cfd modelling of a novel stirred reactor for the bio- production...
TRANSCRIPT
14th European Conference on Mixing Warszawa, 10-13 September 2012 CFD MODELLING OF A NOVEL STIRRED REACTOR FOR THE BIO-
PRODUCTION OF HYDROGEN
G. Montantea, M. Coroneoa, J.A. Francesconib, A. Pagliantia, F. Magellia
a University of Bologna, DICMA – Department of Chemical, Mining and Environmental Engineering, via Terracini 28, 40131 Bologna, Italy;
b INGAR (CONICET-UTN), Avellaneda 3657 (3000), Santa Fe, Argentina
Abstract. In this work the Computational Fluid Dynamics simulation of a stirred bioreactor purposely designed for the production of hydrogen from organic wastes fermentation is presented. The bio-reactor is a vortex-ingesting, dual impeller, mechanically-agitated vessel equipped with a central draft tube. Due to the geometrical and physical complexity, the selected reactor offers a very challenging benchmark for the multiphase flow model capability and poses additional numerical and modelling issues with respect to previously investigated gas-liquid stirred tanks. The fluid dynamics experimental data, available for the same geometrical and operating features of the reactor, allow to strictly evaluate the model capability and to highlight the possible critical issues of the simulation. The applicability of the CFD simulation method proposed in this work for the optimization and the scale up of the bioreactor for the H2 production is discussed through a critical analysis of the prediction results. Keywords: biohydrogen, fermentation, bioreactor, CFD, two-phase model, vortex-ingesting
1. INTRODUCTION The production of hydrogen by biological fermentation of biomass is very promising for
the replacement of fossil fuels by a sustainable and environmental friendly approach and it offers a great potential for the reduction of the H2 production energy requirement with respect to the chemical and electrochemical methods [1-3]. Beside the biochemical and microbiological issues, the engineering aspects, which mainly concern the bioreactor design and the selection of the operating conditions, are of crucial importance for moving towards the technological application. However, these aspects have been less extensively investigated with respect to the former, although in the past few years significant efforts have been devoted to the study of the microbial conversion of organic substrates to bioenergy [4]. Recently, Computational Fluid Dynamics (CFD) has been identified as a viable approach to the design and the scale-up of bio-hydrogen reactors, which are still mainly based on semi-empirical and trial and error approaches [1,5,6].
The bio-hydrogen production process is affected by a number of critical factors, including mixing, H2 partial pressure, medium composition, pH and temperature; in order to provide appropriate conditions mechanical agitation is often selected. For this reason, the experience gained in the experimental and computational analysis of fluid mixing in stirred tanks is expected to provide an important contribution to the industrial production of bio-hydrogen.
In this work, an innovative stirred bioreactor for the hydrogen fermentation of organic wastes is modeled by a CFD simulation strategy based on the Reynolds averaged Navier-Stokes equations and the Eulerian-Eulerian two phase model. The selected bioreactor works under gas-liquid condition, since H2 stripping is adopted for improving efficiency [7,8]. In
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order to isolate the numerical uncertainties associated with the complexity of the computational domain, the results of preliminary single phase simulations are compared with liquid velocity data collected by Particle Image Velocimetry. Afterwards, the capability of the computational approach to predict the two phase flow hydrodynamics is evaluated and the overall strategy for the full simulation of the bio-hydrogen production is outlined.
2. MODELLING APPROACH The simulations of the stirred bioreactor were based on the numerical solution of the
Reynolds averaged Navier-Stokes (RANS) equations in the single-phase and on the relative Eulerian formulation in the two-phase case. The computational domain consisted of about 1.65×106 cells and was divided in three volumes, in order to adopt both a rotating reference frame around each impeller and a steady reference frame including the baffles. The main characteristics of the bioreactor are depicted in Figure 1, where the single phase configuration only is shown. It reproduced closely the experimental bioreactor apart from the two bags containing the bacteria supports, which are placed inside the draft tube. For the simulations, they were schematically designed as shown in Figure 1, while the experimental bags had an irregular shape.
Figure 1. The main geometrical characteristics of the computational domain.
