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Prospective Energy Analysis of Emerging Technology Options for the United States Ethylene Industry Yuan Yao,*,† Diane J. Graziano,‡ Matthew Riddle,§ Joe Cresko,∥ and Eric Masanet*,†,⊥ †

Department of Chemical & Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60201, United States ‡ Global Security Sciences Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States § Energy Systems Division, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, Illinois 60439, United States ∥ Advanced Manufacturing Office, United States Department of Energy, 1000 Independence Avenue, SW, Washington, DC 20585, United States ⊥ Department of Mechanical Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60201, United States S Supporting Information *

ABSTRACT: In this study, a bottom-up technology assessment model is constructed and applied to evaluate potential changes in the cradle-to-gate primary energy consumption and greenhouse gas (GHG) emissions of U.S. ethylene production in the future. Three chemical pathways are modeled: conventional natural gas to ethylene, shale gas to ethylene, and crude-oil-based naphtha to ethylene. State-of-the-art technology and five emerging technologies for the production of ethylene from natural gas are evaluated at the process and national levels. The results quantify the primary energy and GHG emissions reductions achievable with state-of-the-art and emerging technologies, highlight the key parameters influencing their reduction potentials, and shed light on the implications of possible feedstock and technology shifts for U.S. ethylene production over the next several decades. The generalized and flexible modeling framework presented can be further used by energy, policy, and environmental analysts for assessing the savings potential of different technologies, making decisions in research and development investment, and strategic planning for meeting energy and emissions reduction goals.

1. INTRODUCTION Ethylene is one of the largest commodity chemicals produced in the United States. In 2010, 24 million metric tons (Mt) of ethylene were produced domestically, accounting for 40% of total U.S. petrochemical production.1 U.S. ethylene production has increased over the last several years and is expected to continue growing in the future.2 The recent boom in U.S. natural gas production provides abundant natural gas liquids (NGLs), especially ethane, which is the major feedstock used in U.S. ethylene production. Based on announced U.S. ethylene projects, approximately 45% more ethane-fed crackers will be added before 2018.2 This rapid growth of ethylene capacity will have repercussions on U.S. energy consumption and greenhouse gas (GHG) emissions, because ethylene production is one of the largest contributors to these impacts within the chemical industry.3 In the United States, ethylene production accounts for approximately 15% of energy use in the chemical industry.4 Globally, ethylene production is ranked as the second largest contributor of energy consumption (1.9 EJ/year) and GHG emissions (140 Mt of CO2-e/year) in the global chemical industry.3,5 The increase in energy use and GHG emissions associated with the expansion of ethylene capacity could be mitigated by the adoption of state-of-the-art (SOA) and emerging technologies. SOA technology refers to the best practice ethylene production in the United States. Emerging technologies are defined as those that are currently in the research, development, or demonstration stage that show potential for © XXXX American Chemical Society

future commercialization and could lead to energy savings beyond SOA technologies. Future projections of economy-wide energy and GHG emissions reductions achievable from the adoption of these technologies can provide policy makers, ethylene manufacturers, environmental communities, and scientists with insights to inform investment, technology promotion, and research and development (R&D) strategies.6 In previous studies, Ren et al.7,8 evaluated the energy saving potential of SOA and advanced technologies for naphtha cracking and alternative pathways such as methane to olefins. Neelis et al.9 provided a list of energy-saving opportunities by optimizing operational conditions and upgrading or replacing low-efficiency facilities with newly developed equipment. These studies assessed direct energy intensity reductions only (e.g., plant energy use per unit of ethylene product), without further analysis of potential energy savings at the regional or global levels over different time scales. A few studies have evaluated the potential of reducing global or country level energy use and GHG emissions in the ethylene industry, but not specifically for U.S. ethylene production. For example, Worrell et al.10 estimated CO2 emission reduction potential of ethylene production in OECD countries (The Special Issue: Sustainable Manufacturing Received: September 13, 2015 Revised: December 3, 2015 Accepted: December 8, 2015

A

DOI: 10.1021/acs.iecr.5b03413 Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

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Industrial & Engineering Chemistry Research

Figure 1. Schematic of the bottom-up cradle-to-gate model.

ethylene production associated with different technological pathways in prospective fashion. The cradle-to-gate model includes all the common unit processes involved in producing ethylene from NGLs derived from conventional natural gas and shale gas, and from crude-oil-based naphtha.15 Uncertainties and variances within the system are considered to estimate the upper and lower bounds on results. This model is used to evaluate the cradle-to-gate primary energy use and GHG emissions implications of SOA and five emerging technologies, which are described by data collected from the literature and technology suppliers. The development stage and adoption options (i.e., suitable for retrofit and new construction) of each emerging technology are considered explicitly. Two prospective scenarios, an expected adoption scenario and a rapid adoption scenario, are developed to project future ethylene production from 2015 to 2040. For each scenario, shares by year of ethylene from conventional ethylene production processes, SOA, and emerging technologies are estimated based on projections of new and retrofit capacities of ethylene plants. In each scenario, high and low estimates of cradle-to-gate primary energy and GHG emission intensities of ethylene production using different feedstocks and technologies are combined with their corresponding production volumes to estimate annual economy-wide primary energy use and GHG emissions of the

Organization for Economic Cooperation and Development), EIT countries (economies in transition), and the world in 2030. Another example is a roadmap published by the International Energy Agency (IEA) that projected, from 2010 to 2050, the total reduction of global energy consumption and GHG emissions that could be realized by employing improved catalytic processes in the production of 18 chemicals.3 The emerging technologies evaluated in the Worrell et al. and IEA studies focused on naphtha cracking. While naphtha is a feedstock widely used in Asia and European countries, naphtha cracking accounts for only 9% of ethylene production in the United States.11−13 IEA published another energy efficiency analysis report for the chemical sector in selected countries, including the United States, and the world as a whole.14 Their study, however, considered the energy consumed for best practice technology in 2006 and did not include prospective estimations for the future. Lastly, all of the aforementioned studies focused on direct energy use and GHG emissions, without broader consideration of the indirect (or upstream) energy use and GHG emissions associated with the extraction, refining, and distribution of ethylene plant feedstocks. In this study, a bottom-up model is presented and used to evaluate potential changes in direct and indirect (i.e., cradle-togate) primary energy consumption and GHG emissions of U.S. B


