Abstract
Using alternative fuels (AF) in industry high consuming energy where fossil fuels are largely consumed may be a great solution to decrease CO_{2} emission and cost production. Or, when using these alternative fuels, the combustion may be difficult to control regarding the different components of AFs compared to fossil fuels. In this case, the use of the computational fluid dynamics CFD tools is a great solution to predict the AFs combustion behavior. This paper represents a computational study of petcoke and olive pomace (OP) cocombustion in a cement rotary kiln burner, established on the commercial CFD software ANSYS FLUENT. This study presents a useful key to choose an adequate simulation model that well predicts cocombustion problems. The performance of the Kϵ turbulence models varieties (standard, Realizable, and ReNormalization Group) combined with the hybrid finite rate/eddy dissipation model and the simple eddy dissipation model for predicting the cocombustion characteristics was investigated. The particle phase solutions are obtained using the Lagrangian approach. The performance of the mentioned model was evaluated based on the mesh accuracy, convergence time, temperature shape, and important chemical elements concentration. The predicted values of species concentrations and temperature are compared to the results obtained from the real case study and available literature. The standard Kϵ model combined with the hybrid finite rate/eddy dissipation model gives the best results and the lower computational resources required for the 2D model realized.
1 Introduction
In the last few years, there has been a growing interest from the cement industries in the Cocombustion of coal with alternative fuels (AF) for environmental and economic aims [1, 2]. Alternative fuels have different chemical and physical properties from fossil fuels [3]. Thus, particles of alternative fuels differ from coal in shape and size, so in their aerodynamics, heating up, and combustion mechanism. Modeling of coal/olive pomace (OP) Cofiring in cement kiln burner is a complex process that includes gas and particle phases as well as the effect of turbulence on combustion mechanism and heat transfer.
The computational fluid dynamics CFD plays an important role in modeling the combustion of pulverized coal/AF mixture in the rotary kiln burner. In recent years, several publications have appeared documenting the use of kϵ models for cofiring coal/AF. The ReNormalisation Group (RNG) kϵ model is widely used for its ability to account for the swirl in the mean flow on turbulence. An interesting approach to this issue has been proposed by [4–6]. An immediate benefit of the realizable kϵ model (RKE) is that it more accurately predicts the spreading rate of round jets [7–9]. Standard kϵ (SKE) is popular in industrial flow and heat transfer simulation due to its robustness, economy, and reasonable accuracy for a wide range of turbulent flows and combustion [10–12]. Knaus et al. [13] studied the influence of mixing conditions, combustion air distribution, and kiln geometry on the combustion process within the wood heater on complete combustion, applying the kϵ, low Reynoldsnumber kϵ, and the Reynoldsstress turbulence model. They reported different flow field predictions using different turbulence models, whereas combustion characteristics (e.g., temperature and gas concentration) were predicted reasonably well by all models.
One of the turbulencechemistry interaction models most used with the kϵ models is the eddy dissipation model (EDM) proposed by Magnussen and Hjertager [14]. Zhou, W. [15] and Ma, L. [16] have used the eddy dissipation model to evaluate the effect of turbulence on chemistry. But the major drawback of this approach can be observed especially under oxyfuel combustion conditions. Most likely, this model overpredicts the temperatures and largely underpredicts the combustion products level [17]. An alternative to the EDM model is available in fluent [18], known as the finite rate/eddy dissipation model (FR/EDM). Several authors [10, 19–22] have used the (FR/EDM), to define the turbulencechemistry interaction, for its accuracy to describe the turbulent combustion phenomena in pulverized fuel (PF) case and low computational cost compared to the eddy dissipation model.
Ariyaratne et al. [4] studied Meat and Bone Meal (MBM) combustion in rotary cement kiln compared to the coal combustion, using EDM with the RNG Kϵ model, and they showed that the temperature profile given by the MBM is lower from the coal one over a 300 K. Another important conclusion is that the largest MBM particles are not subject to devolatilization or char burning, also MBM particle's combustion needs more oxygen mole fraction. This observation was the subject of another paper [23], whether the MBM particle size effect on combustion parameters was investigated. For modeling combustion, the FR/EDM was used for turbulencechemistry interaction and the RNG kϵ model for turbulent flow simulation. Several publications have appeared in recent years that prove the problem of cofiring coal with AF, such as the large particle sizes, the irregularity in shapes, or either the chemical and physical properties [24–27]. The focus of this research has been on AF combustion flaws, (e.g. the pollution effect, the temperature needed, the combustion residues impact…). The high rotary cement kiln temperature and the intrinsic ability for clinker to absorb and lock contaminants, allow the kiln to manage most of the AF combustion problems and also burn a wide range of alternative fuels.
Although much work has been done on AF cocombustion, the authors indicated that more investigations are necessary to improve the accuracy of calculations. Furthermore, from the literature review, it is clear that there is a lack of cofiring numerical investigations concerning the cement rotary kiln. Even though the efficiency of different kϵ models with the turbulencechemistry approach has been improved in recent years on predicting PF combustion, most improvements have been achieved by minimizing the calculation cost. Nonetheless, it is possible to further improve the efficiency by comparing these submodels to choose the wellpredicting model for novel alternative fuel. With this goal, this work seeks to develop a comparison between the three kϵ models combining with eddy dissipation model variances, in a 2D modeling case of cofiring coal with olive pomace, under rotary cement kiln burner conditions. The object is to explore the possibility of having a model that could predict well the combustion of a solid fuels mixture behavior in the kiln environment, could also take into account the difference in chemical and physical properties of AF versus coal, and could be able to give an overview of the kiln process parameters when changing the cofiring rate. In the previous work [28], we have examined the key issues in the flow field, mainly on how they are affected by turbulence models and coprocessing conditions. The results obtained will be a guideline to well interpreted results obtained in this work.
The remainder of the paper is organized as follows: 1. The proposed mathematical models are presented in section 2. The computational methodology is discussed in section 3. Section 4 shows the results of different cases. Finally, section 5 concludes with a summary.
2 Mathematical models
The timeaveraged steadystate Navier–Stokes equations, as well as the mass and energy conservation equations are solved. The governing equations for the conservations of mass, momentum, energy, and species are the same as given in the previous work [28].
2.1 Turbulence closure models
The turbulent viscosity

