Assessing the energy intensity of alternative chemical

Assessing the energy intensity of alternative chemical and cryogenic natural gas purification processes in LNG production Matteo V. Rocco°, Stefano Langè*, Lorenzo Pigoli, Emanuela Colombo°, Laura A. Pellegrini* *Politecnico di Milano, Dipartimento di Chimica, Materiali e Ingegneria Chimica “G. Natta”, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan, Italy °Politecnico di Milano, Department of Energy, via Lambruschini 4, Milan, Italy E-mail addresses: [email protected] ; stefano.langè@polimi.it ; [email protected] ; [email protected] Corresponding author: M.V. Rocco, Tel.: +39-02-2399-3861; address: Via Lambruschini 4, 21056 Milan, Italy. E-mail: [email protected]

Abstract The production of Liquefied Natural Gas (LNG) from natural gas reservoirs with high content of acidic compounds is expected to be a strategic and crucial issue for the development of the natural gas market in future decades. Therefore, the identification of alternative suitable processes for the synergistic natural gas purification and LNG production, when the amount of CO2 in the raw gas feed is high, and their comparative thermodynamic assessment, is necessary to foster a cleaner and efficient production of LNG. In this paper, the energy intensity of the classical chemical absorption units using aqueous solutions of Methyldiethanolamine (MDEA) and the novel Dual Pressure Low-Temperature Distillation process (DPD) operating in different national contexts is assessed by means of the Net Equivalent Methane analysis and the Energy Life Cycle Assessment. These methods return respectively the equivalent methane and the embodied non-renewable energy invoked by the two alternative processes in one operating year. In general, the primary energy required by the construction phases of both the systems results negligible compared to the operation phase, and most of the latter is due to the consumption of energy utilities, which is strongly dependent by the considered national context. According to the results obtained by means of both the methodologies, the DPD process is generally less energy intensive compared to MDEA: the DPD process results to be an efficient and promising technique to perform the synergistic natural gas purification and LNG production.

1. Introduction: the synergistic potential of natural gas purification and LNG production Despite the great efforts done so far by several countries in increasing their energy efficiency and in displacing non-renewables sources from their energy mix, the world primary fossil energy consumption is 1

expected to increase in future decades according to the IEA “Current Policies” and “New Policies” scenarios (IEA, 2016). The only exception is represented by the more optimistic “450” scenario, which grounds its forecasts on massive investments in renewable technologies. In any case, the most authoritative projections agree in forecasting an increase of the global demand of natural gas with respect to other competing fossil sources (British Petroleum, 2016; IEA, 2016; U.S. Energy Information Administration, 2016). In particular, the role of Liquefied Natural Gas (LNG) will be crucial in satisfying the increase of the natural gas demand, mainly due to its technical advantages compared to the pipeline transportation mode: the flexibility of supply, the absence of a distribution infrastructure, the ease transport and storage and its high energy density. Because of these features, the market share of LNG is currently increasing by about 7% per year since year 2000 (International Gas Union, 2016), and it is expected to reach the share of natural gas transported by pipeline by 2035 (British Petroleum, 2016). Due to the future increase in LNG production, the cleaner, sustainable and profitable exploitation of unconventional, acidic and sour natural gas reserves will be an increasingly important challenge. In particular, acidic natural gas reserves are estimated to be about 40% of the already discovered reserves, and about 30% of these reserves have a CO2 content between 15% and 80% (Carroll and Foster, 2008). Acidic gas reserves are spread in several countries worldwide, especially in South-East Asia, Middle East, USA, Europe and Northern Africa (Bagirov et al., 2015; Burgers et al., 2011). According to the literature, natural gas pre-treatment processes, and the sweetening process among others, have an important role in the LNG production chain, since they are characterized by high costs and energy requirements (Lim et al., 2013a). Natural gas sweetening processes can be classified into six groups, based on the modes of acid gas removal: chemical absorption, physical absorption, physical-chemical absorption, permeation through membranes, adsorption, cryogenic purification. The choice of the most appropriate technique depends on multiple factors, and detailed guidelines for the most appropriate technical choice are provided in the open literature (Mokhatab et al., 2006). The purification of high flow rates of natural gases with high shares of CO2 and/or H2S can be technically performed by using all of the above-mentioned technologies, but the final choice of the proper solution is taken based on their economic cost. Chemical absorption processes based on aqueous MDEA as solvent have been widely applied and studied in the natural gas industry since 1930 (Russell et al., 2004). However, due to the high heat requirements needed to break chemical bonds formed between acidic compounds and solvent itself, the use of such processes for the purification of highly acid gas streams shows some economic limitations (B. T. Kelley et al., 2011; Langè et al., 2015). Over the last decades, cryogenic technologies, particularly based on distillation, have been studied and developed by several companies for the exploitation of high CO2-content gas reserves (Holmes et al., 1983; B T Kelley et al., 2011; Parker, 2011). The issue dealing with cryogenic processes for natural gas purification is the possible formation of solid CO2 inside process equipment, since typical operating conditions of these processes are close to the triple point of CO2: this can cause safety and operational problems and proper techniques are needed to avoid it. Cryogenic distillation processes differ based on the way that this issue is handled: for example, the Ryan-Holmes process needs a solvent to favor the solubility of CO2 in the liquid phase and to prevent its freezing inside process equipment. On the other hand, processes like The CFZ or the Cryocell allow the controlled freezing and re-melting of CO2, but they require dedicated equipment with consequent cost increase. Other solutions combine cryogenic distillation with classical purification processes: the Sprex process merges a cryogenic distillation for bulk acidic gases 2

removal with a classical amine unit in a hybrid process, which requires several equipment and dehydration steps. The DPD process (Pellegrini, 2014) bypasses the freezing conditions of CO2 in mixtures with methane by means of a proper thermodynamic cycle relying on standard process equipment. When CO2 capture for further uses is considered, cryogenic processes based on distillation allow to obtain a discharged stream of carbon dioxide that is already dry, in liquid phase and under pressure, while MDEA, membrane and physical absorption processes releases CO2 as a wet gas at atmospheric pressure. Several other works have been done comparing the energy performances of cryogenic processes and classical chemical or physical absorption processes: Kelley et al. investigate the profitability of the CFZ technology by comparison with the Ryan-Holmes process and the Selexol process, showing the profitability of the CFZ technology (B. T. Kelley et al., 2011). Langè et al. compared the energy demand of classical MDEA units and the DPD process considering different compositions of the raw natural gas feed stream, showing the benefits of the low-temperature process for CO2 contents in the natural gas higher than about 10 mol% (Langè et al., 2015). Pellegrini et al. compared on an energy basis the Ryan-Holmes process, the DPD process and the classical MDEA process for natural gas purification and LNG production for a natural gas containing about 40% of CO2 (in volume), showing the profitability of low-temperature processes by comparison with classical chemical scrubbing processes in a synergistic coupling of low-temperature purification and cryogenic natural gas liquefaction units (Pellegrini et al., 2015). Lallemand et al. propose a hybrid process that couples a cryogenic distillation for acid gases bulk removal with a classical MDEA unit, showing that for high CO2-content gas streams this combination allows to decrease significantly the overall production costs respect to a classical MDEA unit (Lallemand et al., 2013). Hart and Gnanendran compare the amine unit with their Cryocell process, showing the cost benefits of the low-temperature process respect to the amine unit for the treatment of natural gases having 20 and 35% of CO2 (in volume) at process inlet (Hart and Gnanendran, 2009); Turunawarasu et al. compared the relative process performances between the Ryan-Holmes process and the CFZ process, showing that the second solution allows to obtain better technical performances than the first low-temperature process (Turunawarasu et al., 2015). Based on these considerations, two main processes are considered in this work: •

