Chemistry in Combustion Science and Technology: From Molecules to PracticesView this Special Issue
Effect Analysis on Combustion and Emission Characteristics of a Rotary Burner Fueled by Biomass Pellet Fuel
A biomass pellet rotary burner was chosen as the research object, in order to study the influence of excess air coefficient on the combustion phenomenon of biomass rotary burner, the finite element simulation model of a biomass rotary burner was established, and simulation results of a biomass rotary burner were verified by the experiment. The computational fluid dynamics software was applied to simulate the combustion characteristics of biomass rotary burner in a steady-state condition, and the effect of excess air coefficient on temperature field and component concentration field in biomass rotary burner was analyzed. The results show that the flue gas flow rate inside the burner gradually increases with the increase of air velocity, the area with large temperature is mainly concentrated in the middle region of the rotary burner, and the maximum combustion temperature also appeared in the middle region of the combustion chamber, and the formation area of CO decreases with the increase of excess air coefficient. CO2 is mainly concentrated in the middle region of the burner, and the CO2 generating region decreases with the increase of excess air coefficient. The experimental value of the combustion temperature of the biomass rotary burner is in good agreement with the simulation results.
With the rapid development of modern economy and technology, the energy crisis and air pollution from the combustion of fossil fuels have become increasingly serious [1–3]. In order to meet the requirements of resource and environment problems, many researches had been done to improve combustion technologies and look for alternatives to replace fossil fuels [4, 5]. Biomass pellet fuel is a typical biomass solid forming fuel, with high efficiency, clean, easy to ignition, CO2 near-zero emissions, and so on [6–8]. It can replace coal and other fossil fuels used in cooking, heating, and other civilian areas and boiler combustion, power generation, and other industrial areas [9, 10].
In recent years, biomass pellet fuel is being developed rapidly in the EU, North America, and China [11–13]. Another advantage of biomass pellet fuel is that it can be used in small boilers, hot blast furnaces, and heating furnaces. The automatic control system is used to achieve the continuous combustion of biomass pellet fuel burner. After years of research, biomass pellet burners are being developed rapidly. These burners mainly use wood pellets as fuel. Wood pellets have many advantages, such as high calorific value, low ash content, high ash melting point, and being not easy to slag after combustion [14, 15].
Khodaei et al.  addressed a comparative CFD-based analysis of different drying models. Several submodels were simulated to investigate the evaporation process of different geometries based on standard densified wood pellets. Buchmayr et al.  presented an accurate, time-efficient CFD approach for small-scale biomass combustion systems equipped with enhanced air staging. The model can handle the high amount of biomass tars in the primary combustion product at very low primary air ratios. Gas phase combustion in the freeboard was performed by the Steady Flamelet Model (SFM) together with a detailed heptane combustion mechanism. Farokhi et al.  presented a computational study to evaluate the influence of turbulence and combustion models as well as chemistry schemes on the combustion of an 8–11 kW small lab-scale biomass furnace. Fagerström et al.  used a macro-TGA reactor to determine the release of ash forming elements during devolatilization and char combustion of single pellets. Soft wood and wheat straw were combusted at two temperatures (700°C and 1000°C), and the residual ashes were collected and analyzed for morphology, elemental, and phase composition. Roy et al.  presented combustion and emission results obtained using a prototype pellet furnace with 7–32 kW capacity (designed for burning high ash content pellet fuels) for four biomass pellets: one grass pellet and three wood pellets. Fuel property, gas emissions, and furnace efficiency are compared. Vicente and Alves  presented an extensive tabulation of particulate matter emission factors obtained worldwide and critically evaluated. Henderson et al.  explained how local economies can benefit from wood pellet manufacturing by expressing the economic multiplier effect of wood pellet plant operations in terms of employment, wages and salaries, and value-added in each of the 13 states in the U.S. South. Nunes et al.  studied several studies on the torrefaction of biomass for heat and power applications in the literature, which need to be reviewed and analyzed for further actions in the field, because significant gaps remain in the understanding of the biomass torrefaction process, which necessitate further study, mainly concerning the characterization of the torrefaction chemical reactions, investigation of equipment performance and design, and elucidation of supply chain impacts. Mobini et al.  used a dynamic simulation modeling approach to assess the integration of torrefaction into the wood pellet production and distribution supply chain. The developed a model that combined discrete event and discrete rate simulation approaches and allowed considering uncertainties, interdependencies, and resource constraints along the supply chain which were usually simplified or ignored in static and deterministic models.