Standard kϵ Model

Renormalization Group (RNG) kϵ Model

Realizable kϵ Model
In Eq. (6)
In Eq. (7) the constant
The other constant in the three kϵ models are the closure coefficients and are presented in Table 1.
The three turbulence model constants
Turbulence models  Model constants 
Standard kϵ 

RNG kϵ 

Realizable kϵ 

2.2 Turbulencechemistry interaction models
Pulverized fuel combustion process occurs in a highly turbulent environment when the fluctuations have an important effect on the source term
Generally, in cofiring coal with alternative fuels, the volatile matter can be presented by

Eddy dissipation model (EDM)
Alternative and fossil fuels properties as received basis
Fuel properties  Petcock  Olive pomace 
Ultimate Analysis (wt. %)  
Carbon  88.6  51.6 
Hydrogen  3.74  6 
Oxygen  1.4  33.7 
Sulfur  3.98  0.35 
Nitrogen  1.62  1.89 
Proximate Analysis (wt. %)  
Volatile  10.6  57 
Fixed carbon  87.02  16.4 
Ash  0.58  4.7 
Moisture  1.58  21.9 
Physical properties  
High heat value (kJ kg^{−1})  34,805  15,349 
Dry particle density (kg m^{−3})  900  657 
Mean particle diameter (μm)  15  200 
Global gasphase reaction mechanism
N°  Gasphase reaction 
Coalvolatile oxidation  
1 

