Article pubs.acs.org/EF
Three-Dimensional Numerical Simulations and Analysis of a HeatRecovery Coke Oven Qianqian Zheng and Hongyuan Wei* School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, People’s Republic of China ABSTRACT: Three-dimensional numerical simulations of the combustion process in a heat-recovery coke oven were carried out using the eddy dissipate concept combustion model, which give an in-depth understanding of behaviors in the heat-recovery coke oven and provide useful information for optimizing operations and design. Four different coal devolatilization models, including a single-step model, a two competing reaction rate model, and distributed activation energy models (k = constant, and k = aebE), were employed to assess the volatile matter evolution rate and temperature distribution in the oven. In comparison to the experimental data, the distributed activation energy model (k = aebE) gives a more satisfied prediction and was further extended to study the distributions of volatile species and pressure in the oven. All of the simulation results have been proven to be converged, grid-independent, and reliable.
1. INTRODUCTION The modern coke-making industry has been developed over 100 years and has tremendous success in technology and economic fields.1 However, the application of the conventional coke oven (slot-type ovens) brought a series of problems, such as dust pollution, water pollution, and air pollution, which conflicted with the new environmental and related political legislations. The clean production of cokes has therefore become the top priority in the coking industry worldwide. At present, several new and efficient technologies have been developed to control and reduce pollution, for example, the “jumbo coking reactor”, SCOPE21 (super coke oven for productivity and environment enhancement toward the 21st century), and heat-recovery coke oven.2 The heat-recovery coke technology transforms the coal into a high-quality coke for the blast furnaces of the steel plant and the volatile matter into gases, which combust with air in the oven to supply the heat for the coking process. Under the negative pressure, the toxic gases and other harmful substances are burnt directly during the coking cycle. The remaining heat is then used for steam production and power generation. Considering the most energy efficient and minimum pollution that the heat-recovery coking technology provided, it is extensively regarded as one of the best available environmental control technologies. In comparison to the conventional coking technology, the heat-recovery coking process is a very complicated process because it involves several complex, simultaneous, and interdependent processes, such as turbulent flow, moisture transfer, coal devolatilization, and volatile gas combustion. Among them, coal devolatilization is the basic step in the coking process and has the dominant effect on the overall combustion behavior.3 Accordingly, the true rate of coal devolatilization is a matter of some contention, and a number of approaches have been proposed to obtain the kinetics of the complex devolatilization process. Some approaches have been implemented in the computational fluid dynamics (CFD) simulations to obtain the volatile matter evolution rate of coal by many researchers.4−6 © XXXX American Chemical Society
The simplest approach is an empirical model and employs global kinetics, where Arrhenius expressions are used to correlate rates of weight loss with temperature. These simple models can be divided into single- and multiple-step reactions. The single-step model proposed by Badzioch and Hawksley7 is widely used for the devolatilization of coal in many research works because it contains less parameters.8−10 However, employing the single-step model with inappropriate parameters can lead to large errors in the numerical simulations. In the work performed by Gera et al., the effect of kinetic parameters of the single-step model with different values of the activation energy on the flame structure has been conducted.11 The results suggested that the errors in coal devolatilization parameters have the dominant effect on simulated results. In comparison to the single-step model, the multiple-step model considers the effect of the heating rate on the coal devolatilization and has been employed to obtain the rate of coal devolatilization satisfactorily.12 However, the more parameters shown in the model usually cause the complexity of the simulation process. Jones et al. have used single-step and two competing reaction rate models to assess the global devolatilization rates based on a laminar flow of a drop-tube furnace.13 The results indicated that the single-step model coupled with the functional-group, depolymerization, vaporization, and cross-linking (FG-DVC) devolatilization mechanism model can give more satisfied profiles. Another approach is the distributed activation energy model (DAEM). The model is based on the assumption that the devolatilization occurs through several first-order reactions, which occur simultaneously. In this model, it is crucial to estimate the frequency factor and the distribution function of the activation energy. The distribution function is generally assumed by a Gaussian distribution. As for the frequency factor, it is assumed to be a constant for all reactions to avoid the Received: February 5, 2013 Revised: May 1, 2013
