Application of Filtered Model for Reacting GasâSolid Flows and

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Application of Filtered Model for Reacting Gas-Solid Flows and Optimization in a Large-scale Methanol-to-olefin Fluidized Bed Reactor Li-Tao Zhu, Mao Ye, and Zheng-Hong Luo Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b02819 • Publication Date (Web): 04 Nov 2016 Downloaded from http://pubs.acs.org on November 5, 2016

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Industrial & Engineering Chemistry Research

Application of Filtered Model for Reacting Gas-Solid Flows and Optimization in a Large-scale Methanol-to-olefin Fluidized Bed Reactor

Li-Tao Zhu,1 Mao Ye,2 Zheng-Hong Luo1, * 1

Department of Chemical Engineering, College of Chemistry and Chemical

Engineering, Shanghai Jiao Tong University, Shanghai 200240, P. R. China 2

Dalian National Laboratory for Clean Energy, National Engineering Laboratory for MTO, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, P. R. China

* Correspondence to: Z.-H. Luo; E-mail: [email protected] Tel.:+86-21-54745602 Fax: +86-21-54745602

1

ACS Paragon Plus Environment


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ABSTRACT: A reactor model for a methanol-to-olefin (MTO) reaction system was constructed by incorporating a filtered drag model, a filtered gas-solid heat transfer model and a MTO kinetic model to probe large-scale reactor behavior and explore optimization. First, the efficiency of several typical gas-solid heat transfer models and kinetic models was evaluated by comparing predicted results with experimental data. Second, the effect of two significant operation parameters, namely, reaction temperature and water-to-methanol ratio, were studied based on the above-mentioned model. Predictions suggested the optimum catalyst residence time (about 33 min) and average coke content (about 6.74%) of this MTO system. In addition, relatively high temperature maximized ethylene production, and the water introduced into the feed significantly attenuated coke deposition. This work is the first to conduct coarse-grid simulations by using the developed effective filtered-CFD coupled model to probe the reaction flow and explore optimization for a large-scale MTO reactor.

Keywords: Fluidization; Multiphase flow; Computational fluid dynamics (CFD); Filtered models; MTO kinetics.

2


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1. INTRODUCTION The methanol-to-olefin (MTO) reaction performs a significant function in the C1 chemical industry. MTO is a novel technology for producing high value-added petrochemical components from non-oil resources, such as coal and natural gas.1 For MTO processes, a certain amount of coke deposition on catalyst is known to favor light olefin selectivity,2, 3 which is quite different from fluid catalytic cracking (FCC) processes. Recently, the MTO catalyst and reaction kinetics have been extensively studied.2-–16 However, relatively few studies have investigated the simulation of MTO reactors, especially for large-scale reactors. Soundararajan et al.17 suggested a core-annular fluid hydrodynamic model that combines an MTO kinetic model to describe the fluid hydrodynamics in a fast-fluidized bed riser. The solid catalyst residence time is only several seconds in the simulation, thereby providing insufficient time to deposit the desired coke content. To maintain the desired coke level, the catalyst stays in another bed for a period of time prior to entering the reactor. Chang et al.18 extended the above-mentioned work through a computational fluid dynamics (CFD) model coupling MTO kinetics to explore hydrodynamics and reaction performance. Our group developed a CFD method coupled with a discrete element method to capture particle motion patterns and some important flow distributions in a laboratory-scale gas-solid MTO fluidized bed reactor.19 We also developed a direct concurrent multiscale CFD method incorporating a single particle model (SPM) and a TFM to study the effect of intraparticle transfer on flow structures in a small-scale fluidized bed reactor.20 In this coupled model, a TFM captured the 3



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two-phase flow hydrodynamics in the bed, whereas an SPM was built to feature the individual intraparticle reaction-diffusion process. Recently, a coarse-grid CFD method has been applied to an MTO FBR,21 in which the energy-minimization multiscale (EMMS) method22 was adopted to capture interphase drag force. However, the influence of coke deposition, which has a significant effect on the selectivity of gaseous products, was neglected in their model. Jiang et al.23 investigated a two-stage reactor in series with methanol quenched between the two stages, but this mathematical reactor model did not consider the effect of the flow structures on reactor performance. More recently, Lu et al.24 coupled a classic chemical reaction engineering model with CFD to speed up their isothermal simulation of a pilot-scale MTO fluidized bed reactor.

However, this pilot-scale reactor was operated in the

bubbling fluidization regime and an isothermal distribution was assumed. Practically, industrial-scale MTO reactor is operated in the turbulent fluidization regime, which shows complex turbulent disorder. This turbulent disorder includes heterogeneous temperature fields. Contrastingly, the experimental measurement techniques for industrial MTO devices remain a challenging issue because of turbulent flow disorder and complex reactions. Thus, a more realistic new modeling technique for reactor performance and optimization is particularly required. For this aim, coarse-grid simulation approaches employing TFM are desirable, particularly for industrial-scale equipment.25 For coarse-grid simulation, the grid size is approximately several dozens or even O(100) of the particle diameter for the Geldart A type of particles (such as FCC, MTO catalyst particles). However, such an 4


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approach unavoidably neglects sub-grid structures, such as clusters and streamers in gas-particle

flows,

thereby

resulting

in

unreasonable

predicted

results.26

Characterizing these sub-grid structures using TFM closed by kinetic theory of granular flow (KTGF)27 and the Wen and Yu drag model28 is usually unrealistic because of the limitation of high computing cost; the required mesh size should not exceed 10 particle diameters.25 Thus, coarse-grid simulation with reasonable sub-grid constitutive models should be carried out to consider the impact of the unsolved sub-grid scales. To address the above-mentioned challenging issue, a promising sub-grid filtered method that accounts for the influence of the sub-grid flow structures has been developed by Princeton’s multiphase flow group.29-–32 Notably, Li and his co-workers33 recognized the demand for developing sub-grid methods earlier and their efforts promoted the development of an energy-minimization multi-scale (EMMS) model. In this model, filter size is not set as a parameter. In particular, the EMMS model is analogous to the filtered models in the limit of large filter size.32 Thus far, many studies have included coarse-grid simulation with sub-grid models in industrial devices, such as FCC risers and circulating fluidized bed combustors.34-–44 In spite of the practical importance of MTO fluidized bed reactors, studies focusing on coarse-grid simulation of reactor performance and optimization for a large-scale reactor remain extremely scarce. In our previous study, reasonable cold-flow hydrodynamics, such as relatively stable bed expansion height was predicted by coarse-grid simulation with filtered model in a demonstration-scale MTO reactor. In the work of Zhao et al.21, a coarse-grid CFD model coupled kinetics was developed to 5