OPvolatile oxidation  
2 

CO oxidation  
3 


Finite rate/eddy dissipation model (FR/EDM)
2.3 Particle phase
In this work, the combustion of solid particles conversion is treated as heating, devolatilizing, and char burning process. When migrating through the continuous phase in the furnace, the pulverized particles incur sequences of heterogeneous reactions, creating sources for reactions in the gas phase. Proper modeling of this particle participation is an important key role in CFD combustion.
The kinetic rate constant
3 Numerical computation and strategy
The rotary kiln used in this paper is assimilated to a real kiln with 46 m in length and 3.8 m in diameter, and it is specially equipped with a multichannel burner (see Fig. 1). For calculation purposes, the multichannel burner is converted to Fig. 1 with the applied boundary conditions cited below. The velocity and temperature and fuels feed entry are the same as in the real case. For boundary conditions, we apply in all entries an inlet velocity with normal direction to boundary except for swirl entry when both normal and tangential velocity is expected. The walls are treated as adiabatic and a noslip condition is applied [28]. More details are given in Fig. 1. The I.T in Fig. 1 is the turbulence intensity.
A mesh test independence is applied for temperature and velocity with fore mesh size (854, 3,380, 14,040, and 31,512). To optimize convergence time, we use nonuniform mesh with higher density near the burner inlet and the axis direction and coarse in all the rest of the domain. The simulation is implemented in a 2D domain with 14,040 meshes elements for all the cases studied.
3.1 Case study
As mentioned earlier, this paper discusses the effect of the different Kϵ turbulence models and the interaction turbulencechemistry models on the CFD prediction of the cofiring coal in cement rotary kiln. To this aim, six different cases were investigated and are summarized in Table 4.
Investigated cases details
Case  Turbulence model  Turbulence chemistry interaction model  DPM model 
1  SKE  EDM  ON 
2  FR/ED  
3  RNG  EDM  ON 
4  FR/ED  
5  RKE  EDM  ON 
6  FR/ED 
4 Results and discussion
Coal combustion in the rotary kiln is often characterized by very complicated turbulent flows. Cocombustion with AF further complicates the scenario, the AF has different physical properties from the coal. When they are transported into the kiln, they require mixing to the oxidizer, cold reactants, and hot products. In this turbulent flow, there are a lot of eddies of different lengths and velocities. To this aim, a critical view in which the model can give more information on combustion characteristics (i.e., velocity field, temperature prediction, and species distribution) is presented in this section.
4.1 Temperature prediction
Figure 2(a) illustrates temperature distribution using the three kϵ varieties. From this figure, it can be seen that a lowtemperature zone in front of the flame is present in the three turbulence model simulations, which cannot be seen in the real case temperature distribution as shown in Fig. 7. This is due to the drying and devolatilization of the particle model. Even though predicting different flow fields, the three kϵ models have similar trends of temperature profiles but differ in the flame length.
Predicted temperature contour for the FR/ED modelbased (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted temperature contour for the FR/ED modelbased (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted temperature contour for the FR/ED modelbased (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
The RKE model gives a longer flame length because it underestimates the reverse velocity and in consequence the mixing of the cold and hot gas. In cases that use the EDM model, all models over predict the flame temperature (a pic around 2500 K) and give a longer flame, around 15 m for SKE and RNG model, as shown above in Fig. 2(b), considering the RKE model case, we remark a 25 m flame length, this is due to its dissipative nature as discussed previously, which directly impacts the EDM model production rate owing to its infinitely fast chemistry assumption. However, the simulations combined with FR/ED model perform marginally better.
Predicted temperature contour for the EDM modelbased (SKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted temperature contour for the EDM modelbased (SKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted temperature contour for the EDM modelbased (SKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
For example, at 7.5 m away from the burner, all models achieve 2400 K as maximum temperature, which flows the real case, while the RKE model has a large pic temperature region as can be seen in Fig. 8.
Furthermore, along with the kiln at 12.5 m, the SKE and RNG kϵ model gives a uniform temperature 100 K lower than the kiln performs, but the RKE model still over predicting the temperature. Generally, it can be seen that the temperature profile is not affected by turbulence as much as by the turbulencechemistry interaction models. A similar trend has been reported by several authors [41–43].
4.2 Oxygen and CO_{2} fraction distribution
All simulations show low oxygen concentration near the axis and higher oxygen mass fraction in the outer region. Recalling that the fuels are injected near the burner axis thus, a low oxygen mass fraction in the axis should be expected when combustion is occurring, this will be discussed later. The influence of the turbulence model on O_{2} mole fraction prediction is illustrated in Fig. 3(a). The results show that the O_{2} distribution is independent of the turbulence model away from the combustion zone. Although the three turbulence models let on quite different in the low O_{2} concentration zone, this is due to the variance of the predicted flow field (see [28]). Similar to the temperature contours, RKE provides the longer low O_{2} concentration region what explains the longer flame shape obtained in this case. On the other hand, in the standard kϵ model case, a difference from the RNG model can be observed, exactly in the recirculation zone, and hence the O_{2} concentration may be affected by the mixing process.
