Understanding Variability To Reduce the Energy and GHG Footprints

Article pubs.acs.org/est

Understanding Variability To Reduce the Energy and GHG Footprints of U.S. Ethylene Production 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, U.S. 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: Recent growth in U.S. ethylene production due to the shale gas boom is affecting the U.S. chemical industry’s energy and greenhouse gas (GHG) emissions footprints. To evaluate these effects, a systematic, first-principles model of the cradle-to-gate ethylene production system was developed and applied. The variances associated with estimating the energy consumption and GHG emission intensities of U.S. ethylene production, both from conventional natural gas and from shale gas, are explicitly analyzed. A sensitivity analysis illustrates that the large variances in energy intensity are due to process parameters (e.g., compressor efficiency), and that large variances in GHG emissions intensity are due to fugitive emissions from upstream natural gas production. On the basis of these results, the opportunities with the greatest leverage for reducing the energy and GHG footprints are presented. The model and analysis provide energy analysts and policy makers with a better understanding of the drivers of energy use and GHG emissions associated with U.S. ethylene production. They also constitute a rich data resource that can be used to evaluate options for managing the industry’s footprints moving forward.

1. INTRODUCTION Over the past decade, natural gas (NG) production in the United States has been increasing rapidly.1 This trend has been largely driven by the use of hydraulic fracturing to recover unconventional gases, including shale gas (SG), which has also led to lower prices for NG and natural gas liquids (NGLs).2 Low-cost NG and NGLs have provided a competitive advantage for the U.S. chemical industry and spurred growth in its capital investments, production, and exports. As a result, the American Chemistry Council (ACC) projects that U.S. chemical production will grow by 4% per year through 2020 and that capital spending will reach $61 billion by 2018.3 U.S. production of ethylene, which is the largest volume chemical derived from NGLs, is expected to be a key beneficiary of this growth.4 Ethylene is the raw material used to produce polymers such as polyethylene, polyvinyl chloride, polystyrene, and many other organic chemicals.5 These products are widely used in industries such as packaging, construction, transportation, electronics, consumer chemicals, and coatings.5 Growth in ethylene production will affect the energy use and greenhouse gas (GHG) emissions of the U.S. chemical industry, given that © 2015 American Chemical Society

ethylene production accounts for the largest fraction of the industry’s direct fuel use.6,7 However, increased capital investment may also improve the energy efficiency of U.S. ethylene plants if best-practice technologies are adopted during plant retrofits and expansions. While low-cost NG and NGLs are driving increasing ethylene production, they also bring significant indirect or “upstream” energy use and GHG emissions in the recovery, processing, and distribution systems for NG and NGLs. Therefore, understanding the energy use and GHG emission implications of increased ethylene output requires the consideration of both the direct (i.e., ethylene plant) and indirect (i.e., feedstock) components of the production system. Previous research largely focused on estimating the direct energy and GHG emission intensities of ethylene plants, without including a broader consideration of how feedstock system characteristics affect indirect energy use and GHG Received: Revised: Accepted: Published: 14704

August 9, 2015 November 1, 2015 November 2, 2015 November 2, 2015 DOI: 10.1021/acs.est.5b03851 Environ. Sci. Technol. 2015, 49, 14704−14716

14705

Madhav et al.24 Franklin Associates25

PlasticsEurope

23

bottom-up model using GaBi (2014) plant data collected in 2003

bottom-up approach using UMBERTO5 (2012)

approaches/tools (study years)

U.S. U.S. and Canada

Europe

geography

ethane aggregated results of naphtha and NGLs

aggregated results of naphtha and NGLs

feedstock

a natural gas-to-electricity LCA study using primary data from ExxonMobil’s XTO operation for SG (2013) (c) Studies for cradle-to-gate natural gas-to-ethylene system

