# Combustion of Biogas Released from Palm Oil Mill Effluent and the Effects of Hydrogen Enrichment on the Characteristics of the Biogas Flame

Authors: Seyed Ehsan Hosseini; Ghobad Bagheri; Mostafa Khaleghi; Mazlan Abdul Wahid
Journal: Journal of Combustion
 (2015)

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## Abstract

Biogas released from palm oil mill effluent (POME) could be a source of air pollution, which has illustrated negative effects on the global warming. To protect the environment from toxic emissions and use the energy of POME biogas, POME is conducted to the closed digestion systems and released biogas is captured. Since POME biogas upgrading is a complicated process, it is not economical and thus new combustion techniques should be examined. In this paper, POME biogas (40% CO2 and 60% CH4) has been utilized as a fuel in a lab-scale furnace. A computational approach by standard k-ε combustion and turbulence model is applied. Hydrogen is added to the biogas components and the impacts of hydrogen enrichment on the temperature distribution, flame stability, and pollutant formation are studied. The results confirm that adding hydrogen to the POME biogas content could improve low calorific value (LCV) of biogas and increases the stability of the POME biogas flame. Indeed, the biogas flame length rises and distribution of the temperature within the chamber is uniform when hydrogen is added to the POME biogas composition. Compared to the pure biogas combustion, thermal NOx formation increases in hydrogen-enriched POME biogas combustion due to the enhancement of the furnace temperature.


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## Body

## 1. Introduction

The increasingly strict regulations on pollution formation are pushing the energy and environmental research communities to find cleaner fuel and more efficient combustion technologies. Fossil fuel production is slow and taking many years therefore, the natural reserves of fossil fuels are rapidly exhausting. Many investigations have been carried out to find renewable fuels to replace these transient fossil fuels. Hence, biomass was found to have great potential to be applied in current combustion systems and biofuel could be one of the most important alternative fuels in the future energy mix of the world [1]. Meanwhile, biogas released from anaerobic digestion of biomass and organic wastes could be a source of energy for heat and power generation purposes. By capturing biogas from waste materials, not only an acceptable source of energy is provided but also the environment is protected from greenhouse gas emissions [2]. Unlike other alternative fuels, biogas is not limited geographically and biogas simple production process is one of the most important power points of this fuel [3]. Palm oil as one of the most famous biofuel resources has been developed widely in South East Asian countries like Indonesia, Malaysia, and Thailand and tropical countries in Africa and South America. Palm oil, with approximately 28% total annual production, is known as the biggest vegetable oil in the world [4, 5]. However, sustainability of palm oil-based biodiesel production is under question due to POME generation. Huge amount of biogas generated from POME is released to the environment per annum which leads the world to global warming. The low calorific value (LCV) of biogas is the main barrier of biogas utilization development [6, 7]. Therefore, biogas should be upgraded to remove impurities such as CO2 and H2S [8]. Since biogas upgrading is not economical, pure biogas was applied in flameless combustion technology successfully [9]. Today, with biogas utilization development in heat and power generation, comprehensive knowledge about various biogas combustion techniques is needed to select efficient energy conversion by biogas combustion [10]. On the other hand, hydrogen (H2) as a clean fuel with low carbon dioxide (CO2), carbon monooxide (CO), sulfur oxide (SOx), and unburned hydrocarbon (UHC) emissions has great potential as a major fuel in the future [11]. However, H2 is not freely found in nature and when it is used as a fuel, due to its high flammability and the diffusivity of H2, some concerns about safety exist in storage and transport which leads to high explosion risk [12]. Before the 1990s, since most of the researches about H2 enriched combustion were about liquid fuel enrichment, they were not applicable, because atomization and mixing of the species as the mechanical processes play a crucial role in the combustion of H2 enriched liquid fuels. However, the effects of H2 addition to gaseous fuel combustion are more radical because the combustion characteristics of such flames depend heavily on the properties of the gaseous fuels and conditions of the flame. Therefore, H2 enrichment of different gaseous fuels like methane (CH4), propane (C3H8), and natural gas (NG) had been developed during the last decade. Ignition of such fuel mixtures under their lean flammability limits ensures the fuel saving targets under the radical development method [13]. The raised temperature due to H2 combustion as well as quick reaction rate with O2 can justify this physical phenomenon. Although hydrogen-enriched gaseous fuel combustion has been developed experimentally and numerically, the effects of hydrogen enrichment on biogas conventional combustion have not been taken into consideration seriously. Since biogas utilization has become one of the valuable energy sources in the world, the effects of hydrogen enrichment of biogas on the conventional flame stability and pollutant formation are investigated in this paper.


### 1.1. Biogas Composition

Biogas is a flammable renewable gas formed in the anaerobic digestion (AD) of biomass which needs a relatively short formation time. The process of biogas generation and the type of feedstock play important roles in the biogas ingredients mixture [14]. Biogas consists of noncombustible CO2, combustible CH4 with low amounts of hydrogen sulfide (H2S), water vapor (H2O), carbon monoxide (CO), ammonia (NH3), hydrogen (H2), nitrogen (N2), oxygen (O2), dust, and occasionally siloxanes [3]. The most important biogas resources in the world are municipal solid waste (MSW) [15], domestic garbage landfills and old waste deposits [16], palm oil mill effluent [17], sewage sludge [18], cattle ranching and manure fermentation [19], coal mining [20], and agricultural products, rice paddies [21]. The average calorific value of biogas is 21.5 MJ/m3 which is low in comparison with the calorific value of natural gas at 36 MJ/m3. Based on the feedstock, CH4 forms about 40–80% of the composition of biogas. Since the lower heating value of CH4 is around 34,300 kj/m3 at the standard temperature and pressure, the lower heating value of biogas is about 13,720–27,440 kJ/m3. The physical characteristics of biogas are usually modeled by CO2 and CH4 because more than 98% of biogas is a combination of these two gases.


