45 Global warming potential assessment for operation

LALCA Revista Latino-Americana em Avaliação do Ciclo de Vida v.1 n.1 | Ano 2017 | 45 - 63

Global warming potential assessment for operation of thermoelectric power plant in Manaus Avaliação do potencial de aquecimento global para a operação de uma termelétrica em Manaus Evaluación de potencial de calentamiento global para la operación de una central térmica en Manaus Submetido em 9 fevereiro 2017 | Aceito em 17 abril 2017 | Disponível em 07 junho 2017

Cássio de Almeida1 Vinicius Maciel2 Luiz Cybis1

Abstract The electric sector is very important to the strategic growing of any country. It isn’t different in Brazil, which shows a diversified energy matrix, but has a predominance of hydropower sector. However, the thermoelectric sector has grown in the last years to guarantee the electrical safety and, in isolated systems, the thermoelectricity is predominant. It is the case of Amazonas State, which receives energy priority from thermal power plants in the region. They use, mostly, fossil fuels such as Diesel, Heavy Fuel Oil (HFO). Nowadays, it has been incorporated into this system the natural gas use from Amazon oil basin, located in Urucu. In this sense, to analyze the environmental influence of this change on the thermal power plants, this study intends to employ the methodology of Life Cycle Assessment (LCA) of the electricity delivered to the grid by one thermal power plant (TPP), located in Manaus, which uses HFO and Natural Gas as fuel. For observation of differences, it was performed a comparative study of this power plant in two situations: using only HFO and using HFO and Natural gas concomitant. The study was conducted from cradle to gate of the power plant from specific primary data,

1 Institute of Hydraulic Research UFRGS (Federal University of Rio Grande do Sul) e-mail:[email protected] 2 Post-Graduation Program in Materials Engineering and Technology PUCRS (Pontifical Catholic University of Rio Grande do Sul)

Global warming potential assessment for operation of thermoelectric power plant in Manaus

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provided by the power plant and secondary data from the literature. The Life Cycle Impact Assessment (LCIA) was calculated from the CML IA baseline with the use of SimaPro software and it was chosen the impact category of Global Warming Potential (GWP) for analysis. The conversion bifuel resulted in reduction of the impact of the TPP, which previously was 590.50 kg CO2eq / MWh and passed to 521.11 CO2eq / MWh. However, the bifuel power plant has, along the lifecycle, when compared the operation with only HFO, the same magnitude of GWP due to contributions of, for example, natural gas production. Keywords: Carbon Footprint; Thermal Power Plant; Life Cycle Assessment; Natural Gas.

Resumo O setor energético é de suma importância para o crescimento estratégico de qualquer país. Isso não é diferente no Brasil, o qual apresenta uma matriz energética diversificada, mas que tem um predomínio do setor hidrelétrico. No entanto, o setor termelétrico tem crescido nos últimos anos para garantir a segurança energética e, nos sistemas isolados, a termeletricidade é predominante. Este é o caso do estado do Amazonas, o qual recebe energia prioritariamente de usinas termelétricas da região. As usinas da região utilizam, em sua maioria, combustíveis fósseis tais como diesel, óleo combustível pesado (HFO, em inglês). Atualmente, tem sido incorporada a este sistema a utilização do gás natural proveniente da bacia petrolífera amazônica, localizada em Urucu. Nesse sentido, para analisar a influência ambiental desta mudança nas usinas termelétricas, este emprega a metodologia de Avaliação do Ciclo de Vida (ACV) da eletricidade entregue ao grid por uma usina termelétrica, localizada em Manaus, que utiliza óleo combustível pesado e gás natural como combustível. O estudo foi conduzido do berço ao portão da usina a partir de dados primários da própria usina e dados secundários de bibliografia da área. Para a observação das diferenças, fez-se um estudo comparativo entre a mesma usina em duas situações: utilizando somente óleo combustível pesado e o uso concomitante deste combustível com o gás natural. A Avaliação do Impacto de Ciclo de Vida foi calculada pelo método CML IA baseline com o uso do software SimaPro e escolheu-se a categoria de impacto de Aquecimento Global para análise. A conversão bicombustível resultou em redução do impacto da usina, que antes era de 590,50 kg CO2eq/MWh e passou para 521,11 CO2eq/MWh, no entanto ao longo do ciclo de vida o resultado se manteve no mesmo patamar. Palavras-chave: Pegada de Carbono. Planta Termelétrica, Avaliação do Ciclo de Vida, Gás Natural.