Proskurina et al.  presented an overview of the current status of the Finnish wood pellet business and discussed the main opportunities and challenges facing the future development of the industry. Coelho et al.  studied several base case power plants and hybrid biomass/CSP options: wood gasification, refuse-derived fuel pellets, biogas from a wastewater anaerobic digester, and biogas from a landfill and natural gas. Roni et al.  investigated the existing cofiring plants with technologies and the availability of biomass resources in different countries of the world. Finally, this paper summarized the major global biomass cofiring initiatives and the prospects of biomass cofiring in securing renewable energy targets. Toklu  showed that there is an important biomass energy potential for climate change mitigation and energy sustainability in Turkey. Proskurina et al.  evaluated the potential of torrefied biomass in different industries, both power and nonpower generation industries, and considered the impact of such use on the international biomass market. Carvalho et al.  compared different technologies, such as CFB air and oxygen gasification, dual fluidized bed (DFB) steam gasification, and bio-synthetic natural gas (bio-SNG) production, focusing on the use of the product gas in an iron ore pelletizing process located in the Southeast of Brazil. García et al.  analyzed thirteen alternative raw biomass samples and compared them with briquette, wood pellets, and charcoal and considered the data of their proximate, ultimate, and calorimetric analysis and physical properties. Silva et al.  evaluated the application potential of torrefaction in the solid fuel’s production from lignocellulosic biomasses in Brazil. Xu et al.  evaluated the reductions in PM2.5, organic carbon (OC) and elemental carbon (EC) emissions by comparing emission factors (EFs) among 19 combinations of biofuel/residential stove types measured using a dilution sampling system. Ahn and Jang  manufactured and tested a prototype of a 230 kW class wood pellet boiler employing a four-step grate. Ndibe et al.  investigated cofiring characteristics of torrefied biomass fuels at 50% thermal shares with coals and 100% combustion cases. Kraszkiewicz et al.  analyzed the results of the CO, NO, and SO2 emission from burning pellets of oil cake, rape straw, and birch sawdust in the low-temperature water boiler of the top combustion. Chai and Saffron  optimized the biomass upgrading depot capacity and biomass feedstock moisture to obtain the minimum production cost at the depot gate to produce woody biofuels.
In this paper, a biomass pellet rotary burner with the spiral cleaning and slagging device was chosen as the research object in order to study the influence of excess air coefficient on the combustion efficiency. According to the combustion characteristics of the biomass (wood pellet), a high-efficiency double-layer combustion drum device has been designed to achieve a better flow distribution in this study. The flow velocity distribution characteristics in the burner have an important effect on the combustion efficiency of the biomass (wood pellet). The flow field distribution and combustion field distribution of the new biomass rotary burner are studied, which can reduce the test workload and shorten the design cycle. The CFD software was applied to simulate the combustion characteristics of biomass rotary burner in steady condition, and the effects of the excess air ratio on the temperature field and component concentration field in the biomass rotary burner were analyzed.
2. Numerical Model
2.1. Physical Model of Biomass Rotary Burner
In order to improve the thermal efficiency of the biomass rotary burner, a rotary combustion chamber has been used in the biomass rotary burner for reducing the pollutants. A screw feed device is used to control the appropriate amount of biomass (wood pellet). The schematic diagram of the biomass rotary burner is shown in Figure 1 which is mainly constituted by the spiral feeding mechanism, rotary combustion chamber, small combustion chamber, connecting flange, and other components.
When the burner is working, the biomass pellets are fed from the feed spiral tube into the rotary combustion chamber. The air is blown into the rotary combustion chamber through the secondary air pores to make the biomass particles burn completely. The combustion characteristics of the burner in the steady state are studied in the paper.
