EVALUATION OF Eucalyptus WOODCHIP UTILIZATION AS FUEL FOR THERMAL POWER PLANTS

This paper aims to evaluate the implications of Eucalyptus woodchip utilization as an alternative solid fuel for thermal power plants, highlighting its energy properties, power generation expenses and unit variable costs. Woodchip samples were collected from different sources and a proximate analysis was carried out in order to determine their moisture content, basic and bulk densities, ash content and higher heating value. Then, with these properties, empirical indices of a 10-megawatt thermal power plant were used to simulate the potential woodchip consumption, the forest area needed and the unit variable cost (US$·MWh−1) for each sample. The results indicate that woodchip samples with lower moisture content and improved higher heating value presented: reduced woodchip consumption for the same power generation, decreased generation expenses, reduced unit variable costs and smaller Eucalyptus plantations area needed to supply the woodchip consumption. Greater energy density may result in lower transportation and storage expenses, however, does not indicate better generation performance, since it is influenced by biomass field conditions. All samples obtained satisfactory levels of ash content, which may result in lower emissions of pollutants and superior operational efficiency. Finally, all samples presented unit variable costs below the limit established by the government for participation in the regulated energy market, which might be an economic attraction for this kind of project. Therefore, Eucalyptus woodchip moisture content, higher heating value and energy density are key issues in sustainable thermal power generation and should be managed by Eucalyptus power plants in order to reach better generation performance and reduced expenses. 1Federal University of Viçosa, Department of Forest Engineering, Viçosa, MG, Brazil. 2Federal University of Viçosa, Department of Forest Engineering, Viçosa, MG, Brazil. 3CEPEL, Electrical Energy Research Center, Rio de Janeiro, RJ, Brazil. 4Pontifícia Universidade Católica do Rio de Janeiro, Department of Industrial Engineering, Rio de Janeiro, RJ, Brazil. ♠Corresponding author: gabriel.deus@ufv.br Received: 30.10.2019 Accepted: 21.01.2021 Maderas. Ciencia y tecnología 2021 (23): 29, 1-12 Universidad del Bío-Bío 2