Laurenzi et al.22

study

estimation approach (study years)

six LCA studies for natural gas-to-electricity conventional NG and SG16−21 are harmonized by the authors (2011−2012)

ethane ethane ethane propane butane

study

(b) studies for U.S. natural gas recovery, processing, and distribution

estimated fossil fuel and electricity use based on previous estimates in 1980 and 199614,15 derived energy intensity from previous analysis in 199615 plant-specific data for steam crackers operating in Western Europe in 2003, adjusted to U.S. by 110%

ethane

feedstock

results

22.4 1.44 0.84 23 0.7

results

12.4−19.5 11−21 450−567

17−21 16−19 13−25 19.4 1.0 1.1 1.2

results

MJ/kg HVC kg CO2-e/kg HVC kg CO2-e/kg ethylene MJ/kg of olefin productsb kg CO2-e/kg olefin products

intensity basisa

gCO2-e/MJ conventional NG gCO2-e/MJ SG kg CO2-e/MWh SG

intensity basis

GJ/t ethylene GJ/t HVC GJ/t ethylene GJ/t ethylene ton CO2/t ethylene ton CO2/t ethylene ton CO2/t ethylene

intensity basisa

On an ethylene basis, all ethylene plant fuel use and emissions are attributed entirely to ethylene production outputs. On a high-value chemical (HVC) basis, plant fuel use and emissions are attributed to all HVC production outputs including ethylene. The HVC basis therefore inherently results in lower reported intensities than does the ethylene basis. bUnlike the HVC basis, here the intensity basis includes only olefin products, mostly ethylene and propylene.

a

9

estimated energy intensity of ethylene production based on literature in 2000

estimation approach with data reporting years

(a) studies for NGL-based U.S. steam cracking process

Weber et al.13

Energetics9 Worrell et al.10 2006 IPCC guideline11

Ren et al.

8

study

Table 1. Previous Studies of Natural Gas-to-Ethylene Systems

Environmental Science & Technology Article

DOI: 10.1021/acs.est.5b03851 Environ. Sci. Technol. 2015, 49, 14704−14716

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Environmental Science & Technology

Figure 1. System boundary and unit process systems model for estimating cradle-to-gate life-cycle energy use and GHG emissions of ethylene production.

emissions. Furthermore, there is a large variability in existing estimates of direct intensities, which are driven by methodological differences related to the type of NGL feedstock (e.g., ethane, propane, and butane), process technology efficiencies, reporting year, estimation methods, and intensity reporting bases, as summarized in Table 1a.8−11 These methodological differences and the lack of detailed process data make it difficult to harmonize and compare past results. The large variability in past results also underscores the need for parametric models that can isolate key system drivers that explain this variability and can be targeted for future system improvements. Previous work also exists related to the energy use and GHG emissions of upstream systems for recovering, processing, and distributing NG from both conventional and SG wells. The majority of the studies focused on conventional NG or SG for electric power generation, as summarized in Table 1b. A key finding of the previous studies is the large variability in fugitive methane emissions during the recovery, processing, and distribution steps and that this variability can be particularly pronounced for SG systems.12,13 As indicated in Table 1a, the type of NGL feedstock used for ethylene production also influences the direct energy and GHG emissions of ethylene plants. Therefore, a consideration of the feedstock type and the variability in upstream system parameters is also important, especially as the industry shifts to SG as a source of NGLs. A few life-cycle assessment (LCA) studies have analyzed both indirect and direct energy use and GHG emissions of ethylene production; the results are summarized in Table 1c. These studies quantify the energy and GHG emissions of cradle-to-gate ethylene production by using either proprietary or aggregated data without process-level details. This lack of resolution and transparency makes it difficult to adapt the data for use in other analyses with different system conditions or for

use in a credible analysis of system variability and potentials for indirect and direct impact reductions. The model and analyses described in this paper contribute to a greater understanding of U.S. ethylene production systems in several important ways. First, the bottom-up framework models all direct and indirect unit processes in a consistent, interdependent, and parametric fashion. This approach enables replication and applicability to a broad range of possible system conditions for U.S. ethylene production. Second, credible ranges for all major system parameters are established on the basis of a comprehensive literature review, which enables bounded energy-use and GHG-emission footprint analyses for different system configurations. Third, these ranges are leveraged in a global sensitivity analysis to isolate the key system parameters that contribute the most to variability in U.S. ethylene production impacts. Fourth, best-practice efficiency states are modeled in cascading fashion for each key system parameter to illustrate its overall footprint reduction leverage. Therefore, the model and analyses can inform energy analysts and policy makers on the drivers of energy use and GHG emission footprints associated with U.S. ethylene production, as well as provide them with a rich source of data for assessing opportunities for reducing the industry’s footprint moving forward. For engineers and project managers at ethylene plants, the framework can help identify technology strategies with the largest saving potential, and can also be integrated with in-plant cost models to aid cost-benefits analyses for investment decision making.