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study hydrodynamic features in a demonstration-scale reactor, and the hydrodynamic features were predicted well in comparison with experimental data. However, as previously mentioned, the influence of coke deposition on the controlling of the selectivity of products was neglected. Lu et al.24 extended the above-mentioned work by considering the effect of coke deposition and to accelerate the simulation, a classic chemical reaction approach was integrated with CFD model. For improving hydrodynamic predictions, a sub-grid method (such as EMMS model) was used to consider the influence of the sub-grid structures on the drag model. Their contribution showed that the selectivity of products is highly comparable with the experimental data. But hydrodynamics was not validated, and the reactor was actually operated in bubbling fluidization regime at pilot scale, whereas the practical units at demonstration or commercial scales are operated in turbulent fluidization regime. In the turbulent regime, mass and heat transfer are enhanced, thereby motivating the study of reactor behavior in a large-scale turbulent fluidized bed. This contribution aims to explore reaction flow and probe optimization by using coarse-grid filtered-CFD modeling method in a large-scale MTO turbulent fluidized bed. The kinetic model and filtered gas–solid heat transfer model were integrated with our previous cold-flow filtered TFM model. The constructed reactor model was validated against experimental data. The main objective of model analysis was to evaluate the ability of the models to capture reactor performance, including hydrodynamic behavior, catalyst deposition and component selectivity, and then to select suitable models for the design and optimization of large-scale MTO reactor 6


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processes.

2. MODEL DESCRIPTION In this study, a widely-employed Eulerian–Eulerian TFM closed by KTGF and filtered drag model32 were used to feature gas–solid two-phase flow hydrodynamic behavior. Igci et al.29 pointed out the demand of the constitutive closures for interphase momentum exchange force and the effective solid stresses in the momentum conversation equations. By contrast, the drag force model exerts a dominant effect on the accuracy of CFD modeling results.25, 45 Thus, only the filtered drag model was employed to calculate the effective drag coefficient, whereas filtered solids stresses were not used. In addition, the effect of the solids stresses was assessed to show several discrepancies (see Supporting Information.) through filtered solids stresses model and KTGF. The energy and species transport equations were introduced to describe component mass fraction and temperature distributions. The filtered gas–solid interphase heat transfer model46 was used to obtain the effective gas–solid heat transfer coefﬁcient and a seven-lumped MTO kinetic model4 was used to describe reaction source terms. Given that the TFM and the KTGF are widely used for the gas–solid two-phase reactive flows in fluidized bed reactors, the models are briefly summarized in Table 1. For concision, further detailed model descriptions and the literature to which the readers can refer to for details are available in the Section APPENDIX of the Supporting Information.

3. MODELING METHOD AND CONDITIONS In this work, a simplified demonstration-scale MTO fluidized bed reactor reported 7



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by Zhao et al.21 was explored, as shown in Figure 1. In Figure 1, the total height is 8.50 m and an initial 3.00 m bed height is set. The freeboard at the top is 1.95 m. The stripper at the bottom is 0.44 m. The dense bed in the middle part is 1.00 m. The gas inlet distributor and solid jet inlet have been simplified as an isosceles trapezoid and a square to ensure that the gas and solid catalyst phases can be injected into the bed uniformly. Three-dimensional (3D) simulations are usually better implemented for the purpose of capturing quantitative numerical predictions.47–49 Coke deposition lasts for minutes or hours to reach the desired content, thereby resulting in costly computational resources unaffordable for 3D industrial-scale simulations at present. Moreover, the main objective of the present investigation is to exhibit the ability of the proposed reactor model to qualitatively feature reactor performance and explore optimization. Therefore, a two-dimensional geometrical model was employed in this work and the comparisons of prediction differences between 3D and 2D simulations were implemented to show several discrepancies in the following section. Gambit software (Ansys Inc., USA) was employed to obtain geometry and mesh. Usually, coarse mesh sizes typically that are used for large-scale devices are O(100) of the solid particle diameter when using the filtered model.31, 50-52 Therefore, three sets of coarse grids were created with 133, 267, and 400 solid particle diameters, to study grid size independence. For convenience, the three grid resolutions were shortened to “fine grids”, “medium grids”, and “coarse grids”, respectively. On top of the distributor, structural grid elements with smaller aspect ratio than 1.52 and skewness of less than 0.23 were created. Under the distributor, hybrid grid elements 8


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with smaller aspect ratio than 1.87 and skewness of less than 0.39 were generated. Simulations were performed by means of industrial CFD code, FLUENT (Ansys Inc., USA) in the double-precision mode. Discretization for governing equations was accomplished through a finite volume approach. To incorporate pressure and velocity, a SIMPLE algorithm was utilized. The discretization for volume fraction was a Quick method and the momentum and the discretization for each species term were achieved through a second-order upwind scheme. Moreover, the 1.0×10−3 s time step and 1.0×10−3 convergence criteria were used. Discretization for unsteady time was an implicit first-order approach. A no-slip wall boundary was adopted for two phases. In addition, a sub-relaxation factor was utilized to ensure the simulation converged. The CFD modeling was accomplished on 2.6 GHz Intel® with two CPUs (16 cores) and 16 GB of RAM. Moreover, the coarse-grid simulations using medium grids required approximately 45 min of CPU (16 cores) to compute a one-second physical process. Regarding that the formation rate of coke deposition are required dozens of minutes to achieve the desired average coke content. The coke content in Section 4.1 was set as a constant 6.5% (empirically 6%-7%) to study grid sensitivity and model verification. The physical flow time in these two sections was approximately 40 s and the latter 20 s were used to obtain the time-average variables for our study. The physical flow time in Section 4.4 was approximately 2400 s and lasted for about 75 days, during which the influences of a completely increasing coke-deposition process on product selectivity and methanol conversion are characterized. To investigate the qualitative 9