INTRODUCTION
Historically, Brazil has established its power generation system based on a high share of renewable sources, despite the worldwide predominance of fossil fuels (ENERDATA 2018). Until the last decade, the Brazilian power system was mostly developed on hydro generation, with a thermal generation being a necessary complement due to the possibility of critical hydrological periods, then characterizing its system as a hydrothermal system (EPE 2017). However, as part of the Brazilian government expansion policy, new renewables were introduced in its electrical matrix during the last decade, in particular wind and solar PV. As of June 2019, renewables accounted for 79,33 % of the Brazilian installed capacity, in which 60,83 % came from hydropower, wind 8,71 %, biomass 8,58 %, and solar photovoltaic 1,21 %, as presented in Table 1 (ANEEL 2019). Even presenting a high share of renewables, according to the Ten-Year Energy Expansion Plan 2027 (EPE 2018), the hydro, wind, solar PV, sugarcane, biogas and forest biomass will be the main drivers of renewable expansion in Brazil. Until 2027, it is expected addition of 38,310 MW from those sources in its electrical matrix, representing 63 % of the system expansion. One of the challenges faced by systems with a high share of renewables is that most of them are non-dispatchable sources, such as wind and solar PV. It means that the system may not have the capacity to produce enough electricity to reach its demand due to the lack of wind or solar radiation at the same moment the demand happens (Morato et al. 2018). For this reason, it is comprehended that Brazil must also invest in renewable fuels that are not intermittent, i.e., may be dispatched whenever there is a scarcity of other sources, and which are relatively inexpensive (Lamas and Giacaglia 2013). In this context, the use of the planted forest is presented as an alternative, since Brazil is a major Eucalyptus producer with high productivity rates on an industrial scale. In 2019, the total area of planted forests in Brazil was 9,0 million hectares, of which 6,97 million hectares were of Eucalyptus plantations. Those were mainly concentrated in Minas Gerais, São Paulo and Mato Grosso do Sul states. Brazil is also recognized for its high forest productivity: in the last five years, the average Eucalyptus productivity rate was 35,7 m 3 ·ha −1 , and in the last 10 years, the average growth rate of Eucalyptus plantations in Brazil was 4,21 % p.a., which is expected to remain stimulated by pulp and energy sectors (IBÁ 2020).
In the actual scenario, thermal power plants (TPPs) using forest biomass are increasing their participation in cogeneration and industrial-specific projects, through the utilization of Eucalyptus woodchips, with a high degree of national content and reduced operational costs (Deboni et al. 2019, Miranda et al. 2017). In addition, it can be considered that woodchips present technical improvements, such as i) high degree of mechanization, from the harvest until its combustion in power plants; ii) almost complete use of the trees for energy (all aerial components) in well-managed forest plantations; iii) better steam quality when compared to firewood, as woodchips allow uniform burning and flow in thermal systems (Do et al. 2014, Ignacio et al. 2019).
Most of the projects are being developed with a small installed capacity (less than 100 MW), which allows its development where the demand is located or via distributed generation. This strategy seems to be necessary since some existing woodchip TPPs have been facing technical and operational issues (e.g., low forest productivity rate, insufficient woodchip volume for power demand, lack of knowledge of woodchip fuel parameters, etc.) (EPE 2018).
Furthermore, despite the potential of Eucalyptus woodchip for power generation, there are few studies that address its use and its fuel properties for thermal power generation. According to two Brazilian institutional reports (CGEE 2015) and (IAB 2015), the main studies are related to Eucalyptus for charcoal production aimed at the steel industry, and Eucalyptus use for boilers to meet industrial steam demand, as can be observed in previous studies (De Oliveira Vilela et al. 2014, Carneiro et al. 2014, Miranda et al. 2017. It should also be noted that there is a lack of knowledge on part of the electrical industry about the characteristics of Eucalyptus woodchip and its use in the combustion process for power generation. Therefore, this work aims to address some of these issues, as a way to track an even cleaner energy mix in Brazil and worldwide. In this context, this study goals to characterize the utilization of Eucalyptus woodchip as an alternative solid fuel for thermal power plants, highlighting its fuel properties and power generation expenses. The paper's main contributions include the following areas: i) for the government: considering that there are few studies related to the use of Eucalyptus woodchips in power generation, these results should be used to address regulatory issues related to the use of a non-intermittent fuel; ii) for investors: the energy properties obtained may be used to simulate the project's viability as well as to point out some fuel features that may improve power generation efficiency; and iii) for forest producers: these findings are relevant for forest producers in order to trade biomass for power generation with better fuel quality.

Sampling and samples preparation
Five different Eucalyptus woodchip samples commonly used as fuel in boilers were analysed in this study Figure 1. All the samples were obtained from different companies that use woodchips for steam and power generation. Samples 1 and 2 came from distinct Eucalyptus plantations located in Viçosa's region (Minas Gerais state) and originated from clones of Eucalyptus hybrids E. urophylla x E. grandis, harvested at seven years old. Samples 3, 4 and 5 came from distinct plantations in Rondonópolis' region (Mato Grosso state), and originated from clones of Eucalyptus hybrids E. urophylla x E. grandis, harvested at seven years old as well. All samples were randomly selected from their respective piles of woodchips. The drying period informed by the companies was around 90-120 days for all the samples. Sampling and sample preparation was conducted in order to be representative of the combustion process in thermal power generation.