2. MATERIALS AND METHODS Figure 1 depicts the discrete unit operations and energy and mass flows characterized by the systems modeling framework. Four different production stages are considered: NG recovery, 14706


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an amine sweetening process,17 which is the most common method used for H2S removal in the United States.53 After acid gas removal, the gas stream is sent to a dehydration unit. The water content in the raw NG is estimated to be 0.04−0.11% based on previous studies54−56 and is reduced to 0.01% after dehydration to meet the requirement for pipeline-quality dry NG.17 Because glycol-based dehydration is commonly used in NG plants for water removal,51,53 the fuel consumed for dehydration is estimated based on glycol usage and related fuel use factors derived from a National Energy Technology Laboratory (NETL) study17 and chemistry handbooks.57,58 In the hydrocarbon recovery unit, methane is separated from NGLs at a low temperature.53 Electricity is used in the compression stage (to raise pressure) and in the refrigeration stage (to condense NGLs). On the basis of previous studies,59,60 electricity use for compression and refrigeration system is 2.7 × 10−3 kWh/mol of feeds and 0.4 × 10−3 kWh/ mol of condensate, respectively. The feed for the hydrocarbon recovery unit is the gas output from the previous dehydration unit. The condensate is calculated based on the condensation ratio of each component in the feed, which is derived from the OPGEE59 model and Nawaz’s and Jobson’s work.60 Liquids processing separates NGLs into ethane, propane, and butane using fractionation trains composed of a deethanier, depropanizer, and debutanizer. In the depropanizer and debutanizer, heat is provided by combusting NG in a reboiler for distillation, and cooling for condensing products is provided by cooling water. The depropanizer and debutanizer were modeled by using engineering parameters for distillation53,55,61 and the properties of condensed products.57,58,62 In the deethanizer, products must be refrigerated63 for condensation. Thus, in addition to the distillation model, a refrigeration model is added for the deethanizer. Dry NG undergoes nitrogen rejection after hydrocarbon recovery if its nitrogen content exceeds the allowable limit for pipeline NG, which is 6% to ensure a sufficient NG heat content.64 The fuel use for nitrogen rejection is estimated as 0.072 kWh/kg of nitrogen removed based on cryogenic distillation,65,66 which is the most common method used in the United States.55 After nitrogen has been rejected, dry NG is compressed. Three types of compressors are considered in this study: NG-powered reciprocating, NG-powered centrifugal and electric-powered centrifugal. Fuel-related GHG emissions are calculated by applying the emission factors35−39 for different fuels used in the unit operations. Fugitive emissions from NG processing are estimated to be 0.2−0.5% of the NG processed based on recent studies.32,34,67−70 See Supporting Information Section 2 for more details on the NG processing model. 2.3. Natural Gas Liquids and Dry Natural Gas Transportation. In the United States, NG and NGLs are transported predominantly through pipelines.25,55 The fuel consumption factor for NG transportation is assumed to be 0.3 MJ/(ton·km), as derived from the GREET model.35 The amount of fuel used for NGL transportation is 0.15 MJ/(ton· km) based on petroleum products transportation.25 With regard to distance estimates, first, data on the NG processed and NGL reserves in different states are collected and ranked,40,41 as shown in Figure SI-1. Second, the destination plant is assumed to be located at Houston, Texas, which is the largest ethylene-producing region in the United States.71 Third, the distances from NG plants located in the top