Chemical absorption processes with amine solvents. This kind of processes is widely used industrially as state-of-the-art technology for natural gas purification. In these processes, gaseous acid compounds are removed by means of absorption and chemical reaction into an aqueous phase containing a basic solvent. Amine solvents are mostly used for this purpose, and methyldiethanolamine (MDEA) has gained success with respect to other amines thanks to its lower volatility than MEA, less corrosivity, and better selectivity towards H2S (Moioli et al., 2013a). If the production of LNG is needed, the sweetened natural gas needs to be liquefied by means of a subsequent gas liquefaction process after chemical absorption.

•

Cryogenic processes. These techniques perform the purification of acid natural gas streams by acting on pressure and temperature, hence providing the synergistic production of a flow of purified methane at about 40 bar and a flow of liquefied CO2 at 50 bar that can be furthermore used in other processes, such as Enhanced Oil Recovery (EOR) applications in particular (Burgers et al., 2011). The produced methane from the distillation unit is already available at low-temperature, suitable conditions for the integration between these technologies with LNG production. In other words, the 3

overall cooling duty required for LNG production is in part supplied during the purification stage. Several types of cryogenic processes have been extensively described in the literature (Chang, 2015; Hart and Gnanendran, 2009; Lim et al., 2013b; Pellegrini et al., 2015), and all of them result to be profitable for the purification of natural gas with high contents of acid compounds (Langè et al., 2015; Meisen and Shuai, 1997). Recently, the Dual Pressure Low-Temperature Distillation process (DPD) emerged as simpler and less expensive technique compared to other cryogenic processes, allowing to purify natural gas or biogas streams with high shares of CO2 or H2S without incurring in freezing conditions in the distillation unit (Baccanelli et al., 2016). So far, chemical absorption processes have been compared with standard Ryan-Holmes cryogenic processes, resulting more economically convenient for a natural gas with low CO2 content (about 11%) (Habibullah, A., Krusen, 2002). However, chemical absorption processes have never been compared with Dual Pressure Distillation technique in performing the synergistic purification of natural gas with high CO2 content and LNG production.

1.1. Assessing energy intensity of engineering processes The assessment of the energy intensity of engineering processes is of paramount importance in fostering the development of sustainable and low carbon national economies (Koesling et al., 2017). Generally, engineering processes are related to other systems and to the surrounding environment by means of energy interactions of different nature and quality (i.e. heat, work, bulk flow) (Gyftopoulos and Beretta, 2005). Even if the energy intensity should not be used as a measure of the process efficiency (Proskuryakova and Kovalev, 2015), it is also true that comparing two different systems that produce the same output, the most efficient system would be the one that consume the less amount of energy (i.e. the system with the lower energy intensity). Energy intensity can be thus considered as an efficiency indicator, provided that it is used to compare systems that produce the same output (either measured in energy units or not). Energy intensity quantified by means of different approaches, mainly distinguished based on their scope. Conventional Energy analysis accounts for the total energy interactions occurring between the system and the environment in a simple way, but it is unable to distinguish the quality of energy flows and to correctly interpret their direction (Bejan, 2006), thus it may provide biased evaluations of the energy intensity of the analyzed process. To solve this problem, Exergy analysis is usually adopted, enabling to convert energy flows of different nature into homogeneous thermodynamic ideal work equivalents required to provide the such energy interactions (Kotas, 2013). Because of the limited scope of Energy and Exergy analyses, such methods can be properly used to assess the efficiency of energy-related processes: an extensive review about the possible approaches to assess the thermodynamics efficiency indicators has been carried out in the literature, focusing on First and Second Law indicators (Lior and Zhang, 2007). However, these methods fail in quantifying the energy intensity of generic production systems with high consumption of secondary energy utilities (e.g. high temperature steam, electricity, refined petroleum products, etc.), since other essential processes that support the life cycle of the analyzed system are excluded from the analysis (e.g. the primary energy required to provide steam or electricity) (Rocco et al., 2017). To deal with these issues, two approaches are usually adopted: 4

•

Net Equivalent Methane method. This method enables to convert each energy interaction in the amount of natural gas that should be consumed in order to deliver the energy interaction by means of conventional reference processes, namely steam boilers, electricity plants, heat pumps, etc. (Pellegrini et al., 2015). This method is extremely simplified and straightforward; on the other hand, it is subjected to a strong degree of arbitrariness, and it does not capture the energy intensities of nonenergy-related goods and services invoked by the analyzed process during its whole life cycle. Moreover, the equivalent methane does not accurately reflect the real primary non-renewable intensities of products of different economies, each characterized by different productive structures, energy mixes, and by a continuously increasing penetration of renewable energy sources (Rocco and Colombo, 2016).

•

Energy Life Cycle Assessment. Differently with respect to the Net Equivalent Methane method, LCA is based on a standardized approach and detailed supply chain models that enables to capture the energy intensity of both goods and services provided to the system during its whole life cycle (Ekvall et al., 2016; ISO, 2006). However, these advantages are counterbalanced by an increase in complexity and uncertainty in final results. LCA can be performed by relying on Process models, Input-Output models, or hybrid models: such approaches present advantages and drawbacks described in detail in the literature (Giljum et al., 2004; Islam et al., 2016; Suh and Huppes, 2005). Due to the recent development of freely available databases, the so-called Environmentally Extended Input-Output models are becoming increasingly relevant and widely adopted in the LCA field (Liang et al., 2017; Reutter et al., 2017; Yang et al., 2017).

While several works that compare results of standard energy/exergy analysis with results of LCA can be easily found in literature, comparative applications of Net Equivalent Methane method and LCA are currently missing.

1.2. Objectives of the work This paper aims to assess and to compare the energy intensity of two alternative processes widely adopted for acid gas purification in LNG production: Dual Pressure Low-Temperature Distillation (DPD) and Methyldiethanolamine (MDEA). Energy intensity of the processes are assessed through the Net Equivalent Methane method and the Energy LCA. This study adds to and extends the existing literature in several ways: •

The energy intensity of MDEA and DPD processes have been comparatively investigated for the first time with the aim of understanding the effective impact of a technology transition for natural gas purification in a greater context than only the technical one.

•

Energy intensities of the two processes are assessed in terms of primary non-renewable energy depletion and, for the first time, the alternative approaches of Net Equivalent Methane and the Energy LCA have been comparatively applied.

