f is the energy of the manufacturing facility (MJ), and p is the number of parts produced at the facility. Transportation: The transportation stage was calculated by multiplying the transportation intensity by the shipment weight and the distance traveled. The energy for each part to be transported was then summed to acquire the total energy for the transportation stage, as shown in equation (3).
ET ¼
X
Htk *dk *sk
Ashby
(3)
k2T
where T is all of the transportation modes and k2T represents the individual transportation modes. Ht is the transportation energy intensity (MJ/t*km), d is the distance traveled (km), and s is the shipment weight in metric tonnes (t). Use: The use phase was calculated by multiplying the life expectancy by operation time, fuel efficiency, and calorific value of the fuel, as shown in equation (4).
EU ¼ LE*OT*e*CV Assumed scenarios Manufacturer
(1)
i2M
(4)
where U represents the use phase, LE is life expectancy (yrs), OT is operation time (hrs/yr), e is fuel efficiency (gal/h), and CV is calorific value of fuel (BTU/gal). The calorific value used in the calculation was specified by the manufacturer as 130,000 BTU/gal.
K. Benton et al. / Journal of Cleaner Production 149 (2017) 265e274
End of Life: The EoL stage is more complex than the others. Ashby (2013) described the activities included in each of the three disposal routes considered for this study: 1. Landfill e Collect and transport to landfill site. 2. Recycling e Collect, sort by material family and class, recycle. 3. Remanufacturing e Collect, dismantle, replace or upgrade components, re-assemble. To account for the possible net energy saving or consumption associated with the EOL stage, an energy credit/debit was calculated. Recycling and remanufacturing are usually allocated an EoL credit (or benefit) as they save energy for the materials stage; while the landfill disposal route receives an EoL debit (or energy allocation) as landfill demands energy input. In the overall energy balance equation, the credits are represented as a negative value because it reduces the total energy demand, whereas the debits are expressed as a positive value because it adds to the total energy demand and has a negative impact on the environment. Ashby (2013) provided an equation for each disposal option. Equation (5) shows the energy debit of the landfill disposal route.
EL z
X
0:1*l*mi *Hi
Scenario 1: This scenario was modeled to show the negative impact of sending the entire product to the landfill. This is not realistic, but provides a baseline for the other three scenarios. Scenario 2: This scenario was based on the remanufacturing rates of the engine. The manufacturer knows that 85 wt% (Note: wt% refers to mass percent) of an engine can be remanufactured. Given the engine makes up about 38 wt% of the entire genset and assuming that the engine is only part that could be remanufactured, that would mean that 32 wt% (85%*38%) of the genset could be remanufactured. The recycling rate for scenario 2 came from a USEPA report on waste generation, recycling, and disposal in the United States for 2012 (Environmental Protection Agency, 2012). The report listed metals as having an average recycling rate of 34 wt%. The remainder of the genset was then assumed to be sent to the landfill. Scenario 2 then serves as a baseline for 3 and 4 because realistically, more of the genset can be remanufactured. Scenario 3: This scenario was generated with a remanufacturing rate in middle ground between scenario 2 and 4. The recycling rate was kept the same as scenario 2. Scenario 4: This scenario was created to analyze what the EoL impacts would be if the remanufacturing rate of the entire genset was equal to that of the engine e a possibly future goal for the manufacture to pursue. The remaining 15 wt% of the genset was divided into a recycling and landfill rate of 10 wt% and 5 wt%, respectively.
(5)
i2M
where l is fraction of a material landfilled, and mi is the mass of a material to be disposed. Equation (6) shows the energy credit for recycling.
ERC ¼
X
r*mi *ðH Hrc Þi
(6)
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3. Results and discussion
i2M
3.1. Life cycle impact assessment where r is fraction of a material recycled, Hrc is recycled embodied energy (i.e., energy needed to recycle unit mass of a material; MJ/ kg), and H is effective embodied energy (MJ/kg) averaged from those of virgin and recycled materials, as shown in equation (7).
H ¼ RHrc þ ð1 RÞHm
(7)
where R is recycle content of the material at start of life, and Hm is the embodied energy (MJ/kg). R varies for each different material depending on the embodied energy data source. To remanufacture a product, the potential EoL credit is described by equation (8).
ERM ¼
X
0:9*rm*mi *Hi
(8)
i2M
where rm is fraction of a material remanufactured. Remanufacturing recovers almost all of the original embodied energy. The total energy for the production of a genset is then given in equation (9).
ETotal ¼ EM þ EF þ ET þ EU þ EL ERC ERM
The life cycle impact assessment (LCIA) evaluates the magnitude and significance of the potential environmental impacts of the life cycle of the product (International Standard Organization, 2006a, 2006b). A variety of results were calculated based on the LCI. First, the overall results will be presented, and then the results for each stage will be presented. In order to protect propriety information, several results are presented in a scaled format. The energy demands of each process or material will remain proportional, so the relationship can be described as a relative energy demand. 3.1.1. Overall results The energy for each stage is shown in Fig. 3. The EoL impact is not represented here since there are four different scenarios for this stage, rather, it will be explained in its designated results section. The use phase dominates the energy demand at nearly 95% of the total demand, which is consistent with other studies (Ashby, 2013; Li et al., 2013). The materials stage is the second most energy intensive, at 4% of the total energy demand, then transportation at 1%, and then manufacturing at less than 1% of the total energy demand.
(9)
Table 2 shows the four different EoL scenarios analyzed in this study. The methodology used to develop each of these scenarios is as follows:
3.1.2. Material results The original list of materials was simplified by grouping similar material types into categories defined by Ecoinvent, Ashby, and ICE. The final list of materials consisted of the following eighteen material categories:
Table 2 Four different scenarios analyzed for end of life. Scenario
Landfill
Recycle
Remanufacture
1 2 3 4
100% 34% 16% 5%
0% 34% 34% 10%
0% 32% 50% 85%
Aluminum Alloy Cast Aluminum Cast Iron Copper Epoxies Ferromanganese (Fe-Mn)
Ferrosilicon (Fe-Si) Lead Low Alloy Steel Low Carbon Steel Molybdenum Nickel
PCB Stainless Steel Steel, Bar, & Rod Tin Titanium Alloys Zinc
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Fig. 3. Energy demand per life cycle stage.
The primary production EE values and sources used to calculate the material energy can be found in Table S1. For materials with a range of EE values, an average was used. Fig. 4 shows the percentages of total mass and energy demand contributed by each material. It is clear that energy does not always have a proportional relationship to mass, as shown in Fig. S1. The data point on the far right represents PCB (Printed Circuit Board) materials in the electronic control system. The typical composition of a PCB is over 70 wt% nonmetals (i.e. plastic, resins, glass fibers, etc.), and about 16 wt% copper, 4 wt% solder, 3 wt% iron (ferrite), 2 wt% nickel, 0.05 wt% silver, 0.03 wt% gold, and 0.01 wt% palladium (Zhou and Qiu, 2010). The materials in a PCB are very energy intensive, as illustrated in Fig. 5 where the PCBs (Controls) make up less than 1% of the mass, but make up 36% of the total embodied energy of the genset. In analyzing the results, it is helpful to understand the mass and energy breakdown of the individual parts of the genset as well. The allocation of mass and energy by parts are shown in Fig. 5. The heaviest components of the genset are the alternator and the engine, at a respective 44.7% and 37.8% of the total mass. The most energy demanding part is the control system because of the energy intensive materials described earlier.
3.1.3. Manufacturing results The relative energy requirements for each manufacturing facility are presented in Fig. 6. The 51% energy demand of the assembly
Fig. 5. (a) Mass allocation by part; and (b) energy allocation by part.