### 1.2. Hydrogen-Enriched Gaseous Fuel Combustion Modelling

Arrhenius reaction rate of hydrogen-enriched fuel increases due to the growth of the temperature; consequently, the rate of O2 consumption in the lean mixture increases. In the simulation of hydrogen-enriched gaseous fuel combustion, fast chemistry models are superior due to their low computational cost. Indeed, the conserved scalar model as a subcategory of some other models such as flame sheet, laminar flamelet, and conserved scalar models with equilibrium chemistry is defined based on the relationship of the flame thermochemical characteristics as a function of the mixture fraction [22]. Ilbas et al. [23] applied the conserved scalar model to simulate a nonpremixed turbulent combustion of hydrogen-enriched methane. Similar flame was simulated with the eddy dissipation concept (EDC) by Frassoldati et al. [24] and Mardani and Tabejamaat [25] and an unacceptable accuracy was found for mass fraction of minor species like O and OH which conducts to wrong expectation for NO formation behavior. It was found that, for modelling the combustion of hydrogen-enriched methane, the steady laminar flamelet method has better performance in terms of minor species prediction [26]. Probability density function (PDF) was calculated for mass fraction of minor and major species, mixture fraction, and temperature by Suo [27]. It was reported that, compared to the equilibrium model, better results within the reaction zone were gained by flamelet model. The simulation of a nonpremixed hydrogen-enriched methane bluff-body flame with respect to laminar flamelet model, equilibrium chemistry, flame sheet model, and constrained equilibrium chemistry which was done by Hossain and Malalasekera [28] proves that just major species could be predictable by flame sheet model. Although acceptable results for H2O mass fraction and combustion temperature could be achieved by the flame sheet model, overprediction of CO2 is one of the weak points of this model. On the other hand, the reliable results were recorded for temperature, CO2, and H2O mass fraction by using the constrained equilibrium model; however, the predicted levels of CO and OH mass fraction were not accurate. Similarly prediction of temperature and species mass fraction were reported poor when the equilibrium model was applied. It was concluded that accurate results for temperature and major and minor species mass fraction could be achieved by laminar flamelet model. Hossain and Malalasekera [29, 30] modeled a similar flame applying flamelet model and pointed out that reasonable results could be achieved at upstream locations when a coupled radiation/flamelet model is utilized [31].


### 1.3. Chemical Reaction Mechanisms in Hydrogen-Enriched Gaseous Fuel Combustion

Various researchers applied different chemical reaction mechanisms in their simulation and it was found that simulation of chemical reaction mechanism plays an important role in the performance of the laminar flamelet model. For instance in the combustion simulation carried out by Ilbas et al. [23] only seven species were employed and Suo [27] applied GRI mechanism with 18 species. GRI2.11 was used by Ravikanti et al. [26] and it was found the simulated results are in good agreement with the results of a reduced DRM-22 mechanism [25]. In the combustion simulation done by Frassoldati et al. [24], 600 reactions were implemented when 48 species were considered.


### 1.4. Hydrogen-Enriched Gaseous Fuel Turbulence Modelling

A great variety of models are introduced for modeling turbulence, chemical reaction, and interaction of these two; yet there is no universal suitable model for all turbulent combustion applications achieved. Each model shows several advantages and disadvantages and poses better performance only in specific applications. Reynolds averaged Navier-Stokes modeling (RANS), large eddy simulation (LES), and direct numerical simulation (DNS) are the main methods that could be applied for numerical investigation of nonpremixed combustion. In turbulence modelling, despite the Reynolds Stress Model (RSM) yielding proper results for prediction of high strain rate flows and streamline curvature, its function is under question due to different results issued by various researchers. The inability of RSM to predict flow field in the simulation of swirling flame was reported by Meier et al. [32]. The failure of RSM in swirling flow modelling with a processing vortex core (PVC) in the case of local velocity gradients capturing prediction was reported by Erdal and Shirazi [33]. Moreover, the convergence problems of RSM simulation have been reported by some researchers [34, 35]. Besides, some reports indicate thatk-ε mode has great capacity to model various combustion systems [36]. Dally et al. [37] modelled a bluff body flame with standard modifiedk-ε and RSM flame sheet model applied with a beta probability density function and claimed that both standardk-ε and RSM are not capable of predicting the flow field with acceptable accuracy. To improve projecting the flow field, a fine-tuningk-ε model constant (Cε1 from 1.44 to 1.6) was assumed. Based on this assumption, Kim and Huh [38] simulated a bluff body methane/hydrogen flame using a conditional moment closure combustion model to predict NO formation in a turbulent condition. It was concluded that, although the local results of velocity fields and the variations of the mixture faction were not reliable, the modifiedk-ε model is eligible to predict the overall mixture fraction fields and velocity. Indeed, acceptable accuracy was reported by [23–25] when fine-tuning of the standardk-ε model with EDC combustion was simulated. Also, the reliability of both RSM and fine-tunedk-ε model is enhanced when they applied with laminal flamelet model [26]. The performance of large eddy simulation (LES) was compared to fine-tunedk-ε model by Suo [27] when the Smagorinsky-Lilly and RNG/k-ε models were applied as the subgrid models. It was found that the results of both LES and modifiedk-ε model are in good agreement with experimental records.


## 1.1. Biogas Composition

Biogas is a flammable renewable gas formed in the anaerobic digestion (AD) of biomass which needs a relatively short formation time. The process of biogas generation and the type of feedstock play important roles in the biogas ingredients mixture [14]. Biogas consists of noncombustible CO2, combustible CH4 with low amounts of hydrogen sulfide (H2S), water vapor (H2O), carbon monoxide (CO), ammonia (NH3), hydrogen (H2), nitrogen (N2), oxygen (O2), dust, and occasionally siloxanes [3]. The most important biogas resources in the world are municipal solid waste (MSW) [15], domestic garbage landfills and old waste deposits [16], palm oil mill effluent [17], sewage sludge [18], cattle ranching and manure fermentation [19], coal mining [20], and agricultural products, rice paddies [21]. The average calorific value of biogas is 21.5 MJ/m3 which is low in comparison with the calorific value of natural gas at 36 MJ/m3. Based on the feedstock, CH4 forms about 40–80% of the composition of biogas. Since the lower heating value of CH4 is around 34,300 kj/m3 at the standard temperature and pressure, the lower heating value of biogas is about 13,720–27,440 kJ/m3. The physical characteristics of biogas are usually modeled by CO2 and CH4 because more than 98% of biogas is a combination of these two gases.