Characterization of the collected samples
Samples characterization followed the standardized methods that conduct to consistent and accurate evaluation of fuel properties. Moisture content (%), bulk density (kg·m −3 ) and ash content (%) were determined following standards DIN EN 14774-2 (2010a), DIN EN 15103 (2010c), DIN EN 14775 (2012), and the basic density (kg·m −3 ) according to (Vital 1984). Higher heating value (HHV), expressed in MJ·kg −1 , was achieved with an IKA ®™ C200 calorimeter and followed the standard DIN EN 14918 (2010b). Net heating value (NHV), expressed in MJ·kg −1 , was estimated from the higher heating value using Equation 1, according to standard DIN EN 14918 (2010b).
From kcal·kg −1 to J·g −1 , it was considered that a kcal·kg −1 corresponds to 4,184 J·g −1 . The energy density, expressed in GJ·m −3 , was obtained by the multiplication of the sample's basic density (kg·m −3 ) and their respective HHV (MJ·kg −1 ). All parameters were determined in the Panels and Wood Energy Laboratory (LA-PEM), Department of Forestry Engineering, Federal University of Viçosa, Brazil. All determinations were as received, i.e., on a wet basis.

Statistical analysis
The experiment was analysed according to a completely randomized design, with five different Eucalyptus woodchip samples and three replicates, totaling 15 sampling units for each energy property. Data normality for all variables was assessed by Lilliefors' test, and Cochran and Bartlett's test was performed for homogeneity of variances. The analysis of variance (ANOVA) was performed and whenever significant differences were observed, the treatments were compared by Tukey's test at 5 % of probability level. Statistical analyses were implemented in the Statistica program (Statsoft 2007).

Woodchip consumption and required forest area
In this study, several empirical parameters were used to evaluate the fuel consumption that each woodchip sample would represent. These parameters were obtained with ICAVI Caldeiras S/A (ICAVI 2019), a Brazilian company that specialized in the development, construction and, assistance of boilers and thermal power projects. All the empirical indices may be considered a mean of what ICAVI Caldeiras S/A observes and/or determines on its business. For the study's purpose, a thermal power project was developed using empirical parameters in order to simulate steam production, power generation and the woodchip consumption required for this production. The main characteristics of this project are: i) gross installed capacity of 10 MW, which is close to the average installed capacity of the woodchips thermal power plants currently operating in Brazil: about 8 MW, according to the Brazilian Electricity Regulatory Agency (ANEEL 2019); ii) adoption of Rankine steam cycle technology, the most used in Brazil and considered a well-developed technology. Rankine is comprehended as a simple technological route based on the use of water/steam as transportation fluid and energy storage. In this process Figure 2, direct combustion of forest biomass occurs in a boiler for the generation of the superheated steam, which, in turn, feeds steam turbines for power generation of thermal origin (Lora and Andrade 2009).  Table 2 presents the empirical parameters; the equations used to reach the woodchip consumption and the forest area needed; and the acronyms used in each equation. Two forest parameters were considered for calculations: Eucalyptus mean annual volume increment (MAI) equal to 35 m 3 ·ha −1 ·year −1 , and forest stands cut age of 700 years, both are considered the Brazilian average, according to the Brazilian Tree Industry Annual Report (IBÁ 2020). The other forest parameters used in calculations were determined in Laboratory. The fuel consumption calculation was made considering the use of all the aerial Eucalyptus tree volume, since in Brazil Eucalyptus plantations for energy purposes are entirely managed for it. For fuel consumption calculation, HHV and NHV were expressed in kcal·kg −1 . From J·g −1 to kcal·kg −1 , it was considered that a kcal·kg −1 corresponds to 4,184 J·g −1 .
The effective fuel consumption index (kg woodchip·kg steam −1 ) represents the efficiency of the fuel employed in the thermal process, i.e., the less this index, the smaller the amount of fuel required to generate the same quantity of energy.