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Cássio de Almeida, Vinicius Maciel, Luiz Cybis


Resumen El sector energético es de suma importancia para el crecimiento estratégico de cualquier país. Esto no es diferente en Brasil, que tiene una matriz energética diversificada, pero que tiene un predominio del sector hidroeléctrico. Sin embargo, el sector termoeléctrico ha crecido en los últimos años para garantizar la seguridad energética y, en sistemas aislados, termoelectricidad es predominante. Este es el caso de estado del Amazonas, que recibe energía principalmente de centrales térmicas de energía en la región. Las plantas de la región utilizan, sobre todo, combustibles fósiles como el diesel, fuelóleo pesado (HFO en inglés). En la actualidad, se ha incorporado a este sistema, el uso de gas natural de la cuenca petrolífera del Amazonas, situado en Urucu. En este sentido, para analizar el impacto ambiental de este cambio en las centrales térmicas, este estudio emplea la metodología del Análisis de Ciclo de Vida (ACV) de la electricidad entregada a la red por una central térmica, que se encuentra en Manaus, que utiliza fuelóleo pesado y gas natural como combustibles. El estudio se realizó a partir de datos primarios de la central térmica y datos secundarios de literatura del área. Para observar las diferencias, se hizo un estudio comparativo de la misma planta en dos situaciones: utilizando sólo el fuelóleo pesado y el uso concomitante de este combustible con gas natural. La evaluación del impacto del ciclo de vida se calculó por el método de CML IA baseline usando el software SimaPro y optó por categoría de impacto del calentamiento global para análisis. La conversión bi-combustible resultó en una redución del impacto de la planta, que antes era de 590.50 kg CO2eq / MWh y aumentó a 521.11 CO2eq / MWh. Sin embargo a lo largo del ciclo de vida, el resultado se mantuvo en el mismo nivel. Palabras clave: Huella de carbono; Planta Térmica; Análisis de Ciclo de Vida; Gas Natural

1. Introduction Brazil is considered one of the countries with the cleanest electric matrix in the world with about 71% of its energy from renewable sources, mainly hydroelectric plants (61%) (EPE, 2015). While 27% of the electric energy consumed in Brazil is produced by Thermoelectric Power Plants (TPPs) (ANEEL, 2016a), in the Amazon region, the electricity generation by TPP represents 87% of the electric energy generation (EPE, 2016). This is due to geographic characteristics of the region, composed of dense and heterogeneous

Global warming potential assessment for operationof thermoelectric power plant in Manaus

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forest, as well as large and extensive rivers, which difficult the construction of transmission lines of great extension that allows the connection to the National Interconnected System (NIS), which is the system of generation and transmission of electric power with size and characteristics that allow to consider it as unique in the worldwide scope (ANEEL, 2008). In Amazon, the major consumer is Manaus Free Trade Zone (MFTZ), that was conceived as a free import and export trade area with special tax incentives (Almeida, 2011). According to the Manaus Free Trade Zone Superintendence (SUFRAMA, in Portuguese), the Manaus industrial center has approximately 600 high-tech industries, which generates more than half million jobs (SUFRAMA, 2016). With the existence of this area, the Manaus region became quite dependent on the industries that are there. In 2001, for example, the share of MFTZ’s industrial activity represented 2/3 of the Amazonas State’s Gross Domestic Product (GDP) (Filho, 2005). In this sense, a great part of the products placed in the national market is produced in the Amazon region, since more than 90 % of MFTZ’s products are directed to the national market (SUFRAMA, 2015). The products, in 2015, with the highest revenues were: telephones, radios, bicycles, toys, cell phones, televisions, air conditioning, motorcycles, among others (SUFRAMA, 2015). In 2010, started the electric energy generation with use of Natural Gas (NG) from Amazon basin, whose objective was to bring economic and environmental gains to the region (PETROBRAS, 2016), diversifying the regional energy matrix. The NG is extracted from Amazon oil basin and produced at Operational Base Geologist Pedro de Moura (BOGPM, in Portuguese), located in Urucu, which is unique Petrobras’ Petroleum and Gas Production Asset in the Amazon (PETROBRAS, 2008) as showed in Figure 1.