NG processing, transportation, and ethylene production. The modeling approaches for each stage are discussed briefly in the following subsections and are further described in the Supporting Information. 2.1. Natural Gas Recovery. In NG recovery, a NG well is constructed, and raw NG is extracted from the ground. Conventional NG and SG recovery have similar unit operations: well construction, well completion, and well operation. Well construction is the preparation and drilling process for an NG well.26 Horizontal drilling is widely used for SG, whereas the usual design for conventional NG is a vertical well.18 Once construction is finished, the well is prepared for production in the well completion process, which involves setting and perforating the casing.12 For SG, a hydraulic fracking process, which pumps chemicals and water to stimulate the production of gas, is conducted. This step is not required for conventional gas well completion.18 After well completion, the well can be operated for gas production. During well operation, well workover may be conducted to repair or maintain a well in response to a possible change in conditions.27 Another activity during well operation is liquids unloading in which liquids that have accumulated in the well and can slow or impede gas production are removed.28 Fugitive emissions coming from each step discussed above are modeled based on recent literature,12,13,18,29−34 including reported data of fugitive emissions that are higher than data from the U.S. Environmental Protection Agency (EPA).31−34 The fuel used in the NG recovery stage for both conventional NG and SG is derived from the literature.18,25,35 On the basis of the fuel use, fuel-related GHG emissions (CO2, CH4, and N2O) are calculated by applying emission factors for different fuels.35−39 Electricity is assumed to be purchased from the grid. This study only considers the energy use and GHG emissions of electricity generation and distribution (i.e., upstream extraction, processing, and transport of power plant fuels are excluded). The ranges for the electricity emission factors are derived according to an Energy Information Administration (EIA) analysis37 for seven states that together account for 89% of the NG processed in the United States.40,41 See Supporting Information Section 1 for model details. The compositions of the extracted conventional NG and SG are also modeled explicitly in the framework, given that the gas composition influences the fuel requirements of the downstream unit operations. The variability of the SG composition is modeled by using data from 142 samples obtained from the literature.42−49 The variability of the conventional NG composition is modeled based on data collected by the Gas Technology Institute.50 See Table SI-5 for detailed composition data. 2.2. Natural Gas Processing. Unit operations for NG processing are depicted in Figure 1. The input for NG processing is raw NG from the NG recovery stage. Two major outputs from the NG processing stage are used in ethylene production: (1) NGLs that are used as feedstocks and (2) pipeline-quality, dry NG that is used as a fuel. Fuel usage and GHG emissions are allocated to dry NG and NGLs on a mass basis. The first unit operation in NG processing is acid gas removal, which removes hydrogen sulfide (H2S), a toxic and corrosive gas, prior to further NG separation.51 On the basis of data from the EPA,52 H2S content in raw NG ranges from 0 to 3.9%. For the fuel use estimation, the acid gas removal step is modeled as 14707