•

The Energy LCA analysis is based on an Environmentally Extended Multi-Regional Input-Output analysis (EE-MRIO), taking life cycle inventory inputs from detailed economic analyses of the life cycle phases of the process. The energy intensity of the plants in different economic contexts rich in acid gas reserves: Indonesia (IDN), Italy (ITA), United States of America (USA). 5

•

Uncertainty in background Energy LCA data is assessed by considering alternative Environmentally Extended MRIO databases used as models for the world economy. More specifically, WIOD, EORA26 and EXIOBASE have been used, each characterized by different aggregation levels of national economies and economic segments.

The rest of this paper is organized as follows: in section 2 the net equivalent methane method and the LCA model based on Hybrid Input-Output analysis are presented and explained. In section 3 the plant layouts and specifications are described. In section 4 methods are applied, and results showed and commented. Concluding remarks and possible future research alternatives are finally reported in section 5.

2. Methods and models The essential description of the Net Equivalent Methane and the Energy LCA methods is provided in this section, with reference to the schematic representations provided by Figure 1.

2.1. Net Equivalent Methane method The energy intensity of engineering processes can be assessed as the amount of methane that should be consumed in order to provide the energy utilities invoked by the process during its operation. The Net Equivalent Methane method is performed by converting heat and work flows that cross system boundaries into an equivalent amount of methane that need to be burned in order to provide such energy transfers by means of conventional engineering processes. Other interactions different than heat and work are not considered by the analysis, such as material streams or services. The basic idea of the equivalent methane method is to enable the analyst to assess in a simple way the methane consumption required by “standard processes” in order to deliver the desired energy effect to the system. The rationale of this method is based on the “ceteris paribus” principle: it aims at accounting for the equivalent methane required by the chemical process only, assuming constant features for all the exogenous processes useful to deliver energy utilities to the chemical plant (i.e. power plants, boilers, refrigerators). For such reason, the method receives the detailed features and performance of the analyzed system (i.e. the chemical process only) as input data, while standard average processes for producing such energy effects are considered as follows: •

Work interactions. The mechanical or electrical energy consumed (or produced) by the process is converted into the equivalent methane consumed (or saved) by a classic combined cycle power plant. The plant has an average first law efficiency 𝜂𝜂𝐼𝐼,𝐶𝐶𝐶𝐶 equal to 0.55, defined by relation (1) as the

ratio between the energy interaction by work 𝑊𝑊 and the thermal power input coming from the 𝑊𝑊 combustion of methane (𝑄𝑄𝐶𝐶𝐶𝐶4 , with an average heating value 𝐿𝐿𝐿𝐿𝐿𝐿𝐶𝐶𝐶𝐶4 of 50 MJ/kg). W ECH 4 =

•

W

η I ,CC

(1)

Heat absorbed by the process. If the heat flow is absorbed by the process, it is supplied by means of low-pressure steam produced by a methane-fired boiler with an energy efficiency 𝜂𝜂𝐼𝐼,𝐵𝐵 equal to 0.8

(Langè et al., 2015).

6

Q← ECH 4 =

•

Q←

ηB

(2)

Heat released by the process. Heat flows rejected by the analyzed process are is accounted as the equivalent methane required to drive a classic vapor compression refrigeration cycle according to the following procedure, resumed by equation (3). (1) Based on environmental temperature (𝑇𝑇0 =

298.15 K) and on the average temperature at which the heat exchange process occurs (𝑇𝑇), the ideal

COP of the refrigerator is derived as 𝐶𝐶𝐶𝐶𝐶𝐶 = 𝑇𝑇⁄(𝑇𝑇0 − 𝑇𝑇). (2) The work absorbed by the ideal

refrigerator 𝑊𝑊𝑖𝑖𝑖𝑖 is calculated based on the COP definition. (3) Assuming a constant value of the

second law efficiency of the refrigerator 𝜂𝜂𝐼𝐼𝐼𝐼,𝐹𝐹 equal to 0.6 (Kanoğlu, 2002), the work absorbed by the

“real” average refrigerator W is calculated. (4) The equivalent methane is finally calculated based on equation equation (1). Q→ ECH 4 =

η II , F

Q→ T η I ,CC T0 − T

(3)

The energy intensity of the process is thus defined as the sum of all the equivalent methane consumed or saved by the process.

2.2. Energy LCA based on Environmentally Extended MRIO model The steps and quality requirements in the application of LCA has been determined by the ISO 14000 standards (Finkbeiner et al., 2006). The practical application of such method can be performed by means of three approaches: Process-based method, Input-Output method and Hybrid Input-Output method. An extensive literature review about these methods can be found in literature (Hauschild et al., 2017; Suh and Huppes, 2005) The Process-based method consists in the attempt to characterize the energy requirements (or the environmental impact in general) caused by the analyzed process using facility-level data and building the network of related industrial activities, stopping when either data limitations or other considerations make further expansion infeasible, hence setting the boundaries of the analysis. Several commercial software and databases are currently available, widely adopted and continuously improved for the application on this method, such as the Ecoinvent database (Frischknecht et al., 2005), collecting the accurate description of several processes. However, the application of such method is difficult, extremely data-intensive, and subjected to great arbitrariness (Reap et al., 2008a, 2008b). Differently, Input-Output analysis is based on the application of Leontief’s models to the Input-Output tables of national economies, which collect economic transactions between national economic sectors, enabling to assess the energy and environmental impact of average products of each sector of the analyzed national economy (Miller and Blair, 2009). Considering a national economy in a given year (with subscript N), the embodied energy requirements of each of its n economic processes 𝐄𝐄𝐍𝐍 (𝑛𝑛 × 1) can be synthetically expressed by means of equation (4), where 𝐟𝐟𝐍𝐍 (𝑛𝑛 × 1)

represents the households’ final demand vector (i.e. the Gross Domestic Product) of the analyzed economy, the exogenous transactions coefficients vector 𝐁𝐁𝐍𝐍 (1 × 𝑛𝑛) collects the direct energy transactions with the

7

environment per unit of product by each sector, the technical coefficients matrix 𝐀𝐀𝐍𝐍 (𝑛𝑛 × 𝑛𝑛) collects the direct intermediate production of goods and services required by each sector, and 𝐈𝐈𝐍𝐍 (𝑛𝑛 × 𝑛𝑛) is the Identity matrix.