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Figure 1- Localization of extraction site, gas pipeline, refinery and Thermal Power Plant in the Amazonas State.

Source: Adapted from (BRASIL, 2017a) and (BRASIL, 2017b).

Therefore, given this new supply of NG, the TPPs of the region started to be converted to use this fuel too. In this sense, this study carried out an environmental comparison, in terms of Global Warming Potential (GWP), of a TPP in two situations using only Heavy Fuel Oil (HFO) (monofuel) and using HFO and NG concomitant (bi-fuel). The comparison of environmental performances of power plant operation (bi-fuel and monofuel) will be evaluated by Monte Carlo Analysis.


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2. Methodology To evaluate the differences between these two types of operation it was used Life Cycle Assessment (LCA) methodology. LCA is a tool to analyze environmental performances of products, processes or services, it has been widely recognized as an important tool to understand the environmental impacts of human activities (Guinée; et al, 2002). In this study, the environmental profile was performed following ISO 14040 and 14044 specifications (ISO, 2006a, 2006b). 2.1. Goal and Scope definition The objective of this study is to compare the GWP impacts of the electric energy production in a TPP in Amazonas. The TPP installed power capacity is 85 MW (ANEEL, 2016b), and use six Wärtsilä engines which were recently converted to the bi-fuel operation (Ojutkangas, 2011). With this conversion, it is possible to operate the system with HFO and NG in different proportions (WÄRTSILÄ, 2014). According to the manufacturer, the Gas-Diesel (GD) engine uses the diesel cycle in all operation modes. The scope of this LCA is a cradle-to-gate system boundary, which is presented in Figure 2, where the process and flows in green are related only to bi-fuel operation. The Functional Unit (FU) for this study is 1 MWh as net electricity generated from the TPP. All the inputs and outputs are related to the FU.

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Figure 2- System boundaries.

2.2. Life Cycle Inventory Data Collection The data for this study are based on, mostly, primary data. Secondary data was used to complete the Life Cycle Inventory (LCI) because it was impossible to obtain all primary data, mainly for upstream processes, such as NG and HFO production. The company, that has monopoly in fuel production, does not share data. For these processes, it was used database support from Ecoinvent v3.0 available in SimaPro® 8.0.3.14. Then, the quantities of products used in TPP (chemicals, volumetric use of HFO, Lubricating Oil and Natural Gas) were collected from 2013 operation reports and by personal visits in TPP. As TPP was still changing its technology, the first three months was used only HFO (monofuel operation) as fuel and the rest of this year it was used NG and HFO to produce energy (bi-fuel operation). Emissions to air were calculated from quality air reports, which were produced under the responsibility of TPP due to regulations in Brazil (Brasil, 2011).


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The emission rate was calculated by equation (1), with the average concentrations and exhaust gas flow. The numbers of engines and the operation time were considered.

Equation 1

Where: n= Numbers of Engines; TE = Emission Rate (kg/h); C = Average Concentration (mg/Nm³) Q = Exhaust Gas Flow (Nm³/h); For the pollutants not listed in the reports (CO and CO2), it was used emission factors presented by the responsible company, which are presented in Table 1.