Contour plots of oxygen mol fraction using the FR/ED model current results for (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Contour plots of oxygen mol fraction using the FR/ED model current results for (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Contour plots of oxygen mol fraction using the FR/ED model current results for (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Contour plots of oxygen mol fraction using the EDM model current results for (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Contour plots of oxygen mol fraction using the EDM model current results for (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Contour plots of oxygen mol fraction using the EDM model current results for (SKE, RNG, and RKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Figure 4(a) shows the comparison of the contour plot of the CO_{2} mole fraction for the SKE, RNG, and RKE models. Figure 4(a) and 4(b) reveals that by adopting the different kϵ models, no effect on the CO_{2} distribution SKE model is observed. All models give similar trends, the difference in the CO_{2} concentration, due essentially to the O_{2} distribution. Referring to the turbulencechemistry interaction models, it can be observed that the whole difference is in the combustion region see Fig. 3(b). Away from this region, less formation of CO_{2} and less consumption of O_{2} are predicted.
CO_{2} mole fraction contours current results for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO_{2} mole fraction contours current results for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO_{2} mole fraction contours current results for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO_{2} mole fraction contours current results for the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO_{2} mole fraction contours current results for the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO_{2} mole fraction contours current results for the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Otherwise, Fig. 4(b) shows that by comparing the prediction of CO_{2} between EDM and FR/ED, EDM prediction of CO_{2} drops away from the flame region, this is confirming the improvement of FR/ED prediction of slow forming species. However, in the hightemperature region, the EDM model overpredicts the CO_{2} concentration, this is due to its assumption to use the same model constant A and B (see Eq. 8) which may overestimate the reaction rate.
In summary, the results show that the prediction of CO_{2} and O_{2} is mostly independent of the turbulence model.
4.3 Volatiles and char combustion
The process of kiln destabilization has been reported in cofiring coal combustion due to the different thermodynamic and transport properties [5]. Maintaining stability and keeping the combustion of cofiring coal characteristics similar to coal combustion need special attention to volatile production near the burner, and the char combustion downstream.
Figure 5(a1) and 5(a2) show the volatile mole fraction in coal and OP mole fraction contours for FR/ED cases. Because of the fine particle size, and the high density of coal, all particles are dewatering out as soon as they are injected compared to the OP particles where the evaporation process takes place deep in the kiln. Summarizing all species distribution figures in Figs 5 and 6, it can be observed that the effect of turbulence modeling on the prediction of volatiles and CO is negligible.
Predicted coal volatile mole fraction contours using the FR/ED model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted coal volatile mole fraction contours using the FR/ED model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted coal volatile mole fraction contours using the FR/ED model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted OP volatile mole fraction contours using the FR/ED model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted OP volatile mole fraction contours using the FR/ED model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted OP volatile mole fraction contours using the FR/ED model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
The effect of the turbulencechemistry interaction model on volatiles and CO production is presented in Figs 5 and 6. Figure 5(b1) and 5(b2) show that the EDM model gives faster consumption of volatile compared to the FR/ED model case, therefore, it is clear from Fig. 6(a) and 6(b) that using the EDM model high concentration of CO formation is predicted. Hence, the FR/ED model gives a longer char reaction zone, and this fits well with real case data (see Table 5) and with the O_{2} and temperature prediction given with the same model.