{

−1 EN =fˆN ⋅ B N ⋅ ( I N − A N )

}

Τ

(4)

Notice that the same approach can be used for the analysis of network of national economies, providing that international trade flows are known (Rocco and Colombo, 2016). Beside the individual country tables, provided by statistics departments of the majority of the world countries, several world Input-Output databases with different features have been developed and freely distributed for the purpose of economic and environmental analyses: WIOD (Rahman et al., 2017), EORA (Lenzen et al., 2013), EXIOBASE (Wood et al., 2015) are some of the most known databases. These and other databases have been extensively analyzed and compared by Owen (Owen, 2017). Input-Output analysis is simpler and less data intensive compared to the Process-based method, and it is based on freely available data sources. However, these advantages are compensated by a low accuracy of results, which are strictly dependent by the disaggregation level of the Input-Output tables adopted as reference database. The Hybrid Input-Output method has been adopted to exploit the advantages of Process-based and InputOutput techniques, reducing boundary cutoff error and data requirements in the former, and aggregation error in the latter. The mathematical structure of this approach has been formalized by several authors (Heijungs and Suh, 2002; Hendrickson et al., 2010; Joshi, 1999; Suh and Huppes, 2005), and it is presented by equation (5) as an expansion of the basic model (4). The embodied energy of a detailed system (with subscript S), composed by a number of s processes and operating in the economy N, is a function of its production level 𝐟𝐟𝐒𝐒 (𝑠𝑠 × 1), its direct energy transactions with the environment per unit of production

𝐁𝐁𝐒𝐒 (1 × 𝑠𝑠), its own technical coefficients matrix 𝐀𝐀𝐒𝐒 (𝑠𝑠 × 𝑠𝑠), an Identity matrix 𝐈𝐈𝐒𝐒 (𝑠𝑠 × 𝑠𝑠), and the Upstream and Downstream Cutoff matrices (𝐂𝐂𝐍𝐍𝐍𝐍 (𝑛𝑛 × 𝑠𝑠) and 𝐂𝐂𝐒𝐒𝐒𝐒 (𝑠𝑠 × 𝑛𝑛) respectively), representing the goods and services

transactions between the nation N and the system S (and vice-versa). Definition of these last two matrices is a fundamental step in performing the Hybrid Input-Output analysis, and it is the output of the Life Cycle Inventory Analysis phase (LCIA). In particular, the Upstream Cutoff matrix is based on process-specific data collected in monetary units, representing the inventory of goods and services inputs for all the analyzed life cycle phase of the system.  E N  fˆN   =  ES  0

    ⋅ [ B N S   

0 fˆ

 I BS ] ⋅   N  0

0   AN − I S  CSN

CNS    A S  

−1

    

Τ

(5)

The uncertainty of final results mainly depends on the national coverage and disaggregation level of the Input-Output database (defined as background uncertainty) and to the quality of data used to fill the Cutoff matrices (defined as foreground uncertainty). While the latter depends on the quality of process-related data, the former is strictly related to the adopted Input-Output database.

8

ENERGY LCA METHOD

NET EQUIVALENT METHANE METHOD

Utilities production (standard processes)

Energy utilities

Primary energy (natural gas equivalent)

National economy (EE-MRIO model)

LNG

Natural Gas purification and LNG production process

CO2 (liquid)

Other goods and services production

Energy utilities production

Acid Natural Gas

Other goods and services Energy utilities

Natural Gas purification and LNG production process

Primary energy (coal, oil, natural gas)

LNG

CO2 (liquid)

Acid Natural Gas

Figure 1. Schematic representation of the Net Equivalent Methane method and the Energy LCA method.

3. Processes description, layouts and economic analysis In this section, a general description of the analyzed MDEA and DPD processes is provided. The two systems deal with the same material input and output streams with properties reported in Table 1. Therefore, the objective of both the process is the same: to perform the purification of an acid natural gas stream (Feed input), by producing a flow of LNG (CH4 output) and a flow of liquefied CO2 (CO2 output) with given specifications. The commercial software Aspen Plus® has used to simulate the amine unit (absorber and regenerator), relying on the Electrolyte-NRTL model to account for the non-ideality in the liquid phase (Chen et al., 1979; Chen and Evans, 1986), coupled with an in-house developed kinetic subroutine used to better represent the behavior of MDEA absorption units (Moioli et al., 2013b). Statoil-Linde Mixed Fluids Cascade process for natural gas liquefaction has already been simulated and optimized in literature, while other refrigeration cycles for LNG liquefaction and DPD process have been both simulated by means of Aspen Hysys®, relying on the Soave-Redlich-Kwong SRK equation of state, which is adequate in modeling the behavior of simple hydrocarbon systems. Plant equipment have been sized considering literature guidelines (Peters et al., 2003; Turton et al., 2012). In particular, energy requirements of dehydration process have been evaluated through heuristic rules.

Table 1. Reference stream of raw feed gas treated by DPD and MDEA systems.

Stream name Raw feed gas CH4 output CO2 output

x_CH4 0.6 1 0

x_CO2 0.4 0 1

Mol. Flow kmol/h 5000 3000 2000

T K 308.15 111.75 287.21

P bar 50 1.101 50

3.1. Methyldiethanolamine process (MDEA) Among the chemical absorption processes, the ones based on alkanolamine are currently the most used purification methods (Moioli et al., 2013b). Despite the availability of different types of amines, the processes 9

based on methyldiethanolamine (MDEA, Figure 2) obtained the highest commercial success, mainly due to the highest maximum allowable loading, the lowest expenses related to solution recirculation, the reduced volatility and corrosivity, and lower heat flows required for the solvent regeneration.

Sweetened CH4 50 ppm CO2 (+ water)

H2O, MDEA makeup 2.2 atm 30 °C

2

Pump

CO2 (l) 14 °C 50 bar

3

LP amine column

HP amine column

RL=0.8 50 bar

LNG cooler 2

TEG Unit

5

CO2 compressor

1

2.2 atm

LNG cooler

7

Water discharge T≈30 °C 1 atm

CO2 (+ water)

50 °C 2.2 atm

≈50 °C 50 bar Feed NG 35 °C 50 bar

30°C 1.99 bar

4 CO2 cooler

T≈136 K 2.2 atm LL=0.008

LNG -161 °C 1.01 bar

6

Figure 2. Process flow diagram of the Methyldiethanolamine process (MDEA) with Mixed Fluid Cascade liquefaction process. Streams that cross system boundaries have been highlighted: material flows in green, work flows in red and heat flows in blue. Dashed blue lines represent the heat flows rejected to the environment. Only the most relevant flow properties have been showed.

The raw natural gas feed stream (Feed NG) is fed at 35°C and 50 bar at the bottom of the absorption column (HP Amine Column), where it is contacted counter currently with the lean aqueous MDEA solvent fed at 50°C at the top of the absorber. During this operation, acid gases pass from the gas to the liquid phase, in order to obtain a purified top gas product LNG quality methane stream (Sweetened CH4) and a bottom liquid product, which is the rich solution containing the aqueous MDEA solvent together with dissolved acidic components captured from the gas phase. This stream is expanded to low pressure (about 2.2 atm) in order to desorb part of the acidic gases from the liquid phase by means of flash expansion. This stream is then heated up in the cross-heat exchanger and sent to the solvent regeneration column (LP Amine Column). The regeneration is made by means of a distillation operation to obtain at the top a wet gas stream at low pressure which contains the acidic gases and at the bottom the lean regenerated solvent. During this step, heat is provided to the reboiler in order to break chemical interactions formed between the dissolved acids and create the vapor flow needed for the distillation operation. The top acidic gas stream is then compressed (CO2 Compressor) and cooled (CO2 Cooler) to separate water from the main CO2 stream. The bottom hot lean MDEA solution is cooled down in the cross-heat exchanger, mixed with the fresh solvent make-up (H2O, MDEA Make-up), furthermore cooled down to 50°C and pressurized (Pump) before to be recirculated to the absorber. The produced LNG from the top of the absorber is then cooled down and send to a TEG dehydration unit in order to remove the water that remains in the gas phase after the absorption step. This unit is needed to prevent ice and hydrates formation during cryogenic liquefaction. At this point, the dry 10