Power generation expenses and unit variable cost
Each sample's parameters were simulated in the thermal power project and resulted in five different woodchip consumption scenarios, as well as distinct power generation expenses and unit variable costs (UVCs). UVC is the TPP's operational cost, which mostly represents the fuel costs (EPE 2017) and is expressed in US$·MWh −1 . For example, in the Brazilian power sector, the UVC is used by the System Operator in order to define the dispatch's merit order of thermal power plants and to compose the cost-benefit index of such plants that sell energy in the Brazilian Regulated Market, among other uses (EPE 2017 Where: C fuel = represents the fuel expenses (annual-basis), expressed in US$; C O&M = represents the operational and maintenance expenses (annual-basis), in US$. C O&M was fixed at 2,57 US$·MWh −1 (10 R$·MWh −1 ) which is a proxy, and then multiplied by the annual maximum power generation to reach the annual value.
Where: i = Conversion Factor necessary to transform the fuel price in US$·MWh −1 . This conversion factor is expressed by the Equation 4; G max = annual maximum power generation; h y = heat yield or efficiency. In this study heat yield was assumed in 28 %.
To obtain the UVC in US$·MWh −1 , the annual value in US$ was divided by the maximum power generation in MWh·year −1 .

Woodchips energy characterization
The results of energy characterization per sample are presented in Table 3. Means followed by the same letters within a row do not statistically differ from each other by the Tukey's test at 5 % of significance.
As may be observed in Table 3, the obtained values for basic density varied between 449,47 kg·m −3 (sample 5) and 546,60 kg·m −3 (sample 3), and sample 5 basic density was significantly different from the others. For higher heating value, the obtained values varied between 19,35 MJ·kg −1 (sample 3) and 19,73 MJ·kg −1 (sample 5) and no statistical differences were observed between the samples. All the obtained values reside within the normal range for Brazilian Eucalyptus genres, which is 450 kg·m −3 -550 kg·m −3 for basic density and 18,5 MJ·kg −1 -20,0 MJ·kg −1 for HHV, according to Castro et al. (2016) and Magalhães et al. (2017).
The energy density, which was obtained by the multiplication of HHV and the basic density, indicates the potential of energy generation per sample and also the viability of transport and storage of biomasses. As this calculation is made on a dry-basis, it excludes the influence of the moisture content, and represents the fuel property of the genetic material. In this way, sample 4 would be more efficient in the energy generation, being able to supply up to 10,69 GJ·m −3 . In contrast, the lowest value was observed in sample 5 (8,87 GJ·m −3 ). For this sample, even presenting the highest HHV, its lower energy density was influenced by its reduced basic density, making sample 5 possibly the most expensive in terms of storage, transport and handling, compared to the other samples. This point can also be observed in Martinez et al. (2019), for biomass residues, where the authors have observed that the increased the energy density, the reduced the transport and handling costs of the biomasses. The results of energy density obtained in this work are relatively similar to those found by Magalhães et al. (2017) when studying Eucalyptus genetic materials for energy.
For the ash content, the obtained values varied between 0,35 % (sample 4) and 0,54 % (sample 2) and statistical differences were observed between samples 4 and 2. The results of this study can be considered valuable when compared with other studies for Eucalyptus (Ignacio et al. 2019, Almeida et al. 2010. For Eucalyptus urosemente woodchips, Ignacio et al. (2019) observed values between 1,05 % and 1,54 %. The authors considered that the presence of bark in the extracted samples increased the ash content. Fernández et al. (2012a) also observed higher ash content for Eucalyptus (1,9 % ± 0,9 %), reaching values over 30 % higher than expected in the bibliography.
It is also important to note that almost all the samples in this study presented ash content below 0,50 %, which may be considered satisfactory in relation to gases emissions and operational efficiency, since ash content is related to the reduction of the HHV, to residues generation in the combustion process and, depending on the composition, to the corrosion or formation of deposits in boilers (Deboni et al. 2019, Fernández et al. 2012b. The moisture content varied between 22,88 % (sample 2) and 38,47 % (sample 5) and statistical differences were observed between the samples. These variations directly impacted samples NHV, which varied between 10,40 MJ·kg −1 (sample 5) and 13,41 MJ·kg −1 (sample 2). The highest NHVs were observed from samples 2 and 1, and the lowest from samples 5 and 3. It is important to mention that under field/practical conditions, it is normal to observe woodchip moisture content around 40 -50 % due to local weather and humidity variations.
Bulk density is an essential factor that may influence the economic viability of biomass materials utilization, since it affects transport costs. In this study, bulk density varied between 236,81 kg·m −3 (sample 3) and 283,57 kg·m −3 (sample 5), being directly influenced by the basic density and moisture content of the biomass. Significant differences were observed between the samples.
Concerning the combustion process, it is desirable for woodchips to present as reduced moisture content as possible, which can provoke greater NHV. In these terms, samples 2 and 1, both from Viçosa's region, presented the greatest net heating values and lowest moisture contents, respectively, which should impact the fuel consumption and project operational expenses. On the other hand, samples 5, 3 and 4, from Rondonópolis' region, presented the lowest net heating values due to the highest moisture contents, and although they have demonstrated some decreased fuel properties, when compared to Viçosa' samples, they should not be discarded since their other indicators are satisfactory. For example, sample 4 showed the highest energy density, which may reduce its transport and handling costs.