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first, then methane is separated with NGLs. Methane-rich gas is further processed by removing excess nitrogen and then compressed and delivered as pipeline-quality natural gas in the transportation stage. NGLs, including ethane, propane, and butane, are separated one by one through fractionation and transported to ethylene plants as feedstocks. The allocation of energy use and GHG emissions to pipeline-quality natural gas and NGLs is conducted on a mass basis that aligns with guidance from the ISO Standard 14044 for partitioning based on the physical relationships between systems inputs and outputs.17 Mass-based allocation is chosen for two reasons: (1) NGLs are used as the feedstock (as opposed to use as fuel) in ethylene production; therefore, a mass-based allocation better reflects the relationship between the environmental burdens and the inputs into the ethylene system than an energy/exergy based allocation. (2) An allocation based on economic values is highly sensitive to market conditions, which can change over time. In the ethylene production stage, steam cracking, which is the dominant method used for producing ethylene in the United States,15 converts feedstock into multiple products. Products from the cracking reactors are cooled and compressed. Water content is removed through drying. Next, ethylene is separated with other byproducts through a series of separation units. To estimate the cradle-to-gate primary energy and GHG emission intensities of conventional natural gas and shale gas to ethylene pathways, unit operations involved in the ethylene production system, such as steam cracking, cooling, compression, gas drying, and ethylene separation, are modeled based on process characteristics and thermodynamic properties of chemical inputs and outputs.15 The ranges of cradle-to-gate intensities are estimated using Monte Carlo simulation (10 000 trials in Crystal Ball18) in which uncertainty and system variances are captured by setting upper and lower bounds on system parameters. For naphtha cracking, crude oil is recovered from oil fields and sent to refineries for further processing.19 Naphtha produced at the refinery is transported to ethylene plants for naphtha cracking production. Most of the naphtha in the United States is transported to ethylene plants by pipeline.20 The cradle-to-gate primary energy and GHG emissions intensities of petroleum extraction, refining, and naphtha transportation are estimated based on the GREET model and various literature sources.3,7,8,20−24 See Supporting Information section 1.2 for detailed data. Because NGLs are the major feedstocks used in U.S. ethylene production,11−13 in this study SOA and emerging technologies are assessed only for conventional natural gas and shale gas pathways. Regarding the selection of emerging technologies, only those aimed at energy efficiency improvement for the ethylene production stage are reviewed and included in this work; a choice made because ethylene production is the most energy intensive among four cradle-to-gate stages according to previous studies.15 Additionally, the modeling framework presented in this study can be easily used and extended for evaluating other emerging technologies or pathways beyond those considered here given its flexible unit process structure. With respect to allocation methods for ethylene production, energy and emission intensities in the ethylene industry are generally reported on two bases: the ethylene basis or the highvalue chemical basis (HVC).7,24−26 On the ethylene basis, ethylene plant energy and GHG emissions are allocated to the ethylene product only. On the HVC basis, ethylene plant

U.S. ethylene industry. The details of the modeling methodology are discussed in section 2. Section 3.1 presents cradle-to-gate results for ethylene production from different feedstocks and technologies. These results are useful for benchmarking present levels of energy use and GHG emissions and for understanding cradle-to-gate reduction potentials associated with adopting SOA and emerging technologies. They are also transparent data resources that can be used by the life-cycle assessment (LCA) community or energy policy analysts. In section 3.2, the annual cradle-to-gate primary energy consumption and GHG emissions of U.S. ethylene production associated with adopting SOA and emerging technologies under different scenarios are presented and compared. The modeling framework and results address important stages and impacts within the broader life cycle of chemicals and particularly for emerging manufacturing technologies for which there are considerable knowledge gaps. The results also shed light on potential energy use and GHG emissions reductions of possible large-scale feedstock and technology shifts for producing ethylene over the next several decades. These implications are important for assessing the role of ethylene production in U.S. energy use and emissions, as well as for identifying technologies and actions for reducing energy use and GHG emissions that can be encouraged by policies and incentives in a proactive fashion.

2. METHODOLOGY Figure 1 depicts the modules included in the model employed in this study: a cradle-to-gate energy and emission analysis module, a technology assessment module, and a prospective analysis module. The cradle-to-gate energy and emission analysis module estimates primary energy and GHG emission intensities (including both fuel-related emissions and fugitive emissions) for U.S. ethylene production systems. The model is described in Yao et al.,15 with further discussion of data sources and assumptions provided as Supporting Information. The technology assessment module evaluates the energy-saving potential of SOA and emerging technologies. Integrated with the cradle-to-gate energy and emission analysis module, the technology assessment module is able to estimate cradle-to-gate primary energy and GHG emission intensities of both SOA and emerging technologies. The intensity results are then used in the prospective analysis module for projecting annual cradle-togate primary energy and GHG emissions of U.S. ethylene production from 2015 to 2040. The following subsections provide a brief introduction to each module. 2.1. Cradle-to-Gate Energy and Emission Analysis Module. In the United States, the majority of ethylene is produced from NGLs and approximately 9% is produced from naphtha,11−13 which is mainly produced in crude oil refining.16 NGLs can be derived from conventional natural gas or shale gas. The cradle-to-gate energy and emission analysis module includes all three feedstock pathways, namely, shale gas to ethylene, conventional natural gas to ethylene, and naphtha to ethylene. For each pathway, four stages are included: raw feedstock recovery, processing, transportation, and ethylene production. For conventional natural gas and shale gas to ethylene pathways, feedstock recovery is a series of activities including well construction and extraction of natural gas. The raw natural gas from feedstock recovery is sent to a processing plant for further purification and separation of different products. In this stage, impurities like hydrogen sulfide and water are removed at C


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Industrial & Engineering Chemistry Research Table 1. Summary of Emerging Technologies Considered in This Study technology catalyst-assisted production of olefins (CAMOL)27−29 microwave-enhanced cracking30−32 (MEC) microchannel process technology (MPT)4,33 membrane/distillation hybrid process (MDP)34,35 enhanced distillation through hollow fiber technology (HFT)36,37

reference section in existing plants

on-site fuel saving rate compared to reference sectiona

fuel type change

TRL

introduction year

steam cracking

15−25% 30−50% 10−14% 15−23% 7−9%

no yes no yes no

6 4 5 5 4

2020 2030 2025 2025 2030

separation

a

Fuel used in steam cracking process is predominately natural gas. Therefore, for CAMOL, MPT, and HFT whose fuel types are not changed, reduction rates refer to the saving percentage of the natural gas requirements. For MEC and MDP whose fuel types are changed, the fuel requirements after applying reduction rate need to be split between natural gas and electricity. The split ratio of two fuels and electricity generation efficiency are further discussed in section 2.2.1.