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Environmental Science & Technology seven states to Houston are estimated by Google Map.72 For states with more than one NG plant, the longest and shortest distances to Houston are used as the bounds for the transportation distance. Finally, the shortest and longest distances from gas fields in the seven states to the destination plant are weighted based on the NG that is processed and the NGL reserves in these states; these distances are 394 to 893 miles (630 to 1429 km) for NG transportation and 345 to 937 miles (552 to 1500 km) for NGL transportation. Fuel-related GHG emissions can be calculated on the basis of the fuel use discussed above. The fugitive emission factor is assumed to be 0.29−2% of natural gas throughput based on recent published data.32,73−76 See Supporting Information Section 3 for more details. 2.4. Ethylene Production. In the United States, ethylene is produced predominantly by steam cracking of NGLs.5 After steam cracking, hot gas products are cooled, compressed to a high pressure, and sent to cracked gas processing for separation. The following subsections briefly describe the modeling approach used to estimate the fuel use for each unit operation and for three different NGL feedstocks, namely ethane, propane, and butane. 2.4.1. Steam Cracking. In the steam cracking, feedstocks are preheated and then reacted at temperatures of 750−875 °C in tubular reactors known as crackers.5 Steam is injected to dilute the NGL feedstock and assist the cracking reaction.5 The model for steam cracking consists of three stages: preheating, cracking reaction, and steam use. The heat required for preheating is estimated by integrating the heat capacity function57,58 for each feedstock from room temperature (25 °C) to cracking temperature. The heat requirement of the cracking reaction is estimated by calculating the heat of reaction using the thermodynamic property data for feedstocks and product outputs, which include ethylene, propylene, butadiene, aromatics, hydrogen, methane, and other hydrocarbons. Because the yields of chemical outputs vary depending on the specific NGL feedstock, the heats of reaction for cracking ethane, propane, and butane are individually calculated based on the corresponding yield ranges of their chemical products. The fuel consumed in preheating and cracking is then calculated based on their respective heat requirements and on the furnace thermal efficiencies that range from 89% to 95%.5,15 The fuel required to generate the steam injected into the crackers is estimated based on the amount of steam used, the minimum heat requirement for steam generation, and the overall thermal efficiency of the steam system. The steam usage is determined by the steam-to-feed ratio (in kg steam/kg feeds), which is 0.25−0.35 for ethane cracking, 0.3−0.4 for propane cracking, and 0.4−0.6 for butane cracking, according to industry practices.5 The typical properties of the steam used in crackers are 473 K and 1000 kPa.77 The minimum heat requirement is 2.4 MJ/kg steam, as calculated by using the U.S. Department of Energy (DOE) Steam Calculator.78 The overall thermal efficiency of the steam system, including steam generation and delivery, ranges from 51% to 64% in the U.S. chemical industry, based on Energetics’s analysis for DOE.79 See Supporting Information Section 4.1 for a further explanation. 2.4.2. Quenching and Cooling. After steam cracking, the high-temperature cracked gas is cooled to preserve its composition.80 A fraction of the heat released by the cracked gas during cooling is typically recovered by using a transfer-line

heat exchanger (TLE).81 The quantity of recovered heat is calculated based on the heat recovery rate and the total heat released by cracked products. The heat recovery rate typically ranges from 40% to 63%, according to statistics from DOE.82 The total heat released can be calculated by integrating the heat capacity function of each chemical product from the cracking temperature to the cooled temperature. The typical cooled temperature for the cracked products from ethane and propane cracking is 200 °C.5 For butane cracking, this temperature is higher; 250 °C is used in this study.5,14 See Supporting Information Section 4.2 for more details. 2.4.3. Compression. After quenching and cooling, cracked gas is compressed to 3200−3800 kPa in a multistage compression process prior to separation.5,62 The model calculates the fuel used for compression based on classical adiabatic compression,59,83 the thermodynamic properties of the gas components,62,84−87 the compressor efficiency, and the machine drive efficiency. In ethylene plants, compression is typically performed by a centrifugal compressor,5,88 the efficiency of which ranges from 68% to 90%.59,87,89 This large variability is due to the variances in compressor design and operation conditions.88 Outdated design, aged components and off-design operating conditions (e.g., partial-load) are common reasons contributing to the low efficiency of compressors.88,90,91 In ethylene plants, a steam turbine is commonly used as the prime mover driving the compressors.88,90 Therefore, the machine drive efficiency is dependent on the efficiency of the steam turbine and steam generation system. The efficiency of steam generation systems is discussed in the Steam Cracking Section. The efficiency of the steam turbine is estimated as 65−90%,92−94 a range that considers differences in compressor capacity, design and operation conditions (e.g., steam quality).92,95 High steam turbine efficiency is usually achieved in new plants with large capacity and optimized process operations.92,95 2.4.4. Cracked Gas Processing. Cracked gas processing involves a series of separation units to recover ethylene from other cracked gas components. This model includes three unit operations: gas drying to remove water content, chilling with a demethanizer to remove methane and hydrogen, and recovery of ethylene from other products with an ethylene separator. For gas drying, the model used for the dehydration unit discussed in the Natural Gas Processing Section is employed to estimate the fuel use. Chilling with a demethanizer is similar to hydrocarbon recovery in NG processing with the exception that the feed enters at a higher pressure. Therefore, the fuel is primarily consumed for lowering the temperature5 and is estimated on the basis of the classical model of refrigeration cycles.96 In the ethylene separator, the ethylene is separated by highpressure fractionation in which a propene refrigerant serves as an indirect heat pump, shifting the heat from the condenser to the reboiler.53 The distillation model developed for the depropanizer and debutanizer is used and modified according to the classical heat pump model.96 The amounts of fuel used for each unit operation discussed above are summed to arrive at the total fuel consumption of the ethylene production stage for the different NGL feedstocks. Fuel-related GHG emissions are calculated by applying the emission factors for each fuel.35−39 The ranges of fugitive emissions of CH4 are 4.2−7.8 kg CH4/ton of ethylene for ethane cracking and 2.1−3.9 kg/ton of ethylene for propane and butane cracking, derived from IPCC data.11 14708