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effects of two key process parameters on reactor performance, the physical flow time in Section 4.5 was performed for about 180 s. In this work, the experimental data of the axial solid concentration were provided by the Dalian Institute of Chemical Physics (DICP), China. These experimental data was obtained based on a demonstration-scale reactor with a 16 kt/a methanol feed rate. The experimental data of hydrocarbon selectivity with a bed temperature of 723 K and 773 K were obtained from Qi et al.53 The species thermodynamic data and the kinetic parameters are available in the Supporting Information. The other main model parameters are shown in Table 2.

4. RESULTS AND DISCUSSION 4.1. Study of mesh size independence and model identification. Mesh-size independent time-average profiles could reportedly be achieved when the filter-to-grid ratio is not less than 2.0 for a cold-flow system.31, 50 To further evaluate sensitivity for the reaction system, we selected two filter-to-grid ratios (2.0 and 4.0). Figures 2a and 2b show that a small difference exists in the near-wall regions. Meanwhile, Figure 2c shows that the predicted results qualitatively match the experimental data. Basically, a core-annulus structure and an S-shaped axial solid concentration profile was predicted based on different grid resolutions, indicating that coarse-grid simulation was effectively sufficient to capture the main hydrodynamics. Moreover, mesh-size independent time-average profiles are observed when filter length is not less than twice the mesh size. This observation is consistent with the results of a literatures reported conducted by Igic et al.31, 10


50

Figure 3 shows the

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selectivity

of

products

simulated

at

different

resolutions.

Herein,

the

ethylene-to-propylene ratio represents the ratio of ethylene mass fraction to propylene mass fraction. As displayed, the selectivity values of ethylene and propylene were approximately 41.2%–42.5% and 36.6%–37.3%, respectively. Evidently, product selectivity predicted through different resolutions matched the experimental data. As presented in Table 3, the ethylene-to-propylene ratio simulated with medium grids agrees slightly better with the experimental data compared with those with coarse and fine grids. The conversion of menthol predicted with coarse and fine resolutions are slightly better match the experimental data compared with that obtained with medium resolution. Xie et al.48 and Li et al.49 pointed out that both 2D and 3D CFD modeling could yield qualitative results. However, quantitative differences between 2D and 3D simulations could still be observed. Therefore, comparisons between 3D and 2D simulations for the MTO reaction system were studied by setting a constant coke deposition content (6.5%), as shown in Figure 4. First, a 3D simulation was carried out, but an unexpected bed expansion height was observed because the grid resolution was insufficiently fine for the present reaction system. Then, the 3D grid resolution was further refined until the bed expansion height remained stable. The 3D and 2D simulations were both performed for 40 s and it took about 15 days wall-clock time was required for the current 3D simulation to accomplish a 40 s physical time. In Figure 4a, extremely similar bed expansion heights for 3D and 2D simulations were shown. However, the local solid concentration close to wall regions of the lower area 11



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of the bed as predicted by 3D simulation was higher than that predicted by 2D simulation. Figure 4b shows that the predictions for solid concentrations match the experimental data. In comparison with experimental data, the main products, including C2 and C3 lumps, were slightly lower than those predicted by both 3D and 2D simulations, as shown in Figure 4c. The main reactor behaviors can be described by both 2D and 3D simulations. The function of time step has also been studied, as shown in the Supporting Information. Both of the two simulations that used time steps of 5×10−4 and 1× 10−3s could characterize the main performances of the reactor. Notably, the dimensionless Courant number ( N c = U ∆ t / ∆ y , where U , ∆ t , ∆ y represent the gas velocity, time step and grid size in the direction of the flow, respectively) through the medium grids in this work was about 0.093, which satisfied the required Courant number ranging between 0.03 and 0.30 as suggested by several researchers54, 55. This range ensures numerical results that are independent of convergence criterion, grid size and time step in this work. As a consequence of the above discussions, medium grids with filter-to-grid ratio equal to 2.0 and a step of 0.001 s were selected in all subsequent simulations. 4.2. Evaluation of the gas-solid heat transfer model. Figure 5 shows the comparisons of product selectivity among three different gas-solid heat transfer models46, 56, 57. In Figure 5, a qualitative agreement among the results based on these three models is illustrated. Figure 6 displays temperature distributions in the bed. Figures 6a and 6b show that similar distributions were simulated between the gas and 12


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catalyst particle phases for each model. Meanwhile, Figure 6c shows an evident difference (about 5–7 K) near the gas inlet between the gas and solid phases. This observation suggests that a hotspot could be captured based on the filtered gas–solid heat transfer model (Figure 6c). As shown in Figure 7a, the gas–solid heat transfer coefficient predicted by the first two classic models is higher by one to two orders of magnitude than that by the filtered model. According to eqs 38 and 42, the

γ gs, filt is

mainly determined by (1-Q)*Nu in this work. As shown in Figure 7b, the (1-Q)*Nu calculated through the filtered model is smaller than that calculated by the Ranz– Marshall and Gunn models at relatively low Reynolds number. Namely,