Predicted coal volatile mole fraction contours using the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted coal volatile mole fraction contours using the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted coal volatile mole fraction contours using the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted OP volatile mole fraction contours using the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted OP volatile mole fraction contours using the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Predicted OP volatile mole fraction contours using the EDM model (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO mole fraction distribution predicted for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO mole fraction distribution predicted for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO mole fraction distribution predicted for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO mole fraction distribution predicted for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO mole fraction distribution predicted for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
CO mole fraction distribution predicted for the FR/ED model cases (RKE, RNG, and SKE respectively from the top)
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Species average masse fraction in the combustion region
SKE FR/ED  RNG FR/ED  RKE FR/ED 
SKE EDM 
RNG EDM 
RKE EDM 
Real case  
CO  0.00447  0.00431  0.00426  0.0068  0.0068  0.0062  0.0044 
O_{2}  0.0159  0.0145  0.0126  0.007  0.0072  0.0061  0.017 
5 Comparison with real data validation
The rotary kiln in cement industries operates at a high temperature, so the installation of equipment that can register information into the kiln is complicated. For this reason, only the external wall temperature is measured using an infrared handheld pyrometer. In this chapter, we compare the trend of the temperature simulated to the external wall real temperature.
Compared the temperature profile on the symmetry position presented in Fig. 8 to the real case profile presented in Fig. 7 we remark the same trend of temperature. The position in the real case data where the temperature is low is due to the ventilator position; this affects the kiln envelope temperature only but still gives an idea on how the temperature changes in the kiln.
Thermal profile of the real kiln wall
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Thermal profile of the real kiln wall
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Thermal profile of the real kiln wall
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Temperature profile for all Kϵ models combined with the two turbulence interaction model on the symmetry position
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Temperature profile for all Kϵ models combined with the two turbulence interaction model on the symmetry position
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
Temperature profile for all Kϵ models combined with the two turbulence interaction model on the symmetry position
Citation: International Review of Applied Sciences and Engineering 13, 2; 10.1556/1848.2021.00326
It has also been remarked that the pic temperature is in the same position as in the real case when standard kϵ is used. The realizable kϵ model gives a large pic as in the real case but retarded pic is observed with the same model. Also as recommended technically the temperature with the used fuel feed the temperature must reach a range of 2100–2500 K, we remark that all the model gives approximately a temperature in the same range.
For the environmental and technical constraints, several chemical components are controlled. In the case of combustion quality control the CO and O_{2} are the primordial element that gives global information on the combustion process into the kiln. In this study the CO and O_{2} are measured with the differential optical absorption spectroscopy. Moreover, Table 5 summaries the CO and O_{2} analysis in the combustion region in the real kiln case, it is clear that the EDM model falls in the prediction of this species, due to its based assumption, otherwise, the SKE model combined with the FR/ED model gives better agreement with the real case. Also, we remark the model that underestimated O_{2} concentration is the same model that overestimates CO who confirms the good coordination of the simulation used.
6 Conclusion
The effect of the turbulence model (i.e., SKE, RNG, and RKE Kϵ models), and the turbulencechemistry model (i.e., the EDM model and its modification FR/ED model) on the temperature distribution, and species (i.e., O_{2}, CO_{2}, CO, and volatile) of the cofiring coal combustion gas phase of real rotary kiln case is investigated.
Six different cases are simulated and predicted, results are verified with real case data and available literature study. Based on the temperature profiles it can be seen that the temperature is not affected by turbulence as much as by the turbulencechemistry interaction models. The main difference is observed in the flame zone and near the recirculation zone where the turbulence level is high. The same SKE model seems to fit adequately with the real kiln temperature profiles and gives the same trend of the temperature profile.
The different performance of the turbulencechemistry model leads to different species concentration predictions, the EDM model fails to predict the O_{2} concentration and over predict the CO_{2} concentration which improves the good performance of the FR/ED model to predict the slow reaction species. Accordingly, while the EDM model predicts a fast consumption of volatile and high CO formation, the FR/ED model gives a longer CO reaction zone this fits well with the temperature and O_{2} prediction. Generally, away from the flame zone, the level of turbulence and strain rates decay, all parameters have the same trends and keep unchangeable until the exit boundary.
On the other hand, OP conversion mechanisms, such as devolatilization and burnout are also responsible for the shift off flame from the real case, this is due to the model of volatilization, burnout, and size distribution model. Therefore, the influence of these models needs to be investigated to increase the accuracy of the simulations.
Nomenclature