2.2. Mathematical Model
In this work, the combustion characteristics of the biomass rotary burner were studied by Fluent software. In the numerical calculation, the turbulent flow adopts the k-ε equation model, and the P-1 model was used to simulate the radiative heat transfer. The discrete phase particle trajectory was adopted. A power-diffusion limiting model was used in the stochastic tracking model for combustion. A two-step competitive reaction model was adopted for calculating volatile pyrolysis, and a mixture fraction/probability density function (PDF) was used for simulating gas phase turbulent combustion.
In the fluid simulation of biomass rotary burner, it is regarded as steady-state turbulent motion. The flow and heat transfer conservation equations of mass, momentum, and energy are as follows.
The mass conservation equation iswhere ρ is the fluid density and U is the fluid velocity vector.
The momentum conservation equation iswhere p is the fluid pressure, μ is the dynamic viscosity, and Su, , and are the generalized source terms for the momentum conservation equation.
The energy conservation equation iswhere cp is the specific heat capacity of fluid, T is the fluid temperature, h is the heat transfer coefficient of fluid, and St the is viscous dissipation term.
The turbulence kinetic equation and the turbulence stress equation which reflect the influence of the turbulence pulsation on flow can be obtained by the k-ε equation in the following form:where k is the turbulent kinetic energy, μt is the turbulent viscosity, Gk is the generation term of the turbulent kinetic k caused by mean velocity gradient, Sk is the source term of turbulent kinetic energy, and σk is the Prandtl number corresponding to the turbulent kinetic k.where ε is the turbulent dissipation rate, Sε is the turbulent dissipation source, σε is the Prandtl number corresponding to the turbulent kinetic energy dissipation ε, and C1ε and C2ε are the empirical constants, σk = 1.0, σε = 1.3, C1ε = 1.44, and C2ε = 1.92.
In the mixed fraction model, the instantaneous thermochemical parameters of the fluid are related to the mixing fraction. The mixing fraction can be calculated by equation (6) according to the atomic mass fraction of the fuel and oxidant including the inert component:where Zi is the elemental mass fraction of element i, the subscript ox is the value at the inlet of oxidant stream, and fuel is the value at the inlet of the fuel stream. The mixing fraction is the local mass fraction of all components (CO2, H2O, O2, etc.) including the combusted and unburned fuel stream elements (C, H, etc.). The average (time average) mixed fraction and its mean square equation arewhere is the average velocity vector, m/s; μt is the turbulent viscosity, kg/(m·s); σk is the turbulent Prandtl number; Sm is the source term caused by chemical reaction, kg/(m3·s); and and Cd are the model constants which are 2.86 and 2.00, respectively.
For radiant heat flow qr, it can be expressed by where a is the absorption coefficient; σs is the scattering coefficient; G is the incident radiation, W/m2; and C is the linear anisotropic phase function coefficient.
Introducing the parameter Γ = 1/[3 (a + σs) − Cσs], equation (8) can be turned into
The transport equation for G iswhere σ is the Stephen-Boltzmann constant, 5.67 × 10−8 W/(m2·K4); and SG is the source term of user-defined radiation, W/m2.
When using the P-1 model, the local radiation intensity is used to solve this equation. Equation (11) can be calculated by combining equations (9) and (10):where −▽qr is an expression that can be directly substituted into the energy equation to obtain a heat source due to radiation.
The precipitation of biomass volatiles was simulated using a two-competing-rate model, and the parameter was determined based on the properties of biomass.where is the mass of volatiles precipitated at time t, kg; is the initial volatile mass fraction of the particles; mp, 0 is the initial particle mass of the jet source, kg; ma is the ash content of the particles, kg; α1 and α2 are production rate factors; and R1 and R2 are competitive precipitation rate constants, controlling the precipitation rate in different temperature ranges, Ri = Aiexp (−Ei/RTp), kg/(kg·s).