Single phase and two-phase conditions were considered for the simulations, as in the
parallel experimental activity [9]. The purpose was to separately evaluate the numerical issues associated to the geometrical complexity of the reactor from the effects of the modelling approximation, which are due to physical complexity of the vortex-ingesting turbulent flow.
In all cases, the stirred vessel was operated under fully turbulent flow conditions and water and air were the fluids selected for the hydrodynamics characterization.
The k-ε and the Reynolds Stress (RSM) turbulence models were considered for closure of the RANS equations in single phase conditions, while a simple extension of the standard k-ε model to two phase flow was adopted for the gas-liquid simulations. The preliminary evaluation of the turbulence model effect was performed since, as a difference with the case of standard geometry fully baffled stirred tanks, the flows inside the draft tube similarly to that generated in unbaffled tanks, could be critically dependent on the turbulent model closure [10-12].
The simulation of the vortex-ingesting condition was based on the Eulerian formulation of the continuity and momentum equations for each of the phases, which are considered as
Vesselfully baffled, cylindrical, flat bottomedT [m] 0.232H [m] 2TW [m] T/10ImpellersLower impeller: 4-bladed 45° PBTD1 [m] 0.094C1 [m] 0.137Upper impeller: 6-bladed 45° PBTD2 [m] 0.0774C2 [m] 0.314Draft tubecylindrical, provided with 5 holesD [m] 0.092L [m] 0.316
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interpenetrating continua interacting through the interphase transfer terms. Both the mono-disperse assumption and the two-bubble classes were considered for tackling the gas phase, while renouncing to a fully predictive prevision of the bubble size distribution. This simplification was forced by the overall complexity of the final model, which is aimed at describing the H2 production process, thus requiring the additional incorporation of a kinetic model of fermentation reaction and the hydrogen mass transfer flux from the liquid phase to the stripping gas. The numerical solution of the model equations was achieved by a finite-volume method in the realm of the general purpose CFD code FLUENT 6.3. The model equations and the details of the numerical solution strategy are omitted here for the sake of brevity.
3. RESULTS AND DISCUSSION In the reference single phase condition, the bioreactor was filled with water up to the lid
(as in Figure 1). The impeller speed was set at 6 s-1, corresponding to a rotational Reynolds number referred to the lower impeller of 5.3×104.
A preliminary evaluation of the prediction accuracy is obtained from the power dissipation, which was measured by a strain gauge technique in the experiments and evaluated from the torque on the steady and the moving surfaces in the simulations. A slight difference was found between the values predicted by the two turbulence models, which were equal to 3.5W and 3.45W in the k-ε and the RSM calculations respectively, both resulting in a fair agreement with the experimental value of 3.3 W. The predicted power numbers were equal to 1.8 and 1.2 for the lower and the upper PBT impeller, respectively.
The computed flow fields exhibited the typical features of the PBT hydrodynamics, although the specific geometry of the bioreactor leads to significantly different pumping actions of the two impellers. As can be observed in the comparison between the PIV data and the CFD results around the lower impeller shown in Figure 2, the main features of the flow field are correctly captured by both the turbulence models.
(a) (b) (c)
Figure 2. 2D liquid velocity field in single phase conditions around the 4-PBT. (a) PIV data; (b) CFD results - RSM model; (c) CFD results - k ε model. The colour scale is in m/s.
The outcome of the turbulence model is similar in the two impeller regions, as can be
observed in Figure 3, where a quantitative comparison of the experimental and computed velocity components is reported. Two radial profiles of the main velocity component above the upper impeller and below the lower one are selected, that are the radial and the axial components, respectively.
As can be observed, the CFD simulation results are in good agreement with the PIV data, although the accuracy of the prediction is lower than that usually obtained in single phase simulations of standard geometry stirred tanks; this result was expected due to the unfeasibility to describe the exact geometry of the experimental stirred tank. A slightly better agreement is obtained with the RSM in the lower vessel region. Overall, a realistic pictures of
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the flow field is obtained with both the models and for this reason the less computational intensive k-ε turbulence model has been selected for the following more complex conditions.
z=0.093m-0.3
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Figure 3. Experimental and computed velocity profiles along the radial coordinate. (a) Axial velocity below the 4-PBT; (b) radial velocity above the 6-PBT.