## 1.2. Hydrogen-Enriched Gaseous Fuel Combustion Modelling

Arrhenius reaction rate of hydrogen-enriched fuel increases due to the growth of the temperature; consequently, the rate of O2 consumption in the lean mixture increases. In the simulation of hydrogen-enriched gaseous fuel combustion, fast chemistry models are superior due to their low computational cost. Indeed, the conserved scalar model as a subcategory of some other models such as flame sheet, laminar flamelet, and conserved scalar models with equilibrium chemistry is defined based on the relationship of the flame thermochemical characteristics as a function of the mixture fraction [22]. Ilbas et al. [23] applied the conserved scalar model to simulate a nonpremixed turbulent combustion of hydrogen-enriched methane. Similar flame was simulated with the eddy dissipation concept (EDC) by Frassoldati et al. [24] and Mardani and Tabejamaat [25] and an unacceptable accuracy was found for mass fraction of minor species like O and OH which conducts to wrong expectation for NO formation behavior. It was found that, for modelling the combustion of hydrogen-enriched methane, the steady laminar flamelet method has better performance in terms of minor species prediction [26]. Probability density function (PDF) was calculated for mass fraction of minor and major species, mixture fraction, and temperature by Suo [27]. It was reported that, compared to the equilibrium model, better results within the reaction zone were gained by flamelet model. The simulation of a nonpremixed hydrogen-enriched methane bluff-body flame with respect to laminar flamelet model, equilibrium chemistry, flame sheet model, and constrained equilibrium chemistry which was done by Hossain and Malalasekera [28] proves that just major species could be predictable by flame sheet model. Although acceptable results for H2O mass fraction and combustion temperature could be achieved by the flame sheet model, overprediction of CO2 is one of the weak points of this model. On the other hand, the reliable results were recorded for temperature, CO2, and H2O mass fraction by using the constrained equilibrium model; however, the predicted levels of CO and OH mass fraction were not accurate. Similarly prediction of temperature and species mass fraction were reported poor when the equilibrium model was applied. It was concluded that accurate results for temperature and major and minor species mass fraction could be achieved by laminar flamelet model. Hossain and Malalasekera [29, 30] modeled a similar flame applying flamelet model and pointed out that reasonable results could be achieved at upstream locations when a coupled radiation/flamelet model is utilized [31].


## 1.3. Chemical Reaction Mechanisms in Hydrogen-Enriched Gaseous Fuel Combustion

Various researchers applied different chemical reaction mechanisms in their simulation and it was found that simulation of chemical reaction mechanism plays an important role in the performance of the laminar flamelet model. For instance in the combustion simulation carried out by Ilbas et al. [23] only seven species were employed and Suo [27] applied GRI mechanism with 18 species. GRI2.11 was used by Ravikanti et al. [26] and it was found the simulated results are in good agreement with the results of a reduced DRM-22 mechanism [25]. In the combustion simulation done by Frassoldati et al. [24], 600 reactions were implemented when 48 species were considered.


## 1.4. Hydrogen-Enriched Gaseous Fuel Turbulence Modelling

A great variety of models are introduced for modeling turbulence, chemical reaction, and interaction of these two; yet there is no universal suitable model for all turbulent combustion applications achieved. Each model shows several advantages and disadvantages and poses better performance only in specific applications. Reynolds averaged Navier-Stokes modeling (RANS), large eddy simulation (LES), and direct numerical simulation (DNS) are the main methods that could be applied for numerical investigation of nonpremixed combustion. In turbulence modelling, despite the Reynolds Stress Model (RSM) yielding proper results for prediction of high strain rate flows and streamline curvature, its function is under question due to different results issued by various researchers. The inability of RSM to predict flow field in the simulation of swirling flame was reported by Meier et al. [32]. The failure of RSM in swirling flow modelling with a processing vortex core (PVC) in the case of local velocity gradients capturing prediction was reported by Erdal and Shirazi [33]. Moreover, the convergence problems of RSM simulation have been reported by some researchers [34, 35]. Besides, some reports indicate thatk-ε mode has great capacity to model various combustion systems [36]. Dally et al. [37] modelled a bluff body flame with standard modifiedk-ε and RSM flame sheet model applied with a beta probability density function and claimed that both standardk-ε and RSM are not capable of predicting the flow field with acceptable accuracy. To improve projecting the flow field, a fine-tuningk-ε model constant (Cε1 from 1.44 to 1.6) was assumed. Based on this assumption, Kim and Huh [38] simulated a bluff body methane/hydrogen flame using a conditional moment closure combustion model to predict NO formation in a turbulent condition. It was concluded that, although the local results of velocity fields and the variations of the mixture faction were not reliable, the modifiedk-ε model is eligible to predict the overall mixture fraction fields and velocity. Indeed, acceptable accuracy was reported by [23–25] when fine-tuning of the standardk-ε model with EDC combustion was simulated. Also, the reliability of both RSM and fine-tunedk-ε model is enhanced when they applied with laminal flamelet model [26]. The performance of large eddy simulation (LES) was compared to fine-tunedk-ε model by Suo [27] when the Smagorinsky-Lilly and RNG/k-ε models were applied as the subgrid models. It was found that the results of both LES and modifiedk-ε model are in good agreement with experimental records.