γgs is

predicted higher by the two classical models because the presence of sub-grid structures of the gas-catalyst flows is ignored. As a result, the reaction heat could not be rapidly transferred to the solid phase due to the limited gas-solid heat transfer coefficient predicted by the filtered model. Consequently, a hotspot was easily formed in gas phase near the inlet, as observed in Figure 6c; a similar observation has also been reported in the literature11, 58. At the same time, the other two models did not predict this evident macroscopic performance. Therefore, this investigation proposes the necessity of characterizing macroscopic flow features based on an effective filtered gas-solid heat transfer model. 4.3. Evaluation of the MTO kinetic model. To evaluate the applicability of kinetic models in describing reactor behavior, including hydrodynamics and product selectivity, we analyzed three typical lumped kinetic models (see Section Appendix C in the Supporting Information). For convenience, the three kinetic models are 13



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shortened to “Model A,” “Model B,” and “Model C,” respectively. Figure 8 displays the comparison of hydrodynamic features with three different kinetic models. Qualitatively, main characteristics, including similar profiles of the axial and radial solid concentrations and bed expansion height, could be indicated in Figures 8a and 8b. Results of the axial solid volume fraction calculated through the three models basically match the experimental data. If the same amount of methanol flowed into the bed, nearly the same predicted amount of main gaseous component products (C2, C3, C4, C5 and water lumps) in the three kinetic models studied will be formed in the bed. This formation will lead to a highly similar gas expansion ratio, meaning that the kinetic models utilized in this work exert a minor effect on bed expansions; similarly, main hydrodynamic behaviors can be observed. Figure 8c shows that gaseous species selectivity that was obtained based on Model C was better matched with the practical data than those based on Models A and B. Further details could be found in Table 4, showing that highly similar methanol conversions were simulated based on the three kinetic models. Nevertheless, the prediction based on Model A seems to produce a higher ethylene-to-propylene ratio (about 1.92), whereas the prediction based on Model B produces a ratio of 0.58. Although the kinetic parameters of the models were all based on the SAPO-34 catalyst, the selectivity of ethylene as simulated by Model A was about 55.43%, implying that the pore sizes inside the catalyst used in this model would be smaller than those of the other two models. Meanwhile, the selectivity of C4 lumps simulated by Model B was approximately 20.94%, implying that the catalyst employed in this model possessed larger pore sizes. However, the 14


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kinetic parameters of Model C were obtained over an industrial SAPO-34 catalyst. Both the water effects and the catalyst deposition were considered in Model C, indicating that gaseous species selectivity predicted by Model C could better respond to the changes in water and coke distributions compared with the other two models. In conclusion, this investigation indicates that the Model C is more suitable for our reaction system. 4.4. Study of coke deposition. Figure 9 displays transient solid concentration and velocity vector distributions of flow field in the MTO reactor at 1500 s. In Figure 9, solid catalyst particles are carried upward by bubbles and these bubbles, which break upon reaching at bed surface. The particles then flow downward at the vicinity of the bounding wall. All of these movement lead to turbulent inhomogeneous distribution59, 60. On one hand, relatively reasonable macroscopic flow hydrodynamics was predicted by both of the present simulation with reactions and the previous work60 without reactions. For example, a classic core-annulus structure and S-shaped profiles were shown in both studies. Both studies show that the surface was unclear at the top part of the dense bed and that numerous catalyst particles were entrained into the freeboard, in a typical phenomenon in the turbulent fluidization regime. On the other hand, the bubble sizes in the present study seem to be larger than in our previous work. In addition, the total number of bubbles generated in the bed of the present study is essentially higher than that of previous work. This higher number may be attributed that MTO reaction processes are exothermic and involve volume expansion; as the reactions proceed, a large amount of gaseous species are generated in the bed. 15



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Figure 10 delineates the transient distributions of macroscopic flow characteristics in the reactor at 1500 s. Figure 10a further confirms that the coke deposits near the reactor feed where the hotspot (as displayed in Figure 6) is located. A similar result was observed by Aguayo et al.11 and Liang et al.58 eq 42 shows that coke deposition is sensitive to reaction temperature, suggesting that higher temperature leads to faster coke deposition. Correspondingly, the coke deposits more severely near the hotspot, in which the reaction mainly occurs and attenuates along the bed. The contours in Figure 11 also show that as the catalyst particles are deactivated, coke content grows; coke is distributed from the gas feed toward the exit of the reactor bed. A certain degree of coke deposition on catalyst can typically favor light olefin selectivity (see Figure 12c), but excessively high coke deposition content will lead to a rapid decrease of methanol conversion. Thus, to obtain the corresponding optimum value, the desired coke content should be accumulated within a suitable period of time. As shown in Figure 11, coke content near the gas inlet increases rapidly in the initial few minutes of the reaction. During coke deposition, the coke formation rate decreases considerably. Simultaneously, virtually 100% methanol conversion remains constant for a certain period (about 2000 s) prior to a sharp decrease of methanol conversion. On the other hand, it could be found that the total selectivity of the desired light olefins (C2 and C3) increased during coke deposition (see Figure 12c). The above-mentioned discussion suggests that for our MTO reaction system, the optimum catalyst residence time and average coke content along the fluidized bed were about 33 min and 6.74%, respectively. Industrially, once the desired coke 16


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deposition is reached, the provision of a regeneration unit will perform a significant function in recycling parts of the catalyst stream for continuous processing.10 Figures 12a and 12b compare the results between the present work and those from literatures. The ethylene selectivity predicted in the present work is higher than in Soundararajan et al.’s work. This finding may be attributed to the different types of the reactors used in these works. In their riser reactor, product selectivity and methanol conversion increased along the reactor height; even at the outlet, obvious growing rates were still observed. However, the product selectivity and methanol conversion observed in our study basically reaches a stable value in the middle part of the bed. First, the riser reactor used in their work was actually operated in the fast-fluidized regime; hence, a high gas velocity could be expected. Second, the riser was a dilute-phase fluidized bed, whereas the reactor used in the present study was a dense-phase bed. Under this high gas velocity and low catalyst concentration condition, the MTO reaction in the riser was probably insufficient. Thus, the present turbulent dense fluidized bed favors the methanol conversion and product selectivity, but the riser reactor may be not suitable for MTO units. Meanwhile, the ethylene selectivity predicted in the present work is basically close to that based on Bos et al.’s work at lower coke content. This similarity may due to the kinetic frame used in this work being a simplification of Bos et al. Then the kinetic parameters were obtained over an industrial MTO catalyst. 4.5. Optimization of key process parameters. It is well known that reaction temperature and inlet water content perform a significant function in MTO reactor 17