depicted in Figure 1. Third, if the emerging technology uses a different fuel type compared with the conventional steam cracking process, new emission factors and primary energy conversion factors were applied. See Supporting Information section 1.1 for details on the emission factors and primary energy conversion factors of each fuel. Fourth, Monte Carlo simulation was run (10 000 trials) to estimate ranges on the overall energy and GHG emissions savings of emerging technologies compared to the existing plants without retrofit. When emerging technologies are adopted as additions to new ethylene plants, the cradle-to-gate primary energy and GHG emission intensities were estimated using the similar approach as discussed above but with one additional step. In this step, a group of key process parameters were set to the best practice values to simulate the combination of SOA technology and one emerging technology. Then Monte Carlo simulation was run again (10 000 trials) to estimate the mitigation potentials of emerging technologies employed in new ethylene plants. The identification process for key process parameters used to simulate SOA technology performance is further discussed in SOA Technology Assessment. In addition, the maturity of each technology was assessed as it governs when the technology would be available in the future for commercial use. This assessment is conducted by using the technology readiness level (TRL) following protocols published by the U.S. Department of Energy (DOE).38 It was assumed that an emerging technology would be adopted in 2020 when its TRL is equal to or larger than 6 based on industry experience.4,38 As TRL decreases by 1, it was assumed that the technology adoption will be delayed by 5 years.4,38 Table 1 summarizes the information discussed in this section. The following subsections provide brief descriptions of emerging technologies considered in this study. 2.2.1.1. Catalyst-Assisted Production of Olefins. CAMOL is a technology applied to the steam cracking furnace. During steam cracking, coke accumulates in the inside surface of the radiant coil or tube. This coking is a problem because the operation has to be periodically stopped to remove the coke. CAMOL technology applies an advanced coating to the inner surface of the tube and coils.28 The catalytic coating surface is inert to filamentous coke and gasifies amorphous coke such that the frequency of off-line operation for decoking is reduced; therefore, the run-length of the furnace can be extended and the heat transfer can be improved.27,28 The TRL of CAMOL is assessed as 6 because it already has a prototype demonstration.29 2.2.1.2. Microwave-Enhanced Cracking. MEC is a new technology that can replace traditional energy-intensive cracking furnaces. In conventional furnaces, heat is transferred

energy use and emissions are allocated to all HVC production outputs including ethylene (see Table S2 for a detailed list of production outputs). Both bases are calculated using massbased allocation. In this study, the ethylene basis is chosen for two reasons: (1) As discussed previously, technologies assessed in this study are only for NGLs-based ethylene production whose major product is ethylene. (2) None of the SOA and emerging technologies considered in this study will change the product yields of the steam cracking process. However, the allocation method can be readily changed in the framework for future analysis as needed. 2.2. Technology Assessment Module. In the technology assessment module, SOA technology and five emerging technologies are evaluated: catalyst-assisted production (CAMOL) 2 7 − 2 9 and microwave-enhanced cracking (MEC)30−32 for the steam cracking section and microchannel process technology (MPT),4,33 membrane/distillation hybrid process (MDP),34,35 and hollow fiber technology (HFT)36,37 for the ethylene separation section. This work does not consider combining emerging technologies. Such combinations would require detailed feasibility analyses in practice conducted with the involvement of both technology suppliers, which is beyond the scope of this study. However, such combinations could be easily integrated into the modeling framework in the future when such feasibility analyses have been conducted. The following subsections briefly discuss the methodology and data used in the technology assessment module. 2.2.1. Emerging Technology Assessment. Emerging technologies can be adopted in two ways: (1) in existing plants as retrofit options and (2) in new plants as additions to SOA processes. As a retrofit option, an emerging technology improves the energy efficiency of a specific section (e.g., steam cracking section) while the rest of the unit operations in the existing process remain unchanged. For new plants that are usually equipped with SOA processes,16 emerging technologies can further improve energy efficiency. In this study, the primary energy and GHG emissions intensities of emerging technologies adopted in both ways are assessed explicitly. The cradle-to-gate primary energy and GHG emission intensities of emerging technologies adopted in existing plants as retrofit options were estimated using four steps. First, the fuel reduction potential of each technology on the relevant process step (i.e., steam cracking or the separation section) was estimated and is summarized in Table 1 based on the data reported by the literature and technology suppliers. Because distribution data for fuel saving rates are not available, we assume a uniform distribution for each of them. Second, the fuel saving rate was applied to either the steam cracker model or separation model in the cradle-to-gate system model as D


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potential to be used in large-scale distillation columns.36,37 The TRL of HFT is assessed to be 4 based on the existence of projects demonstrating the feasibility of major components at a lab scale.36,37 2.2.1.6. Other Technologies. In addition to the technologies discussed in the previous sections, there are several alternative pathways for ethylene production: methanol to olefins (MTO), methane oxidative coupling (OCM), ethane oxidative dehydrogenation (EOD), and ethanol dehydration (EDH).5 Based on previous studies,7,8 MTO and OCM may have significantly higher energy consumption than the conventional steam cracking process; therefore, they are excluded in this study. Regarding EOD, energy data are rare because the process is still under lab development. Assessing this technology requires development of a conceptual chemical process design including possible configurations, which is beyond the scope of this paper. EDH is a biobased technology producing ethylene from biomass-derived ethanol, whose cost, energy consumption, and GHG emissions are highly subject to biomass availability, ethanol locations, and costs (estimated at 2−3 times higher than that of ethane steam cracking).46 If the trend of low-cost NGLs continues into the future, this technology is not expected to compete well with steam cracking in the United States. Therefore, EDH is not included in this study. 2.2.2. SOA Technology Assessment. SOA technology represents best practice ethylene production in the United States, which is usually achieved in newly built plants.16 Without adopting any emerging technology, new ethylene plants commonly have higher energy efficiency than existing plants due to the use of more efficient facilities (e.g., steam turbines and compressors) and better process control.16 Therefore, it is necessary to analyze cradle-to-gate primary energy and GHG emission intensities of SOA technology to understand the evolution of the ethylene industry in the absence of emerging technologies, which can also be used as one of the baselines for comparison. Furthermore, as discussed in Emerging Technology Assessment, emerging technologies will be adopted in new ethylene plants as additions to SOA technology. Thus, SOA technology intensity results are useful for assessing the cradle-to-gate primary energy and GHG emission intensities of emerging technologies adopted in new ethylene plants. In previous studies, Saygın et al. reported primary energy consumption for ethylene production as 13.8 GJ/t of HVC for SOA technology.14 According to a study conducted for the U.S. DOE,4 on-site SOA ethylene production consumes 15.5 GJ/t primary energy of ethylene. However, data from both studies are only for the ethylene production stage without considering upstream processes. In this work, a unit-operation-specific approach is used to estimate the cradle-to-gate energy and GHG emissions intensities of SOA ethylene production. First, sensitivity analysis was conducted to identify key modeling parameters contributing to variances in the cradle-togate primary energy intensity of U.S. ethylene production. The ranges and distributions of parameters were derived from literature or data sample fitting; for parameters whose distribution were not available, uniform distributions were assigned. Second, these key parameters were categorized into two groups: controllable parameters (i.e., those that can be changed by process or operating decisions, such as compressor efficiencies and heat recovery rates) and uncontrollable parameters (i.e., those that are exogenous to technology processes, such as raw natural gas composition). Third,