& controls facility is representative of the large number of activities that occur at this site. In addition to the production of the controls and the assembly of the genset, there are other activities like painting, testing, and validation that occur in the facility that all contribute to the production of the genset. The radiator and skid are made by a supplier, and different suppliers have different methods
Fig. 4. Mass and energy percentages of materials.
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Table 5 Comparison of on-highway engine and generator set use phase. Life Cycle Stage
Generator Set
Enginea
Materials Manufacturing Transportation Use
4.15% 0.29% 1.00% 94.57%
0.19% 0.00% 0.05% 99.76%
a
transportation mode, transportation energy, and the percentage of overall energy contributed by the transportation for each part. Except for the controls, each part is made at a manufacturing facility other than the genset assembly facility that is located in the United States. The transportation of the alternator from Mexico to the United States accounts for 62% of the overall transportation energy because of the long distance traveled by truck. It should be noted that the transportation considered in this study only accounts for a portion of the transportation involved with the production of a genset. Therefore, this stage would most likely account for a larger percentage of the overall energy demand if a full analysis was conducted for transportation.
Fig. 6. Manufacturing energy allocation.
Table 3 Transportation energy intensities. Mode
Lower (MJ/t*km)
Upper (MJ/t*km)
Average (MJ/t*km)
Truck Rail Ship
1.70 0.49 0.15
7.30 0.70 0.61
4.50 0.56 0.38
of reporting energy consumption. The facilities for the engine, alternator, and assembly & controls all report the consumption of electricity, diesel, natural gas, and other fuels, namely propane, gasoline, ethanol, and biodiesel. The electricity usage reported by the manufacturer is electricity purchased from the grid. The United States Department of Energy (DOE) requires the manufacturer to include the source energy for electricity. To do this, the energy usage in kWh is multiplied by a factor of 3 to account for the generation and transmission losses from the utility. Since this level of detail is unknown for the supplier facilities, the most uncertainty lies in the skid and radiator facility energy usage. 3.1.4. Transportation results Table 3 contains the energy intensities for different transportation modes. Transportation by truck has the highest range of values and highest average energy intensity. Therefore, the products transported by truck have higher energy demands than other transportation methods. Table 4 shows the country of origin,
Table 4 Transportation information by part (per genset unit). Part Skid Controls
a
Radiator Engine Alternator a
Country of Origin
Transportation Mode
Average Energy (MJ)
Energy Percentage
United States United States China
Truck
287
1%
n/a
0
0%
Ship Truck Truck
877 2546 10,291
2% 7% 28%
Truck
23,172
62%
United States Mexico
The controls are built at the genset assembly facility.
Data are acquired from Bolin (2013).
3.1.5. Use results As discussed in the overall results, the use phase has the highest energy demand of all of the life cycle stages. The energy demand for the use phase is a direct result of the hours of use. A graph showing the linear relationship between operation time and energy use is shown in Fig. S2. The operation limit is 200 h per year, which is based on the warranty. However, a more realistic option is in the range of 50e100 h. This figure shows that even based on the lowest operation time of 50 h/yr, the use phase demands 3500 GJ, which is 95% of the energy demand for all of the life cycle stages combined (Fig. 3). If the standby generator is operated at its maximum of 200 h/yr, the energy demand is 14,099 GJ, which is 99% of the total energy demand. While it is common for the use phase to dominate the life cycle energy demand when the product uses fuel, it is surprising that this is still the case for a product that operates as an emergency power supply. In order to justify these results, a comparison of the use phase of an engine in an on-highway application was made, and the energy demand was calculated to account for 99.76% of the total energy demand, as shown in Table 5. While the use phase energy demand of the genset is large, it is not as large as that of an engine used in on-highway trucks. An on-highway engine has nearly 160,000 lifetime gallons, whereas a genset uses 28,000 gallons in a lifetime. Therefore, the energy demand is a result of the gallons of fuel burned. 3.1.6. End of life results As previously mentioned, four different EoL disposal route scenarios were analyzed. The EoL credit and debit were calculated for each scenario and then added to the energy demand of the material stage in order to represent a potential increase or decrease in material energy demand as a result of the disposal route. The results are reported as positive or negative percent changes in the materials stage energy. These results are a tribute to the positive effect that recycling and remanufacturing has on the gensets. By remanufacturing the product, it is possible to reduce the initial energy consumption by 52% (Table 6), based on the scenario #2 assumed in the calculation section. These results are similar to those of a study that found a remanufactured engine could be produced with 26%e 90% less raw material consumption than a brand new engine (Smith and Keoleian, 2004). Again, the specific disposal route of the genset was not determined, but these scenarios provide a good
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3.2. Sensitivity and uncertainty analysis
Table 6 Percent change in materials energy for each EoL scenario. Scenario
Percent Change in “Materials” Energy
1: Landfill 100% 2: Landfill 34%, Recycle 34%, Remanufacture 32% 3: Landfill 16%, Recycle 34%, Remanufacture 50% 4: Landfill 5%, Recycle 10%, Remanufacture 85%
þ0.24% - 52.20% - 68.30% - 82.79%
estimate of possible energy reductions that can be achieved by choosing sustainable disposal routes.
3.1.7. Limitations There are inherent limitations with all LCAs, but they still provide valuable information. Estimates and assumptions have to be created for each life cycle stage. Those used to perform this study were approved by the manufacturer and based on background information, previous studies, and logical thought. Limitations specific to this study are described here: This LCA only addresses energy demand. Other impact metrics such as GHGs emission, eutrophication potential, and acidification potential are not presented in this study. There are gaps in the material inventory that limit the accuracy of the results. However, 87% of the total genset mass was accounted for by using the data collection methods described earlier, which was greater the 85% cut-off criterion set for this study. The system boundary described in Fig. 2 also limits the impact assessment. The transportation stage is more expansive than that considered in this study. The uncertainty of the EoL disposal route is also a limitation.
A sensitivity analysis was conducted in order to test the extent to which overall energy demand is sensitive to a ±10% change in the primary production EE values for each component. The top ten most sensitive EE values are shown in Fig. 7. The centerline represents the baseline case, and the values to the left and right represent a 10% decrease and increase, respectively. A combination of material mass and primary production EE values produce these results. The overall energy demand is most sensitive to the EE value of PCBs (Controls) that account for the largest portion of overall material energy (Fig. 5b). A similar analysis was done to study the relative importance of different life cycle stages. The use phase was found to be most sensitive to change. This means that changes in the use parameters, such as fuel efficiency, will have a significant impact on the overall energy demand. A Monte Carlo simulation was conducted for the uncertainty analysis. Twenty-one parameters used to calculate the overall energy demand were tested for uncertainty. To perform the simulation, transportation and material energy intensities were replaced with random numbers generated by Microsoft Excel based on the specified distribution type. A lognormal distribution was assumed for material energy intensities with standard deviation and average data. In the case where a lower and upper energy intensity was provided, a triangular distribution was assumed. A complete list of the parameters varied and the corresponding distribution can be seen in Table S3. The overall energy demand was calculated with a fixed manufacturing and usage energy value because there are no statistical data for these stages. For each parameter that was replaced with a random value, 1000 iterations were used, producing a total of 21,000 iterations. Each iteration produced an overall energy demand value per genset unit. After all of the iterations were completed, the results were represented by a histogram shown in Fig. 8. Also shown in Fig. 8 is the actual overall energy demand of 3,727,318 MJ. The bin with the largest frequency is 3,708,000 MJ, which only has a percent difference of 1.0% from the calculated value. An additional uncertainty analysis was also performed for materials energy demand. The histogram (Fig. S3) reveals that if the energy demand
Fig. 7. Tornado plot showing the sensitivity of material energy demand to primary production embodied energy values.