## 2. Methodology

The objective of this project is mainly to study the impacts of hydrogen addition on the biogas conventional flame stability and pollutant formation. In experimental step, biogas (40% CO2 and 60% CH4) is injected to the combustor as fuel. In numerical modeling, biogas flow field and related chemical reactions are simulated. After model validation, hydrogen is added to the biogas and the impacts of such change in fuel composition on the temperature distribution in the furnace, flame stability, and pollutant formation are investigated in three cases (Case 1: 0% H2, 40% CO2, 60% CH4, Case 2: 5% H2, 40% CO2, 55% CH4, and Case 3: 10% H2, 40% CO2, 50% CH4).


### 2.1. Experimental Setup

The diameter and the length of the chamber are 264 mm and 600 mm, respectively, made of carbon steel. The inside diameter of the furnace is 150 mm after installation of refractory. Five holes are located at the top of the furnace in a specific distance from burner to record temperature and pollutants byK-type thermocouples and gas analyzer, respectively. The diameter of fuel inlet jet is 5 mm surrounded by holes with 5 mm diameter for air inlet. A spark ignition system is used to ignite the reactants. Some flow meters are applied to check the flow of air, biogas, hydrogen, and the hydrogen-enriched biogas line. At the first step, the combustion system is run by biogas and for the second and third steps hydrogen is added to th biogas components 5% and 10%, respectively. Figure 1 displays the experimental setup.Figure 1
Experimental setup.


### 2.2. Numerical Solution

Three-dimensional (3D) simulation is done by ANSYS 14 using ANSYS Modeler to design the chamber and ANSYS Meshing to mesh the combustor [39]. Mesh refinement could be effective to improve the convergence rate and scalar properties; thus grid resolution for smooth flow representation could be ensured. Due to symmetry, only one-eighth of the furnace is simulated. The grid consists of 7769 nodes and 33798 elements.


#### 2.2.1. Boundary Conditions

In each cell, it has been assumed that all the properties have an average value at the center of the cell. The second order upwind scheme is set to calculate the temperature, velocity, and pressure through the governing equations with coupled algorithm. A mass-weighted averaging method is set to interpolate the velocity values of cell-center to face values. The temperature of fuel and air inlet and pressure outlet is 300 K and 1.013 × 105 Pa, respectively, and free stream turbulence is set at 5%. The residual energy equation should drop below 10−6 and for all other variables it is set at 10−3 to ensure the convergence of the solution. The swirl velocity of components is neglected in this steady-state CFD simulation and stoichiometric equivalence ratios (Φ=1) are taken into consideration. The density of biogas includes 40% CO2 and 60% CH4 in the standard temperature and pressure is considered 1.2146 g/l. Table 1 displays various fuel conditions.Table 1
Fuel data in various cases.
Case
CO2
CH4
H2
Fuel density (kg/m3)
V
air (m/s)
V
fuel (m/s)
m
˙
air
(kg/s)
m
˙
fuel (kg/s)
H0
40
60
0
1.2146
30
22.64
0.005544
0.00054
H5
40
55
5
1.1834
30
24.78
0.005544
0.000576
H10
40
50
10
1.1521
30
27.27
0.005544
0.000617


#### 2.2.2. Grid Independent Check

The results of numerical solution with 7769 nodes and 33798 elements are in good agreement with the experimental records. The grid independent of the simulation could be tested by changing the number of meshes to the finer meshes. In this simulation the number of elements was adopted 33798, 68453, and 100356. These adoptions were motivated by the fact that the most significant conformity to the experimental measurements could be achieved when the mesh is so fine. However, meaningful changes were not observed in the results and the grid independent of solution is confirmed by this test. Figure2 compares the radial temperature profile of experimental results and simulation data related to biogas conventional combustion. This figure reveals that the captured data from experimental and simulated study have the same trend in terms of the temperature profile except at X=100 mm.Figure 2
The radial temperature profile of experimental results and simulation data.


#### 2.2.3. Turbulence Modelling

The standardk-ε model is employed to model conventional flame with the laminar flamelet combustion model. In the standardk-ε model,k and ε are modelled with following transport equations:(1)∂∂tρk+∂∂xiρkui=∂∂xjμ+μtσk∂k∂xj+Pk+Pb-ρε-YM+Sk,∂∂tρε+∂∂xiρεui=∂∂xjμ+μtσε∂k∂xj+C1εεkPk+C3εPb-C2ερε2k+Sε,where μt, Pk, and YM represent turbulent viscosity, turbulence kinetic energy production, and the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate, respectively, which are described by(2)μt=ρCμk2ϵ,Pk=-ρui′uj′-∂uj∂xi,YM=2ρεMt2.


#### 2.2.4. Combustion Modelling

The nonpremixed modelling approach is usually applied to the simulation of turbulent diffusion flames with fast chemistry. This model offers many benefits over the eddy dissipation (ED) formulation and allows radical species prediction, dissociation impacts, and precise turbulence-chemistry coupling. The steady laminar flamelet method models a turbulent flame as an ensemble of discrete, steady laminar flames. It is assumed that the individual flamelets have the same structure as laminar flames in simple configurations and are achieved by calculation or experiments. Using detailed chemical mechanisms, FLUENT can calculate laminar opposed-flow diffusion flamelets for nonpremixed combustion. The laminar flamelets are then set in a turbulent flame using statistical PDF methods. The sensible chemical kinetic impacts can be incorporated into turbulent flames by using the laminar flamelet approach. The chemistry can be preprocessed and tabulated, offering enormous computational savings. A set of flamelet profiles in a flamelet library is built in terms of independence scalar dissipation rate and immediate mixture fraction. It means that(3)PZ,X=PZPX,where the mean value of the scalar dissipation rate is calculated by(4)X=Cxε~k~Z′′2.Cx is equal to 2.5.The flamelet library is then integrated with a probability density function (PDF) to compute the average scalar properties:(5)∅~=∫0∞∫01∅Z,xPZ,xdZdx.Mixture fraction could be estimated based on Drake formula [40]. Since GRI3.0 mechanism includes 325 reactions and 53 species, it is superior to GRI 2.11 in terms of up-to-date kinetics and accuracy; thus for developing the flamelet library, GRI3.0 mechanism was employed. Indeed, kinetics related to prompt NOx calculation have been improved in this revision [41].