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Table 2. Variability in Energy and GHG Emission Intensity Results for Conventional NG-to-Ethylene and SG-to-Ethylene Pathways intensity

upstream production and transportation conventional NG

Eethylene MJ/kg ethylene EHVC MJ/kg HVC GHGethylene kg CO2-e/kg ethylene GHGHVC kg CO2-e/kg HVC a

ethylene production

SG

total conventional NG

SG

lowa

higha

low

high

low

high

low

high

low

high

6.1 5.2 1.0 0.8

7.5 6.3 1.3 1.2

6.1 5.2 1.2 1.0

7.6 6.5 2.8 2.4

17.1 15.2 1.1 0.9

21.3 18.2 1.3 1.1

23.6 20.8 2.1 1.8

28.5 24.3 2.6 2.2

23.4 20.5 2.3 2.0

28.9 24.3 3.9 3.4

The “low” and “high” refer to the lower and upper 95% confidence interval, respectively.

Figure 2. Sensitivity analysis results for the energy intensities of conventional NG-to-ethylene and SG-to-ethylene pathways (ethylene output basis).

Information Section 1 for detailed conversion factors. Low and high values representing the 95% confidence interval of estimated primary energy use and GHG emission intensities of U.S. ethylene production are shown in Table 2. In general, the results shown in Table 2 are consistent with the previous results for ethylene LCA and on-site ethylene production shown in Table 1. This consistency validates the methodology and model used in this study. The most significant discrepancy is found in the GHG emission results, which are higher than those of previous LCA studies.23−25 Two reasons exist for this difference. First, fugitive emission factors are adopted from recent papers, some of which claim much higher emission rates than those used in previous studies, which relied on prior U.S. Environmental Protection Agency estimates.31−34 Second, previous LCA studies included only upstream NGL used as feedstock, whereas this study’s system boundaries also include dry NG used as fuel. The conventional NG and SG pathways have similar ranges of energy intensities. However, with regard to the GHG emissions results, the SG pathway has higher values and wider ranges due to the large variance in upstream fugitive emissions. To understand the drivers of energy use and GHG emissions of NG to ethylene system, sensitivity analysis was conducted. Figure 2 shows the results for the energy intensities of NG to

The weighted average fuel use and GHG emissions of cradleto-gate ethylene production are calculated by using a weighting factor for each NGL feedstock; these factors are developed based on the market share (mass basis) of ethylene production from ethane, propane, and butane in the United States for years 2008−2013.97 See Supporting Information Section 4.4 for more details.

3. RESULTS AND DISCUSSION As discussed in Section 1, previous results for the direct and indirect energy use and GHG emissions of U.S. ethylene production exhibit large variability. A sensitivity analysis is conducted to identify which parameters contribute most to the variances in energy and GHG intensities with the goal to identify best-practice values for each. Results are derived from the model and system parameter values described in Section 2. The summary of ranges and distributions for all uncertain parameters are provided in the Supporting Information Section 5. Each input variable is assumed to be independent. Crystal Ball is used to run Monte Carlo simulation for 10 000 runs.98 The results are shown in the form of primary energy use and CO2-equivalents, which are converted based on the total fuel use and GHG emissions using conversion factors from the IPCC36 and EIA.39 See Supporting 14709


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Figure 3. Cumulative changes of energy intensity ranges by setting key parameters to best-practice values.