methane stream is liquefied under pressure (LNG Cooler) and expanded to 1.01 bar in order to reach typical LNG operating conditions needed for storage. LNG liquefaction is achieved by means of the Statoil-Linde Mixed Fluids Cascade process, selected among the available commercial processes for its higher thermodynamic efficiency according to literature (Castillo et al., 2013; Ding et al., 2017; Jensen and Skogestad, 2006; Mehrpooya and Ansarinasab, 2015). In this process, pressurized dry natural gas is contacted with four different mixed refrigerants in four different heat exchangers in series. In the first heat exchanger the natural gas is cooled down to -1.9°C; then, in the second step of the series, natural gas is cooled to -39.3°C. Natural gas leaves the third heat exchanger at a temperature level equal to -72.1°C and it is finally liquefied and subcooled to -155 °C in the fourth heat exchanger. Pressure is then decreased to the atmospheric level by means of a throttling valve downstream to the series of heat exchangers. Part of the LNG vaporizes while flashing in the throttling valve and is then liquefied through a nitrogen refrigeration cycle.

3.2. Dual Pressure Distillation process (DPD) Among other cryogenic processes, the Dual Pressure low-temperature Distillation process (DPD, Figure 3) has emerged in recent years as a promising technique to couple in a synergistic way the natural gas purification and the LNG production. The DPD process has been developed by Politecnico di Milano and patented in 2014 (Pellegrini, 2013). The process is designed to produce a flow of pure liquefied natural gas (LNG) and a flow of pure carbon dioxide starting from a sour natural gas stream, avoiding the formation of dry ice during process operation. The plant is divided into a high pressure section (HP) and a low pressure section (LP): the HP section performs a bulk removal of the carbon dioxide contained in the raw inlet gas stream, while the LP section performs the final purification to produce a methane stream at the desired purity. More in detail, a wet Feed acid natural gas stream is sent to a TEG dehydration unit for water removal. This unit is needed to prevent ice and hydrates formation during the cryogenic separation of methane and CO2. Then, the acid natural gas is fed to the HP at 50 bar and it is separated in a bottom stream, containing pure CO2, and in a gas stream at the column top rich in methane. The latter stream is divided into two flows: one is heated and expanded to the pressure of the LP section (40 bar), reaching a final temperature nominally 5 K above its dew point temperature, in order to avoid the freezing of CO2 and the dry ice formation. On the other hand, stream 3 is cooled down and expanded to its bubble point at 40 bar, and it is subsequently fed to the LP section, few theoretical stages above the bottom. The products of the LP section are a top product stream, containing pure methane, and a bottom liquid stream, rich in methane with up to 8% of CO2 (molar composition). The bottom product of the LP section is pumped and recycled back to the HP section, and its content of carbon dioxide is controlled to avoid the formation of dry ice inside the process. In particular, the split ratio of the stream out of the top of the HP column is chosen in order to keep the CO2 content of the bottom product of LP column below the desired limit. The reflux ratio of the distillation process in the LP section is chosen between 1.4 and 1.5 in order to minimize the energy requirements in terms of cooling duty and to avoid excessively large columns diameter. The purification level that can be achieved in the HP section is limited by the critical point of the mixture at 50 bar. The LP section is operated at pressures below the critical point of pure methane (~46 bar) in order to perform the final purification. The pressure of the HP section is chosen 11

in order to remain above the maximum of the solid-liquid-vapor locus of the system CH4-CO2 that occurs around 49 bar (Davis et al., 1962). In this way, the process bypasses this three-phase line of the system avoiding the formation of a solid phase during distillation operation.

7

6

5 CH4 + CO2 (50 ppm) -87.43 °C 40 bar

LNG -161 °C 1.01 bar

LNG cooler 2 -68.4 °C 50 bar

8

-79 °C 40 bar

4

-82 °C 50 bar

Natural gas cooler 2

LP distillation column

Pump

Natural gas cooler 1

TEG Unit Feed NG 35 °C 50 bar

-84.3 °C 40 bar

LNG cooler

3

50 bar

T=Tdew 50 bar

HP distillation column

1

CH4 + CO2 (6.5%) -74 °C 50 bar

CO2 (l) 14 °C 50 bar 2

Figure 3. Process flow diagram of the Dual Pressure Distillation process (DPD). Streams that cross system boundaries have been highlighted: material flows in green, work flows in red and heat flows in blue. Only the most relevant flow properties have been showed.

LNG liquefaction is achieved by means of a propane/ethylene/methane cascade process. In the first heat exchanger the natural gas is cooled down to -98.7 °C; then, in the second step of the series, natural gas is cooled to -115 °C. Natural gas leaves the third heat exchanger at a temperature level equal to -135 °C and it is finally liquefied and subcooled to -155 °C in the fourth heat exchanger. Pressure is then decreased to the atmospheric level by means of a throttling valve downstream to the series of heat exchangers. Part of the LNG vaporizes while flashing in the throttling valve and is then liquefied through a nitrogen refrigeration cycle. Notice that the liquefaction process for DPD differs from the one considered for MDEA: this is justified by the different temperature and pressure of the natural gas streams treated by the two plants, for which liquefaction processes with different features represent the most suitable choice from the technical and economic points of view. Further details about the thermodynamics and other technical features of the process can be found in the literature (Baccanelli et al., 2016; Langè et al., 2015).

12

3.3. Detailed economic analysis of MDEA and DPD In conducting an LCA based on a Hybrid Input-Output model, the Life Cycle Inventory Analysis (LCIA) is represented by a detailed economic analysis for the construction and operation phases of the analyzed processes. This analysis is useful to identify the type and the amount of products (i.e. goods or services expressed by means of their economic value) that are provided by the national economy to the analyzed process. Based on location of the acid gas reserves (Burgers et al., 2011), it is assumed that the MDEA and DPD processes are owned and operated in Indonesia (IDN), Italy (ITA) and United States of America (USA). First, a detailed estimation of the capital and operating expenditures of the processes is performed (results are respectively shown in Figure 8 and Figure 9 in the appendix, expressed in MUSD for year 2007). For both the processes, costs have been estimated based on literature guidance, industrial standards and best practice rules (Peters et al., 2003; Turton et al., 2012; Woods, 2007). Several expenditures categories have been distinguished: construction expenses, engineering and supervision, electrical systems, purchase equipment and installation, maintenance and repair, energy utilities, and so on. DPD has an overall total investment cost of 44.19 MUSD, which is more than 40% lower compared to the cost of MDEA of about 79.06 MUSD: this is mainly due to the amine column and the CO2 compressors, highly expensive components that are missing in the DPD, and that contribute in increasing also the expenses in other categories. While capital expenditures are considered as equal for the three analyzed economies, operating expenditures are more dependent by the nation where the plant is located. Operating expenditures of DPD range between 38.3 up to 68.6 MUSD per year while MDEA range between 56.4 up to 107.4 MUSD per year (average values have been considered, disregarding uncertainties reported in Figure 9), depending on the national context: again, the operative costs for the DPD are on average 20% lower compared to MDEA for all the national contexts. With reference to Figure 9, it can be inferred that annual operative costs in Italy are about two times with respect to IDN and USA, and this is mainly due to the high cost of energy utilities. Again, the operating costs for the two processes are distributed in a similar way among in the identified expense categories. Once the economic expenditures have been derived, they must be assigned to the national economic sectors according to a standard classification, such as the ISIC or NACE protocols (European Commission, 2008; United Nations, 2008), hence defining the Upstream Cutoff matrices 𝐂𝐂𝐍𝐍𝐍𝐍 described in sub-section 2.2. A rigorous classification of expenditures and allocation of them among national economic sectors has been