#### 2.2.5. Radiation Modelling

Prediction of radiative heat transfer is a crucial factor in the simulation of turbulent combustion. Notable discrepancies between numerical predictions and experimental results in terms of pollutant formation and combustion characteristics could emerge if an accurate radiative heat transfer method is not applied. SinceNOx formation is sensitive to the trend of furnace temperature, overprediction of NOx formation takes place if the radiative heat loss is not considered. For prediction of radiation, discrete ordinates (DO) radiation model is employed in this simulation because of its reasonable computational cost. DO is widely used in such similar computational investigation with no significant error. The related formula and more details of this radiative model can be found in [42, 43].


#### 2.2.6. NOx Formation Modelling

In conventional combustion regime,NOx formation reduction plays an important role to control acid rain, smog, ozone depletion, and greenhouse effects. NOx is usually formed in the presence of nitrogen and oxygen within a locally high temperature region. Thermal NOx, prompt NOx, N2O intermediate mechanism, and fuel-bound nitrogen are mentioned as the main regimes for NOx formation in the combustion process. At extremely high temperatures within the combustion chamber, N2 and O2 can react through chemical mechanisms that are named Zeldovich formulation. The rate of thermal NOx formation increases quickly with increasing temperature. Prompt or Fenimore NOx formation occurs in fuel rich conditions and it was found that the prompt NOx formation increases near equivalence ratio of 1.4 [44]. N2O intermediate mechanism takes place in fuel lean, elevated temperature, and low pressures. NOx formation by N2O mechanism was proposed because of lower flame temperature in the combustion process [45]. Fuel-bound NOx formation mechanism is related to the presence of nitrogen species in the molecular structure of the fuel. Due to the characteristics of biogas conventional combustion, thermal NOx and prompt NOx are considered in the simulation. The transport equation for NO species formation is(6)∂∂tρYNO+∇·ρv→YNO=∇·ρD∇YNO+SNO.SNO includes thermal NOx which should be determined by Zeldovich equations and prompt NOx. Zeldovich equations could be written as [46](7)O+N2⟷NO+NN+O2⟷NO+ON+OH⟷NO+HThe reaction rate constants of reactions are selected from [47] and partial equilibrium approach is considered to estimate the concentration of OH and O radicals. The calculation of prompt NOx formation is done from global model presented in [48]: (8)CH+N2⟷HCN+NN+O2⟷NO+OHCN+OH⟷CN+H2OCN+O2⟷NO+COTo consider the interaction of NOx formation and turbulence, a PDF of temperature is applied to compute a time average rate of NOx constitution when generation rate of thermal NOx and prompt NOx is calculated. The computed time averaged NOx results are applied in (6).


## 2.1. Experimental Setup

The diameter and the length of the chamber are 264 mm and 600 mm, respectively, made of carbon steel. The inside diameter of the furnace is 150 mm after installation of refractory. Five holes are located at the top of the furnace in a specific distance from burner to record temperature and pollutants byK-type thermocouples and gas analyzer, respectively. The diameter of fuel inlet jet is 5 mm surrounded by holes with 5 mm diameter for air inlet. A spark ignition system is used to ignite the reactants. Some flow meters are applied to check the flow of air, biogas, hydrogen, and the hydrogen-enriched biogas line. At the first step, the combustion system is run by biogas and for the second and third steps hydrogen is added to th biogas components 5% and 10%, respectively. Figure 1 displays the experimental setup.Figure 1
Experimental setup.


## 2.2. Numerical Solution

Three-dimensional (3D) simulation is done by ANSYS 14 using ANSYS Modeler to design the chamber and ANSYS Meshing to mesh the combustor [39]. Mesh refinement could be effective to improve the convergence rate and scalar properties; thus grid resolution for smooth flow representation could be ensured. Due to symmetry, only one-eighth of the furnace is simulated. The grid consists of 7769 nodes and 33798 elements.


### 2.2.1. Boundary Conditions

In each cell, it has been assumed that all the properties have an average value at the center of the cell. The second order upwind scheme is set to calculate the temperature, velocity, and pressure through the governing equations with coupled algorithm. A mass-weighted averaging method is set to interpolate the velocity values of cell-center to face values. The temperature of fuel and air inlet and pressure outlet is 300 K and 1.013 × 105 Pa, respectively, and free stream turbulence is set at 5%. The residual energy equation should drop below 10−6 and for all other variables it is set at 10−3 to ensure the convergence of the solution. The swirl velocity of components is neglected in this steady-state CFD simulation and stoichiometric equivalence ratios (Φ=1) are taken into consideration. The density of biogas includes 40% CO2 and 60% CH4 in the standard temperature and pressure is considered 1.2146 g/l. Table 1 displays various fuel conditions.Table 1
Fuel data in various cases.
Case
CO2
CH4
H2
Fuel density (kg/m3)
V
air (m/s)
V
fuel (m/s)
m
˙
air
(kg/s)
m
˙
fuel (kg/s)
H0
40
60
0
1.2146
30
22.64
0.005544
0.00054
H5
40
55
5
1.1834
30
24.78
0.005544
0.000576
H10
40
50
10
1.1521
30
27.27
0.005544
0.000617


### 2.2.2. Grid Independent Check

The results of numerical solution with 7769 nodes and 33798 elements are in good agreement with the experimental records. The grid independent of the simulation could be tested by changing the number of meshes to the finer meshes. In this simulation the number of elements was adopted 33798, 68453, and 100356. These adoptions were motivated by the fact that the most significant conformity to the experimental measurements could be achieved when the mesh is so fine. However, meaningful changes were not observed in the results and the grid independent of solution is confirmed by this test. Figure2 compares the radial temperature profile of experimental results and simulation data related to biogas conventional combustion. This figure reveals that the captured data from experimental and simulated study have the same trend in terms of the temperature profile except at X=100 mm.Figure 2
The radial temperature profile of experimental results and simulation data.