Table 1- Emission Factors for NG and HFO (kg/MWh) Emission Factors (kg/MWh) Pollutant

NG

HFO

CO2

1.82E+2

2.54E+2

CO

6.21E-2

5.12E-2

NG: Natural Gas; HFO: Heavy Fuel Oil Source: Adapted from (Wärtsilä, 2013)

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Finally, the emission rates are calculated by equation (2). TE=LHV*FE Equation 2

Where: TE= Emission Rate (kg pollutant/ kg or m³ of fuel) LHV= Lower Heating Value (kcal/kg) or (kcal/m³); FE= Emission Factor (kg pollutant/ kcal fuel energy) The emission rate values (kg/h) were related to the operating time of each month, resulting in the mass emission rates for each month. So, this information was related to the energy delivered to the grid during those months. Due unavailability of primary data for wastewater and solid waste, it was used bibliography data, which analyzed the quality and quantity parameters of wastewater and solid waste for another TPP in Manaus (França, 2015), that uses the same reciprocating engine and technology of this work. 2.3. Study considerations and Limitations The data quality was based on operational conditions and similar technology used in TPP, in order to show the real implications of the operation. For upstream processes, it was used Ecoinvent v3.0 database for all processes that are outside of the TPP boundary. Moreover, Ecoinvent supported enough data to model emissions amounts in reference to supplies processes, transport truck and power grid. Due to the complexity of chemical characteristics of some products used in TPP, some of them were not available in the Ecoinvent v.3 database. With the idea to overcome this limitation, it was analyzed similar substances those used in TPP in order to compose the inventory. When one substance was not available in the database, its quantity was summed up with another substance (available in the database), which presents potential impacts, i.e, it was used a worst case approach.


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The transport data is very important, mainly in Amazon region, once it is a difficult place to transport goods due to the lack of logistic infrastructure (Ribeiro et al., 2016). Thus, each product has a different route that is presented in Table 2. The distances were considered by information received in TPP visit. A portion of HFO is imported (50 % volumetric), that it comes from Venezuela by navy. The other portion is national and comes from Urucu (in the Amazon), where is extracted the NG and it is transported by pipeline and ship until Manaus. After all products arrive in Manaus, they are transported by road to TPP. The background data (production of inputs used in TPP, and transports) have been sourced from the Ecoinvent v3.0 database. Table 2- Transport distances between products production and Thermal Power Plant (TPP). Product

Amount (km)

Source

Comment

Lubricating Oil

4300

Visit at TPP

Distance between Rio de Janeiro and Manaus

HFO (import)

4837.42

Visit at TPP

Distance between ports in Venezuela and Manaus

Chemicals

3996

Visit at TPP

Distance between Cotia (São Paulo) and Manaus

All products /Landfill

50

Visit at TPP

Distance between Manaus and TPP/ Distance between TPP and Landfill

Natural Gas (gas pipeline)

663.2

(PETROBRAS, 2008)

Extension of Urucu-Coari-Manaus gas pipeline

HFO (national)

452

Visit at TPP

Distance between ports Coari and Manaus

HFO (oil pipeline)

281

Transpetro (2006)

Extension of Urucu-Coari oil pipeline

HFO: Heavy Fuel Oil.

2.4. Impact Assessment and Interpretation The method for calculating GWP chosen for this study was CML baseline, which is one of the most widely used (Agrawal et al., 2014; Mori et al., 2014; Vinodh et al., 2012). Besides that, this method is recommended to application

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in Brazil (Mendes et al., 2016). This method, in its last version (4.7) calculates the climate change based on the GWP factors extracted from the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC) published in 2013 (CML, 2016). In LCA, there are uncertainties in the various data inputs all add up and can heavily influence your LCA results (Golsteijn, 2015). Because of this situation, the comparison monofuel to the bi-fuel operation was performed by application of stochastic approach based on Monte Carlo (MC) analysis (BOGUSLAW, 2014), which varies entry data of the model calculation randomly according to the uncertainty distributions (Winter et al., 2015). The MC simulation, in SimaPro® 8.0.3.14, takes a random value from the uncertainty distribution for each uncertain data input and calculates and stores the LCA results for this set of sampled values.(Golsteijn, 2015). SimaPro® 8.0.3.14 supports four types of distributions: uniform, triangular, normal and lognormal (PRé, 2016a). The comparative simulation, using MC analysis, performs the subtraction of two systems, where the results indicate the probability of one option generating more damage than the other (QUANTIS, 2011) or the number of times that a product system has more or less load than another (PRé, 2016a). In this work, uniform distribution was used, which needs the minimum and maximum values for each data (PRé, 2016b). MC was conducted with 95% confidence level and 1000 cycles. Besides MC, it was performed a comparison in relation to contribution analysis to show the processes that have a major contribution for GWP.