Through the experimental study, the components attached to the combustion are similar to coke which is very close to the chemical properties of coke. It is assumed that the coke surface reaction rate in the dynamic diffusion control combustion model is simultaneously controlled by the diffusion process and chemical reaction kinetics. The coke combustion rate iswhere R is the chemical reaction rate constant, R = C2exp (−E/RTp), kg/(kg·s); Mp is the current pellet mass of coke, kg; dp is the current particle diameter of coke, m; and pox is the partial pressure of oxidant in vapor phase around the coke particles.
2.3. Computational Mesh and Boundary
Before calculating by numerical simulation method, a 3D model of biomass pellet rotary burner was established. For making the calculation of the simulation model faster, the burner surface chamfer and fine structure were properly simplified without affecting the accuracy of the calculation. The fluid model in the burner was established by Gambit. The tetrahedral mesh was used to divide the body mesh of the fluid model in the burner. Figure 2 shows a three-dimensional volume model of fluid in the burner with a number of nodes of 133078 and a number of grids of 600240 after grid independence.
In this paper, the boundary conditions are set including the inlet velocity boundary, the pressure outlet boundary, and the wall boundary, and the near-wall region simulation uses the wall function method. The k-ε model is used to simulate turbulent gas phase flow, the scalar conserved mixed fractional probability density function is used to simulate volatile combustion, and the P-1 radiation model is used for simulation research to calculate the radiation exchange. The kinetic/diffusion control reaction rate model is used to simulate the coke combustion. The distribution characteristic of the solid phase particle is set to uniform. The Lagrange discrete phase model is used to calculate the interaction between the particle phase and the gas phase, and the random orbit model is used to track the particle motion. The WSGGM-domain-based model is used to calculate the absorption coefficient. The inlet boundary is set to the velocity inlet, and the temperature T is set to 300 K. The turbulence intensity is determined by the method of turbulence intensity and hydraulic diameter. The outlet boundary condition is set to the type of pressure outlet. In the process of simulating the adiabatic combustion, the wall is set to an adiabatic wall surface with no heat flux and mass flux. It is assumed that the velocity on the wall surface is zero. The proximate and ultimate analyses of the biomass pellet used in the simulation are listed in Table 1.
The productions of the volatile pyrolysis of the biomass fuel are CO, H2, H2O, and so forth. The pyrolysis equations of the combustion process are as follows:
For simulating the combustion of the biomass pellet rotary burner, it is necessary to determine the independence between the grid and the calculated results. In the paper, four different density grids of the burner are divided, and the results are shown in Table 2.
It can be seen from the table that when the grid is increased to 845772, the maximum temperature of the outer burner remains substantially stable. When the grid is too dense, the simulation time and the computational cost will increase; therefore, the second grid is adopted in the analysis.
2.4. Model Validation
In order to test the simulation model, the team established a test bench for the biomass rotary burner. A schematic diagram of the test device for the biomass rotary burner is shown in Figure 3. It can be seen that the test instruments include thermocouples, flue gas analyzers, fan flow meters, and feed screw speed detection devices. Because this research mainly discusses the combustion efficiency of the burner, the temperature is the best evaluation index. Therefore, temperature measurement is the key condition of model validation. The measuring instruments used in the test are specially customized.
The test steps of the main experiments are as follows:(1)At the test site, all test devices required for the test platform are installed(2)Check that all test devices are complete and whether they are consistent with the predetermined size(3)Check the air tightness of the whole device, and check whether there is any loose connection(4)Install various sensors and measuring devices(5)Power on the instrument and ensure that all pipelines are connected(6)Carry out the experiment according to the experiment manual
The biomass pellet fuel used in the test was fir wood pellet fuel which was compressed into a cylindrical shape by a fuel molding machine with a diameter of 5 mm. The processes are as follows:(1)Convey the wood pellet fuel by the screw conveyor; the conveying capacity of the conveyor was kept at 12 kg/h.(2)The hot air gun was energized, and the burner was heated by the hot air for about 10 minutes.(3)When the hot air gun was energized, the fan started to work, and then the wind was blasted into the combustion chamber.(4)When the flame was detected, the hot air gun was powered off and stops working. After that, the inlet valve was adjusted to change the intake air volume of the combustion chamber.