### 2.2.3. Turbulence Modelling

The standardk-ε model is employed to model conventional flame with the laminar flamelet combustion model. In the standardk-ε model,k and ε are modelled with following transport equations:(1)∂∂tρk+∂∂xiρkui=∂∂xjμ+μtσk∂k∂xj+Pk+Pb-ρε-YM+Sk,∂∂tρε+∂∂xiρεui=∂∂xjμ+μtσε∂k∂xj+C1εεkPk+C3εPb-C2ερε2k+Sε,where μt, Pk, and YM represent turbulent viscosity, turbulence kinetic energy production, and the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate, respectively, which are described by(2)μt=ρCμk2ϵ,Pk=-ρui′uj′-∂uj∂xi,YM=2ρεMt2.


### 2.2.4. Combustion Modelling

The nonpremixed modelling approach is usually applied to the simulation of turbulent diffusion flames with fast chemistry. This model offers many benefits over the eddy dissipation (ED) formulation and allows radical species prediction, dissociation impacts, and precise turbulence-chemistry coupling. The steady laminar flamelet method models a turbulent flame as an ensemble of discrete, steady laminar flames. It is assumed that the individual flamelets have the same structure as laminar flames in simple configurations and are achieved by calculation or experiments. Using detailed chemical mechanisms, FLUENT can calculate laminar opposed-flow diffusion flamelets for nonpremixed combustion. The laminar flamelets are then set in a turbulent flame using statistical PDF methods. The sensible chemical kinetic impacts can be incorporated into turbulent flames by using the laminar flamelet approach. The chemistry can be preprocessed and tabulated, offering enormous computational savings. A set of flamelet profiles in a flamelet library is built in terms of independence scalar dissipation rate and immediate mixture fraction. It means that(3)PZ,X=PZPX,where the mean value of the scalar dissipation rate is calculated by(4)X=Cxε~k~Z′′2.Cx is equal to 2.5.The flamelet library is then integrated with a probability density function (PDF) to compute the average scalar properties:(5)∅~=∫0∞∫01∅Z,xPZ,xdZdx.Mixture fraction could be estimated based on Drake formula [40]. Since GRI3.0 mechanism includes 325 reactions and 53 species, it is superior to GRI 2.11 in terms of up-to-date kinetics and accuracy; thus for developing the flamelet library, GRI3.0 mechanism was employed. Indeed, kinetics related to prompt NOx calculation have been improved in this revision [41].


### 2.2.5. Radiation Modelling

Prediction of radiative heat transfer is a crucial factor in the simulation of turbulent combustion. Notable discrepancies between numerical predictions and experimental results in terms of pollutant formation and combustion characteristics could emerge if an accurate radiative heat transfer method is not applied. SinceNOx formation is sensitive to the trend of furnace temperature, overprediction of NOx formation takes place if the radiative heat loss is not considered. For prediction of radiation, discrete ordinates (DO) radiation model is employed in this simulation because of its reasonable computational cost. DO is widely used in such similar computational investigation with no significant error. The related formula and more details of this radiative model can be found in [42, 43].


### 2.2.6. NOx Formation Modelling

In conventional combustion regime,NOx formation reduction plays an important role to control acid rain, smog, ozone depletion, and greenhouse effects. NOx is usually formed in the presence of nitrogen and oxygen within a locally high temperature region. Thermal NOx, prompt NOx, N2O intermediate mechanism, and fuel-bound nitrogen are mentioned as the main regimes for NOx formation in the combustion process. At extremely high temperatures within the combustion chamber, N2 and O2 can react through chemical mechanisms that are named Zeldovich formulation. The rate of thermal NOx formation increases quickly with increasing temperature. Prompt or Fenimore NOx formation occurs in fuel rich conditions and it was found that the prompt NOx formation increases near equivalence ratio of 1.4 [44]. N2O intermediate mechanism takes place in fuel lean, elevated temperature, and low pressures. NOx formation by N2O mechanism was proposed because of lower flame temperature in the combustion process [45]. Fuel-bound NOx formation mechanism is related to the presence of nitrogen species in the molecular structure of the fuel. Due to the characteristics of biogas conventional combustion, thermal NOx and prompt NOx are considered in the simulation. The transport equation for NO species formation is(6)∂∂tρYNO+∇·ρv→YNO=∇·ρD∇YNO+SNO.SNO includes thermal NOx which should be determined by Zeldovich equations and prompt NOx. Zeldovich equations could be written as [46](7)O+N2⟷NO+NN+O2⟷NO+ON+OH⟷NO+HThe reaction rate constants of reactions are selected from [47] and partial equilibrium approach is considered to estimate the concentration of OH and O radicals. The calculation of prompt NOx formation is done from global model presented in [48]: (8)CH+N2⟷HCN+NN+O2⟷NO+OHCN+OH⟷CN+H2OCN+O2⟷NO+COTo consider the interaction of NOx formation and turbulence, a PDF of temperature is applied to compute a time average rate of NOx constitution when generation rate of thermal NOx and prompt NOx is calculated. The computed time averaged NOx results are applied in (6).