performed. However, it cannot be easily reported, since the sectors of the adopted MRIO databases differ in term of classification protocols and aggregation levels. However, it is worth mentioning that the expenditure categories are often overlapped with the standard classification of national economic activities, simplifying the procedure.

13

4. Results 1 and discussion In this section, results of the application of the methods described in section 2 to the processes introduced in section 3 are showed and discussed.

4.1. Net Equivalent Methane method Detailed numerical results for the MDEA and DPD processes have been respectively reported in Table 2 and Table 3. Each energy interaction (heat Q or work W, in kW) is related to one defined component of the process, and it correspond to an equivalent amount of primary methane quantified in energy units (Eeq, in toe/y and in % with respect to the total). Notice that the average temperature at which heat interactions cross the boundaries of the process is also reported, and it is used as input data in equation (3) to evaluate the corresponding equivalent methane.

Table 2. Results of the net equivalent methane analysis of the MDEA process. Component name Amine column Pump CO2 compressor CO2 cooler LNG cooler 1 LNG cooler 1.1 LNG cooler 1.2 LNG cooler 1.3 LNG cooler 1.4 LNG cooler 2 TEG unit Total

Stream # 1 2 3 4 5

6 7

Type name Heat Work Work Heat Heat

Heat Heat

T C 136.3 8.4

T K 409.5 281.6

Q/W kW 39950 1190 6880 -5080

Eeq kW 49938 2164 12509 908

Eeq toe/y 34351 1488 8605 624

% 55 2 14 1

9.1 -25.6 -60.7 -118.6 -168.8 -

282.2 247.6 212.5 154.6 104.4 -

-1100 -1397 -1708 -6956 -348 960

188 865 2088 19571 1958 1200 91388

130 595 1436 13463 1347 825 62864

0 1 2 21 2 1 100

Based on the obtained results, the absolute consumption of energy of the MDEA process is about 1.9 times greater than the DPD process: the latter process results thus the most efficient from the energy perspective according to this method. The energy intensity of the MDEA process is mostly due to the heat supplied to the LP amine column (55%), followed by the heat required by LNG liquefaction process (up to 27%) and by the work required to compress the CO2 flow to the required specifications (14%). The other components do not contribute significantly in increasing the energy intensity of the process. Similarly, the methane liquefaction process invokes for the 47% of the energy requirements in the DPD process, followed by the energy required by the natural gas coolers (30%) and by the LP distillation column (21%).

1

Authors are willing to share the source data, the scripts used to elaborate them, and the obtained results for replication purposes: please write to the corresponding author, Dr. Matteo V. Rocco ([email protected]).

14

Table 3. Results of the net equivalent methane analysis of the DPD process. Component name Natural Gas Cooler 1 HP distillation column Natural Gas Cooler 2 Pump LP distillation column LNG cooler 1 LNG cooler 1.1 LNG cooler 1.2 LNG cooler 1.3 LNG cooler 1.4 LNG cooler 2 TEG unit Total

Stream # 1 2 3 4 5 6

7 8

Type name Heat Heat Heat Work Heat Heat

Heat Heat

T C -16.1 25.1 -83.1 -92.5

T K 257.1 298.2 190.1 180.7

Q/W kW -2556.2 4302.3 -7406.4 308.4 -5171.3

Eeq kW 1237 0 12757 561 10185

Eeq toe/y 851 0 8775 386 7006

% 3 0 27 1 21

-103.7 -120.0 -140.0 -161.4 -165.8 -

169.5 153.2 133.2 111.8 107.4 -

-3424 -1103 -1110 -1268 -180 540

7881 3165 4168 6409 969 675 48006

5421 2177 2867 4409 666 464 33022

16 7 9 13 2 1 100

4.2. LCA based on Environmentally Extended MRIO model After sizing and simulating the analyzed processes, the application of the Hybrid Input-Output method has performed through the application of equation (5). For each process described in section 3, the following steps have been applied: 1. Characterization of the background national economy. Economic transactions provided by different MRIO databases, namely WIOD2013, EORA26 and EXIOBASE v.2, have been used to define the technical coefficients matrices 𝐀𝐀𝐍𝐍 and the households’ final demand vectors 𝐟𝐟𝐍𝐍 . The input coefficient vectors 𝐁𝐁𝐍𝐍 represent the direct energy use by each economic sector and it has been taken from the same MRIO databases, integrating missing data with the IEA statistics. The size of the background

system depends on the size of the adopted MRIO database. The economies where the process life cycle takes place (Indonesia, Italy and USA in this case) are enclosed in all these three MRIO databases: this constitutes a prerequisite for the analysis. 2. Characterization of the foreground process. Both the MDEA and DPD processes are assumed to take goods and services inputs from the national systems in order to own and to operate them: these inputs result from a detailed economic analysis for construction and operation phases (see subsection 3.3), and they are collected into the Upstream Cutoff matrix 𝐂𝐂𝐍𝐍𝐍𝐍 . Outputs of the processes

are delivered as final demand only 𝐟𝐟𝐒𝐒 , while Downstream Cutoff matrix 𝐂𝐂𝐒𝐒𝐒𝐒 , technical coefficients and

input coefficients matrices 𝐀𝐀𝐒𝐒 and 𝐁𝐁𝐒𝐒 are empty. Since the objective of these processes is to purify

natural gas and to convert it into LNG, the input and output material flows to the systems (see Table 1) are not included in the LCA data, since these energy flows are actually not part of the energy intensity of the processes. It is worth noticing that the LCA model arranged in this way is often called

Tiered Hybrid Input-Output model in the literature (Stromman et al., 2009). 3. Application of the energy LCA model. Numerical solution of the system of equations (5), hence deriving the energy intensities of the analyzed processes 𝐄𝐄𝐒𝐒 . Notice that the energy intensities of all the products of national economies covered by the MRIO database are also obtained 𝐄𝐄𝐍𝐍 .