through the outer surface of coils or tubes. MEC technology takes advantage of microwave energy to offer heat directly to the targets. MEC enables energy savings by eliminating preheating and steam and lowering the reaction temperature.30−32 The reported savings potential does not consider the primary energy used for generating electricity, which is necessary because MEC reduces steam cracker fuel use from fossil fuels (predominately natural gas in the United States) but increases electricity requirements. In this study, we assume that the electricity used for MEC is generated by on-site combined heat and power (CHP). This assumption is reasonable for MEC which is not expected to be commercialized for 15 years, by which time CHP is expected to be widely adopted in U.S. ethylene plants given the current rapid growth of CHP deployment in the chemical industry.39,40 The electricity generation efficiency of CHP is assumed to vary between 69 and 84% based on the U.S. Environmental Protection Agency data.41 The TRL of MEC technology is evaluated as 4 given ongoing projects to validate the feasibility of major components in lab environments.30−32 2.2.1.3. Microchannel Process Technology. Cryogenic distillation, an energy-intensive process, is used for gas separation in conventional ethylene production. MPT is a novel process which combines heat transfer and gas separation into one unit.4 Two patents for the application of MPT for ethylene production42,43 have been filed in the United States. The U.S. DOE supported a MPT project aimed at incorporating an MPT distillation unit within an ethylene plant.33 Simulation and experiments have explored the technical possibility and economic viability of MPT.33 These results show that MPT holds an advantage for smaller-scale applications compared to cryogenic distillation. However, from an economic perspective, MPT is not yet suitable for large-scale ethylene plants. As the technology develops, MPT may be an option for future ethylene plants. The TRL of MPT technology is evaluated to be 5 based on the existence of lab-scale demonstrations of all key components in the MPT system and quantitative analyses of engineering and economic feasibilities for industrial MPT applications.33,42,43 2.2.1.4. Membrane/Distillation Hybrid Process. MDP is a technology that integrates membrane separation and distillation.34 The specific membrane used for this application has been used widely in separation processes and has promising gas transport performance.34,35 This technology takes advantage of high gas transport of the membrane system to transporting ethylene. Economic analyses suggest that this process may achieve a return on investment up to 67%.34,35 Similar to MEC, MDP technology changes the fuel type used in the separation section of ethylene process. In this study, it is assumed that 66% of the fuel used in MDP system is electricity,44 and the rest of fuel is the same as the distillation unit. As for MEC, electricity is assumed to be generated by an on-site CHP system. The TRL of MDP is assessed to be 5 given the existence of projects to validate the feasibility of all components in the MDP system and for engineering and economic analysis for large-scale applications.34,35 2.2.1.5. Enhanced Distillation through Hollow Fiber Technology. Another enhanced distillation technology is HFT, which is designed to improve the efficiency of a distillation column by replacing packing materials with hollow fibers that have separated channels for both liquids and vapor.45 This novel material can increase separation efficiency and capacity.45 Recent results show that hollow fibers have the E


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Introduction, feedstocks (d) considered in this study include conventional natural gas derived NGLs, shale gas derived NGLs, and crude oil derived naphtha. For the case of adopting emerging technologies, technology n includes the conventional steam cracking process, SOA technology, and one of five emerging technologies discussed in the previous section. For the case of adopting SOA only (one of the baselines for comparison), technology n used in eq 2 includes only the conventional steam cracking process and SOA technology. Another comparison baseline used in this study is the businessas-usual (BAU) case, which is commonly used in time-related energy analyses.3 In the BAU case, technology n used in eq 2 includes only conventional steam cracking process using either NGLs or naphtha as feedstocks. In addition, the BAU case assumes that the current range of the cradle-to-gate primary energy intensity for U.S. ethylene production remains constant from 2015 through 2040. In another words, the primary energy and GHG emission intensities of annual U.S. ethylene production from year 2015 through 2040 (AEt,s and AGt,s in eq 2) are fixed to be equal to the intensities in year 2015. Then the fixed intensities are multiplied with projected annual ethylene production as the calculation shown in eq 1 to give the annual primary energy consumption and GHG emissions of U.S. ethylene production in the BAU case. The share of different feedstock (SFd,t,s) is calculated as follows. First, the quantity of ethylene from naphtha cracking is assumed to be constant (2.2 million ton/year)12,13 throughout the projection period. Naphtha cracking has relatively low yields of ethylene but higher yields of other valuable chemicals such as propylene than NGLs-based cracking.7,23,26,48,49 Consequently, naphtha cracking capacity decisions are based less on ethane supply and more on hedging against future risks by providing manufacturers with flexibility to respond to changes in demand and price margins for propylene relative to ethylene. Then, the share of naphtha for ethylene production is calculated as the ratio of ethylene from naphtha to the quantity of projected ethylene production in each year. Next, the share of total natural gas derived feed is calculated as one minus the fraction of naphtha cracking. Finally, the share between conventional natural gas and shale gas is calculated based on the AEO’s projection of natural gas production from different natural gas sources.40 This share is assumed to be the same for existing plants and new plants given the difficulty of tracing back the sources of natural gas and NGLs used in each ethylene plant. The share of conventional steam cracking process (Pn,t,s where n = conventional steam cracking process), or say, the share of existing ethylene plants without retrofit, is calculated by the following equation:

controllable parameters were set to best practice values based on the best industrial practice for each parameter. Fourth, Monte Carlo simulation was run (10 000 trials) to estimate ranges for the cradle-to-gate primary energy and GHG intensities of SOA ethylene production. See Supporting Information section 2 for further details. 2.3. Prospective Analysis Module. Prospective analysis is conducted to project future changes of the energy use and GHG emissions of the U.S. ethylene industry as functions of ethylene demand, ethane supply, and process technology pathway assumptions. Two prospective scenarios are developed in this study: an expected adoption scenario and a rapid adoption scenario. In the expected adoption scenario, emerging technologies are available in the market in the adoption year shown in Table 1. In the rapid adoption scenario, emerging technologies are assumed to be available five years earlier than their expected adoption years. The purpose of designing two scenarios is to understand the impacts of earlier adoption on the mitigation potential of emerging technologies, which can shed light on the extent to which accelerated investments in bringing technologies to market faster can pay off. In each scenario, the cradle-to-gate primary energy consumption (NEt,s, million GJ/year) and GHG emissions (NGt,s, million ton CO2-e/year) of U.S. ethylene production by year t and scenario s are calculated by the equation below. NEt , s = PVt , s·AEt , s

and

NGt , s = PVt , s·AGt , s

(1)

where PVt,s is the projection of ethylene (million ton/year) production volume in year t in scenario s; AEt,s is the cradle-togate primary energy intensity of annual ethylene production (GJ/ton ethylene) in year t in scenario s; and AGt,s is the GHG emissions intensity of annual ethylene production (ton CO2-e/ ton ethylene) in year t in scenario s. In this study, year t ranges from 2015 to 2040, while scenario s includes the two scenarios discussed previously. The ethylene production projection is estimated based on the annual ethane supply derived from the U.S. DOE’s Annual Energy Outlook 2014 (AEO) projections of NGLs from 2015 to 2040.40 Ethane is the major feedstock used in U.S. ethylene production, and the majority of announced and planned ethylene projects are designed for ethane cracking.2 The average ethane content in NGL is 39% based on reported data for U.S. gas plant production of NGLs from 2010 through May 2014.47 For the ethylene production projections, 80% of the ethane produced in the United States is assumed to be converted to ethylene based on the yields of ethane cracking.7,23,26,48,49 The annual ethylene production volumes projections used in this study are provided in Table S7. The annual cradle-to-gate primary energy and GHG emission intensities of produced ethylene are estimated by eq 2. AEt , s =

∑ SFd ,t ,s ·∑ (Pn,t ,s·EId ,n) and d

AGt , s =

Pn , t , s = Cn , t − 1, s[1 − SP − (1 − SP)RP]·Ut /PVt , s

where Cn,t−1,s is the capacity of the existing plant without retrofit in the previous year (t − 1) in scenario s. SP is the annual fraction of plants that are shut down, mothballed, or degraded in performance. RP is the annual ratio of plants that are retrofitted. Ut is the capacity utilization rate in year t. PVt,s is the total ethylene produced in year t in scenario s, which is discussed in eq 1. The shut down and retrofit fractions are assumed to be 1% and 5%, respectively.1,16,50,51 This is a simplified assumption and it can be improved in future work if data on the vintages of U.S. ethylene plants become publically available. The capacity utilization rate is estimated based on the historical data.52

n

∑ SFd ,t ,s ·∑ (Pn,t ,s·GId ,n) d

n

(3)

(2)

where SFd,t,s is the share of ethylene production (%) using feedstock d in year t for scenario s. Pn,t,s is the percentage of ethylene production (%) by technology n in total quantity of ethylene produced in year t for scenario s. EId,n and GId,n are primary energy and GHG emission intensities of ethylene (GJ/ ton ethylene and ton CO2‑e/ton ethylene) made from different feedstocks d and technologies n. As mentioned in the F


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Industrial & Engineering Chemistry Research Table 2. Cradle-to-Gate Results for U.S. Ethylene Production by Different Pathways and Technologiesa primary energy (GJ/ton ethylene) CNG low Existing Ethylene Plants without Retrofit CSC (NGLs) 23.6 CSC (naphtha)b 27.9 BAUb 23.5 Existing Ethylene Plants with Retrofit CAMOL 21.4 MEC 20.4 MPT 23.2 MDP 22.8 HFT 23.2 New Ethylene Plants CAMOL 18.0 MEC 17.1 MPT 19.9 MDP 19.4 HFT 19.9 SOA 20.2

GHG (ton CO2-e/ton ethylene)

SG

CNG

SG

high

low

high

low

high

low

high

28.5 56.7 30.8

23.4

28.9

2.10 1.85 2.18

2.60 4.33 3.47

2.30

3.90

26.8 27.5 28.0 28.0 28.1

21.1 20.2 23.0 22.9 23.0

27.3 27.9 28.7 28.6 28.7

1.98 1.96 2.09 2.07 2.09

2.49 2.51 2.58 2.57 2.59

2.18 2.16 2.29 2.28 2.29

3.72 3.79 3.88 3.88 3.80

20.6 21.3 21.9 21.8 22.0 22.4

17.8 16.9 19.8 19.5 19.9 20.1

20.5 21.2 22.0 21.8 22.0 22.2

1.67 1.65 1.78 1.76 1.78 1.79

2.06 2.08 2.15 2.14 2.16 2.17

1.85 1.83 1.96 1.95 1.96 1.97

3.29 3.36 3.45 3.45 3.37 3.47

a

CNG: conventional natural gas. SG: shale gas. CSC: conventional steam cracking process. bThe feedstock of naphtha cracking is crude-oil-based naphtha; therefore, the results are not distinguished between CNG and SG. BAU results are not distinguished between CNG and SG because it is the weighted average of current production using different feedstocks.