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complexity of the analysis or correlated with the activation energy based on experimental data. In comparison to the coal devolatilization model described above, an additional model parameter, the standard deviation of the activation energy, is introduced in this model. Besides, very recently, a new coal devolatilization model, named the tabulated devolatilization process (TDP) model, has been proposed by Hashimoto et al.14 In their work, various devolatilization models, such as the single-step model, two competing reaction rate model, and TDP model, have been used to assess the global devolatilization rates. The results indicated that the coal particle and gas-phase behaviors predicted by the TDP model were better than those by the other models.15 However, the TDP model was based on the devolatilization database obtained from experiments or from other models, such as the FLASHCHAIN model. Moreover, considering the complex calculation of the TDP model, the simulation process needs more computational efforts than those of conventional devolatilization models. Therefore, the range of application and extrapolation is limited. As mentioned above, the heat-recovery coke oven is one of the available environmentally friendly technologies for the coking industry. However, up to now, a limited number of studies address the combustion process in the heat-recovery coke oven using the numerical simulation method. In the present work, three-dimensional numerical simulations for heat-recovery coke oven were performed using a commercial code, ANSYS FLUENT 12.1. Meanwhile, the influence of four different devolatilization models, including the single-step model, two competing reaction rate model, DAEM (k = constant), and DAEM (k = aebE), was assessed on the accuracy of simulation results of the combustion behavior. This study is to provide a wide range of information on future studies in the heat-recovery coke oven and to optimize the designs of the combustion chamber and sole flues as well as the coking process operations.
Figure 1. Structure and gas flows in a heat-recovery coke oven.
2. COMPUTATIONAL DOMAIN A heat-recovery coke oven consists of five main sections, coal/ coke bed, combustion chamber, downcomers, sole flues, and uptakes, as shown in Figure 1. During the coking process, coal was charged into the oven and heated to a high temperature until substantially all of the volatile matters have been driven off and the remaining solid residues were coke. The air and volatile gas flow directions in the oven are also illustrated in Figure 1 by arrows in blue and red, respectively. The volatile gas released from the coal/coke bed mixed with the air from the primary air inlets at the top of the coke oven and burnt directly in the combustion chamber above the coal/coke bed. The heat from combustion was then transferred into the coal/coke bed in the form of convection and radiation. However, only part of volatile gas combustion took place in the combustion chamber. The remaining portion of fuel-rich gas flowed through the downcomers in the oven side walls into the sole flues, where flammable species remaining in the gas flow continuously burn out with air from the secondary inlets. Finally, the exhaust gas passed through the uptakes in the oven side walls into the waste-gas-collecting system. The configuration and dimensions of the computational domains, which were designed to exactly match the actual configuration and dimensions of a typical heat-recovery coke oven, are shown in Figure 2. Grids were created in a computeraided design (CAD) program called GAMBIT 2.4.6 and
Figure 2. Configuration and dimensions of the computational domain.
exported into ANSYS FLUENT 12.1. The coking coal used in the present study is a medium-volatile coking coal from the north of China, the properties of which are summarized in Table 1.
3. MODELING 3.1. Coal Devolatilization Models. In this study, four coal devolatilization models, i.e., a single-step model, a two competing reaction rate model, a DAEM (k = constant) model, and a DAEM (k = aebE) model, are employed to obtain the devolatilization rate.