Only the measured temperature is reliable due to the error of measuring instruments caused by high temperatures.
It can be seen from Figures 4 and 5 that the experimental values of the combustion temperature of the biomass rotary burner are in good agreement with the simulation results, and the maximum error is 11.9%, because the model is appropriately simplified during the simulation.
3. Results and Discussions
In order to make a better comparison with the test results, the fir particle fuel has been chosen as the simulated fuel. Its ultimate and proximate analysis is shown in Table 1. The feed amount of biomass pellet fuel is set to 12 kg/h, and the temperature field and CO, CO2, and O2 concentrations of the biomass pellet burner are numerically simulated under four conditions of excess air coefficient of 1.0, 1.4, 1.8, and 2.2.
3.1. Effect of Excess Air Coefficient on Temperature Distribution
Figure 6 shows the longitudinal distribution of the combustion temperature of the rotary burner under different excess air coefficients. It can be seen from Figure 6 that the temperature changes greatly and is mainly concentrated in the middle region of the rotary burner. The combustion temperature reaches the maximum in the middle region of the combustion chamber. With the increase of excess air ratio, the temperature is correspondingly reduced. When the excess air coefficient α is 1.0, 1.4, 1.8, and 2.2, the maximum temperature of the rotary combustion chamber is 1700 K, 1600 K, 1700 K, and 1600 K, respectively.
Figure 7 shows the temperature distribution along the longitudinal centerline of the burner under different excess air coefficients. It can be seen from Figure 7 that the maximum temperature decreases with the increase of excess air coefficient. This is because as the excess air coefficient standing for the fuel is larger, it will be blown to the tail of the burner before the combustion in the combustion chamber is completed. This will lead to an increase in the velocity of the burner outlet, which is consistent with the simulation results.
3.2. Effect of Excess Air Coefficient on CO Concentration Distribution
The CO concentration distribution of the burner is shown in Figure 8.
It can be seen that CO is mainly concentrated near the fuel inlet where the biomass fuel is richer than other areas, so the CO concentration is also richer, and the CO generating region decreases when the excess air coefficient increases. When the excess air coefficient α is 1.0, CO is almost distributed throughout the rotary combustion chamber, indicating that the oxygen supply is insufficient in this area. When the excess air coefficient α is 1.4, the CO concentration range decreases with the increase of the oxygen content, and it is limited to the small combustion chamber of biomass rotary burner. When the excess air coefficient α is 2.2, the CO concentration range is further reduced, and the range of the maximum CO concentration is greatly reduced.
Figure 9 shows the distribution of the CO concentration along the axial centerline of the burner under four operating conditions.
It can be seen from the curve in Figure 9 that the CO concentration under the four working conditions shows an approximate parabolic distribution. It increases rapidly first and then decreases slowly, indicating that the biomass fuel is enough when it enters the combustion chamber, and an incomplete combustion reaction occurs, and then the CO concentration increases. With the increase of O2, CO undergoes further oxidation reaction to form CO2, which causes the CO concentration to decrease, while the CO2 concentration is corresponding to increase. It can be seen that the CO concentration in the burner gradually decreases with the increase of the excess air coefficient.
3.3. Effect of Excess Air Coefficient on CO2 Concentration Distribution
The CO2 concentration distribution of the rotary burner is shown in Figure 10. The CO2 concentration is mainly concentrated in the middle region of the burner. The CO2 generating region decreases with the increase of the excess air coefficient. When the excess air coefficient α is 1.0, CO2 is almost distributed throughout the rotary combustion chamber, indicating that biomass fuel is almost combusted completely. When the excess air coefficient α is 1.4, the area of the CO2 concentration range decreases compared with the excess air coefficient 1.0. When the excess air coefficient α is 1.8, the CO2 concentration range decreases with the increase of the oxygen content, and it is limited to the longitudinal region of the biomass rotary burner. When the excess air coefficient α is 2.4, the CO2 concentration range is further reduced, and the range of the maximum CO2 concentration is also greatly reduced.