The shares of SOA or emerging technologies (Pn,t,s where n = SOA or emerging technologies) are calculated by the equation

be the weighted average of two options in each year as calculated by the equation EId , n ′ ,(t )

Pn ′ , t , s = [Cn ′ , t − 1, s + CNn ′ , t , s + Cn , t − 1, s(1 − SP)RP]·Ut /PVt , s (4)

=

where Cn′,t−1,s is the total capacity of retrofitted plants and new plants using technology n′ in previous year (t − 1) in scenario s. CNn′,t,s is the annual capacity additions using technology n′ in year t in scenario s. Cn,t−1,s(1 − SP)·RP is the capacity of ethylene plants that are retrofitted as discussed in eq 3. The estimated domestic ethylene capacity in year 2015 was based on a reported ethylene capacity of 27.6 million ton/year in 201253 plus capacity additions reported to be on-schedule from 2012 through 2015.54 From these data, total installed capacity in 2015 was estimated to be 29 million ton/year. In the projection of ethylene capacity from 2015 to 2017 year, the capacity is added based on new construction and expansions publically announced by industry54 (see Table S6 for detailed data). For the years beyond 2017, annual capacity additions are estimated using an algorithm that considers the capacity utilization as follows. If the capacity utilization in the previous year is greater than the desired maximum utilization (0.9),55,56 capacity is added at a level required to reduce utilization to a lower level (0.87).55,56 This formulation is meant to reflect capital planning and yields periodic capacity additions over the course of the projection period. See Supporting Information section 3 for detailed data. The cradle-to-gate primary energy and GHG emission intensities of ethylene (EId,n and GId,n) produced by the conventional steam cracking process or SOA technology are obtained from the cradle-to-gate energy and emission analysis module and technology assessment module discussed in previous sections. As mentioned previously, emerging technologies can be adopted as two options, retrofits in existing plants and additions to new plants. Thus, the primary energy and GHG emissions intensities of emerging technology should

[∑t CNn ′ , t , s · NEId , n ′ + ∑t − 1 Cn , t − 1, s(1 − SP) ·RP ·REId , n ′]· Ut Pn ′ , t , s· PVt , s (5)

and GId , n ′ ,(t ) = [∑t CNn ′ , t , s · NGId , n ′ + ∑t − 1 Cn , t − 1, s(1 − SP) ·RP ·RGId , n ′]· Ut Pn ′ , t , s· PVt , s

where NEId,n′ and NGId,n′ are the cradle-to-gate primary energy and GHG emission intensities of emerging technologies adopted by new ethylene plants, respectively. REId,n′ and RGId,n′ are energy and GHG emissions intensities of emerging technologies adopted by existing plants as retrofit options, respectively. The rest of the parameters are discussed in eq 4, and they were used to calculate the shares of new plants and retrofitted existing plants.

3. RESULTS AND DISCUSSION 3.1. Cradle-to-Gate Results for U.S. Ethylene Production by Different Technologies. Table 2 below shows estimated cradle-to-gate primary energy and GHG emission intensities for U.S. ethylene production by different pathways and technologies reported as 95% confidence intervals. The results are shown in three sections: (1) existing ethylene plants that deploy the conventional steam cracking process without retrofit, (2) existing ethylene plants adopting one emerging technology as a retrofit option, and (3) new ethylene plants equipped with SOA technology and one additional emerging technology. Compared with the conventional steam cracking process used in existing ethylene plants, emerging technologies G


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Industrial & Engineering Chemistry Research and SOA technology show reductions of the cradle-to-gate primary energy and GHG emission intensities. Ethylene plants with retrofitting emerging technologies have higher intensities than new ethylene plants because upgrading one section of the production process with an emerging technology will not reduce energy/GHG emissions down to the level of a new plant that has highly efficient facilities and well-optimized operations over their entire process system. Another observation is the existence of variance in the results. The large variance in the cradle-to-gate primary energy intensity of the conventional steam cracking process is majorly caused by the variability of process parameters including steam turbine efficiency, compressor efficiency, and others (see Supporting Information section 2 for further explanation). Therefore, when these key process parameters are set to best practice values to simulate SOA technology, both the quantity and variability of primary energy intensity results have been reduced, as shown in Table 2. For emerging technologies, due to the uncertainty of fuel saving rates, as shown in Table 1, their intensity results have similar or larger ranges compared to the results of SOA technology. To investigate the impacts of high and low estimates of the cradle-to-gate primary energy intensity on national energy consumption, and to test the robustness of the future scenario results, the high and low values shown in Table 2 were used in the prospective analysis module. As a result, the projection of primary energy consumption for annual U.S. ethylene production shown in the following section has high and low estimates in each scenario. Unlike the primary energy results, the variance of GHG emission intensities are largely driven by fugitive emissions occurring during natural gas recovery and processing (see Supporting Information section 2 for more details). To understand the impacts of fugitive emissions in future scenarios, the high and low values of the cradle-to-gate GHG emission intensities shown in Table 2 were used in the prospective analysis module to provide a comparative case. 3.2. Future Scenario Results. Figure 2 shows projections of the cradle-to-gate primary energy consumption of U.S. ethylene production in the cases of BAU and increasing SOA penetration. Because the cradle-to-gate primary energy intensities of BAU and SOA technology have variances (as

shown in Table 2), the annual energy consumption results in Figure 2 are shown within high and low bounds to reflect their ranges. In Figure 2, the projection of BAU case indicates that without energy efficiency improvements the cradle-to-gate primary energy consumption of annual U.S. ethylene production will grow significantly from 2015 and 2040. This trend can be mitigated by the widespread deployment of SOA technology as shown in the same figure. While there is a large overlap area between the BAU and SOA scenario results, it does not indicate that adopting SOA leads to small energy savings, given that the energy intensity of SOA technology is below the ranges of existing ethylene plants as demonstrated in Table 2. Instead, the overlap between the two scenarios merely implies that the reduction of energy use contributed by the application of SOA technology is not large enough to cancel out the increase of energy consumption led by capacity growth. The cradle-to-gate primary energy consumption and energy intensities for adopting emerging technologies in the two future scenarios are shown in Figures 3 and 4, respectively. SOA results are also depicted in both figures as a baseline for comparison. The “wave” shape of lines is caused by the time required for new plant construction. As mentioned in the previous section, in each scenario, the high and low values of primary energy intensities shown in Table 2 are used in the prospective analysis module. Therefore, in each scenario in Figures 3 and 4, two projections based on high and low estimates are shown. The results of high and low estimates in Figure 3 show a robust result that adopting emerging technologies can slow or even reverse the growing trend of the cradle-to-gate primary energy consumptions of ethylene industry. This phenomenon can be explained by the fact that emerging technologies can lead to further reductions of primary energy intensities beyond SOA technology as demonstrated in Figure 4. An interesting observation is that MEC shows much larger energy-saving potential at low estimate (even larger than CAMOL in Figures 3 and 4) than it does at high estimate. This is caused by the large uncertainty of the fuel saving rate that can be enabled by MEC technology, as demonstrated in Table 1. This phenomenon indicates the importance of taking the uncertainty into consideration when conducting a prospective analysis. Furthermore, it reveals the need to reduce the uncertainties related to the energy-saving potential of emerging technologies in future R&D work to enable more robust prospective analysis. The comparison between the expected adoption scenario and rapid adoption scenario shown in Figures 3 and 4 indicates that the adoption year is a critical factor driving the results. More rapid adoption of emerging technologies brings greater and faster reductions in the cradle-to-gate primary energy intensities of annual U.S. ethylene production (as demonstrated in Figure 4), leading to 5−38% more cumulative energy savings by 2040 (as demonstrated in Figure 3). This indication is critical to policy makers, investors, and researchers who are interested in knowing whether intensive R&D investment for faster commercialization is worthwhile. Furthermore, the modeling framework and results shown in this study can be further used in economic analysis to understand the net benefits between R&D investment for earlier adoption and cost reductions enabled by faster energy savings. Regarding the comparison among different technologies, the results in Figures 3 and 4 reveal that CAMOL and MEC, two technologies applied to the steam cracker, tend to have larger