Table 3. Energy-Saving Opportunities for NG-to-Ethylene Production Systems key parameter

energy-saving opportunity

possible measure 99,100

steam turbine efficiency

reduce energy loss

compressor efficiency

increase compressor efficiency

steam system efficiency

increase efficiency of boiler/distribution/recovery

heat recovery rate and preheating temperature

improve heat transfer/reduce deposit coke haltering heat transfer

furnace thermal efficiency

improve combustion

ethylene yields

increase yields

reboiler efficiency

improve heater efficiency

steam-to-feed ratio

maintain optimal steam to feed ratio

ethylene systems (ethylene output basis). See Supporting Information Section 6 for detailed results and explanations. On the basis of the results, the differences in the sensitivities of the energy intensity results for conventional NG and SG pathways to ethylene production are minor. On the other hand, the results show that the high variance in fugitive emissions for the SG pathway leads to more significant differences in the GHG emission intensities of the two pathways. The major sources of variance for energy intensity can be organized into three categories as follows:

three-dimensional blading advanced sealing technologies such as brush seal and abradable coating101,102 design and optional optimization94,103 improved thrust bearing104 geometry design optimization105 advanced compressor control106 variable speed drive compressor6 optimized boiler design107 minimized excessive air to boiler108 well insulated distribution system108 high-pressure condensate recovery108 Optimal TLE design5 catalyst-assisted production of olefins7 effectively clean heat transfer area109 Secondary TLEs110 combustion air preheating5 radiant coil design optimization111 optimized heater severity112 better yield prediction model113 accurate steam supply control77 process turbulator installation114 heater optimization by adjusting stack damper, air register and controlling combustible levels115 contaminate control116 process optimization117

1. Process parameters, such as steam turbine efficiency, compressor efficiency, heat recovery rate, furnace thermal efficiency, steam system efficiency, ethylene yields, reboiler efficiency, steam-to-feed ratio, preheating temperature (accounting for 94.5% of the variance); 2. The shares of the different feedstocks (accounting for 3% of the variance); 3. The composition of the raw NG, especially the NGL content (accounting for 1.5% of the variance). 14710


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Figure 4. Cumulative changes of life-cycle GHG emission intensities by changing key parameters to best-practice values.

Table 4. GHG Emission-Reduction Options for NG-to-Ethylene Production Systemsa key parameter

GHG reduction opportunity

possible measure

well workover venting well completion venting emissions reduction in well completion/ workovers number of workovers

control/reduce the fugitive gas emitted

emissions reduction equipment118 green completion18,119 work within sealed systems55

reduce the risk of equipment failure and wellbore problems

equipment leakage in NG recovery

install/retrofit equipment with better emission control features

plunger lift systems120 real-time monitoring and predictive maintenance121 corrosion control56 valve controllers55 interruptible intermittent-bleed nozzles55 regular equipment repair122 dry seals in centrifugal compressor123 replacement of pneumatic controllers with mechanical controllers124 regularly scheduled leak detection122 optimized operation settings such as system supply pressure or controller gain setting55 maintenance and repair of pressure safety valves125 automated air/fuel ratio controls for combustion126 contaminant control116 process optimization117 plunger lift systems120 smart automation127

fugitive emission factor of NG processing emissions reduction during well operation

control fugitive emissions by improving well operations

fugitive emissions in ethylene production

reduce equipment leakage

feedstock losses

maintain optimal operational conditions

liquids unloading venting

control/reduce venting

a

Energy-related measures are provided in Table 3.

To explore the implications of shifting process parameters to their best practice value, additional Monte Carlo simulations were performed as follows. First, the parameters were ranked in order, from largest to smallest, of their contribution to variance in the energy intensity. Second, the first parameter was set to its best-practice value, and the Monte Carlo best-practice value, and the Monte Carlo simulation was run again was run again. This process was repeated for each subsequent process parameter, while previous parameters were held at their bestpractice values. The purpose of these simulations was to quantify the leverage of each process parameter to shift the overall energy use intensity toward its lowest pathway value.