K. Benton et al. / Journal of Cleaner Production 149 (2017) 265e274
Fig. 8. Monte Carlo simulation results for overall energy demand per genset unit.
with the highest frequency was used, the materials stage energy would account for 3.67% of the overall energy demand rather than the current 4.1%, which is a minor difference. 4. Conclusion A streamlined LCA was conducted for a standby diesel genset manufactured by a leading diesel engine company in the United States. The study aimed to determine the energy demands of each life cycle of the genset: materials, manufacturing, transportation, use, and end-of-life disposal. Similar to the case of on-highway engines, the use phase dominated the lifetime energy demands for the genset even though the genset has much shorter operation time than on-highway engines. The energy demand during the use phase is proportional to the amount of diesel fuel consumed, and the fuel consumption is equal to the product of operation time and fuel efficiency. Obviously, a most feasible way to reduce energy demand during the use phase is to improve the fuel efficiency. This can be done by such as increasing compression ratio and fuel injection pressure, optimizing combustion conditions, and harvesting waste heat for electricity generation. The second most energy intensive stage was materials. Among different genset components, the control accounted for the largest energy demand but only made up 0.5% of the overall mass of the genset. This is largely due to the exceptionally high embodied energy values of PCBs in the control. It is important to note that only 87% of genset mass was included for assessment of material stage energy demands. Thus, the total material stage energy demands may be significantly underestimated. The end of life disposal has a significant impact on material stage energy consumption. The analysis showed that if 34% of the genset is remanufactured, 34% is recycled, and 32% sent to a landfill, the materials energy could be reduced by 52% compared to the 100% landfill scenario. Therefore, recycling and remanufacturing are of effectiveness in reducing energy intensive material extraction and processing. The transportation stage in this study only considered the shipping from the genset part suppliers to the final assembly facility. Thus, the actual energy demands for transportation would be likely contribute a larger percentage to the overall energy demand if a full analysis was conducted. Among the transportation activity investigated, the shipping of alternators from Mexico to the United States accounted for 62% of the overall transportation energy because of the long distance traveled by truck. Locating a genset part supplier near a port would significantly reduce energy demands for transportation.
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Several limitations are associated with the present study, as listed earlier. The most significant one is the relatively low level of data completeness. It is difficult to compile a complete material inventory for the diesel genset as it consists of >15,000 parts. There also lacks a complete database about the distribution, operation, maintenance, and disposal of the genset product. To address this issue and enable a more accurate LCA, a better communication and documenting mechanism needs to be established between the manufacturer and part suppliers, between the manufacturer and dealers, and between the dealers and end-users. Efforts will be made to find estimates or surrogates for the missing data, and to conduct a more comprehensive assessment on the environmental impact of the genset over the entire life cycle. In addition to energy, other impact metrics, such as global warming potential and eutrophication potential, will be considered. It is anticipated that diesel gensets will show somehow different environmental impacts than on-highway diesel engines, due to their different components, operation conditions, and emission control strategies. Acknowledgement The authors wish to acknowledge all engineers, workers and managers in the diesel generator manufacturing company who offered their generous support during this study e unfortunately, their names cannot be released due to a confidentiality agreement. The authors also want to thank Dr. Kumar Ganesan, Ms. Jeanne Larson and Dr. Jack Skinner at Montana Tech for their suggestions and comments. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2017.02.082. References Ashby, M., 2013. Materials and the Environment, second ed. Butterworth-Heinemann, Boston, MA. Bolin, C.E., 2013. Iterative Uncertainty Reduction via Monte Carlo Simulation: a Streamlined Life Cycle Assessment Case Study. Master thesis. Massachusetts Institute of Technology, Cambridge, MA. Cooney, G., Hawkins, T.R., Marriott, J., 2013. Life cycle assessment of diesel and electric public transportation buses. J. Ind. Ecol. 17 (5), 689e699. Curran, M.A., 1996. Environmental life-cycle assessment. Int. J. Life Cycle Assess. 1 (3), 179e179. Curran, M.A., 2013. Life cycle assessment: a review of the methodology and its application to sustainability. Curr. Opin. Chem. Eng. 2 (3), 273e277. Diesel Service and Supply, 2016. Generator Industry Outlook. http://www. dieselserviceandsupply.com/Generator_Industry_Market_Forecast.aspx (Accessed 31 March 2016). Environmental Protection Agency, 2006. Life-cycle Assessment Principles and Practice. http://brevard.ifas.ufl.edu/communities/pdf/chapter1_frontmatter_ lca101.pdf (Accessed 13 April 2016). Environmental Protection Agency, 2012. Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for 2012. https:// www3.epa.gov/epawaste/nonhaz/municipal/ (Accessed 06 February 2016). Fleck, B., Huot, M., 2009. Comparative life-cycle assessment of a small wind turbine for residential off-grid use. Renew. Energy 34 (12), 2688e2696. Frost, Sullivan, 2013. Demand from Booming Data Center Industry Sustains Diesel Generator Sets Market in Asia-Pacific. https://frost.com/prod/servlet/pressrelease.pag?docid¼287274429 (Accessed 31 March 2016). Gmünder, S.M., Zah, R., Bhatacharjee, S., Classen, M., Mukherjee, P., Widmer, R., 2010. Life cycle assessment of village electrification based on straight jatropha oil in Chhattisgarh, India. Biomass Bioenergy 34 (3), 347e355. Hammond, G.P., Jones, C.I., 2011. Inventory of Carbon & Energy (ICE), Version 2.0. University of Bath, Bath, UK. International Standard Organization, 2006a. ISO14040:2006 e Environmental ManagementdLife Cycle Assessment: Principles and Framework. Geneva, Switzerland. International Standard Organization, 2006b. ISO14044:2006 e Environmental ManagementdLife Cycle Assessment: Requirements and Guidelines. Geneva, Switzerland.
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Kouchaki-Penchah, H., Sharifi, M., Mousazadeh, H., Zarea-Hosseinabadi, H., 2016a. Life cycle assessment of medium-density fiberboard manufacturing process in Islamic Republic of Iran. J. Clean. Prod. 112, 351e358. Kouchaki-Penchah, H., Sharifi, M., Mousazadeh, H., Zarea-Hosseinabadi, H., NabaviPelesaraei, A., 2016b. Gate to gate life cycle assessment of flat pressed particleboard production in Islamic Republic of Iran. J. Clean. Prod. 112, 343e350. Li, T., Liu, Z., Zhang, H., Jiang, Q., 2013. Environmental emissions and energy consumptions assessment of a diesel engine from the life cycle perspective. J. Clean. Prod. 53, 7e12. Pascale, A., Urmee, T., Moore, A., 2011. Life cycle assessment of a community hydroelectric power system in rural Thailand. Renew. Energy 36 (11), 2799e2808. RNCOS Market Research, 2014. India Diesel Genset Market Outlook. http://www. prnewswire.com/news-releases/india-diesel-genset-market-outlook-2018272001001.html (Accessed 31 March 2016). Schau, E.M., Traverso, M., Finkbeiner, M., 2012. Life cycle approach to sustainability assessment: a case study of remanufactured alternators. J. Remanufacturing 2 (1), 1e14. Smith, V.M., Keoleian, G.A., 2004. The value of remanufactured engines: life-cycle environmental and economic perspectives. J. Ind. Ecol. 8 (1e2), 193e221.