3. Results and Discussion 3.1. Life cycle Inventory Results The LCIs were developed for the two operation modes and are presented in Table 3. In this table is shown maximum and minimum values, which were later used for MC comparison. In relation to inputs, there was a decrease of 6


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products, with the greatest highlight to Adipic Acid, that use in bi-fuel mode represents 38 % of monofuel use. On the other hand, lubricating oil had an increase in use in 20 % due to compressors system, which compresses natural gas, and uses lubricating oil in operation (França, 2015). In relation to outputs, there was a reduction in flows of 73% for all compartments, but there were some increases, such as, for example, CO emission.

Table 3 - Life Cycle Inventory to monofuel and bi-fuel operation Monofuel

Bi-fuel

Compartment

Unit/ FU

Average

Amount Minimum

Maximum

Average

Amount Minimum

Maximum

HFO

tecnosphere

kg

2.02E+02

1.86E+02

2.11E+02

7.56E+01

4.24E+01

1.54E+02

Natural Gas

tecnosphere

m³

-

-

-

1.90E+02

6.61E+01

2.54E+02

Lubricating Oil

tecnosphere

kg

5.27E-01

4.93E-01

5.95E-01

6.33E-01

5.36E-01

7.73E-01

Sodium Nitrite

tecnosphere

kg

1.42E-03

0.00E+00

1.70E-03

7.55E-04

9.02E-05

1.62E-03

Phosphoric Acid

tecnosphere

kg

1.22E-05

1.09E-05

1.45E-05

1.41E-05

7.29E-06

1.82E-05

Sodium Hydroxide

tecnosphere

kg

6.71E-04

6.79E-05

8.37E-04

2.88E-04

0.00E+00

5.47E-04

Adipic Acid

tecnosphere

kg

6.11E-06

5.43E-06

7.24E-06

2.34E-06

6.83E-07

4.10E-06

Ammonium Chloride

tecnosphere

kg

3.17E-06

2.04E-06

4.75E-06

2.34E-06

6.83E-07

4.10E-06

Formaldehyde

tecnosphere

kg

2.26E-05

1.13E-05

3.39E-05

2.70E-05

1.14E-05

4.56E-05

Monoethanolamine

tecnosphere

kg

1.58E-04

7.92E-05

2.38E-04

1.89E-04

7.97E-05

3.19E-04

Nitrogen Monoxide (NO)

air

kg

4.16E+01

2.95E+01

5.04E+01

Nitrogen Oxides (NOx)

air

kg

6.43E+01

4.31E+01

7.91E+01

2.51E+01

1.99E+01

2.92E+01

Sulfur Dioxide (SO2)

air

kg

4.83E+00

2.21E+00

7.73E+00

Sulfur Oxides (SOx)

air

kg

2.29E+00

1.35E+00

3.05E+00

Flow

Inputs

Outputs

Particulate Matter

air

kg

3.41E+00

2.64E+00

4.05E+00

1.10E+00

7.42E-01

1.28E+00

Carbon Dioxide (CO2)

air

kg

5.90E+02

5.42E+02

6.16E+02

5.22E+02

2.29E+02

8.54E+02

Carbon Monoxide (CO)

air

kg

1.19E-01

1.09E-01

1.24E-01

1.47E-01

6.08E-02

2.28E-01

HFO: Heavy Fuel Oil; NO: Nitrogen Monoxide; NOx: Nitrogen Oxides; SO2: Sulfur Dioxide; SOx: Sulfur Oxides; CO2: Carbon Dioxide; CO: Carbon Monoxide; FU: Functional Unit (1 MWh); Source: primary data.