Table 1. Properties of Coking Coal proximate analysis (wt %)
B
moisture
ash
volatile matter
fixed carbon
high heating value (kcal kg−1)
1.65
9.76
25.45
63.14
7559
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3.1.1. Single-Step Model. The single-step model was commonly employed for modeling the evolution of volatile matters from coal during the pyrolysis process. The Arrhenius expression was used to correlate rates of mass loss with temperature. The overall process of devolatilization can be represented by a single-step first-order reaction k0
coal → V (volatiles) + (1 − V )(char)
∫0
(1)
(2)
⎛ E ⎞ k 0 = A 0 exp⎜− 0 ⎟ ⎝ RT ⎠
(3) −1
−1
where R is the universal gas constant in J K mol and T is the absolute temperature in K. Referring to the study by Arenillas et al., the pre-exponential factor A0 of 114 s−1 and apparent activation energy E0 of 7.44 × 104 J mol−1 are used in this study.16 3.1.2. Two Competing Reaction Rate Model. Kobayashi et al. proposed the two competing, irreversible, and first-order reactions with different rate parameters and volatile yields to describe the thermal decomposition of coal17 k1
coal → α1(volatiles I) + (1 − α1)(char I) k2
coal → α2(volatiles II) + (1 − α2)(char II)
⇀ ∂(ρϕ) + div(ρu ϕ) = div(Γ grad ϕ) + S ∂t −3
(5)
where the coal is taken to be on dry and ash-free (daf) basis, k1 and k2 are the rate constants to determine the rate of conversion of the coal, and α1 and α2 are the mass stoichiometry coefficients. α1 = 0.2139 is obtained from proximate analysis of the coal. With regard to α2, it was taken as 0.8, referring to the literature.18 Although the two reactions occurred simultaneously, the thermal decomposition of coal was dominated by the first reaction (eq 4) at lower particle temperatures and the second reaction (eq 5) at higher particle temperatures. Similar to k0 shown in eq 3, k1 and k2 can also be represented by the Arrhenius equation shown as follows: (6)
⎛ E ⎞ k 2 = A 2 exp⎜ − 2 ⎟ ⎝ RT ⎠
(7)
where A1 and A2 are pre-exponential factors and E1 and E2 are the activation energies. Values of A1 and A2 and E1 and E2 taken from literature are 3.70 × 105 and 1.46 × 1013 s−1 and 7.37 × 104 and 2.51 × 105 J mol−1, respectively.18 Therefore, the rate of volatile matters is expressed by the following equation: t
(α1k1 + α2k 2)exp(−
∫0
4. NUMERICAL APPROACH 4.1. Numerical Algorithm. Differential equations mentioned in section 3 were discretized by a first-order upwind differencing scheme and solved by a finite volume method by Patankar39 in the ANSYS FLUENT 12.1. The moisture evaporation model and coal devolatilization models were specified by user-defined functions (UDFs) written in the C programming language and compiled to the FLUENT solver. The semi-implicit method for pressure-linked equations (SIMPLE) algorithm was used for the pressure−velocity coupling and correction. The normalized absolute residuals for all of the variables in each cell were limited to be less than 10−6. The simulations were started in transient mode of 60 h, which was counted as a coking cycle. All cases of the simulations were carried out in the “TH-1A” supercomputer, which is the one of the fastest speed computers (2566 trillion
t
(k1 + k 2)dt )dt
(8)
where Vcoal is the daf mass of the raw coal. 3.1.3. DAEM. The DAEM, originally developed by Vand19 in 1942, has been frequently used to represent the change in overall conversion.20 In this model, the total amount of volatile material released up to time t is given by
V = V*
∫0
∞
exp(− k
∫0
t
e−E / RT dt )f (E)dE
(12) −1
where ρ is the density in kg m , u̅ is the mean velocity in m s , and Φ, Γ, and S in eq 12 are the generalized variable, diffusion coefficient, and source term, respectively. The descriptions of the model in detail can be found in the literature.30 The effect of the turbulence on fluid flow can be represented by the standard k−ε model with default parameters,31 which was the simplest but reliable complete model to describe the turbulence and has been widely adopted in the industrial flow and heat-transfer simulations. In most previous work, for the simplification of the simulation, moisture transfer in the coking process was normally ignored or expressed by the Arrhenius equation. In the present study, the moisture nonlinear evaporation model was employed to simulate the water evaporation in the coal/coke bed during the coking process.32 Gaseous combustion between volatile matters and air was calculated using the eddy dissipation concept (EDC) combustion model,33 which considers detailed chemical mechanisms in turbulent flows. The rates of volatile combustion are controlled by the Arrhenius law. The volatile matters consist of 10 species (methane, oxygen, carbon monoxide, carbon dioxide, water vapor, ethane, ethylene, benzene vapor, hydrogen, and nitrogen), whose fraction compositions are determined on the basis of previous research.34 In the combustion chamber of the heat-recovery coke oven, radiation, which has been ignored in the study by Jones et al.,13 is actually the dominant mode of heat transfer. The discrete ordinates (DO) radiation model,35,36 which has generally been chosen in the CFD applications to simulate the industry process because of higher accuracy,37,38 was used in this study.