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design and optimization. To investigate the effect of these two key process parameters on the selectivity of gaseous components and coke content, we executed 180 s physical flow time for each case in this section. 4.5.1. Effect of reaction temperature. Cases with two different temperature levels were explored. As observed in Figure 13a, ethylene selectivity increases along with temperature, whereas propylene and C4 and C5 lumps decrease. Meanwhile, the selectivity of ethylene plus propylene increased as temperature increased, as is qualitatively consistent with the practical data. Figure 13b shows that higher temperature intensifies the coke formation rate on the stream. Reportedly, species resultants, including coke, are mainly formed inside the pore of the catalyst, and coke formation inside the pore can cause catalyst pore blockage.2, 9 An intensified coke formation rate will speed up the blockage of these pores, thereby resulting in a decrease in pore size. In this case, small-size molecule alkenes, such as ethylene, are more easily formed inside the smaller pores.61,

62

higher

feasible

temperature

will

be

effectively

This investigation suggests that to

achieve

a

larger

ethylene-to-propylene ratio. 4.5.2. Effect of water. Cases with two different water-to-methanol ratios were discussed in this section. Figure 14a illustrates the influence of initial water mass fraction on the selectivity of the four lumped products versus the time on stream. Evidently, higher water-to-methanol ratio leads to higher selectivity of alkenes in larger molecules but also leads to lower ethylene selectivity. Figure 14b shows that the coke formation of both water-to-methanol ratios is extremely rapid at the 18


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beginning of the entire flow time, whereas a gentle formation rate was found across the entire flow time. In addition, a higher water-to-methanol ratio slowed the formation rate of coke (Figure 14b). This finding is probably because more acid sites inside the pore of the heterogeneous catalyst are occupied by water as the water-to-methanol ratio increases, which agrees with reports in the literature.9, 10 On the other hand, according to the mathematical formula of the reaction rate ( Ri = υi kiθwCCH3OHϕi Mwi ,θw = 1/1+ Kw X w ), the reaction rate slows down as the water content increases. Correspondingly, the reaction rate, including the coke formation rate, slows down. In other words, higher water-to-methanol ratio leads to the attenuation of catalyst deactivation.

5. CONCLUSIONS The present coarse-grid simulation aims to comprehensively understand reacting flow behaviors and study optimization in a demonstration-scale MTO multiphase flow reactor. Coarse-grid sizes that are typically used for large-scale devices are O(100) of the solid particle diameters when employing a filtered drag model integrated into KTGF. For 2D simulations in this work, the filtered model is expected to be ~O(100) times faster than the classic Wen and Yu drag model and kinetic theory model based on TFM.50 In addition, to further accelerate the simulation, we executed a UDF of macro program for reaction rate for every iteration time step for each time, instead of every iteration a time, which means the information of reaction rate was updated for every iteration time step for each time. As a consequence of the above predicted results, basic conclusions were drawn: 19



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(1) Study of grid sensitivity confirms that, even for a reaction system, mesh-size independent time-average profiles are achieved when the filter size does not exceed twice of the grid size. (2) Compared with our previous study under a cold modeling condition, predictions to basic core-annulus structures and S-shaped profiles can still be observed for a reaction condition through the coupled model. (3) The CFD modeling results suggest the necessity of capturing flow behaviors on the basis of the filtered model that considers the effects of sub-grid flow structures. Moreover, the simulation also confirms that the seven-lumped kinetic model precisely describes the product selectivity for our reaction system. (4) For our MTO reaction system, the optimum catalyst residence time and average coke content along the fluidized bed are about 33 min and 6.74%, respectively. (5) Investigations on two key process parameters indicate that a relatively higher temperature can maximize ethylene, whereas the existence of water in the feed will weaken catalyst deactivation. Further investigations should be extended to a multi-scale chemical reaction kinetic model that involves the presence of sub-grid flow structures and the influences of intraparticle transfer on flow fields and main component distributions. Strictly speaking, the filtered heat transfer model still needs to be further validated in comparison with experimental data in future studies. ASSOCIATED CONTENT 20


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The Supporting Information is available free of charge on the ACS Publications website at DOI…. Comparisons of time step, effect of the ﬁltered solids stresses, the thermodynamic data of the components and the kinetic parameters of Models A-C are provided as Supporting Information. AUTHOR INFORMATION Corresponding Author Professor Z. H. Luo; E-mail: [email protected]; Tel.: +86-21-54745602; Fax: +86-21-54745602 Notes The authors declare no competing financial interests.

ACKNOWLEDGMENTS The authors thank the National Natural Science Foundation of China (No. U1462101 and 21625603), the National Ministry of Science and Technology of China (No. 2012CB21500402) and the Center for High Performance Computing, Shanghai Jiao Tong University for supporting this work.

NOMENCLATURE =

−1 coke content over the catalyst, gcoke·(100gcat )