Sverdlik, Y., 2013. Data Centers Drive Backup Generator Demand Growth in Asia Pacific. http://www.datacenterdynamics.com/news/data-centers-drive-backupgenerator-demand-growth-in-asia-pacific/83224.fullarticle (Accessed 31 March 2016). Weidema, B.P., Bauer, Ch, Hischier, R., Mutel, Ch, Nemecek, T., Reinhard, J., Vadenbo, C.O., Wernet, G., 2013. The Ecoinvent Database: Overview and Methodology, Data Quality Guideline for the Ecoinvent Database Version 3. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland. Weitz, K.A., Sharma, A., 1998. Practical life cycle assessment through streamlining. Environ. Qual. Manage 7 (4), 81e87. Weitz, K., Sharma, A., Vigon, B., Price, E., Norris, G., Eagan, P., Oens, W., Veroutis, A., 1999. Streamlined Life-cycle Assessment: a Final Report From the SETAC North America Streamlined LCA Workgroup. SETAC North America, Pensacola, FL. Zhang, H.C., Li, T., Liu, Z., Jiang, Q., 2015. Diesel engine block remanufacturing: life cycle assessment. In: Nee, A.Y.C. (Ed.), Handbook of Manufacturing Engineering and Technology. Springer, London, UK, pp. 3313e3341. Zhou, Y., Qiu, K., 2010. A new technology for recycling materials from waste printed circuit boards. J. Hazard. Mater. 175 (1), 823e828.
Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
Life cycle energy assessment of a standby diesel generator set Kelly Benton a, Xufei Yang a, *, Zhichao Wang b a b
Department of Environmental Engineering, Montana Tech of the University of Montana, Butte, MT 59701, USA EcoEngineers, Des Moines, IA 50309, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 17 August 2016 Received in revised form 4 January 2017 Accepted 11 February 2017 Available online 16 February 2017
The global demand for emergency standby power (ESP) diesel generators continues to grow because of increasing population and urbanization in developing countries. In order to better understand and further reduce the environmental impact of these products, the life cycle assessment (LCA) methodology was applied to an 455 kW ESP diesel generator set to quantify the energy demands of each life cycle stage: materials, manufacturing, transportation, use, and end-of-life disposal. The life cycle inventory (LCI) was completed based on the information acquired from the manufacturing company and its suppliers, and the impact assessment, i.e., energy demand calculation was done using the data from the Ecoinvent and the Inventory of Carbon and Energy (ICE) databases. The results revealed that, similar to on-highway engines, diesel generators consumed most energy (>95% of the entire life cycle) during the use phase, followed by materials, transportation, and then manufacturing. Therefore, increasing fuel efficiency will have the largest energy and potentially environmental benefits. Printed circuit boards (PCBs), although of small mass, accounted for ~35% of energy demands during the materials stage. The materials-related energy demands can be considerably reduced by increasing remanufacturing and recycling rates. Results from this study are expected to help the genset manufacturers to optimize their product design, supply chain, and service so as to minimize the lifetime environmental impact of the product. © 2017 Elsevier Ltd. All rights reserved.
Keywords: Life cycle assessment Standby generator set End of life Recycle Remanufacture
1. Introduction Increased environmental awareness among the public urges industries to proactively evaluate the impact of their operations on the environment. Industries are now moving beyond environmental compliance by incorporating sustainability in the list of company values, which sends a message to the public that their employees are taking actions to protect the environment and conduct business in a sustainable manner. These actions prompt environmental managers and decision makers to look at their products and services from cradle to grave. As a result, the need for Life Cycle Assessment (LCA) continues to grow. LCA is a method for evaluating the cumulative environmental impacts resulting from all stages in the product life cycle (Environmental Protection Agency, 2006). It started as a tool to evaluate individual products but has now developed into a standardized method for providing a scientific basis for environmental sustainability in various
* Corresponding author. E-mail address: [email protected] (X. Yang). http://dx.doi.org/10.1016/j.jclepro.2017.02.082 0959-6526/© 2017 Elsevier Ltd. All rights reserved.
industries (Curran, 2013; Kouchaki-Penchah et al., 2016a, 2016b). This study describes a life cycle assessment (LCA) performed on a standby diesel generator set in cooperation with a large diesel engine manufacturing company in the United States, which also produces power generation products. A standby diesel generator set, hereafter referred to as a genset, is a combination of a diesel engine with an alternator to convert chemical energy in diesel fuels to electricity (Fig. 1). Emergency standby power (ESP) gensets are used to supply power to electrical appliances during the power interruption of the utility source. ESP gensets are essential for applications that require an uninterrupted power supply. Today, nearly every industry needs an ESP genset, as economic loss can be far more expensive than the capital expenditure for the backup power equipment (RNCOS Market Research, 2014). The genset market is driven by the rapidly expanding global population and urbanization of cities throughout the world (Diesel Service and Supply, 2016). The genset demand will continue to increase as industries such as oil and gas, electronics, semiconductors, textiles, food processing units, automotive, shopping malls, and data centers turn to diesel generators to deal with unexpected power outages (Sverdlik, 2013). This demand is especially
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Fig. 1. A typical diesel generator set and its major components. Air filter, turbo charger, and connecting hoses were excluded for assessment due to limited data availability. Note the picture is for demonstration only and may not represent the actual unit investigated in this study.
prevalent in Asia-Pacific, where the data center industry is rapidly expanding, especially in Singapore, Malaysia, Philippines, Thailand, and Australia. Data centers require gensets with a capacity of up to 20 MW (MW) for ESP applications, and therefore, the demand for large diesel gensets with a power output capacity between 1 MW and 3 MW is on the rise (Frost and Sullivan, 2013). It was predicted that the market for 1e3 MW diesel gensets will grow from ~$590 million in 2012 to ~$800 million in 2017 (Sverdlik, 2013). Another study found that the Indian diesel generator market grew 9.5% between 2012 and 2013, and the market would grow at a compound average growth rate of around 11% in value terms during 2014e2018 (RNCOS Market Research, 2014). The global genset market will continue to be driven by the lack of grid infrastructure in remote locations and increasing industrialization in developing countries. Despite the rapid growth in market demand, only a few LCA studies have been done on diesel gensets. Most of them entailed a comparison of different energy production devices including gensets. Gmünder et al. (2010) compared jatropha oil fueled gensets with diesel gensets, photovoltaic panels, and power grids with respect to greenhouse gases (GHGs) emission and other environmental impacts such as acidification and eutrophication, and concluded that jatropha oil fueled gensets significantly reduced GHGs emission when compared to the other three systems. However, no information was provided regarding the consumption of energy and materials during the manufacturing, transportation, or disposal of the diesel genset. Fleck and Huot (2009) compared a small wind turbine with a diesel genset for residential off-grid use, and reported that although the wind turbine was slightly more expensive than the diesel genset over the entire life cycle, it delivered 93% reduction in GHGs emission. Numerous simplifications and assumptions were made during the assessment of the genset. For example, the material composition of the diesel genset was approximated to be 60% steel, 35% aluminum, and 5% copper. Obviously, this and similar simplifications may cause considerable uncertainties in the final LCA results. Pascale et al. (2011) compared a 3 kW community-scale hydroelectric system with a 7 kW diesel genset in rural Thailand, and found that the hydroelectric system offered better environmental and financial benefits than the genset. However, similar to the study by Gmünder et al. (2010), no information was provided regarding materials and energy consumption during the genset manufacturing or transportation. LCA studies have also been done on diesel engine and alternator,
the two major parts of a diesel genset. Li et al. (2013) investigated the energy consumption and environmental impacts of an on-road diesel engine over its entire life cycle, and reported that the use phase accounted for >99.0% of the total primary energy demand, 97.7% of the total GHGs emission, and 94.2% of the total acidification potential. Cooney et al. (2013) compared mass transit buses driven by diesel engines and electric motors, and concluded that the use phase was dominant in causing global warming, carcinogens and other environmental impacts for both diesel-powered and electric buses. Zhang et al. (2015) compared remanufactured diesel engines with newly built ones, and found that engine remanufacturing reduced the eutrophication potential by 79% and the GHGs emission by 67%. Schau et al. (2012) investigated the economic and environmental benefits from remanufacturing of alternators, and revealed that remanufactured products caused only 12% of the GHGs emission and costs when compared with new ones. Although efforts have been made to assess the life cycle of diesel gensets and their key components, a detailed systematic investigation is still lacking, especially for large-capacity gensets. To manufacturers, an LCA will allow decision makers to better report, understand, and interpret the environmental impact of their product in a manner that promotes sustainable product and process choices in the future (Curran, 1996). In 2013, the company who manufactured the diesel genset in this study partnered with a master student from the Massachusetts Institute of Technology (MIT) to perform an LCA on a 15 L displacement engine used in the on-highway application (Bolin, 2013). The primary focus of that study was to understand the energy demands of the life cycle stages prior to the use phase because it was well recognized that the use phase was the most energy intensive for on-highway applications. This genset study not only includes the engine information, but extends the analysis to the full life cycle of the engine as a part of the genset. 