The high and low values of the feedstock share and raw NG composition parameters in the system remained unchanged to capture potentially irreducible variability. The results of these simulations are shown in Figure 3 for both the conventional NG and SG pathways. These figures depict the range of variability between high and low energy use footprint results, and from left to right how this variability is reduced as additional controllable parameters are set to their best practice values. The parameters that are changed with each “step” in the graph are labeled above that step for convenience. In Figure 3, the conventional NG and SG pathways have similar results because the two systems have similar energy 14711


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Environmental Science & Technology intensities. For both pathways, the variability and the energy intensity are reduced when process parameters are set to their best-practice values. Figure 3 shows that three parameters, steam turbine efficiency, compressor efficiency, and steam system efficiency, have the greatest influence on reducing the energy intensity variability. This graph also sheds light on the way to improving process factors where feasible, and to show where the best decisions should be made for new systems when we have the chance to invest in them. Table 3 lists available technology measures for each process parameter that can adjust its value closer to a best-practice energy-efficiency state. The adoption potential and appropriateness of each technology must be determined on a case-bycase basis; nevertheless, the technologies in Table 3 underscore the range of energy saving technology options in existing and future U.S. ethylene production systems that could be implemented as capital is invested in retrofits and expansions to meet a growing demand for ethylene. Options for reducing GHG emission intensities were explored by the same Monte Carlo simulation approach as used to generate Figure 3. The results for conventional NG and SG pathways are shown in Figure 4. The variability and intensity of GHG emissions for both pathways are reduced when fugitive emission sources and energy-related parameters are set to their best-practices values. For SG pathways, reducing the well workover venting contributes most to mitigating the life-cycle GHG emission intensity. For both conventional NG and SG pathways, steam turbine efficiency and equipment leakage in NG recovery are the most important parameters driving the GHG footprint down. Table 4 lists options for GHG emissions reduction and possible measures for realizing the best practice values of key parameters. Although the mitigation potential of each measure should be assessed based on specific cases, the results highlight GHG reduction opportunities that can be captured by industry and provide a useful resource of technology options that can be adopted for retrofits or capacity expansion. In conclusion, large variances in cradle-to-gate energy consumption and GHG emissions have been observed in both conventional NG and SG pathways for ethylene production. Compared to the conventional NG pathway, the SG pathway has similar energy consumption values but higher intensity and larger variances in GHG emissions. The analysis and results from studying these two pathways provide a transparent data resource for further energy and policy analyses. Moreover, the bottom-up modeling framework can be replicated and applied to a wide range of U.S. ethylene lifecycle configurations. The sensitivity analysis highlights opportunities for energy savings and GHG emissions mitigation and provides insights on measures for realizing these reductions. Increasing the efficiency of steam turbine, compressor, and steam generation system are identified as options with the greatest potential for energy savings. These three options and reducing SG well workover venting and equipment leakage in NG and SG recovery offer the opportunities with the greatest leverage for reducing the cradle-to-gate GHG emissions intensity. On the basis of the results of sensitivity analysis, measures that can adjust key parameters to their best practice values are presented. In general, operational improvements such as process control and optimization are more likely to be adopted first because they require little or no capital cost.128,129

Similarly, in practice, improved heat recovery rates and steam system efficiencies (e.g., due to operations and maintenance improvements) are more easily implemented than replacements of steam turbines, compressors and furnaces because this core equipment is often locked-in for long periods due to large capital investments.128,129 However, the selection of technologies must be determined on a case-by-case basis as the adoption potential and appropriateness of each technology depends on many factors at the plant level, including process configurations, achievable energy savings, local fuel costs and investment costs.128 The study results are useful for policy makers, environmental analysts, engineers and project managers for more robust understanding of potentials for energy and GHG emissions savings in ethylene production in the future and for targeting and enabling the most effective measures for investment in plant retrofits or capacity expansions. The modeling framework can also be coupled with plant-specific data or economic analysis models to support decision making and to enhance strategic planning for meeting short-term and long-term sustainability goals at both the plant and industrial scales.

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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.est.5b03851. Detailed explanations of the modeling framework, additional results, and references (PDF).

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AUTHOR INFORMATION

Corresponding Author

*E. Masanet. Phone/fax: (847) 467-2806. E-mail: eric. [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS 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 No. DE-AC0206CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said paper to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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