## 2.2.1. Boundary Conditions

In each cell, it has been assumed that all the properties have an average value at the center of the cell. The second order upwind scheme is set to calculate the temperature, velocity, and pressure through the governing equations with coupled algorithm. A mass-weighted averaging method is set to interpolate the velocity values of cell-center to face values. The temperature of fuel and air inlet and pressure outlet is 300 K and 1.013 × 105 Pa, respectively, and free stream turbulence is set at 5%. The residual energy equation should drop below 10−6 and for all other variables it is set at 10−3 to ensure the convergence of the solution. The swirl velocity of components is neglected in this steady-state CFD simulation and stoichiometric equivalence ratios (Φ=1) are taken into consideration. The density of biogas includes 40% CO2 and 60% CH4 in the standard temperature and pressure is considered 1.2146 g/l. Table 1 displays various fuel conditions.Table 1
Fuel data in various cases.
Case
CO2
CH4
H2
Fuel density (kg/m3)
V
air (m/s)
V
fuel (m/s)
m
˙
air
(kg/s)
m
˙
fuel (kg/s)
H0
40
60
0
1.2146
30
22.64
0.005544
0.00054
H5
40
55
5
1.1834
30
24.78
0.005544
0.000576
H10
40
50
10
1.1521
30
27.27
0.005544
0.000617


## 2.2.2. Grid Independent Check

The results of numerical solution with 7769 nodes and 33798 elements are in good agreement with the experimental records. The grid independent of the simulation could be tested by changing the number of meshes to the finer meshes. In this simulation the number of elements was adopted 33798, 68453, and 100356. These adoptions were motivated by the fact that the most significant conformity to the experimental measurements could be achieved when the mesh is so fine. However, meaningful changes were not observed in the results and the grid independent of solution is confirmed by this test. Figure2 compares the radial temperature profile of experimental results and simulation data related to biogas conventional combustion. This figure reveals that the captured data from experimental and simulated study have the same trend in terms of the temperature profile except at X=100 mm.Figure 2
The radial temperature profile of experimental results and simulation data.


## 2.2.3. Turbulence Modelling

The standardk-ε model is employed to model conventional flame with the laminar flamelet combustion model. In the standardk-ε model,k and ε are modelled with following transport equations:(1)∂∂tρk+∂∂xiρkui=∂∂xjμ+μtσk∂k∂xj+Pk+Pb-ρε-YM+Sk,∂∂tρε+∂∂xiρεui=∂∂xjμ+μtσε∂k∂xj+C1εεkPk+C3εPb-C2ερε2k+Sε,where μt, Pk, and YM represent turbulent viscosity, turbulence kinetic energy production, and the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate, respectively, which are described by(2)μt=ρCμk2ϵ,Pk=-ρui′uj′-∂uj∂xi,YM=2ρεMt2.


## 2.2.4. Combustion Modelling

The nonpremixed modelling approach is usually applied to the simulation of turbulent diffusion flames with fast chemistry. This model offers many benefits over the eddy dissipation (ED) formulation and allows radical species prediction, dissociation impacts, and precise turbulence-chemistry coupling. The steady laminar flamelet method models a turbulent flame as an ensemble of discrete, steady laminar flames. It is assumed that the individual flamelets have the same structure as laminar flames in simple configurations and are achieved by calculation or experiments. Using detailed chemical mechanisms, FLUENT can calculate laminar opposed-flow diffusion flamelets for nonpremixed combustion. The laminar flamelets are then set in a turbulent flame using statistical PDF methods. The sensible chemical kinetic impacts can be incorporated into turbulent flames by using the laminar flamelet approach. The chemistry can be preprocessed and tabulated, offering enormous computational savings. A set of flamelet profiles in a flamelet library is built in terms of independence scalar dissipation rate and immediate mixture fraction. It means that(3)PZ,X=PZPX,where the mean value of the scalar dissipation rate is calculated by(4)X=Cxε~k~Z′′2.Cx is equal to 2.5.The flamelet library is then integrated with a probability density function (PDF) to compute the average scalar properties:(5)∅~=∫0∞∫01∅Z,xPZ,xdZdx.Mixture fraction could be estimated based on Drake formula [40]. Since GRI3.0 mechanism includes 325 reactions and 53 species, it is superior to GRI 2.11 in terms of up-to-date kinetics and accuracy; thus for developing the flamelet library, GRI3.0 mechanism was employed. Indeed, kinetics related to prompt NOx calculation have been improved in this revision [41].


## 2.2.5. Radiation Modelling

Prediction of radiative heat transfer is a crucial factor in the simulation of turbulent combustion. Notable discrepancies between numerical predictions and experimental results in terms of pollutant formation and combustion characteristics could emerge if an accurate radiative heat transfer method is not applied. SinceNOx formation is sensitive to the trend of furnace temperature, overprediction of NOx formation takes place if the radiative heat loss is not considered. For prediction of radiation, discrete ordinates (DO) radiation model is employed in this simulation because of its reasonable computational cost. DO is widely used in such similar computational investigation with no significant error. The related formula and more details of this radiative model can be found in [42, 43].


## 2.2.6. NOx Formation Modelling

In conventional combustion regime,NOx formation reduction plays an important role to control acid rain, smog, ozone depletion, and greenhouse effects. NOx is usually formed in the presence of nitrogen and oxygen within a locally high temperature region. Thermal NOx, prompt NOx, N2O intermediate mechanism, and fuel-bound nitrogen are mentioned as the main regimes for NOx formation in the combustion process. At extremely high temperatures within the combustion chamber, N2 and O2 can react through chemical mechanisms that are named Zeldovich formulation. The rate of thermal NOx formation increases quickly with increasing temperature. Prompt or Fenimore NOx formation occurs in fuel rich conditions and it was found that the prompt NOx formation increases near equivalence ratio of 1.4 [44]. N2O intermediate mechanism takes place in fuel lean, elevated temperature, and low pressures. NOx formation by N2O mechanism was proposed because of lower flame temperature in the combustion process [45]. Fuel-bound NOx formation mechanism is related to the presence of nitrogen species in the molecular structure of the fuel. Due to the characteristics of biogas conventional combustion, thermal NOx and prompt NOx are considered in the simulation. The transport equation for NO species formation is(6)∂∂tρYNO+∇·ρv→YNO=∇·ρD∇YNO+SNO.SNO includes thermal NOx which should be determined by Zeldovich equations and prompt NOx. Zeldovich equations could be written as [46](7)O+N2⟷NO+NN+O2⟷NO+ON+OH⟷NO+HThe reaction rate constants of reactions are selected from [47] and partial equilibrium approach is considered to estimate the concentration of OH and O radicals. The calculation of prompt NOx formation is done from global model presented in [48]: (8)CH+N2⟷HCN+NN+O2⟷NO+OHCN+OH⟷CN+H2OCN+O2⟷NO+COTo consider the interaction of NOx formation and turbulence, a PDF of temperature is applied to compute a time average rate of NOx constitution when generation rate of thermal NOx and prompt NOx is calculated. The computed time averaged NOx results are applied in (6).