Figure 2. Projection of the cradle-to-gate primary energy consumption of annual U.S. ethylene production in the cases of BAU and increasing SOA penetration. H


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Figure 3. Projection of the cradle-to-gate energy consumption of annual U.S. ethylene production by adopting different technologies.

energy savings than the other three technologies that are applied to separation units. CAMOL or MEC can be implemented together with one of MPT/MDP/HFT as they are applied to different unit operations. For technologies applied to separation units, MDP shows more energy savings than the other two. With respect to competing technologies applied to the steam cracker, CAMOL shows much more energy savings than MEC technology before 2035 as demonstrated in Figures 3 and 4. However, MEC technology has the potential to save more energy than CAMOL after 2035, as shown at the low estimates in the same figures. This phenomenon indicates that in the short-term, CAMOL is more favorable in terms of energy savings but in the long-term, MEC may have greater potential especially considering the fact that MEC is powered by electricity and thus can be integrated with renewable power generation in the future. The selection of technology should be considered on a case-by-case basis as the appropriateness and potential of each technology depends on the reduction target (e.g., short-term or long-term), resource availability and cost (e.g., the availability of catalyst feedstock and electricity cost), and investment costs, all of which will vary by plant. However, the model and the general results presented in this study can still provide a good reference for decision making in technology investment. The projection of the cradle-to-gate GHG emissions and emission intensities for adopting emerging technologies in two future scenarios are shown in Figures 5 and 6, respectively. In each scenario, high and low values of GHG emission intensities shown in Table 2 are used in the prospective analysis module to project national GHG emissions using high and low fugitive

emissions estimates. The results of the BAU and SOA cases are depicted in both figures to better illustrate the impacts of fugitive emissions. In Figure 5, there are four major observations. First, there is an overlap between BAU and SOA cases at the high fugitive emission estimate, while this phenomenon has not been observed at low fugitive emission estimate. This overlap can be explained by the overlap of GHG emission intensities at the high fugitive emission estimate as shown in Figure 6. More specifically, as the penetration of SOA technology increases, the cradle-to-gate GHG emission intensity of annual ethylene production drops in the first two years (2015−2017). However, after 2017, the dramatic increases of fugitive emissions associated with the growth of shale gas use compensate and even exceed the reduction of GHG emissions enabled by the application of SOA technology. As a result, the GHG emission intensity increases after 2017 and reaches the level of the BAU case in 2022 and 2024. In other words, the benefits of adopting SOA technology in reducing the cradle-to-gate GHG emissions of U.S. ethylene production could be canceled out if current natural gas industry remains at a high fugitive emission level in the future. Second, similar trends have been observed for emerging technologies. The adoption of emerging technologies lead to less GHG emission reductions at the high fugitive emission estimate (3−5%) than they have at the low fugitive emission estimate (6−10%) compared to the BAU case in 2040. This phenomenon further supports the conclusion discussed previously that large quantities of fugitive emissions reduce the benefit of adopting new technologies in mitigating the I


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Figure 4. Projection of the cradle-to-gate energy intensity of annual U.S. ethylene production by adopting different technologies.

Figure 5. Projection of the cradle-to-gate GHG emissions of annual U.S. ethylene production by adopting different technologies.

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Figure 6. Projection of the cradle-to-gate GHG emission intensity of annual U.S. ethylene production by adopting different technologies.

low fugitive emission estimates compared to BAU, respectively). As discussed previously, although the selection of technology should be conducted at a case-by-case basis, the results shown in this study are good references to facilitate decision making, especially for those focused on reducing GHG emissions.

cradle-to-gate GHG emissions of U.S. ethylene production. Therefore, efforts should be made to reduce fugitive emissions generated in the upstream natural gas industry to retain the lifecycle benefits enabled by adopting new technologies in the downstream ethylene industry. Third, compared to the expected adoption scenario, more rapid adoption of emerging technologies brings greater cumulative reductions in GHG emissions by 2040 (7−33% more reduction) at the high fugitive emission estimate than they do at the low fugitive emission estimate (2−25% more reduction). This phenomenon can be explained by the impact of high fugitive emissions associated with shale gas. As the share of shale gas use increases year by year, the later an emerging technology enters the market the more reduction of GHG emissions enabled by the emerging technology will be offset by fugitive emissions. Thus, earlier adoption of emerging technologies can deliver more reduction benefits at a high fugitive emission estimate. This observation reveals that faster commercialization of emerging technologies can not only bring more reductions of GHG emissions but also reduce the risk of losing these reduction benefits caused by possible high fugitive emissions in the future. Fourth, compared with other technologies, CAMOL has the largest potential for reducing GHG emissions. For example, from 2015 to 2040, CAMOL has much more cumulative reductions of GHG emissions in the expected adoption scenario (80 and 95 million ton CO2-e reduction at the high and low fugitive emission estimates compared to BAU, respectively) than the other technologies (30−39 million ton CO2-e and 54−70 million ton CO2-e reduction at the high and

4. CONCLUSION In this study, a cradle-to-gate, prospective technology assessment framework for the U.S. ethylene industry is presented. This work evaluates the cradle-to-gate primary energy use and GHG emission intensities of current and future ethylene production in the United States. The intensity results are transparent data resources that can be further used by policy makers, ethylene manufacturers, and the energy and environmental analysis communities for benchmarking, opportunity searching, and policy analysis. The prospective analysis based on intensity results provides useful projections on future trends of energy and GHG emission footprints of the U.S. ethylene industry and offer useful insights on aspects and key parameters influencing impact reduction potentials from the application of SOA and emerging technologies. The modeling framework presented here can also be easily used and extended for evaluating other emerging technologies or pathways beyond those considered in this study. Based on the results, the primary energy consumption and GHG emissions of the U.S. ethylene industry will dramatically increase given current practices and technologies. This impact can be mitigated by the application of SOA technology; however, the mitigation is not large enough to cancel out the K