Estimated woodchip consumption and forest area required
The simulated values are presented in Table 4, considering the maximum energy generation for this thermal power project, that was 73973,33 MWh·year −1 . All sample' parameters were simulated for the same energy production as well as the same empirical generation indices. As can be observed, the smallest fuel consumption, in t·h −1 , occurred when using samples 2 and 1, which were those with the highest NHVs and lowest moisture content, both from Viçosa's region and from different companies. The highest fuel consumption occurred when simulated for sample 5, the one with the highest moisture content and lowest NHV.
In cubic meters per hour, the consumption varied between 41,06 (using sample 1) and 50,95 (using sample 3). This difference occurred due to the different bulk density observed in each case, as sample 3 presented the lowest bulk density. In terms of woodchip per kg of steam, simulated values for the use of sample 2 presented again the lowest relationship (0,259) and the highest occurred when using sample 5 (0,334). This index means that when using sample 2 the project should need less fuel to generate the same amount of steam, and consequently, power, than using other samples, since sample 2 reported the greatest NHV and the smallest moisture content.
Regarding woodchip tons per MWh and cubic meters per MWh, the relationship observed above remained: the project simulated with sample 2 presented the lowest fuel consumption (1,17 t·MWh −1 ) and with sample 5 the highest consumption (1,51 t·MWh −1 ). In cubic meter per MWh, sample 1 presented the lowest (4,62 m 3 ·MWh −1 ) and sample 3 the highest (5,73 m 3 ·MWh −1 ). On a year-basis, sample 2 would be consumed 86373,76 woodchip tons and with sample 5 it was observed the highest amount needed: 111344,44 tons (28,91 % more than sample 2). These results demonstrate that greater NHV -which normally occurs when the sample presents reduced moisture content and elevated HHV-represents a valuable quality for thermal energy production.
For the required forest area analysis, the project simulated with samples 2 and 1 (separately) showed the lowest level of forest area required 3689,42 and 3832,93 hectares (in total) respectively, and with sample 5 the greatest: 5111,35 hectares. In this case, two fuel properties influenced the calculation: moisture content and basic density. Despite not having the highest basic density (sample 3 is the highest), sample 2 presented the smallest moisture content, which directly impacted the required area. Similar results may be observed in (Miranda et al. 2017). Viçosa's region (Minas Gerais state), is composed of 20 municipalities and is recognized for its forestry and agricultural vocation. According to data from the Brazilian Institute of Geography and Statistics, this region has 23833 hectares of Eucalyptus (IBGE 2017). With that, it is possible to infer that this kind of project is able to demand a reasonable portion of the existing forest biomass of this region: 15,5 % using sample 2 fuel properties and 16,1 % for sample 1, considering the maximum power generation. Comparatively, Rondonópolis' region (Mato Grosso state) is composed of 10 municipalities and is also known for its forestry importance. This region actually has 27347 hectares of Eucalyptus (IBGE 2017). Using the fuel properties of the samples from there, it may be pointed that sample 3 would demand 14,6 % of the region's forest plantations; sample 4: 14,5 %; and sample 5: 18,7 %.
Therefore, a small to the medium-sized thermal power plant for these regions may be an interesting economic activity. It also may be noted that projects of this size would not necessarily need to buy or lease land to plant forests, and may maintain supply with local producers. Even considering that part of this forest stock is unavailable, these projects would not have to travel long distances to purchase wood, which corresponds to an advantage for the business.
Another point to be discussed is related to the technical empirical indices used in this study and how they can be supported. Although some indices are considered Eucalyptus-exclusive for the combustion process, some empirical parameters can be observed in studies: the power-to-heat ratio of 4500 kg steam·MWh −1 (Nzotcha and Kenfack 2019); the overall efficiency of 85,00 % (Stoppato 2012); the steam delta enthalpy for fuel consumption (Buchmayr et al. 2015); the heat yield of 28,00 % (Saidur et al. 2011).