(4)
⎛ E ⎞ k1 = A1 exp⎜− 1 ⎟ ⎝ RT ⎠
(11)
where a and b are constants and set as 2.8905 and 0.0001, respectively.25 3.2. Governing Equations in CFD and Radiation Model. To establish three-dimensional numerical simulations of the heat-recovery coke oven, except for the previous four devolatilization models, other models, including the governing equations for fluid dynamics, species transport equations, the turbulent model, the moisture-transfer model, and the radiation model, are also needed. The conservation equations for mass, momentum, energy, and species can be typically represented by the following general form:
where V and V* represent the mass of volatile matter evolved from the coal at specific time t and the total volatile content of the coal, respectively. The rate constant k0 can be obtained by the Arrhenius law.
1−
(10)
k = aebE
dV = k 0(V * − V ) dt
∫0
f (E)dE = 1
Generally, the distribution curve f(E) is assumed by Gaussian distribution,21−23 with mean activation energy E̅ and standard deviation σ. In this work, E̅ and σ are 2.427 × 105 and 4.105 × 104 J mol−1, respectively.24,25 As mentioned above, the frequency factor k can be treated as a constant (1.67 × 1013 s−1) or variables depends upon the activation energy E26−29
The devolatilization rate of coal pyrolysis can be then expressed by
V = Vcoal
∞
(9)
where f(E) is a distribution function of the activation energy and k is the frequency factor. According to the assumption that the activation energy had a continuous distribution, the function f(E) must satisfy C
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Figure 3. Instantaneous temperature profiles at three mesh cases (left, 1.42 × 106; middle, 2.76 × 106; and right, 4.94 × 106).
close to each other, which indicates that the numerical solution is essentially not sensitive to the number of cells and both of the mesh cases can provide the same results. Moreover, the simulation time of medium grid size was half of that of the fine case. Thus, the medium grid size was employed for all of the simulations discussed in this study. 5.2. Volatile Matter Evolution Rate. Four simulation cases were carried out in the present study, and the detail information is listed in Table 3. The total volatile matter evolution rate of coal/coke bed, which is the volume integral of the instantaneous volatile matter evolution rate in the whole coal/coke bed, predicted by four devolatilization models versus the coking time is shown in Figure 4. It can be seen that different devolatilization models gave different results. Among them, cases 3 and 4 gave the fastest and slowest evolution rates, respectively, although the DAEM model was used in the two cases. It indicates that the selection of the frequency factor k in the DAEM model has a significant influence on prediction results. Many researchers have suggested that k = constant cannot reflect the devolatilization behavior of coal and the compensation effects between k and the activation energy E must be considered.40,41 Also, the volatile matter evolution rates predicted in cases 1 and 2 showed an apparent difference. The rate predicted by the two competing reaction rate model in case 2 was relatively rapid, which was in coincident with the results obtained by Jones et al.13 The comparison of the experimental value of total volatile mass according to the proximate analysis to the simulated values obtained by four coal devolatilization rates is list in Table 4. The percentage error in total volatile mass for four simulations and experimental value was within 1%, which indicated that the simulations were converged and all of the numerical results were mass-balanced. 5.3. Temperature Distribution. Figure 5 shows the temperature profiles of different coal devolatilization models at measuring points with the comparison to the temperature measured by the thermocouple probes in the production. It can be clearly noticed that the trend of temperature profiles predicted in cases 1 and 4 are in good agreement with the measured data. However, the temperature values obtained in case 1 show different values at the initial stage (t < 10 h) and the end stage (t > 55 h) of the coking process. The former is due to the combined effect of the lack of volatile matter and the inflow of cold air. The latter is caused by the faster volatile matter evolution rate, which can be clearly observed in Figure 4. For case 4, it gives more accurate temperature values, which