Ci

=

mole concentration, mol·L−1

ds

=

particle diameter, m

Eai

=

the activation energy

f

=

extrapolation to infinite resolution

Cc

21



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H

=

the correction factor for interphase momentum exchange coefficient

Hd

=

heterogeneity index

keff ,g

=

the bulk molecular conductivity

Ksg

=

interphase exchange coefficient, kg·m-3·s-1

Mwi

=

molecular weight, g ·mol −1

Nu

=

the Nusselt number

t

=

flow time, s

Pr

=

the Prandtl number

Q

=

correction to interphase heat transfer

vt

=

particle terminal velocity, m/s

v

=

Velocity, m·s-1

vslip

=

dimensionless slip velocity, m·s-1

Vcell

=

grid cell volume, m3

Xw

=

water content in the feed

Greek symbols

α = volume fraction

ρ = Density, kg·m-3 ∆f

=

dimensionless filter size

∆f fil

=

filter size

∆g

=

the grid size

γ

=

gas-solid heat transfer coefficient

θw

=

water function

22


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ϕi

=

catalyst deactivation function for specie i

Subscripts

env = envelope

g = gas phase

s

= solid phase

fil = filtered

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fluidization. Chem. Eng. Sci. 2011, 66, 5541-5555. (23) Jiang, B.; Xiang, F.; Yan, L.; Jiang, Y.; Liao, Z.; Wang, J.; Yang, Y. Methanol to propylene process in a moving bed reactor with by products recycling: Kinetic study and reactor simulation. Ind. Eng. Chem. Res. 2014, 53, 4623-4632. (24) Lu, B.; Luo, H.; Li, H.; Wang, W.; Ye, M.; Liu, Z.; Li, J. Speeding up CFD simulation of fluidized bed reactor for MTO by coupling CRE model. Chem. Eng. Sci. 2016, 143, 341-350. (25) Agrawal, K.; Loezos, P. N.; Syamlal, M.; Sundaresan, S. The role of meso-scale structures in rapid gas–solid flows. J. Fluid Mech. 2001, 445, 151-185. (26) Andrews IV A. T.; Loezos N. L.; Sundaresan S. Coarse-grid simulation of gas-particle flows in vertical risers. Ind. Eng. Chem. Res. 2005, 44, 6022-6037. (27) Gidaspow, D. Multiphase flow and fluidization: continuum and kinetic theory descriptions; Academic press: New York, 1994. (28) Wen, C.; Yu, Y. Mechanics of fluidization. Chem. Eng. Prog. Symp. Ser. 1966, 62, 100-111. (29) Igci, Y.; Andrews, A. T.; Sundaresan, S.; Pannala, S.; O'Brien, T. Filtered two– fluid models for fluidized gas-particle suspensions. AIChE J. 2008, 54, 1431-1448. (30) Igci, Y.; Sundaresan, S. Constitutive models for filtered two-fluid models of fluidized gas–particle flows. Ind. Eng. Chem. Res. 2011, 50, 13190-13201. (31) Igci, Y.; Sundaresan, S. Verification of filtered two–fluid models for gas–particle 26


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flows in risers. AIChE J. 2011, 57, 2691-2707. (32) Milioli, C. C.; Milioli, F. E.; Holloway, W.; Agrawal, K.; Sundaresan, S. Filtered two‐fluid models of fluidized gas–particle flows: New constitutive relations. AIChE J. 2013, 59, 3265-3275. (33) Li, J. Particle-fluid two-phase flow: the energy-minimization multi-scale method: Metallurgical Industry Press; 1994. (34) Zhang, N.; Lu, B.; Wang, W.; Li, J. Virtual experimentation through 3D full-loop simulation of a circulating fluidized bed. Particuology 2008, 6, 529-539. (35) Wang, J.; Van der Hoef, M.; Kuipers, J. Coarse grid simulation of bed expansion characteristics of industrial-scale gas–solid bubbling fluidized beds. Chem. Eng. Sci. 2010, 65, 2125-2131. (36) Zhang, N.; Lu, B.; Wang, W.; Li, J. 3D CFD simulation of hydrodynamics of a 150MW e circulating fluidized bed boiler. Chem. Eng. J. 2010, 162, 821-828. (37) Lu, B.; Zhang, N.; Wang, W.; Li, J.; Chiu, J. H.; Kang, S. G. 3‐D full‐loop simulation of an industrial–scale circulating fluidized–bed boiler. AIChE J. 2013, 59, 1108-1117. (38) Chen, X.; Wang, J.; Li, J., Coarse grid simulation of heterogeneous gas–solid flow in a CFB riser with polydisperse particles. Chem. Eng. J. 2013, 234, 173-183. (39) Zhou, Q.; Wang, J. Coarse grid simulation of heterogeneous gas–solid flow in a CFB riser with EMMS drag model: Effect of inputting drag correlations. Powd. Technol. 2014, 253, 486-495. 27



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(40) Benyahia, S., On the effect of subgrid drag closures. Ind. Eng. Chem. Res. 2009, 49, 5122-5131. (41) Igci, Y.; Pannala, S.; Benyahia, S.; Sundaresan, S. Validation studies on filtered model equations for gas-particle flows in risers. Ind. Eng. Chem. Res. 2011, 51, 2094-2103. (42) Cloete, S.; Johansen, S. T.; Amini, S. Evaluation of a filtered model for the simulation of large scale bubbling and turbulent fluidized beds. Powd. Technol. 2013, 235, 91-102. (43) Schneiderbauer, S.; Puttinger, S.; Pirker, S.; Aguayo, P.; Kanellopoulos, V. CFD modeling and simulation of industrial scale olefin polymerization fluidized bed reactors. Chem. Eng. J. 2015, 264, 99-112. (44) Ozarkar, S. S.; Yan, X.; Wang, S.; Milioli, C. C.; Milioli, F. E.; Sundaresan, S. Validation of filtered two-fluid models for gas–particle flows against experimental data from bubbling fluidized bed. Powd. Technol. 2015, 284, 159-169. (45) Hartge, E. U.; Ratschow, L.; Wischnewski, R.; Werther, J. CFD-simulation of a circulating fluidized bed riser. Particuology. 2009, 7, 283-296. (46) Agrawal, K.; Holloway, W.; Milioli, C. C.; Milioli, F. E.; Sundaresan, S. Filtered models for scalar transport in gas–particle flows. Chem. Eng. Sci. 2013, 95, 291-300. (47) Reuge, N.; Cadoret, L.; Coufort-Saudejaud C, Pannala S, Syamlal M., Caussat B. Multifluid