Heat capacity at constant pressure, J kg^{−1} K^{−1} 

Current particle diameter, m 

External body force vector per unit volume, N m^{−3} 

The fraction of the heat absorbed by the particle 

Mass fraction of volatiles initially present in the particle 

Mass fraction of the evaporating/boiling material 

The heat released by the surface reaction, J/Kg 

Enthalpy, J kg^{−1} 

Convective heat transfer coefficient, W/m^{2}k 

Diffusion flux of species i, kg/m^{2}s 

Current mass of the particle, kg 

The initial mass of the particle, kg 

The partial pressure of oxidant species in the gas surrounding the combusting particle, Pa 

The net rate of production of species i by chemical reaction, kg/m^{3}s 

Rate of creation of species i by addition from the dispersed phase, kg/m^{3}s 

Mass added to the continuous phase from the dispersed second phase, kg/m^{3}s 

Turbulent Schmidt number 

Particle temperature, K 

The local temperature of the continuous phase, K 

Axial velocity, m s^{−1} 

Particle velocity vector, m s^{−1} 

Turbulent viscosity, Pa s 

The density of continuous phase, kg/m3 density of the particle, kg m^{−3} 

Stress tensor, Pa 
References
 [1]↑
K. G. Kolovos , G. Kyriakopoulos , and M.S. Chalikias , “Coevaluation of basic woodfuel types used as alternative heating sources to existing energy network,” J. Environ. Prot. Ecol., vol. 12, pp. 733–742, 2011.
 [2]↑
J. Koppejan , The Handbook of Biomass Combustion and Cofiring. 1st ed., Routledge, 2012, https://doi.org/10.4324/9781849773041.
 [3]↑
Z. Ngadi , and M.L. Lahlaouti , “Impact of using alternative fuels on cement rotary kilns: experimental study and modeling,” in Procedia Engineering, 2017, https://doi.org/10.1016/j.proeng.2017.02.465.
 [4]↑
W.K.W.K. Hiromi Ariyaratne , A. Malagalage , M.C. Melaaen , and L. André Tokheim , “CFD modeling of meat and bone meal combustion in a rotary cement kiln,” Int. J. Model. Optimization, vol. 4, pp. 263–272, 2014, https://doi.org/10.7763/ijmo.2014.v4.384.
 [5]↑
C. Ghenai , and I. Janajreh , “CFD analysis of the effects of cofiring biomass with coal,” Energ. Convers. Manag., vol. 51, pp. 1694–1701, 2010, https://doi.org/10.1016/j.enconman.2009.11.045.
 [6]
S.R. Gubba , L. Ma , M. Pourkashanian , and A. Williams , “Influence of particle shape and internal thermal gradients of biomass particles on pulverised coal/biomass cofired flames,” Fuel Process. Technol., vol. 92, pp. 2185–2195, 2011, https://doi.org/10.1016/J.FUPROC.2011.07.003.
 [7]↑
C. Yin , S. Knudsen Kaer , L. Rosendahl , and S. Hvid , “Modeling of pulverized coal and biomass cofiring in a 150 KW swirlingstabilized burner and experimental validation,” in Proceedings of the International Conference on Power Engineering09, Japan: Kobe, 2009, https://doi.org/10.1299/jsmeicope.2009.2._2305_.
 [8]
S.R. Gubba , D.B. Ingham , K.J. Larsen , L. Ma , M. Pourkashanian , H.Z. Tan , A. Williams , and H. Zhou , “Numerical modelling of the cofiring of pulverised coal and straw in a 300 MWe tangentially fired boiler,” Fuel Process. Technol., vol. 104, pp. 181–188, 2012, https://doi.org/10.1016/J.FUPROC.2012.05.011.
 [9]
S. Somwangthanaroj , and S. Fukuda , “CFD modeling of biomass grate combustion using a steadystate discrete particle model (DPM) approach,” Renew. Energ., vol. 148, pp. 363–373, 2020, https://doi.org/10.1016/J.RENENE.2019.10.042.
 [10]↑
A. Guessab , A. Aris , and A. Bounif , “Simulation of turbulent piloted methane nonpremixed flame based on combination of finiterate/eddydissipation model,” Mechanika, vol. 19, pp. 657–664, 2013, https://doi.org/10.5755/j01.mech.19.6.6000.
 [11]
F. Tabet , V. Fichet , and P. Plion , “A comprehensive CFD based model for domestic biomass heating systems,” J. Energ. Inst., vol. 89, pp. 199–214, 2016, https://doi.org/10.1016/J.JOEI.2015.02.003.