2. Methodology 2.1. Goal and scope definition The goal of this study is to perform an LCA on a standby genset in order to quantify the energy demand for each life cycle stage and identify which is the most energy intensive one. The life cycle stages of this analysis include materials, manufacturing, transportation, use, and end of life (EoL), making the study a “cradle-tograve” analysis. This LCA has been streamlined in order to align the results of the assessment with the goal of the study. A combination of the streamlining techniques described by Keith Weitz at the United States Environmental Protection Agency (USEPA) conference on streamlining LCA was used to perform this streamlined LCA (Weitz and Sharma, 1998, Weitz et al., 1999). 2.1.1. Functional unit The subject of this study is a standby diesel genset. This particular model is equipped with a heavy-duty 15 L engine with a 455 kW rating. As an emergency standby power supply, the primary function of the genset is to convert chemical energy in diesel fuels to mechanical energy (by a diesel engine) and then to electricity (by an AC alternator) during power grid disruptions. The functional unit of the genset is based on the amount of diesel fuels consumed, which is a function of fuel economy and operation time. Under normal conditions, the genset has a life expectancy of 20 years and an operation time of 50e100 h per year, according to the manufacturer. 2.1.2. Process description and system boundaries To conduct the LCA, the generator was divided into five main
K. Benton et al. / Journal of Cleaner Production 149 (2017) 265e274
components, which are the engine, alternator, radiator, electronic controls, and skid (Fig. 1). There are additional parts such as the air filter, turbo charger, and connecting hoses that were not considered due to limited data availability, and they account for 13% of the genset mass. Individual components are manufactured at different facilities of the company in the United States and other countries, or purchased from other providers before delivered to the company's central facility in the United States for assembly into gensets. The processes that are specific to the production and use of the genset are summarized as follow: The individual parts are made in their respective manufacturing facilities and then shipped to the assembly facility. Upon completion of assembly, the product is distributed to the customer, where it is used until it has reached its end of life. Then it can be recycled, remanufactured, or sent to a landfill. Most likely, the disposal route is a combination of these three options. This study assesses the entire life cycle of a genset with varying level of detail for each stage. The “materials” stage includes the raw material extraction and processing required to make the raw material into a usable form. The “manufacturing” stage for this LCA is defined as the stage in which each of the five main parts of the genset is built in its respective manufacturing facility. This stage also includes the step in which the individual parts are assembled into a genset because of the impact metric and similarity in data type. The “use” phase is fairly straight-forward and was taken as a category of its own. The “end of life” stage, however, is more complicated because of the variety of disposal routes, which will be explained in detail in a later section. While transportation occurs between each of these stages, the only piece considered is between the part manufacturing facilities and final assembly site. Downstream transportation that occurs after the product is built was not considered due to a lack of relevant data. This will lead to a significant underestimate of energy consumed during the “transportation” stage. This LCA considered the processes discussed above and the energy inputs associated with them (Fig. 2). Each process has outputs such as air and water emissions that have an impact on the environment but were not considered in this LCA because the goal
267
of the study is to quantify the energy demand for each stage.
2.1.3. Impact metric The impact metric used for this study is energy in units of megajoules (MJ). Energy will then serve as an indicator of the overall environmental impact. Energy is a simple metric and is generally understood by the public, and can be used as a proxy for CO2 if necessary (Ashby, 2013). The relationship between the two impact metrics for diesel engines was further validated by Bolin in the MIT study, in which a strong correlation between embodied energy and greenhouse gas emissions was presented. Energy is also most closely related to production costs more than any other environmental metrics (Bolin, 2013), so this metric has the benefit of relating to not only greenhouse gases, but cost as well. The purpose of this method is to quantify the total energy use throughout the life cycle of the genset, including the direct and indirect energy usage during the extraction of raw materials, manufacturing, transportation, use, and waste disposal. There are four different types of energy considered in this study: (1) embodied energy, primary production; (2) embodied energy, recycling; (3) electricity; and (4) fuel. The embodied energy (EE) is defined as the total primary energy consumed from direct and indirect processes relevant to the materials within the cradle-to-gate boundaries. This includes all activities from raw material extraction, manufacturing, transportation, and through fabrication until the product is ready to leave the factory gate. It is important to note that the energy consumption at each life cycle stage can be supplied by unsustainable (e.g., fossil fuels) and/or sustainable energy sources (e.g., wind power), and this has a significant impact on the overall environmental impact of the diesel genset. However, due to a lack of relevant data, this study does not further distinguish the type of energy sources. This simplification will not affect the life cycle energy balance within the system boundary given in Fig. 2, but will be influential if energy supplies are included in the boundary.
Fig. 2. System boundary of this study.
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2.2. Life cycle inventory analysis 2.2.1. Data requirements and data collection The data requirements for each life cycle stage are listed in Table 1. Engineers and specialists from a variety of departments within the manufacturing company provided information for this study. The multi-disciplinary involvement required for this study is a tribute to the complexity and broad nature of LCAs. In addition to the manufacturer, a variety of data sources were used to complete this study. Table 1 summarizes the data type and source used for each life cycle stage. The most challenging stage to collect data for was the materials stage because the genset is made of over 15,000 parts. This expansive list consists of pieces as small as nuts and bolts. To make the part list manageable and realistic, the list was grouped into five parts (engine, alternator, radiator, electronic controls, and skid) described in the scope of this study. A list of material types and masses for each part is not typically kept on record, so the level of accuracy was dependent on the data available. Part drawings, purchasing data, and general part knowledge shared by design engineers were all compiled to generate the data. It is important to note that these values are an estimate and are not exact. The EE values for calculating primary production of materials were acquired from a combination of Ecoinvent (Weidema et al., 2013), Materials and the Environment: Eco-informed Material Choice (Chapter 15) (Ashby, 2013), and the Inventory of Carbon and Energy (ICE) Version 2.0 (Hammond and Jones, 2011). The “manufacturing” data was collected from plant records of the manufacturer and suppliers. The data included facility energy usage and production data for the year of 2014. The “transportation” data was simply acquired by identifying the manufacturing and assembly location and calculating a travel distance between the two. The transportation mode was identified by the manufacturer, and the transportation energy intensities were acquired from Ecoinvent. Product warranty and life time data was collected from the manufacturer for the use phase assessment. The EoL disposal route is difficult to identify. The product is in the customer's hands when it has reached its EoL, so the customer is at liberty of choosing the disposal route, which is not usually reported back to the original manufacturer. Therefore, it is difficult to acquire this data. In order to explore the energy demands of different disposal routes, four scenarios were used in which different combinations of disposal Table 1 Data requirements and sources for each life cycle stage. Life Cycle Stage
Data Required
Units
Data Source
Materials
Material composition of parts Material mass Embodied energy, primary production values Facility energy usage Production volume Travel mode Travel distance Weight of shipment Energy intensity of transportation mode Lifetime Run time Fuel type Fuel efficiency Calorific value of fuel Disposal Route Material type Material mass Embodied energy, recycling values
e kg MJ/kg
Manufacturer and suppliers Ecoinvent, Ashby, and ICE Manufacturer and suppliers Manufacturer
Manufacturing Transportation
Use
End of Life
MJ e e km Ton MJ/t*km Ecoinvent yr hrs/yr e gal/hr BTU/gal e e kg MJ/kg
Manufacturer
routes were used. The recycling embodied energy used for these scenarios was acquired from Ashby (2013). For the few materials that lacked recycling data, it was assumed that the recycling energy was one-fifth of the embodied energy (Ashby, 2013). This assumption will inevitably cause uncertainties in calculated energy consumption (or saving) during the EoL stage. However, since only five materials (out of 19) used assumed recycling energy values, it is expected that the uncertainties caused would be relatively minor.