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gas–solids

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hydrodynamics: influence of the dissipation parameters. Chem. Eng. Sci. 2008, 63, 5540-5551. (48) Xie, N.; Battaglia, F.; Pannala, S. Effects of using two-versus three-dimensional computational modeling of fluidized beds: Part I, hydrodynamics. Powd. Technol. 2008, 182, 1-13. (49) Li, T.; Pannala, S.; Shahnam, M. Reprint of CFD simulations of circulating fluidized bed risers, part II, evaluation of differences between 2D and 3D simulations. Powd. Technol. 2014, 265, 13-22. (50) Igci, Y. Closures for coarse-grid simulation of fluidized gas-particle flows. Ph.D. Thesis, Princeton University, May 2011. (51) Parmentier, J. F.; Simonin, O.; Delsart, O. A functional subgrid drift velocity model for filtered drag prediction in dense fluidized bed. AIChE J. 2012, 58, 1084-1098. (52) Schneiderbauer, S.; Puttinger, S.; Pirker, S. Comparative analysis of subgrid drag modifications for dense gas–particle flows in bubbling fluidized beds. AIChE J. 2013, 59, 4077-4099. (53) Qi, Y.; Liu, Z.; Lv, Z.; Wang, H.; He, C.; Xu, L.; Zhang, J.; Wang, X., Method for producing light olefins from methanol or/and dimethyl ether. U.S. Patent 8,148,587, April 3, 2012. (54) Cornelissen, J. T.; Taghipour, F.; Escudié, R.; Ellis, N.; Grace, J. R. CFD modelling of a liquid–solid fluidized bed. Chem. Eng. Sci. 2007, 62, 6334-6348. 29



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(55) Coroneo, M.; Mazzei, L.; Lettieri, P.; Paglianti, A.; Montante, G. CFD prediction of segregating fluidized bidisperse mixtures of particles differing in size and density in gas–solid fluidized beds. Chem. Eng. Sci. 2011, 66, 2317-2327. (56) Ranz, W.; Marshall, W. Evaporation from drops. Chem. Eng. Prog. 1952, 48, 141446. (57) Gunn, D. Transfer of heat or mass to particles in fixed and fluidised beds. Int. J. Heat Mass Transfer. 1978, 21, 467-476. (58) Liang, J.; Li, H.; Zhao, S.; Guo, W.; Wang, R.; Ying, M. Characteristics and performance of SAPO-34 catalyst for methanol-to-olefin conversion. Appl. Catalysis. 1990, 64, 31-40. (59) Gao, J.; Lan, X.; Fan, Y.; Chang, J.;, Wang, G.; Lu, C. Xu, C. CFD modeling and validation of the turbulent fluidized bed of FCC particles. AIChE J. 2009, 55, 1680-1694. (60) Zhu, L.T.; Xie, L.; Xiao, J.; Luo, Z. H. Filtered model for the cold-model gas-solid flow in a large-scale MTO fluidized bed reactor. Chem. Eng. Sci. 2016, 143, 369-383. (61) Chang, C. D. Hydrocarbons from methanol. Catal. Rev. 1983, 25, 1-118. (62) Stöcker, M. Methanol-to-hydrocarbons: catalytic materials and their behavior. Microporous Mesoporous Mater. 1999, 29, 3-48.

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Figure captions Figure 1 Geometry of the demonstration-scale MTO fluidized bed reactor studied in this study. Figure 2 Averaged distributions simulated with different grid resolutions: (a) radial solid concentration; (b) axial solid velocity along the radial position; (c) axial solid volume fraction. (Note:

Cc represents the weight percent of coke on the

catalyst and Y is the axial bed height.) Figure 3 Selectivity of products simulated with different resolutions and comparison using experimental data. Figure 4 Comparisons between 3D and 2D simulations for MTO reacting system: (a) Transient solid volume fraction distributions of flow field contours; (b) Axial solid volume fraction distributions; (c) Selectivity of gaseous products. Figure 5 Comparison of gaseous species selectivity using different gas-solid heat transfer models. Figure 6 Comparison of temperature distributions using different gas-solid heat transfer models: (a) Ranz-Marshall; (b) Gunn; (c) Filtered. Figure 7 (a) Correction to the gas-solid heat transfer coefficient (1-Q) vs. solid volume fraction; (b) Comparison of (1-Q)*Nu using different gas-solid heat transfer models. Figure 8 Comparisons of: (a) axial solid concentration; (b) radial solid concentration and (c) selectivity of products predicted using different MTO kinetic models. (Note: Model A, B and C are mainly based on Gayubo et al.10, Chen et al.14 31



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and Ying et al.4 respectively.) Figure 9 (a) Transient solid concentration and (b) solid velocity vector distributions of flow field in the MTO reactor at 1500 second. Figure 10 Transient distributions of flow field in the MTO reactor at 1500 second: (a) Coke content; (b)~(i) gaseous species mass fraction on a water present basis. Figure 11 Methanol conversion and MTO inlet catalyst coke content vs. flow time. Figure 12 (a) Effect of inlet coke content on ethylene selectivity; (b) Effect of inlet coke content on ethylene-propylene ratio (wt%/wt%) ; (c) Effect of flow time on components selectivity. Figure 13 (a) Effect of the temperatures on the four lumped main product selectivity vs. the time on stream with a 20% initial water mass fraction. (b) Coke content vs. the time on stream at different temperatures with a 20% initial water mass fraction. Figure 14 (a) Effect of the initial water mass fraction on the four lumped main product selectivity vs. the time on stream at 723 k. (b) Coke content vs. the time on stream at 723 k with different initial water mass fractions.