 [12]
J. Chaney , H. Liu , and J. Li , “An overview of CFD modelling of smallscale fixedbed biomass pellet boilers with preliminary results from a simplified approach,” Energ. Convers. Manag., vol. 63, pp. 149–156, 2012, https://doi.org/10.1016/J.ENCONMAN.2012.01.036.
 [13]↑
H. Knaus , S. Richter , S. Unterberger , U. Schnell , H. Maier , and K.R.G. Hein , “On the application of different turbulence models for the computation of fluid flow and combustion processes in small scale wood heaters,” Exp. Therm. Fluid Sci., vol. 21, pp. 99–108, 2000, https://doi.org/10.1016/S08941777(99)00059X.
 [14]↑
B.F. Magnussen , and B.H. Hjertager , “On mathematical modeling of turbulent combustion with special emphasis on soot formation and combustion,” Symp. (International) Combustion, vol. 16, pp. 719–729, 1977, https://doi.org/10.1016/S00820784(77)803664.
 [15]↑
W. Zhou , and D. Moyeda , “Process evaluation of oxyfuel combustion with flue gas recycle in a conventional utility boiler,” Energy & Fuels, vol. 24, pp. 2162–2169, 2010, https://doi.org/10.1021/ef9012399.
 [16]↑
L. Ma , J.M. Jones , M. Pourkashanian , and A. Williams , “Modelling the combustion of pulverized biomass in an industrial combustion test furnace,” Fuel, vol. 86, pp. 1959–1965, 2007. https://linkinghub.elsevier.com/retrieve/pii/S0016236106005199 [accessed November 24, 2018].
 [17]↑
C. Yin , L.A. Rosendahl , and S.K. Kær , “Chemistry and radiation in oxyfuel combustion: a computational fluid dynamics modeling study,” Fuel, vol. 90, pp. 2519–2529, 2011, https://doi.org/10.1016/j.fuel.2011.03.023.
 [18]↑
ANSYS Fluent theory guide, in: ANSYS Fluent Theory Guide 15.0. Canonsbourg, USA: Ansys,Inc, 2013, p. 193.
 [19]↑
J. Collazo , J. Porteiro , J.L. Míguez , E. Granada , and M.A. Gómez , “Numerical simulation of a smallscale biomass boiler,” Energ. Convers. Manag., vol. 64, pp. 87–96, 2012, https://doi.org/10.1016/J.ENCONMAN.2012.05.020.
 [20]
D. Djurović , S. Nemoda , B. Repić , D. Dakić , and M. Adzić , “Influence of biomass furnace volume change on flue gases burn out process,” Renew. Energ., vol. 76, pp. 1–6, 2015, https://doi.org/10.1016/J.RENENE.2014.11.007.
 [21]
Y. Bin Yang , R. Newman , V. Sharifi , J. Swithenbank , and J. Ariss , “Mathematical modelling of straw combustion in a 38 MWe power plant furnace and effect of operating conditions,” Fuel, vol. 86, pp. 129–142, 2007, https://doi.org/10.1016/J.FUEL.2006.06.023.
 [22]
T. Zadravec , B. Rajh , F. Kokalj , and N. Samec , “CFD modelling of air staged combustion in a wood pellet boiler using the coupled modelling approach,” Therm. Sci. Eng. Prog., vol. 20, 2020, Art no. 100715, https://doi.org/10.1016/J.TSEP.2020.100715.
 [23]↑
W.K.H. Ariyaratne , A. Malagalage , M.C. Melaaen , and L.A. Tokheim , “CFD modelling of meat and bone meal combustion in a cement rotary kiln  investigation of fuel particle size and fuel feeding position impacts,” Chem. Eng. Sci., vol. 123, pp. 596–608, 2015, https://doi.org/10.1016/j.ces.2014.10.048.
 [24]↑
A. Panahi , M. Tarakcioglu , M. Schiemann , M. Delichatsios , and Y.A. Levendis , “On the particle sizing of torrefied biomass for cofiring with pulverized coal,” Combustion and Flame, vol. 194, pp. 72–84, 2018, https://doi.org/10.1016/J.COMBUSTFLAME.2018.04.014.
 [25]
S. Seepana , S. Arumugam , K. Sivaramakrishnan , and M. Muthukrishnan , “Evaluation of feasibility of pelletized wood cofiring with high ash Indian coals,” J. Energ. Inst., vol. 91, pp. 1126–1135, 2018, https://doi.org/10.1016/J.JOEI.2017.06.011.
 [26]