Estimated power generation expenses and unit variable cost
Simulated expenses and UVC for each sample are presented in Table 5. Since the conversion factor is dependent on the NHV, this factor followed the results already discussed. The use of sample 2 presented the lowest total annual expenses, and sample 5 the highest. The obtained values varied between 3,467 US$ million·year −1 (using sample 2) and 4,414 US$ million·year −1 (using sample 5). These results were already expected since sample 2 was responsible for the smallest amount of fuel to generate the energy proposed for this project, and sample 5 required the highest amount.
As the sample 2' project required less fuel to generate the same level of energy, this sample demonstrated the best results regarding the unit variable cost: 46,86 US$·MWh −1 . The worst economic results were obtained when using sample 5: UVC of 59,67 US$·MWh −1 . All samples' UVCs were below the cap established by the Brazilian government for new projects in the regulated market (71,87 US$·MWh −1 ). In this way, the project would participate in the regulated market using these different samples. Thus, it is the investor's responsibility to purchase woodchips with fuel parameters that keep the project's viability. Working efficiently with these parameters can lead to a reduction of the risks in the face of fuel price fluctuations. Therefore, from the investor and forest producer point of view, forest plantations with better quality for energy can positively affect the operations of the plant, the economics of the projects and the remuneration for the producer.

CONCLUSIONS
This work characterized fuel properties of five different Eucalyptus woodchip samples obtained in two distinct Brazilian states, taking into account its possible use as fuel for a thermal power plant connected to the national system. Several indicators were calculated and a complete methodology was exposed, bringing relevant information that may be replicated in other forest biomass studies, and that may contribute to improving the interest in a non-intermittent source for the Brazilian power sector. However, some limitations might be observed, such as the limits of the sampling method for the identification of the five samples; the standardization of all empirical coefficients and energy properties for the full operational year, which requires further studies with real operational systems.
The work results indicate that woodchip samples with lower moisture content and improved higher heating value presented: reduced woodchip consumption for the same power generation, decreased generation expenses, reduced unit variable costs and smaller Eucalyptus plantations area needed to supply the woodchip consumption. Greater energy density may result in lower transportation and storage expenses, however, does not indicate better generation performance, since it is influenced by biomass field conditions. All samples obtained satisfactory levels of ash content, which may result in lower emissions of pollutants and superior operational efficiency. Finally, all samples presented unit variable costs below the limit established by the government for participation in the regulated energy market, which might be an economic attraction for this kind of project.
Therefore, Eucalyptus woodchip moisture content, higher heating value, and energy density are key issues in sustainable thermal power generation and should be managed by Eucalyptus power plants in order to reach better generation performance and reduced expenses.