calculations per second) in the world, and a total of 100 processors have been employed for parallel computing in this study. 4.2. Boundary and Initial Conditions. All of the boundary conditions in the simulation were set at the same conditions as the actual operations. The coal/coke bed was treated as a porous media, and the porosity of the bed was measured in the previous experiments with the value of 35%. To simplify the calculation, the value of porosity was assumed to be constant during the coking period, although it changed with the coking process. Two types of inlets (primary and secondary inlets) were designated as the pressure inlet, where the direction of fluid flow was normal to the boundary. The outlets for the computational domain on the top of uptakes of the coke oven were specified as the pressure outlet, and the relative pressure was −150 Pa. The surface on the coal/coke bed was set as interior boundary conditions, so that the volatile matter can flow from the bed to the combustion chamber. The computational domain was predefined by a set of initial conditions. According to the actual operating condition, the initial relative pressure and temperature for coal/coke bed and fluid zone were set to be 0 Pa and 298 and 1273 K, respectively. The measured composition and concentration of gas remaining in the combustion region after the coking batch were taken as the initial conditions for the gas species.
5. RESULTS AND DISCUSSION 5.1. Grid Independency. The dependence of the grid size on the numerical simulation results has been investigated. Three different non-uniform grid sizes (1.42 × 106, 2.76 × 106, and 4.94 × 106) were employed for 1 h of coking time. The instantaneous temperatures of three mesh cases in the center of the oven are shown in Figure 3. It can be noticed that the temperature distributions obtained using the three grid sizes showed similarly. Table 2 presents the prediction of temperature values at the measuring point, as shown in Figure 2, for three grid size cases. The results show that the temperature value obtained using the coarse-mesh case was higher than those from the medium- and fine-mesh cases. The temperature values obtained using medium- and fine-mesh cases were very Table 2. Grid Independency Results mesh temperature at the measuring point (K) simulation time (s)
1.42 × 106 2.76 × 106 4.94 × 106 1284.17
1237.11
1228.81
7339
10160
16328 D
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Table 3. Models Used in the Four Simulations case
devolatilization model
turbulent model
combustion model
radiation model
grid
1 2 3 4
single-step model two competing reaction rate model DAEM (k = constant) DAEM (k = aebE)
standard k−ε model
EDC model
discrete ordinates model
2.76 × 106
Figure 4. Total volatile matter evolution rate of coal/coke bed during the coking process.
Figure 5. Comparison of the temperature at the measuring point predicted by the simulations to the experiment.
indicates that the application of case 4 is more appropriate for the simulation of heat-recovery coke oven in this study. The results from cases 2 and 3 do not adequately predict the distribution of temperature over the operating period. Although the predictions of temperature distribution at the early stage (t < 30 h) from cases 2 and 3 were reasonable, they gave a totally wrong trend after 30 h of operating time. It can be explained by the overestimation of volatile matter evolution rates predicted by the two competing reaction rate model and DAEM (k = constant) model at the early stage and then only few volatile matters maintained to react at the late stage. On the basis of the previous discussion, the DAEM (k = aebE) model was applied as the coal devolatilization model to simulate the combustion behavior in further studies. The temperature contours in the heat-recovery coke oven predicted in case 4 at different coking times are shown in Figure 6. It can be noticed that the temperature in the combustion chamber was higher than that in other parts of the oven. The temperature in the middle part of the combustion chamber was higher than that near both oven doors. The higher temperature in the combustion chamber denoted that the combustion mainly occurred in the combustion chamber under the present operating condition. The maldistribution of temperature in the combustion chamber can be attributed to the location of primary inlets. The absence of primary inlets near the both oven doors results in the slightly lower temperature. Moreover,