32


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Tables Table 1．．Governing equations and constitutive laws. Continuity equation (i=g, s) → ∂ (α i ρ i ) + ∇ ⋅ (α i ρi vi ) = 0 ∂t

(1)

Momentum conservation equations ur ∂ r r r r r (α g ρ g vg ) + ∇ ⋅ (α g ρ g vg ⋅ vg ) = −α g ∇p + ∇ ⋅ (τ g + τ gRe ) + K gs (vs − vg ) + α g ρ g g ∂t → ∂ r r r r r (α s ρs vs ) + ∇⋅ (α s ρs vs ⋅ vs ) = −αs∇p − ∇ps + ∇⋅ (τ s + τ sRe ) + Ksg (vg − vs ) + α s ρs g ∂t Gas and solid phases stress tensors r r 2 r τ g = α g µ g (∇ ⋅ v g + ∇ ⋅ v g T ) + α g ( λ g − µ g ) ∇ ⋅ v g ⋅ I 3 r rT 2 r τ s = α s µ s (∇ ⋅ vs + ∇ ⋅ vs ) + α s (λs − µ s )∇ ⋅ vs ⋅ I 3 Gas and solid phases Reynolds stress tensors 2 r r r τ gRe = − (α g ρ g k g + µ g ,t ∇ ⋅ vg ) I + µ g ,t (∇vg + ∇vg T ) 3 2 r r r τ sRe = − (α s ρ s k s + µ s ,t ∇ ⋅ vs ) I + µ s ,t (∇vs + ∇vsT ) 3 Filtered drag force coefficient r r α sα g ρ g vs − vg −2.65 3 K sg , fil = K sg (1 − H ) = CD α g (1 − H ) 4 dp

CD =

24 3 [1 + ( α g Re s )0.687 ] α g Re s 20 r

Re s =

(2) (3)

(4) (5)

(6) (7)

(8)

(9)

r

ρ g d p vs − vg

(10)

µg

Species transport equations ∂ (α g ρ g X i ) ∂t

r + ∇ ⋅ (α g ρ g vg X i ) = −∇ ⋅ (α g J g ,i )] + α g M i ∑ Ri

Energy balance equations for two phases r ∂ (α g ρ g hg ) + ∇ ⋅ (α g ρ g vg hg ) = α g ∇ ( keff , g ∇Tg ) + H sg (Ts − Tg ) + α g ∑ Ri ⋅ ∆H i ∂t ∂ r (α s ρ s hs ) + ∇ ⋅ (α s ρ s vs hs ) = α s ∇( keff , s ∇Ts ) + H gs (Tg − Ts ) ∂t Solid phase pressure ps = α s ρ s Θ s [1 + 2 g 0α s (1 + es )]

(11)

(12) (13)

(14) 33



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Solid phase bulk viscosity Θ 4 λs = α s ρ s d p g 0 (1 + es )( s )1/ 2 π 3 Radial distribution function

g0 =

Page 34 of 52

(15)

1

(16)

1 − (α s / α s ,max )1/3

Granular temperature 1 Θ s = vs' vs' 3 Granular temperature equation 3 ∂ r r [ (α s ρ s Θ s ) + ∇ ⋅ (α s ρ s vs Θ s )] = ∇ ⋅ ( kΘ s ∇Θ s ) + ( − ps I + τ s ) : ∇vs − γ Θ s + φ gs 2 ∂t Collisional energy dissipation

γΘ = s

12(1 − es2 ) g 0 ρ sα s2Θ1s.5 ds π

(17)

(18)

(19)

The diffusion coefficient

kΘ s =

150ρ s d s πΘ s 6 Θs [1 + α s g 0 (1 + e)]2 + 2 ρ sα s2 (1 + e) g 0 384(1 + e) g 0 5 π

(20)

Solid phase shear viscosity

µ s = µ s ,col + µ s , kin + µ s , fr 4 5

(21)

µs ,col = α s ρ s d p g0 (1 + es )

µs ,kin = µ s , fr =

Θs

(22)

π

10d p ρ s πΘ s

4 [1 + (1 + es )α s g 0 ]2 96α s (1 + es ) g0 5 ps sin θ

(23)

(24)

2 I2D

34


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Table 2．．Model parameters. Description

Value

Catalyst density

1500 Kg·m-3

Particle Diameter

75 µm

Velocity inlet boundary condition

1.86 m·s-1

Catalyst heat capacity

800 J·Kg-1·m-3

Catalyst thermal conductivity

0.192 W·m-1·K-1

Gas thermal conductivity

0.0242 W·m-1·K-1

Diffusion coefficient in CFD model

2.88×10-5 m2·s-1

Gas viscosity

2.4×10-5 Pa·s

Catalyst circulation rate

400 kg·h-1

Outlet boundary condition

Pressure outlet

Wall boundary condition

No slip for both gas and solid phases

Wall Thermal conditions

Constant Temperature(723 K )

Particle-particle restitution coefficient

0.9

Angle of internal friction

30○

Gravitational acceleration

9.81 m·s-2

Operating pressure

101.325 kPa

Inlet gas and catalyst temperature

Constant Temperature (723 or 773K)

Outlet gas and catalyst temperature

Constant Temperature (723 or 773K)

Mass Ratio of H2O : CH3OH

0:1.0; 0.2:0.8

Transport & Reaction

Volumetric Reaction (By UDFs)

Solid phase packing limit

0.63

Initial bed height

3.0 m

Convergence criteria

10-3

Time step

10-3 s

35



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Table 3．．Predicted results of product simulated with different grid resolutions.

C2H4/C3H6

Coarse

Medium

Medium

Fine

∆f/∆g=2.0

∆f/∆g =2.0

∆f/∆g =4.0

∆f/∆g =2.0

1.15

1.10

1.11

1.14

1.10

99.31

99.50

99.11

99.13

Conversion/% 99.08

36


EXP.

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Table 4．． Predicted results of product simulated with three different kinetic models. Model A

Model B

Model C

EXP.

C2H4/C3H6

1.92

0.58

1.10

1.10

Conversion/%

100.00

99.82

99.31

99.13

37



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For Table of Contents Only

38


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Figure 1 50x63mm (600 x 600 DPI)



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Figure 10 81x72mm (300 x 300 DPI)


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Figure 11 48x39mm (600 x 600 DPI)



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Figure 12 59x46mm (600 x 600 DPI)


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Figure 13 57x77mm (600 x 600 DPI)



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Figure 14 63x86mm (600 x 600 DPI)


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