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3.2. Life Cycle Assessment Results This study showed that in monofuel operation, the life cycle impact was 690.19 kg de CO2 eq/MWh, while in bi-fuel operation was 690.11 kg CO2 eq/ MWh (value without MC analysis). So, it was considered a tie between modes of operation because there are differences between values only in the second decimal place. However, considering MC analysis, GWP was considered inconclusive, since there was little difference between the numbers of times the two systems had a lower environmental load for this impact category. For example, the bi-fuel operation had a lower load 51% of cases, while in monofuel 49%. Therefore, it is possible to ensure in statistical terms that there are no significant differences between the two types of operation for GWP, as it is shown in Figure 3.

Figure 3- Comparison between bi-fuel mode (A) and monofuel (B)

Also a contribution analysis was conducted and its results shows that TPP has the major contribution for GWP in two modes of operation, although is important to highlight that the contribution of TPP in GWP impact had a decrease, since, in monofuel operation, contribution of TPP was 85.56% and


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decreased to 75.61 % with the NG use (bi-fuel operation), which is shown in Figure 4. HFO production had a decrease too, due to the minor use of this fuel. In bi-fuel operation, there was a considerable contribution from NG chain (Production and pipeline transport), which together resulted in approximately 19% of contribution for GWP.

Figure 4- Contribution Analysis for GWP impact.

FFor bi-fuel operation, in terms of substances, 90% of the impact is due to CO2 emission. To produce energy, it’s necessary burning fuel, so TPP contributes with 83% of this emission followed by on-shore NG production, with 10 % of

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emissions. Previous study has pointed out similar contribution, such as 89.1 % for TPP (burning fuel to produce energy) (Phumpradab et al, 2009). Despite the decrease in the plant contribution other processes had growth, so that the impact along the life cycle was equal to the monofuel operation. For example, natural gas production contributed, in this category, with 17.31% of the impact. Similar value was observed in the work of Phumpradab et al (2009), which had natural gas production with about 10% contribution in GWP for combined cycle power plant. Results found in this work are similar with those searched in Literature. For example, Turconi et al (2013) analyzed 167 LCA studies about electricity generation and found that studies that used NG as energy source had life cycle impact values ranging from 380 to 1000 kg CO2 eq/ MWh (net) while TPP that use oil as fuel range from 530 to 900 kg CO2 eq/ MWh. Phumpradab et al (2009) calculated the GWP impact with a value of 689 kg CO2 eq/MWh for a TPP that uses NG and HFO as fuel and 539 kg CO2 eq/MWh for the combined cycle power plant (which uses only NG). A brazilian work, that analyses the potential to reduce GWP in the mix grid energy, calculated the GWP for different types of generation, being that for NG, the GWP impact was 523 kg CO2 eq/MWh and for diesel oil 832 kg CO2 eq/MWh (Delgado and Carvalho, 2016), showing the same magnitude of this work. Therefore, this study shows that is possible to claim that both operation modes have similar statistical results for GWP. Besides that, bi-fuel operation have shown a strong contribution of NG chain (NG extraction and transport) with almost 19% of contribution, influencing decisively for the tie among operation conditions.

4. Conclusions The results show that the change in the operation mode (monofuel to bifuel) did not bring a huge difference in terms of GWP impact. In addition, if


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TPP were implemented in other place or there were a major NG use, should be observed the distance between extraction and final use, because it may become bi-fuel operation worse than which was modeled in this paper. Also, in this work MC simulation was very important to show the real tie between monofuel and bi-fuel operation, which a simple comparison did not bring the same conclusion, because MC takes into account the uncertainties in data, improving the robustness to the result. It is recommended to investigate the NG production in Amazonas state, which it was tried in this work but not succeed due to unavailability of open data of the company responsible. This recommendation is important because NG production had an important role in results, with the greatest contribution (after the TPP, which is the first one). Finally, the results can be useful for future researches for comparison with other feedstocks and others power production technologies. Also, the results can be useful for future LCA studies mainly those involving materials produced in Brazil and, more specifically, in the MFTZ, once the products produced there are fed by the local TPPs. Besides that, this work also contributes to the knowledge base of LCA and energy systems in Brazil.

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