the heat loss caused by the oven door was also taken into account in the simulations. 5.4. Volatile Species Distribution. Figure 7 shows the distribution of the predicted methane mass fraction in the center of the oven at different coking times. The mass fraction distributions of other components in the volatile matter behaved in a similar way. It can be found that the location of the enrichment area of methane gradually shifted from the surface of coal/coke bed toward the bottom, which is coincident with the temperature distribution in the oven and indicates the difference from the conventional oven.42 With respect to the high methane mass fraction near the surface again at the end stage of the coking period, it can be explained by the accumulation of methane evolved from the bottom of the coal/coke bed. In addition, it can also been noticed in Figure 7 that the methane mass fraction in the combustion region at t = 35 h is much higher than that at t = 5 and 60 h, which is in coincident with the fact that a large amount of volatile matters released from the coal/coke bed at t = 35 h. 5.5. Pressure Distribution. Generally, the pressure distribution was influenced by the flow field. Figure 8 shows the distribution of velocity vectors at different coking times. It can be noticed that the velocity vectors at different coking times exhibit similarly. With respect to the vortex at the coking time of 35 h, it can be attributed to the higher volatile matter evolution rate (Figure 4). Further, the volume-weighted average of velocity at different coking times was calculated. The
Table 4. Comparison of the Simulated Total Volatile Mass to the Theoretical Value simulated value total mass of volatile matter (kg) relative error (%)
experimental value
case 1
case 2
case 3
case 4
1.3082 × 104
1.3136 × 104 0.4142
1.3141 × 104 0.4519
1.3172 × 104 0.6854
1.3089 × 104 0.0531
E
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Figure 6. Temperature distributions in the oven at different coking times (left, 5 h; middle, 35 h; and right, 60 h).
Figure 7. Distribution of the methane mass fraction at different coking times (left, 5 h; middle, 35 h; and right, 60 h).
Figure 8. Distribution of velocity vectors at different coking times (left, 5 h; middle, 35 h; and right, 60 h).
relatively lower values (3.475, 3.842, and 3.576 m s−1 at t = 5, 35, and 60 h, respectively) indicate that the impact of the flow field on the pressure distribution can be negligible. To quantify the differences in the pressure distribution in the oven, five typical locations at the center part of oven were selected (Figure 9). Moreover, the points at the primary inlet and outlet were used as references. In Figure 10, the pressure profiles of the typical points at different coking times are presented. It can be noticed that, for the given time, the pressure dropped gradually from the primary inlet to point 5, which reflected the flow direction. Namely, the gas flowed from the primary inlet to point 5 in the oven. With respect to the higher pressure at outlets, it can be attributed to thermal buoyancy, which is the density difference between the hot and cold gas. The thermal buoyancy results in less density of hot gas flow upward in the uptakes. For the pressure at the specific point, it can be noticed in Figure 10 that, for all examined points, the pressure increased with the coking time and then dropped. It needs to be mentioned that the pressure difference between the primary inlet and point 1 determined the air input and then affected the combustion of volatile matters.
6. CONCLUSION The three-dimensional numerical simulations of the heatrecovery coke oven were performed using ANSYS FLUENT 12.1. The effect of the four coal devolatilization models, including the single-step model, the two competing reaction rate model, and two DAEM models (k = constant, and k = aebE), on the distributions of the volatile matter evolution rate and gas temperature have been investigated. Although the predicted temperature distributions using the single-step model and the DAEM (k = aebE) are in good agreement with experimental data, the DAEM (k = aebE) gives more accurate temperature values. On this basis, behaviors in the heatrecovery coke oven were studied. The results show that the combustion mainly occurred in the combustion chamber and the maldistribution of temperature in the combustion chamber can be attributed to the absence of primary inlets near both oven doors. The predicated distribution of the methane mass fraction reflects the characteristic of the coking process in the heat-recovery coke oven. With respect to the pressure distribution, it reflects the flow direction of gas and affects the combustion performance in the oven. F
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Figure 9. Locations in the center of the heat-recovery coke oven.
Figure 10. Pressure distribution at different coking times.
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AUTHOR INFORMATION
Corresponding Author
*Telephone: +86-22-27405754. Fax: +86-22-27400287. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The study was carried out at the National Supercomputer Center in Tianjin, China, and the calculations were performed on TianHe-1 (A).
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REFERENCES
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