2.2.2. Data calculation procedures The data calculation procedures vary for each life cycle stage, so this section is broken down accordingly. Materials: The calculations for this stage are straightforward: the mass of material was multiplied by the embodied energy value (Tables S1 and S2). The sum of each material's energy is then the total energy for the material stage, as shown in equation (1).
EM ¼
X
mi *Hi
where M represents all of the materials in the genset and i2M represents the individual materials. E is energy (MJ), m is mass of a material (kg), and H is effective embodied energy of a material (MJ/ kg), which is defined in equation (7). Manufacturing: The energy for this stage was calculated by dividing the facility energy consumption by the number of parts produced. The sum of each facility's energy is then the total energy for the manufacturing stage, as shown in equation (2).
EF ¼
X f j2F
p
(2) j
where F represents all of the manufacturing facilities, j2F represents the individual facilities, f is the energy of the manufacturing facility (MJ), and p is the number of parts produced at the facility. Transportation: The transportation stage was calculated by multiplying the transportation intensity by the shipment weight and the distance traveled. The energy for each part to be transported was then summed to acquire the total energy for the transportation stage, as shown in equation (3).
ET ¼
X
Htk *dk *sk
Ashby
(3)
k2T
where T is all of the transportation modes and k2T represents the individual transportation modes. Ht is the transportation energy intensity (MJ/t*km), d is the distance traveled (km), and s is the shipment weight in metric tonnes (t). Use: The use phase was calculated by multiplying the life expectancy by operation time, fuel efficiency, and calorific value of the fuel, as shown in equation (4).
EU ¼ LE*OT*e*CV Assumed scenarios Manufacturer
(1)
i2M
(4)
where U represents the use phase, LE is life expectancy (yrs), OT is operation time (hrs/yr), e is fuel efficiency (gal/h), and CV is calorific value of fuel (BTU/gal). The calorific value used in the calculation was specified by the manufacturer as 130,000 BTU/gal.
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End of Life: The EoL stage is more complex than the others. Ashby (2013) described the activities included in each of the three disposal routes considered for this study: 1. Landfill e Collect and transport to landfill site. 2. Recycling e Collect, sort by material family and class, recycle. 3. Remanufacturing e Collect, dismantle, replace or upgrade components, re-assemble. To account for the possible net energy saving or consumption associated with the EOL stage, an energy credit/debit was calculated. Recycling and remanufacturing are usually allocated an EoL credit (or benefit) as they save energy for the materials stage; while the landfill disposal route receives an EoL debit (or energy allocation) as landfill demands energy input. In the overall energy balance equation, the credits are represented as a negative value because it reduces the total energy demand, whereas the debits are expressed as a positive value because it adds to the total energy demand and has a negative impact on the environment. Ashby (2013) provided an equation for each disposal option. Equation (5) shows the energy debit of the landfill disposal route.
EL z
X
0:1*l*mi *Hi
Scenario 1: This scenario was modeled to show the negative impact of sending the entire product to the landfill. This is not realistic, but provides a baseline for the other three scenarios. Scenario 2: This scenario was based on the remanufacturing rates of the engine. The manufacturer knows that 85 wt% (Note: wt% refers to mass percent) of an engine can be remanufactured. Given the engine makes up about 38 wt% of the entire genset and assuming that the engine is only part that could be remanufactured, that would mean that 32 wt% (85%*38%) of the genset could be remanufactured. The recycling rate for scenario 2 came from a USEPA report on waste generation, recycling, and disposal in the United States for 2012 (Environmental Protection Agency, 2012). The report listed metals as having an average recycling rate of 34 wt%. The remainder of the genset was then assumed to be sent to the landfill. Scenario 2 then serves as a baseline for 3 and 4 because realistically, more of the genset can be remanufactured. Scenario 3: This scenario was generated with a remanufacturing rate in middle ground between scenario 2 and 4. The recycling rate was kept the same as scenario 2. Scenario 4: This scenario was created to analyze what the EoL impacts would be if the remanufacturing rate of the entire genset was equal to that of the engine e a possibly future goal for the manufacture to pursue. The remaining 15 wt% of the genset was divided into a recycling and landfill rate of 10 wt% and 5 wt%, respectively.
(5)
i2M
where l is fraction of a material landfilled, and mi is the mass of a material to be disposed. Equation (6) shows the energy credit for recycling.
ERC ¼
X
r*mi *ðH Hrc Þi
(6)
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3. Results and discussion
i2M
3.1. Life cycle impact assessment where r is fraction of a material recycled, Hrc is recycled embodied energy (i.e., energy needed to recycle unit mass of a material; MJ/ kg), and H is effective embodied energy (MJ/kg) averaged from those of virgin and recycled materials, as shown in equation (7).
H ¼ RHrc þ ð1 RÞHm
(7)
where R is recycle content of the material at start of life, and Hm is the embodied energy (MJ/kg). R varies for each different material depending on the embodied energy data source. To remanufacture a product, the potential EoL credit is described by equation (8).
ERM ¼
X
0:9*rm*mi *Hi
(8)
i2M
where rm is fraction of a material remanufactured. Remanufacturing recovers almost all of the original embodied energy. The total energy for the production of a genset is then given in equation (9).
ETotal ¼ EM þ EF þ ET þ EU þ EL ERC ERM
The life cycle impact assessment (LCIA) evaluates the magnitude and significance of the potential environmental impacts of the life cycle of the product (International Standard Organization, 2006a, 2006b). A variety of results were calculated based on the LCI. First, the overall results will be presented, and then the results for each stage will be presented. In order to protect propriety information, several results are presented in a scaled format. The energy demands of each process or material will remain proportional, so the relationship can be described as a relative energy demand. 3.1.1. Overall results The energy for each stage is shown in Fig. 3. The EoL impact is not represented here since there are four different scenarios for this stage, rather, it will be explained in its designated results section. The use phase dominates the energy demand at nearly 95% of the total demand, which is consistent with other studies (Ashby, 2013; Li et al., 2013). The materials stage is the second most energy intensive, at 4% of the total energy demand, then transportation at 1%, and then manufacturing at less than 1% of the total energy demand.
(9)
Table 2 shows the four different EoL scenarios analyzed in this study. The methodology used to develop each of these scenarios is as follows:
3.1.2. Material results The original list of materials was simplified by grouping similar material types into categories defined by Ecoinvent, Ashby, and ICE. The final list of materials consisted of the following eighteen material categories:
Table 2 Four different scenarios analyzed for end of life. Scenario
Landfill
Recycle
Remanufacture
1 2 3 4
100% 34% 16% 5%
0% 34% 34% 10%
0% 32% 50% 85%
Aluminum Alloy Cast Aluminum Cast Iron Copper Epoxies Ferromanganese (Fe-Mn)
Ferrosilicon (Fe-Si) Lead Low Alloy Steel Low Carbon Steel Molybdenum Nickel
PCB Stainless Steel Steel, Bar, & Rod Tin Titanium Alloys Zinc
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Fig. 3. Energy demand per life cycle stage.