15

Notice that all the input data for the model (both the MRIO database and the process specific data) have been derived for the base year 2007, which is the reference year for the latest available version of the EXIOBASE database (version 2). Total energy intensities of the construction phase are collected in Figure 4. Depending on the construction country and the adopted MRIO database, the energy intensity of DPD ranges between 5 and 25 ktoe, and it about a half with respect to MDEA, which ranges between 10 and 45 ktoe. Values of energy intensity differ depending on the adopted MRIO database; despite this, the weights of the impact categories are about the same in percentage across all the databases: indeed, purchase equipment, piping, buildings and service facilities always represent the major contributions with respect to the total. The energy intensity of construction phase in Indonesia is always greater (two up to four times, depending on the adopted MRIO database) compared to other countries. Since Indonesia has a lower share of non-renewable fossil fuels in its Total Primary Energy Supply (TPES, based on IEA data), this result can only be justified by an average lower efficiency of the Indonesian heavy industries compared to the other countries.

Energy intensity [ktoe]

Dual Pressure Distillation (DPD)

Indonesia (IDN)

Energy intensity [ktoe]

United States (USA)

25

25

20

20

20

15

15

15

10

10

10

5

5

5

0

Methyldiethanolamine (MDEA)

Italy (ITA)

25

WIOD

EORA26

EXIOBASE

0

WIOD

EORA26

EXIOBASE

0

50

45

45

45

40

40

40

35

35

30

30

25

25

35 30 25 20 15 10 5 0

WIOD

EORA26

EXIOBASE

20

20

15

15

10

10

5

5

0

WIOD

EORA26

EXIOBASE

0

WIOD

EORA26

EXIOBASE

Legend Contingency Legal expenses Construction expenses Engineering and supervision Land Service facilities (installed) Yard improvements Buildings Electrical systems (installed) Piping (installed) Instrumentation ans controls (installed) Purchased equipment installation Purchased equipment WIOD

EORA26

EXIOBASE

Figure 4. Breakdown of the energy intensity in the construction phases of DPD and MDEA. Notice that the graph scales are different between DPD and MDEA. Please refers to the electronic colored version of the article to properly read the figure.

Total energy intensities of the operation phase are shown in Figure 5. First, it can be observed that the energy intensity of operation phase is much larger that the construction phase, since the latter should be depreciated considering a plant expected lifetime of about 20 years. Much variability has obtained in the values of energy intensity of operation phase compared to construction: indeed, energy intensity ranges 16

between 40 up to 100 toe/y for DPD, while it is between 20 and 140 toe/y for MDEA, depending on the national context and on the adopted database. The contribution of the energy Utilities category is much greater compared to all other categories, that are negligible if considered singularly. The energy Utilities category has been allocated to the national sectors responsible for the electricity and natural gas supply: in WIOD and EORA databases, the production of such utilities is lumped into one single sector (Electricity, gas and water supply), while in EXIOBASE the electricity and natural gas production is disaggregated (and electricity production further disaggregated based on production technology). Because of this reason, energy intensities of utilities obtained from EXIOBASE are expected to be more accurate with respect to the other database, since it has been demonstrated that the disaggregation process is highly beneficial for the numerical accuracy of results (de Koning et al., 2015; Lenzen, 2011; Lindner et al., 2013). Results provided by different databases are generally in agreement except for the case of Italy, where energy intensity of DPD is higher compared to MDEA for EXIOBASE, while the opposite holds for WIOD and EORA.

Italy (ITA)

Energy intensity [toe/y] Energy intensity [toe/y]

Methyldiethanolamine (MDEA)

Dual Pressure Distillation (DPD)

Indonesia (IDN)

United States (USA)

160

160

160

140

140

140

120

120

120

100

100

100

80

80

80

60

60

60

40

40

40

20

20

20

0

0

WIOD

EORA26

EXIOBASE

WIOD

EORA26

EXIOBASE

0

160

160

160

140

140

140

120

120

120

100

100

100

80

80

80

60

60

60

40

40

40

20

20

20

0

0

WIOD

EORA26

EXIOBASE

WIOD

EORA26

EXIOBASE

0

WIOD

EORA26

EXIOBASE

WIOD

EORA26

EXIOBASE

Contingency Research and development Distribution and selling costs Administration costs Plant overhead costs Insurances Depreciation Patents and Royalties Laboratory charges Operating supplies Maintenance and repair Operating Labor Supervision and clerical labor Utilities energy savings

Figure 5. Breakdown of the embodied energy in the construction phases of DPD and MDEA. Please refers to the electronic colored version of the article to properly read the figure.

4.3. Results comparison Energy intensities of the analyzed processes are comparatively shown in Figure 6, expressed in toe/y: values of Net Equivalent Methane are in the left side (red columns), while the rest of the graph report the results of the Energy LCA for all the databases and countries, aggregated into Construction (properly depreciated considering a plant lifetime of 25 years), Operation (energy utilities) and Operation (other goods and services). 17

Several comments can be made from the analysis of Figure 6. First, with respect to the equivalent methane approach, the Energy LCA method is able to comprehensively assess the energy intensity of the analyzed process, including all the indirect contributions of the background national supply chains, and potentially covering the whole life cycle of the analyzed system. This seems particularly relevant in this case, since energy intensities are dependent by the national context where system is owned and operated. Results of the Net Equivalent Methane method are confirmed by the Energy LCA: the DPD process results less energy intensive (i.e. it has a higher energy efficiency) with respect to the MDEA, and this is true for all the national economies and for all the adopted databases. However, results of Energy LCA are affected by great variability due to different features of the adopted databases.

140 120 100 80 60 40

33

Energy intensity [ktoe/y]

Energy intensity [ktoe/y]

Dual Pressure Distillation (DPD)

Indonesia (IDN) 160

20

140 120 100 80 60

160

140

140

140

120

120

100

100

80

63

40 20 0 equivalent methane

67

60 40

0

Energy intensity [ktoe/y]

Energy intensity [ktoe/y]

Methyldiethanolamine (MDEA)

160

United States (USA)

160

20

0 equivalent methane

Italy (ITA)

160

37 31 WIOD

EORA

60

EXIOBASE

40 20 0

100 80

80 60

46 41

120 90

35

41

31

37

WIOD

EORA

81

40 20

EXIOBASE

0

160

140

140

120

120

120

100

100

91

80 60

50

40 20 0

40 WIOD

80

62

53

EORA

144

60 79

40 20

EXIOBASE

54

0

49 WIOD

63

21 15 WIOD

53

32 EORA

EXIOBASE

Operation (Others) Operation (Utilities) Construction

140

79

80 131

20 EXIOBASE

51

60 40

58

EORA

42

160

160

100

63

60

0

26 18 WIOD

65

39 EORA

EXIOBASE

Figure 6. Total energy intensity of DPD and MDEA in IDN, ITA and USA, calculated through the Net Equivalent Methane method (in red) and Energy LCA. Individual contributions of Construction, Operation (Utilities) and Operation (Others) are highlighted, and values of utilizes are reported in white. All values are in toe/y.