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(2) Energy Information Administration (EIA). Growing U.S. HGL production spurs petrochemical industry investment. http://www.eia. gov/todayinenergy/detail.cfm?id=19771. (3) IEA; ICCA; DECHEMA. Technology roadmap, energy and GHG reductions in the chemical industry via catalytic process; IEA, ICCA, DECHEMA: France, 2013. (4) Energetics Incorporated. Bandwidth study on energy use and potential energy saving opportunities in U.S. chemical manufacturing ; Advanced Manufacturing Office, Energy Efficiency and Renewable Energy, U.S. Department of Energy: Washington, DC, 2015. (5) Yao, Y.; Graziano, D.; Riddle, M.; Cresko, J.; Masanet, E. Greener pathways for energy-intensive commodity chemicals: opportunities and challenges. Curr. Opin. Chem. Eng. 2014, 6 (0), 90−98. (6) Hunter, S. E.; Helling, R. K. A call for technology developers to apply life cycle and market perspectives when assessing the potential environmental impacts of chemical technology projects. Ind. Eng. Chem. Res. 2015, 54 (16), 4003−4010. (7) Ren, T.; Patel, M.; Blok, K. Olefins from conventional and heavy feedstocks: energy use in steam cracking and alternative processes. Energy 2006, 31 (4), 425−451. (8) Ren, T.; Patel, M. K.; Blok, K. Steam cracking and methane to olefins: energy use, CO2 emissions and production costs. Energy 2008, 33 (5), 817−833. (9) Neelis, M.; Worrell, E.; Masanet, E. Energy efficiency improvement and cost saving opportunities for the petrochemical industry; Ernest Orlando Lawrence Berkeley National Laboratory: Berkeley, CA, 2008; http://www.energystar.gov/ia/business/industry/Petrochemical_ Industry.pdf. (10) Worrell, E.; Bernstein, L.; Roy, J.; Price, L.; Harnisch, J. Industrial energy efficiency and climate change mitigation. Energy Efficiency 2009, 2 (2), 109−123. (11) Morris, G. D. Shale gas, NGLs fuel large-scale petrochemical investments. http://www.aogr.com/web-exclusives/exclusive-story/ shale-gas-ngls-fuel-large-scale-petrochemical-investments. (12) Lippe, D. 2012 ethylene production bounces back; turnarounds in early 2013 to curb output. Oil Gas J. 2013, 111, (3). (13) Brelsford, R. Rising demand, low cost feed spur ethylene capacity growth. Oil Gas J. 2014, 112, (7). (14) Saygın, D.; Patel, M. K.; Tam, C.; Gielen, D. J. Chemical and Petrochemical Sector Potential of Best Practice Technology and Other Measures for Improving Energy Efficiency; International Energy Agency (IEA): France, 2009. (15) Yao, Y.; Graziano, D. J.; Riddle, M.; Cresko, J.; Masanet, E. Understanding variability to reduce the energy and GHG footprints of U.S. ethylene production. Environ. Sci. Technol. 2015, 49, 14704. (16) Zimmermann, H.; Walzl, R. Ethylene. In Ullmann’s Encyclopedia of Industrial Chemistry; Bellussi, G., Bohnet, M., Bus, J., Drauz, K., Faulhammer, H., Greim, H., Jäckel, K.-P., Karst, U., Kleemann, A., Kutscher, B., Laird, T., Meier, W., Mukherjee, J., Ottow, E., Qiao, G., Röper, M., Sundmacher, K., Ulber, R., van Dyk, B., Wagemann, K., Wietelmann, U., Eds. Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2000. (17) International Standard Organization. ISO 14044 Environmental management - Life cycle assessment - Requirements and guidelines; ISO: Geneva, Switzerland, 2006. (18) ORACLE. Oracle Crystal Ball. http://www.oracle.com/us/ products/applications/crystalball/overview/index.html. (19) Alfke, G.; Irion, W. W.; Neuwirth, O. S. Oil Refining. In Ullmann’s Encyclopedia of Industrial Chemistry; Bellussi, G., Bohnet, M., Bus, J., Drauz, K., Faulhammer, H., Greim, H., Jäckel, K.-P., Karst, U., Kleemann, A., Kutscher, B., Laird, T., Meier, W., Mukherjee, J., Ottow, E., Qiao, G., Röper, M., Sundmacher, K., Ulber, R., van Dyk, B., Wagemann, K., Wietelmann, U., Eds. Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2000. (20) Argonne National Laboratory. GREET. https://greet.es.anl. gov/. (21) Franklin Associates. Cradle-to-gate life cycle inventory of nine plastic resins and four polyurethane precursors; Eastern Research Group, INC.: Prairie Village, KS, 2011; http://plastics.americanchemistry.

increase of energy consumption and GHG emissions. The adoption of emerging technologies can lead to further reductions of primary energy consumption and GHG emissions, but the quantity of these further reductions are subject to the uncertainty of technology performance. Therefore, how to reduce the uncertainty related to the energy-saving potential of emerging technologies should be included in future R&D work to enable more robust prospective analysis. One critical implication obtained from these results is that faster commercialization of emerging technologies can bring more reductions of primary energy consumption and GHG emissions in the near future and reduce the risk of diminishing mitigation potentials for GHG emissions caused by possible high fugitive emissions in the future. This implication is critical for policy makers, investors, manufacturers, and analysts who question whether intensive efforts to bring technology to market faster are worthwhile with respect to added energy use and GHG emission reduction benefits. These stakeholders can use the results shown in this study to support decision making, to assist further economic analysis, or to enhance strategic planning related to investments in emerging technologies. Another important indication delivered by our results is that from a cradle-to-gate perspective, fugitive emissions play a vital role in the realization of expected GHG emissions reductions brought by adopting SOA or emerging technologies. High fugitive emissions generated during natural gas recovery and processing could counteract or even exceed the reduction of GHG emissions enabled by SOA and emerging technologies. Therefore, both the U.S. government and the natural gas industry should make intensive efforts to reduce fugitive emissions.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.5b03413. Detailed explanations of the modeling framework and parameters employed, additional results, and references (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. Funding

The submitted paper has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. Notes

The authors declare no competing financial interest.

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