The primary production EE values and sources used to calculate the material energy can be found in Table S1. For materials with a range of EE values, an average was used. Fig. 4 shows the percentages of total mass and energy demand contributed by each material. It is clear that energy does not always have a proportional relationship to mass, as shown in Fig. S1. The data point on the far right represents PCB (Printed Circuit Board) materials in the electronic control system. The typical composition of a PCB is over 70 wt% nonmetals (i.e. plastic, resins, glass fibers, etc.), and about 16 wt% copper, 4 wt% solder, 3 wt% iron (ferrite), 2 wt% nickel, 0.05 wt% silver, 0.03 wt% gold, and 0.01 wt% palladium (Zhou and Qiu, 2010). The materials in a PCB are very energy intensive, as illustrated in Fig. 5 where the PCBs (Controls) make up less than 1% of the mass, but make up 36% of the total embodied energy of the genset. In analyzing the results, it is helpful to understand the mass and energy breakdown of the individual parts of the genset as well. The allocation of mass and energy by parts are shown in Fig. 5. The heaviest components of the genset are the alternator and the engine, at a respective 44.7% and 37.8% of the total mass. The most energy demanding part is the control system because of the energy intensive materials described earlier.
3.1.3. Manufacturing results The relative energy requirements for each manufacturing facility are presented in Fig. 6. The 51% energy demand of the assembly
Fig. 5. (a) Mass allocation by part; and (b) energy allocation by part.
& controls facility is representative of the large number of activities that occur at this site. In addition to the production of the controls and the assembly of the genset, there are other activities like painting, testing, and validation that occur in the facility that all contribute to the production of the genset. The radiator and skid are made by a supplier, and different suppliers have different methods
Fig. 4. Mass and energy percentages of materials.
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Table 5 Comparison of on-highway engine and generator set use phase. Life Cycle Stage
Generator Set
Enginea
Materials Manufacturing Transportation Use
4.15% 0.29% 1.00% 94.57%
0.19% 0.00% 0.05% 99.76%
a
transportation mode, transportation energy, and the percentage of overall energy contributed by the transportation for each part. Except for the controls, each part is made at a manufacturing facility other than the genset assembly facility that is located in the United States. The transportation of the alternator from Mexico to the United States accounts for 62% of the overall transportation energy because of the long distance traveled by truck. It should be noted that the transportation considered in this study only accounts for a portion of the transportation involved with the production of a genset. Therefore, this stage would most likely account for a larger percentage of the overall energy demand if a full analysis was conducted for transportation.
Fig. 6. Manufacturing energy allocation.
Table 3 Transportation energy intensities. Mode
Lower (MJ/t*km)
Upper (MJ/t*km)
Average (MJ/t*km)
Truck Rail Ship
1.70 0.49 0.15
7.30 0.70 0.61
4.50 0.56 0.38
of reporting energy consumption. The facilities for the engine, alternator, and assembly & controls all report the consumption of electricity, diesel, natural gas, and other fuels, namely propane, gasoline, ethanol, and biodiesel. The electricity usage reported by the manufacturer is electricity purchased from the grid. The United States Department of Energy (DOE) requires the manufacturer to include the source energy for electricity. To do this, the energy usage in kWh is multiplied by a factor of 3 to account for the generation and transmission losses from the utility. Since this level of detail is unknown for the supplier facilities, the most uncertainty lies in the skid and radiator facility energy usage. 3.1.4. Transportation results Table 3 contains the energy intensities for different transportation modes. Transportation by truck has the highest range of values and highest average energy intensity. Therefore, the products transported by truck have higher energy demands than other transportation methods. Table 4 shows the country of origin,
Table 4 Transportation information by part (per genset unit). Part Skid Controls
a
Radiator Engine Alternator a
Country of Origin
Transportation Mode
Average Energy (MJ)
Energy Percentage
United States United States China
Truck
287
1%
n/a
0
0%
Ship Truck Truck
877 2546 10,291
2% 7% 28%
Truck
23,172
62%
United States Mexico
The controls are built at the genset assembly facility.
Data are acquired from Bolin (2013).
3.1.5. Use results As discussed in the overall results, the use phase has the highest energy demand of all of the life cycle stages. The energy demand for the use phase is a direct result of the hours of use. A graph showing the linear relationship between operation time and energy use is shown in Fig. S2. The operation limit is 200 h per year, which is based on the warranty. However, a more realistic option is in the range of 50e100 h. This figure shows that even based on the lowest operation time of 50 h/yr, the use phase demands 3500 GJ, which is 95% of the energy demand for all of the life cycle stages combined (Fig. 3). If the standby generator is operated at its maximum of 200 h/yr, the energy demand is 14,099 GJ, which is 99% of the total energy demand. While it is common for the use phase to dominate the life cycle energy demand when the product uses fuel, it is surprising that this is still the case for a product that operates as an emergency power supply. In order to justify these results, a comparison of the use phase of an engine in an on-highway application was made, and the energy demand was calculated to account for 99.76% of the total energy demand, as shown in Table 5. While the use phase energy demand of the genset is large, it is not as large as that of an engine used in on-highway trucks. An on-highway engine has nearly 160,000 lifetime gallons, whereas a genset uses 28,000 gallons in a lifetime. Therefore, the energy demand is a result of the gallons of fuel burned. 3.1.6. End of life results As previously mentioned, four different EoL disposal route scenarios were analyzed. The EoL credit and debit were calculated for each scenario and then added to the energy demand of the material stage in order to represent a potential increase or decrease in material energy demand as a result of the disposal route. The results are reported as positive or negative percent changes in the materials stage energy. These results are a tribute to the positive effect that recycling and remanufacturing has on the gensets. By remanufacturing the product, it is possible to reduce the initial energy consumption by 52% (Table 6), based on the scenario #2 assumed in the calculation section. These results are similar to those of a study that found a remanufactured engine could be produced with 26%e 90% less raw material consumption than a brand new engine (Smith and Keoleian, 2004). Again, the specific disposal route of the genset was not determined, but these scenarios provide a good
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3.2. Sensitivity and uncertainty analysis
Table 6 Percent change in materials energy for each EoL scenario. Scenario
Percent Change in “Materials” Energy
1: Landfill 100% 2: Landfill 34%, Recycle 34%, Remanufacture 32% 3: Landfill 16%, Recycle 34%, Remanufacture 50% 4: Landfill 5%, Recycle 10%, Remanufacture 85%
þ0.24% - 52.20% - 68.30% - 82.79%
estimate of possible energy reductions that can be achieved by choosing sustainable disposal routes.
3.1.7. Limitations There are inherent limitations with all LCAs, but they still provide valuable information. Estimates and assumptions have to be created for each life cycle stage. Those used to perform this study were approved by the manufacturer and based on background information, previous studies, and logical thought. Limitations specific to this study are described here: This LCA only addresses energy demand. Other impact metrics such as GHGs emission, eutrophication potential, and acidification potential are not presented in this study. There are gaps in the material inventory that limit the accuracy of the results. However, 87% of the total genset mass was accounted for by using the data collection methods described earlier, which was greater the 85% cut-off criterion set for this study. The system boundary described in Fig. 2 also limits the impact assessment. The transportation stage is more expansive than that considered in this study. The uncertainty of the EoL disposal route is also a limitation.