For the vortex-ingesting operating mode, the vessel was filled with water up to the height
of 1.6T and the impeller speed was fixed again at N=6 s-1, that is just above the onset of gas entrainment by vortex ingestion. An external tube was used to re-circulate the gas phase from the external part of the tank to the draft tube. Under these operating conditions, an air volumetric flow rate of about 50 L/h was measured with a Pitot tube, thus the gas recirculation in the external tube, which was one of the main objective of the novel bioreactor design, was achieved.
The CFD calculation were run adding to the computational domain of the single phase simulation the external tube, as in the experiments, and solving a set of Eulerian RANS equations for each phase taking into account the inter-phase momentum transfer term through the drag term only. Overall, the operating mode of the reactor was correctly captured by the simulations performed with either one or two bubble classes. The predicted external gas recirculation flow rate was equal to 30 and 70 L/h for the former and the latter simulation, respectively, thus in both cases the gross behavior of the reactor is properly predicted.
The local fluid dynamics of the liquid phase in the two impeller regions is affected in both cases by the gas phase as reported in Figure 4, where the comparison of the relevant velocity maps around the 4-PBT are shown. A fair prediction of the liquid flow field in the lower part of the vessel is obtained in both cases, although with two bubble classes the discharge flow of the impeller moves from the impeller tip towards the inner edge and is closer to the PIV data.
(a) (b) (c)
Figure 4. 2D liquid velocity field in two phase conditions around the 4-PBT. (a) PIV data; (b) CFD simulation with a single bubble class (db=0.5 mm); (c) CFD simulation with two bubble classes (db1=0.25 mm and db2=1.10 mm). The colour scale is in m/s.
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As for the bubbles flow field, the CFD simulations are able to properly predict the pumping action of the upper impeller which draw the bubbles down to the lower PBT, where reasonable results are obtained, as can be observed in Figure 5. With respect to the PIV data (Figure 5a), which represent the velocity field of the smaller bubbles that are numerically more significant, the simulation results obtained with one (Figure 5b) or two bubble classes (Figure 5c and 5d) show that the discharge flow angle is strongly dependent from the bubble diameter, as is the interphase momentum exchange term.
a b
c d
a b
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Figure 5. 2D gas velocity field around the 4-PBT. (a) PIV data; (b) CFD simulation with a single bubble class (db=0.5 mm); (c, d) CFD results with two bubble classes for db1=0.25 mm (c) and db2=1.10 mm (d). The colour scale is in m/s.
The flow field and the gas hold-up distribution in the upper part of the vessel, where the
gas ingestion takes place, are more critically dependent on the assumption on the bubble size and on the number of bubble classes. The liquid velocity field obtained in the two cases is shown in Figure 6. As can be observed a different velocity magnitude is obtained, however in both cases the dominant central vortex produces a region of almost nil liquid velocity whose shape and extension prediction depends from the bubble size and the number of bubble classes.
(a) (b)
Figure 6. 2D liquid velocity field around the 6-PBT. (a) CFD simulation with a single bubble class (db=0.5 mm); (b) CFD simulation with two bubble classes (db1=0.25 mm and db2=1.10 mm). The colour scale is in m/s.
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4. CONCLUSIONS The numerical simulation of the turbulent flow of a vortex-ingesting dual impeller stirred
tank designed for the production of H2 by biological fermentation of waste organics has been presented in this work. Overall the fluid dynamic and operational features of the bioreactor are correctly predicted with an increasing level of accuracy at increasing number of bubble classes.
This leads to conclude that the CFD strategy already developed for simpler gas-liquid stirred vessels can be usefully applied to the design of bioreactors for hydrogen fermentation, provided that further developments of the model for including fermentation and mass transfer are performed.
The overall process of H2 production by fermentation has been already implemented in the present hydrodynamic model by defining a volumetric reaction kinetic on the bags volumes, defined as porous regions with the same pressure drops as the real support adopted for the biomass growth. As for the gas flow rate, after stripping the hydrogen is allowed to escape from the reactor with the inert recirculated gas previously predicted by the fluid dynamic simulation. The results obtained with this approach will be evaluated in future investigations.
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