For both the processes in all the countries, contributions of the Construction phase are negligible compared to the Operation one. Moreover, energy Utilities are much bigger than the lumped contributions of all the other Operation categories, representing a share between 70 and 90% with respect to the total energy intensity of the processes (numerical values are shown in white due to their relevance). This result is confirmed by all the adopted databases, and it can be concluded that an accurate evaluation of energy intensity of utilities is fundamental to avoid biased assessment of the energy intensity of the process: Figure 7 provides a graphical focus on these intensities, obtained for all the economies, and expressing the variability in results due to the change in the adopted MRIO database. Net Equivalent Methane method strongly underestimates the energy intensities of utilities in Italy for both DPD and MDEA, and in Indonesia

18

for DPD. On the other hand, it overestimates the energy intensity of MDEA in USA. Average estimations of the energy intensity are provided for MDEA in Indonesia and DPD in USA.

Energy intensity [ktoe/y]

Net Eq. CH4

IDN

ITA

140

USA

131

120 100 79

80 63

60 40

33

0

DPD

65

60

53 40

31

20 MDEA

81

DPD

MDEA

49 31

DPD

MDEA

15

18

DPD

MDEA

Figure 7. Energy intensity of energy Utilities only, evaluated through the Net Equivalent Methane and the Energy LCA methods. Values in toe/y. Diamonds and black lines represent results of the net equivalent methane method.

5. Conclusions In this paper, the energy intensity of the Dual Pressure Low-Temperature Distillation (DPD) and the Methyldiethanolamine (MDEA) acid natural gas purification processes has been assessed. The analysis has been performed based on the Net Equivalent Methane and the Energy LCA methods. The latter method has been applied by considering different reference economic context (Indonesia, Italy and USA), and the background data uncertainty has been assessed by comparing different MRIO database (WIOD, EORA, EXIOBASE). Results demonstrates that the DPD process is a less energy intensive alternative compared to the MDEA process, and this holds for both the methodologies in all the analyzed countries, and considering all the databases. The result obtained from the LCA approach is non-trivial, since the processes consume goods, services and energy utilities different in quantities and in types, and thus their energy intensity ultimately depends on the efficiency and production mix of the national economy where these processes are owned and operated. Based on the obtained results, it can also be inferred that the contribution of energy utilities to the total energy intensity of the processes is predominant compared to the other contributions, and the latter may be disregarded in future analyses. Therefore, the reduction in consumption of energy utilities, or the increase in the national energy supply sectors, are probably the most effective way to reduce the energy intensities of the analyzed processes. Beside this, it can be also concluded that due to the increase complexity and variety 19

of energy technologies in national energy mixes, energy intensities calculated through the Net Equivalent Methane method may over- or underestimate the real energy intensity of national energy utilities. Thus, an energy analysis based on a LCA approach seems to be relevant to provide a non-biased perspective about energy intensity of engineering processes. Further research activities can be identified as an outcome of this research. First, it would be interesting to investigate the effects of an increasing penetration of DPD technology for the acid gas purification in the national technology mix through a consequential LCA approach, such as the Rectangular Choice of Technology (RCOT) model (Duchin and Levine, 2017), or other kind of sophisticated macroeconomic approaches (Igos et al., 2015). This would be particularly important to investigate possible cleaner development pathways. Secondly, from a methodological point of view, the disaggregation of energy related sectors in MRIO databases would result in a more accurate estimation of environmental impact of energy utilities production, hence increasing the quality of LCA analysis of energy related systems.

20

Appendix 3.08

Contingency

DPD

5.51

MDEA

1.23 2.20

Contractor's fee

0.62 1.10

Legal expenses Construction expenses

3.08

Engineering and supervision

3.08

5.51 5.51

0.62 1.10

Land

6.16

Service facilities (installed)

11.02

1.54 2.75

Yard improvements

3.08

Buildings

5.51

1.85 3.31

Electrical systems (installed)

4.00

Piping (installed)

3.08

Instrumentation ans controls (installed)

5.51

4.31

Purchased equipment installation

7.16

7.71 8.47

Purchased equipment 0

2

4

6

8

15.15

10 12 14 16 18 20

Economic cost [M$ - 2007]

Figure 8. Results of the economic analysis of the Construction phase for DPD (blue) and MDEA (green).

Italy (ITA)

Indonesia (IDN) 2.06 3.22

1.15 1.69

Contingency

3.39

Distribution and selling costs

4.86

Insurances

0.44 0.79

Local taxes

0.88 1.58

Depreciation

4.36

Patents and Royalties

1.02 1.46

Laboratory charges Operating supplies

2.17 3.47

0.44 0.79

0.44 0.79

1.33 2.37

0.88 1.58

4.36 7.80

4.36

1.93 2.99

0.12 0.13

0.40 0.71

0.40 0.71

0.40 0.71

3.09

3.09 7.12

7.12

supervision and clerical labor

0.14 0.15

Operating Labor

0.12 0.13

0.76 0.81 20.00

Utilities 5

10

15

20

3.09

Economic cost [M$ - 2007]

30

0.82 0.88 42.18

0

7.12

0.15 0.16

24.80

25

7.80

1.11 1.50

0.11 0.12

0.02 0.02

4.99

0.06 0.11

0.02 0.02

Maintenance and repair

0

9.96

2.13 3.42

7.80

MDEA 3.72

0.05 0.10

1.68 2.94

Plant overhead costs

DPD

1.86 2.50 6.43

0.01 0.02

Administration costs

1.25 1.73

3.21 4.98

1.70 2.43

Research and development

United States (USA)

10

20

30

40

50

21.08 24.28

62.84 60

Economic cost [M$ - 2007]

70

0

5

10

15

20

25

30

Economic cost [M$ - 2007]

Figure 9. Results of the economic analysis of the Operation phase for DPD (blue) and MDEA (green). Notice that the graph scale for ITA is different compared to IDN and USA.

21

Nomenclature, Subscripts Symbol

Quantity

Unit

𝜂𝜂𝐼𝐼

First Law Efficiency

-

0

Environment

-

B

Boiler

-

C

Cutoff matrix

-

CC

Combined Cycle

-

CH4

methane

-

F

Refrigerator

-

N

given national economy

-

n

number of economic sectors

-

NN

molar flow

kmol/h

NS

Nation to System

-

S

given process to be analyzed

-

s

number of sub-processes

-

SN

System to Nation

-

x

molar fraction

kmol/kmol

𝐀𝐀

technical coefficients matrix

USD/USD

𝐄𝐄

embodied energy requirements matrix toe/y

𝐟𝐟

𝐁𝐁

exogenous transactions coefficients vector

𝐈𝐈

identity matrix

-

final demand vector

USD

𝐸𝐸

equivalent methane

toe/y

Lower Heating Value

MJ/kg

𝑃𝑃

Pressure

bar

heat interaction

toe/y

Temperature

K

work interaction

toe/y

𝐿𝐿𝐿𝐿𝐿𝐿

𝑄𝑄

𝑇𝑇

𝑊𝑊

-

Acronyms 22

DPD

Dual Pressure Low-Temperature Distillation

IDN

Indonesia

IEA

International Energy Agency

ITA

Italia

LCA

Life Cycle Assessment

LCIA

Life Cycle Inventory Analysis

LNG

Liquefied Natural Gas

MDEA

Methyldiethanolamine process

MRIO

Multi-Regional Input-Output

USA

United States of America

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