A sensitivity analysis was conducted in order to test the extent to which overall energy demand is sensitive to a ±10% change in the primary production EE values for each component. The top ten most sensitive EE values are shown in Fig. 7. The centerline represents the baseline case, and the values to the left and right represent a 10% decrease and increase, respectively. A combination of material mass and primary production EE values produce these results. The overall energy demand is most sensitive to the EE value of PCBs (Controls) that account for the largest portion of overall material energy (Fig. 5b). A similar analysis was done to study the relative importance of different life cycle stages. The use phase was found to be most sensitive to change. This means that changes in the use parameters, such as fuel efficiency, will have a significant impact on the overall energy demand. A Monte Carlo simulation was conducted for the uncertainty analysis. Twenty-one parameters used to calculate the overall energy demand were tested for uncertainty. To perform the simulation, transportation and material energy intensities were replaced with random numbers generated by Microsoft Excel based on the specified distribution type. A lognormal distribution was assumed for material energy intensities with standard deviation and average data. In the case where a lower and upper energy intensity was provided, a triangular distribution was assumed. A complete list of the parameters varied and the corresponding distribution can be seen in Table S3. The overall energy demand was calculated with a fixed manufacturing and usage energy value because there are no statistical data for these stages. For each parameter that was replaced with a random value, 1000 iterations were used, producing a total of 21,000 iterations. Each iteration produced an overall energy demand value per genset unit. After all of the iterations were completed, the results were represented by a histogram shown in Fig. 8. Also shown in Fig. 8 is the actual overall energy demand of 3,727,318 MJ. The bin with the largest frequency is 3,708,000 MJ, which only has a percent difference of 1.0% from the calculated value. An additional uncertainty analysis was also performed for materials energy demand. The histogram (Fig. S3) reveals that if the energy demand
Fig. 7. Tornado plot showing the sensitivity of material energy demand to primary production embodied energy values.
K. Benton et al. / Journal of Cleaner Production 149 (2017) 265e274
Fig. 8. Monte Carlo simulation results for overall energy demand per genset unit.
with the highest frequency was used, the materials stage energy would account for 3.67% of the overall energy demand rather than the current 4.1%, which is a minor difference. 4. Conclusion A streamlined LCA was conducted for a standby diesel genset manufactured by a leading diesel engine company in the United States. The study aimed to determine the energy demands of each life cycle of the genset: materials, manufacturing, transportation, use, and end-of-life disposal. Similar to the case of on-highway engines, the use phase dominated the lifetime energy demands for the genset even though the genset has much shorter operation time than on-highway engines. The energy demand during the use phase is proportional to the amount of diesel fuel consumed, and the fuel consumption is equal to the product of operation time and fuel efficiency. Obviously, a most feasible way to reduce energy demand during the use phase is to improve the fuel efficiency. This can be done by such as increasing compression ratio and fuel injection pressure, optimizing combustion conditions, and harvesting waste heat for electricity generation. The second most energy intensive stage was materials. Among different genset components, the control accounted for the largest energy demand but only made up 0.5% of the overall mass of the genset. This is largely due to the exceptionally high embodied energy values of PCBs in the control. It is important to note that only 87% of genset mass was included for assessment of material stage energy demands. Thus, the total material stage energy demands may be significantly underestimated. The end of life disposal has a significant impact on material stage energy consumption. The analysis showed that if 34% of the genset is remanufactured, 34% is recycled, and 32% sent to a landfill, the materials energy could be reduced by 52% compared to the 100% landfill scenario. Therefore, recycling and remanufacturing are of effectiveness in reducing energy intensive material extraction and processing. The transportation stage in this study only considered the shipping from the genset part suppliers to the final assembly facility. Thus, the actual energy demands for transportation would be likely contribute a larger percentage to the overall energy demand if a full analysis was conducted. Among the transportation activity investigated, the shipping of alternators from Mexico to the United States accounted for 62% of the overall transportation energy because of the long distance traveled by truck. Locating a genset part supplier near a port would significantly reduce energy demands for transportation.
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Several limitations are associated with the present study, as listed earlier. The most significant one is the relatively low level of data completeness. It is difficult to compile a complete material inventory for the diesel genset as it consists of >15,000 parts. There also lacks a complete database about the distribution, operation, maintenance, and disposal of the genset product. To address this issue and enable a more accurate LCA, a better communication and documenting mechanism needs to be established between the manufacturer and part suppliers, between the manufacturer and dealers, and between the dealers and end-users. Efforts will be made to find estimates or surrogates for the missing data, and to conduct a more comprehensive assessment on the environmental impact of the genset over the entire life cycle. In addition to energy, other impact metrics, such as global warming potential and eutrophication potential, will be considered. It is anticipated that diesel gensets will show somehow different environmental impacts than on-highway diesel engines, due to their different components, operation conditions, and emission control strategies. Acknowledgement The authors wish to acknowledge all engineers, workers and managers in the diesel generator manufacturing company who offered their generous support during this study e unfortunately, their names cannot be released due to a confidentiality agreement. The authors also want to thank Dr. Kumar Ganesan, Ms. Jeanne Larson and Dr. Jack Skinner at Montana Tech for their suggestions and comments. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jclepro.2017.02.082. References Ashby, M., 2013. Materials and the Environment, second ed. Butterworth-Heinemann, Boston, MA. Bolin, C.E., 2013. Iterative Uncertainty Reduction via Monte Carlo Simulation: a Streamlined Life Cycle Assessment Case Study. Master thesis. Massachusetts Institute of Technology, Cambridge, MA. Cooney, G., Hawkins, T.R., Marriott, J., 2013. Life cycle assessment of diesel and electric public transportation buses. J. Ind. Ecol. 17 (5), 689e699. Curran, M.A., 1996. Environmental life-cycle assessment. Int. J. Life Cycle Assess. 1 (3), 179e179. Curran, M.A., 2013. Life cycle assessment: a review of the methodology and its application to sustainability. Curr. Opin. Chem. Eng. 2 (3), 273e277. Diesel Service and Supply, 2016. Generator Industry Outlook. http://www. dieselserviceandsupply.com/Generator_Industry_Market_Forecast.aspx (Accessed 31 March 2016). Environmental Protection Agency, 2006. Life-cycle Assessment Principles and Practice. http://brevard.ifas.ufl.edu/communities/pdf/chapter1_frontmatter_ lca101.pdf (Accessed 13 April 2016). Environmental Protection Agency, 2012. Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for 2012. https:// www3.epa.gov/epawaste/nonhaz/municipal/ (Accessed 06 February 2016). Fleck, B., Huot, M., 2009. Comparative life-cycle assessment of a small wind turbine for residential off-grid use. Renew. Energy 34 (12), 2688e2696. Frost, Sullivan, 2013. Demand from Booming Data Center Industry Sustains Diesel Generator Sets Market in Asia-Pacific. https://frost.com/prod/servlet/pressrelease.pag?docid¼287274429 (Accessed 31 March 2016). Gmünder, S.M., Zah, R., Bhatacharjee, S., Classen, M., Mukherjee, P., Widmer, R., 2010. Life cycle assessment of village electrification based on straight jatropha oil in Chhattisgarh, India. Biomass Bioenergy 34 (3), 347e355. Hammond, G.P., Jones, C.I., 2011. Inventory of Carbon & Energy (ICE), Version 2.0. University of Bath, Bath, UK. International Standard Organization, 2006a. ISO14040:2006 e Environmental ManagementdLife Cycle Assessment: Principles and Framework. Geneva, Switzerland. International Standard Organization, 2006b. ISO14044:2006 e Environmental ManagementdLife Cycle Assessment: Requirements and Guidelines. Geneva, Switzerland.
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