B.N. Madanayake , S. Gan , C. Eastwick , and H.K. Ng , “Biomass as an energy source in coal cofiring and its feasibility enhancement via pretreatment techniques,” Fuel Process. Technol., vol. 159, pp. 287–305, 2017, https://doi.org/10.1016/J.FUPROC.2017.01.029.
 [27]
H. Mikulčić , D. Cerinski , J. Baleta , and X. Wang , “Improving pulverized coal and biomass Cocombustion in a cement rotary kiln by computational fluid dynamics,” Chem. Eng. Technol., vol. 42, pp. 2539–2545, 2019, https://doi.org/10.1002/CEAT.201900086.
 [28]↑
Z. Ngadi , and M.L. Lahlaouti , “Coal and biomass Cocombustion: CFD prediction of velocity field for multichannel burner in cement rotary kiln,” Proceedings, vol. 63, p. 18, 2020, https://doi.org/10.3390/proceedings2020063018.
 [29]↑
W. Michal , “Eddy viscosity turbulence models employed by computational fluid dynamic,” Transaction Inst. Aviation, vol. 4, pp. 92–112, 2007.
 [30]↑
W. Jones , and B. Launder , “The prediction of laminarization with a twoequation model of turbulence,” Int. J. Heat Mass Transfer, vol. 15, pp. 301–314, 1972, https://doi.org/10.1016/00179310(72)900762.
 [31]↑
B.E. Launder , and B.I. Sharma , “Application of the energydissipation model of turbulence to the calculation of flow near a spinning disc,” Lett. Heat Mass Transfer, vol. 1, pp. 131–137, 1974, https://doi.org/10.1016/00944548(74)901507.
 [32]↑
V. Yakhot , and S.A. Orszag , “Renormalization group analysis of turbulence. I. Basic theory,” J. Scientific Comput., vol. 1, pp. 3–51, 1986, https://doi.org/10.1007/BF01061452.
 [33]↑
T.H. Shih , W.W. Liou , A. Shabbir , Z. Yang , and J. Zhu , “A new kϵ eddy viscosity model for high Reynolds number turbulent flows,” Comput. Fluids, vol. 24, pp. 227–238, 1995, https://doi.org/10.1016/00457930(94)00032T.
 [34]↑
B.E. Launder , and D.B. Spalding , Lectures in Mathematical Models of Turbulence. London, New York: Academic Press, 1972, https://doi.org/10.1080/10256010903084126.
 [35]↑
D.B. Spalding , “Mixing and chemical reaction in steady confined turbulent flames,” Symp. (International) Combustion, vol. 13, pp. 649–657, 1971, https://doi.org/10.1016/S00820784(71)80067X.
 [36]↑
P. Rosin , and E. Rammler , “The laws governing the fineness of powdered coal,” J. Inst. Fuel, vol. 7, pp. 29–36, 1933.
 [37]↑
J.S. Shuen , L.D. Chen , and G.M. Faeth , “Evaluation of a stochastic model of particle dispersion in a turbulent round jet,” AIChE J., vol. 29, pp. 167–170, 1983, https://doi.org/10.1002/aic.690290127.
 [38]↑
S.A. Morsi , and A.J. Alexander , “An investigation of particle trajectories in twophase flow systems,” J. Fluid Mech., vol. 55, p. 193, 1972, https://doi.org/10.1017/S0022112072001806.
 [39]↑
S. Badzioch , and P.G.W.W. Hawksley , “Kinetics thermal decomposition pulverized coal particles,” vol. 9, pp. 521–530, 1970, https://doi.org/10.1021/i260036a005.
 [40]↑
M.M. Baum , and P.J. Street , “Predicting the combustion behaviour of coal particles,” Combustion Sci. Technol., vol. 3, pp. 231–243, 1971, https://doi.org/10.1080/00102207108952290.
 [41]↑
Z.F. Tian , P.J. Witt , M.P. Schwarz , and W. Yang , “Comparison of twoequation turbulence models in simulation of a nonswirl coal flame in a pilotscale furnace,” Combustion Sci. Technol., vol. 181, pp. 954–983, 2009, https://doi.org/10.1080/00102200902925679.
 [42]
H. Yilmaz , O. Cam , S. Tangoz , and I. Yilmaz , “Effect of different turbulence models on combustion and emission characteristics of hydrogen/air flames,” Int. J. Hydrogen Energ., vol. 42, pp. 25744–25755, 2017, https://doi.org/10.1016/j.ijhydene.2017.04.080.
 [43]
L. Chen , S.Z. Yong , and A.F. Ghoniem , “Oxyfuel combustion of pulverized coal: characterization, fundamentals, stabilization and CFD modeling,” Prog. Energ. Combustion Sci., vol. 38, pp. 156–214, 2012, https://doi.org/10.1016/j.pecs.2011.09.003.