## 3. Results and Discussion

The velocity of air jet is kept constant at 30 m/s in all cases. When hydrogen is added to the fuel the density of the flow reduces; thus the mass flow rate of the mixture should increase to get stoichiometric conditions. As hydrogen is added to the biogas ingredients by 5%, the structure of biogas flame changes noticeably and the peak of temperature increases. Furthermore, hydrogen addition to the bogus combination causes some changes in the pattern of the flame. The high temperature of the flame shrinks and moves slightly further away from the furnace axis. Indeed, a small hot region is formed at the flame tip and when the concentration of hydrogen raised to 10%, this region becomes bigger. However, the flame temperature is not changed significantly when the percentage of hydrogen intensifies to 10%. Indeed, the length of flame increases when hydrogen is added to the biogas mixture and when further growth in hydrogen percentage is happening, the length of the flame increases. Figure3 demonstrates the contour of temperature inside the chamber in the three cases.Figure 3
Temperature distribution in the chamber and the pictures of the flames.It can be interpreted that added hydrogen changes the density of biogas mixture and thus the flow rate of mixture increases; thus the flammability of the biogas increases because of hydrogen addition (the flammability limit of H0, H5, and H10 is 9, 12, and 15 (percent by volume), respectively). To further hydrogen content (10% hydrogen), the flammability of biogas intensifies due to lower density of mixture and higher flow field. Figure4 displays the numerical results of hydrogen addition effects on the axial temperature and radial temperature profile (at x=65 mm) of the biogas flame. From Figure 4(b), it can be seen that the biogas flame becomes narrow when the percentage of hydrogen content of biogas increases. The flame thickness reduction can be attributed to the enhancement of the mixing. In the simulation of nonpremixed combustion, mixture fraction indicates the mixing rate. While the mixture fraction increases, it can be construed that mixing does not occur properly. Figure 5 displays the mixture fraction in axial and radial directions. When the hydrogen content of biogas increases, the radial spreading rate of mixture fraction decreases which is a sign of mixing growth.Figure 4
Effect of biogas hydrogen enrichment on (a) axial temperature distribution and (b) radial temperature profile.
(a)
Axial temperature distribution
(b)
Radial temperature profile atX=65 mmFigure 5
Effect of hydrogen enrichment on the (a) axial profile of mixture fraction and (b) radial profile of mixture fraction in biogas combustion.
(a)
(b)The axial profile of H2, CH4, and O2 mass fraction in hydrogen-enriched biogas combustion is presented in Figure 6.Figure 6
The axial profile of H2, CH4, and O2 mass fraction in hydrogen-enriched biogas combustion.
(a)
(b)
(c)The contour ofNOx formation and the effects of hydrogen enrichment on NOx formation of biogas combustion are presented in Figures 7 and 8, respectively. NOx formation significantly increases when hydrogen is added to the biogas content. Although the contribution of prompt NOx decreases in the total generated NOx (because of the carbon content reduction in the fuel mixture), thermal NOx is increased dramatically due to considerable growth in the flame temperature. It means that hydrogen in the biogas stream contributes to the NOx formation only through an increase in temperature and consequently via thermal NOx. The maximum temperature in cases H0, H5, and H10 was recorded 1860 K, 1790 K, and 1710 K, respectively. Indeed, from numerical simulation (Figure 3) it can be seen that hot spots are developed in case H10. Because of that, NOx formation region changes with the same pattern that peak temperature distribution region changes.Figure 7
NO
x formation contour.Figure 8
The axial profile of NO mass fraction in hydrogen-enriched biogas combustion.It was mentioned that Figure4 indicates that the case H10 has higher temperature from others; however, Figure 7 demonstrates that the NOx concentration of H10 is lower than case H5. The increasing of hydrogen percentage from 5% to 10% led the combustion to be more complete. Therefore, according to Figure 6, the concentration of oxygen (O2) is less than H5. It could be argued that lower rate of NOx production in H10 is related to oxygen (O2) shortage, even though it has higher temperature.


## 4. Conclusion

Although POME biogas could be an acceptable source of energy, LCV of biogas is the most important barrier of this renewable and sustainable fuel development. Since POME biogas upgrading is not economic, new methods should be taken into consideration to improve the combustion of biogas. Since the percentage of hydrogen in the POME biogas components could be controlled by some chemical strategies, the effects of hydrogen enrichment on biogas conventional coflow flame were investigated. Combustion characteristics and flame stability of pure biogas (H0: 40% CO2 and 60% CH4) and hydrogen-enriched biogas (H5: 40% CO2, 55% CH4, 5% H2 and H10: 40% CO2, 50% CH4, 10% H2) were studied. It was found that adding hydrogen to the biogas content could improve LCV of biogas and consequently the stability of biogas flame increases. Also, the distribution of temperature becomes uniform when hydrogen is added to the biogas. Indeed, the length of the biogas flame is stretched when hydrogen is introduced to the fuel mixture. The simulated results show that the mixing process of fuel and air improves rapidly in the presence of hydrogen. When the concentration of H2 is increased, the density of the inlet fuel mixture and thus the flame structure is changed. Compared to the pure biogas combustion, thermal NOx formation increases in hydrogen-enriched biogas combustion due to temperature enhancement.



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*Source: 612341-2015-03-23.xml*