Figure 11 shows the distribution of CO2 concentration along the longitudinal centerline of the burner under four operating conditions. It can be seen from the curve in Figure 11 that the CO2 concentration under the four operating conditions gradually increases with the reaction of combustion, and then it remains stable. As excess air coefficient increases, the CO2 concentration in the burner is gradually reduced.
3.4. Effect of Excess Air Coefficient on O2 Concentration Distribution
The O2 concentration distribution of the burner is shown in Figure 12. As shown in Figure 12, O2 is mainly concentrated near the airflow inlet at a lower excess air coefficient. When the excess air coefficient increases, the biomass fuel will burn faster and the O2 region will increase with the increase of excess air coefficient. Figure 13 is a distribution curve of the O2 concentration along the longitudinal centerline of the burner under four operating conditions. As shown in Figure 13, the distribution of O2 concentration gradually increases with the increase of excess air coefficient under the four operating conditions. At a lower excess air coefficient, oxygen is concentrated mainly near the airflow inlet. The middle and lower regions of the burner form a relatively low oxygen atmosphere.
3.5. Current and Future Developments
It is a very important research topic to improve combustion efficiency [38–40] and reduce pollutant emissions [41–46] by changing the shape and structure size of the burners. As for the swirl burner, the latter research should be based on the existing model research and carry out relevant simulation and experimental research. Relevant model modification and parameter sensitivity should be explored. The internal distribution and main distribution trend of the flow field are obtained. The computational fluid dynamics software can be applied to simulate the combustion characteristics of the biomass rotary burner in steady condition. The structure of the swirl burner will be more reasonable and it can use fuel efficiently.
Through the simulation and experimental research on the combustion characteristics of the biomass rotary burner, the conclusions are obtained as follows:(1)The area where the temperature changes greatly is mainly concentrated in the middle part of the rotary combustor, the combustion temperature reaches the maximum in this region, and the temperature is smaller in other regions. According to the simulation results, the temperature of the burner decreases with the increase of the excess air coefficient.(2)CO is mainly concentrated near the fuel inlet, because the excess air coefficient is bigger than other areas, so CO is richer than other regions. According to the simulation results, the region of CO decreases with the increase of the excess air coefficient. CO2 is mainly concentrated in the middle region of the burner and the CO2 generating region decreases with the increase of excess air coefficient. O2 is mainly concentrated near the airflow inlet at lower excess air coefficient, and the biomass fuel burns faster and O2 region also increases with the increase of excess air coefficient.(3)The experimental values of the combustion temperature of the biomass rotary burner are in good agreement with the simulation results. It is because the model is appropriately simplified in the simulation.
|ρ:||The fluid density, kg/m3|
|cp:||The specific heat of fluid, J/(kg·K)|
|U:||The velocity field, m/s|
|T:||The temperature, K|
|h:||The thermal conductivity, W/(m·K)|
|p:||The fluid pressure, Pa|
|μ:||The dynamic viscosity, Pa·s|
|Su, , :||The generalized source terms for the momentum conservation equation|
|к:||The turbulent kinetic energy, J|
|μt:||The turbulent viscosity, kg/(m·s)|
|Gk:||The generation term of the turbulent kinetic, J|
|Sk:||The source term of turbulent kinetic energy, J|
|σk:||The Prandtl number|
|:||The average velocity vector, m/s|
|Sm:||The source term caused by chemical reaction, kg/(m3·s)|
|a:||The absorption coefficient|
|σs:||The scattering coefficient|
|G:||The incident radiation, W/m2|
|C:||The linear anisotropic phase function coefficient|
|SG:||The source term of user-defined radiation, W/m2.|
The data in this paper are provided by the manufacturer company, which involves product matching and technical parameters of the company.
Conflicts of Interest
The authors declare that they have no conflicts of interest regarding the publication of this paper.
This work was supported by the China Post-doctoral Science Foundation (2016M592455), Hunan Provincial Education Department’s Scientific Research Project (17B278), Hunan Provincial University’s Science and Technology Innovation Team Support Plan (2014207), and Central South University of Forestry and Technology’s School-level Youth Science Foundation Project (QJ2017006B).
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