Using Computational Fluid Dynamics Software to Estimate Circulation Time Distributions in Bioreactors
Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circu...
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| Vydáno v: | Biotechnology progress Ročník 19; číslo 5; s. 1480 - 1486 |
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2003
American Institute of Chemical Engineers |
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| ISSN: | 8756-7938, 1520-6033 |
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| Abstract | Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circulation time distribution (CTD). There are few examples of CTDs in the literature, and experimental determination of CTD is at best a challenging task. The goal in this study was to determine whether computational fluid dynamics (CFD) software (FLUENT 4 and MixSim) could be used to characterize the CTD in a single‐impeller mixing tank. To accomplish this, CFD software was used to simulate flow fields in three different mixing tanks by meshing the tanks with a grid of elements and solving the Navier‐Stokes equations using the κ‐ϵ turbulence model. Tracer particles were released from a reference zone within the simulated flow fields, particle trajectories were simulated for 30 s, and the time taken for these tracer particles to return to the reference zone was calculated. CTDs determined by experimental measurement, which showed distinct features (log‐normal, bimodal, and unimodal), were compared with CTDs determined using CFD simulation. Reproducing the signal processing procedures used in each of the experiments, CFD simulations captured the characteristic features of the experimentally measured CTDs. The CFD data suggests new signal processing procedures that predict unimodal CTDs for all three tanks. |
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| AbstractList | Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circulation time distribution (CTD). There are few examples of CTDs in the literature, and experimental determination of CTD is at best a challenging task. The goal in this study was to determine whether computational fluid dynamics (CFD) software (FLUENT 4 and MixSim) could be used to characterize the CTD in a single-impeller mixing tank. To accomplish this, CFD software was used to simulate flow fields in three different mixing tanks by meshing the tanks with a grid of elements and solving the Navier-Stokes equations using the kappa-epsilon turbulence model. Tracer particles were released from a reference zone within the simulated flow fields, particle trajectories were simulated for 30 s, and the time taken for these tracer particles to return to the reference zone was calculated. CTDs determined by experimental measurement, which showed distinct features (log-normal, bimodal, and unimodal), were compared with CTDs determined using CFD simulation. Reproducing the signal processing procedures used in each of the experiments, CFD simulations captured the characteristic features of the experimentally measured CTDs. The CFD data suggests new signal processing procedures that predict unimodal CTDs for all three tanks.Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circulation time distribution (CTD). There are few examples of CTDs in the literature, and experimental determination of CTD is at best a challenging task. The goal in this study was to determine whether computational fluid dynamics (CFD) software (FLUENT 4 and MixSim) could be used to characterize the CTD in a single-impeller mixing tank. To accomplish this, CFD software was used to simulate flow fields in three different mixing tanks by meshing the tanks with a grid of elements and solving the Navier-Stokes equations using the kappa-epsilon turbulence model. Tracer particles were released from a reference zone within the simulated flow fields, particle trajectories were simulated for 30 s, and the time taken for these tracer particles to return to the reference zone was calculated. CTDs determined by experimental measurement, which showed distinct features (log-normal, bimodal, and unimodal), were compared with CTDs determined using CFD simulation. Reproducing the signal processing procedures used in each of the experiments, CFD simulations captured the characteristic features of the experimentally measured CTDs. The CFD data suggests new signal processing procedures that predict unimodal CTDs for all three tanks. Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circulation time distribution (CTD). There are few examples of CTDs in the literature, and experimental determination of CTD is at best a challenging task. The goal in this study was to determine whether computational fluid dynamics (CFD) software (FLUENT 4 and MixSim) could be used to characterize the CTD in a single‐impeller mixing tank. To accomplish this, CFD software was used to simulate flow fields in three different mixing tanks by meshing the tanks with a grid of elements and solving the Navier‐Stokes equations using the κ‐ϵ turbulence model. Tracer particles were released from a reference zone within the simulated flow fields, particle trajectories were simulated for 30 s, and the time taken for these tracer particles to return to the reference zone was calculated. CTDs determined by experimental measurement, which showed distinct features (log‐normal, bimodal, and unimodal), were compared with CTDs determined using CFD simulation. Reproducing the signal processing procedures used in each of the experiments, CFD simulations captured the characteristic features of the experimentally measured CTDs. The CFD data suggests new signal processing procedures that predict unimodal CTDs for all three tanks. Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circulation time distribution (CTD). There are few examples of CTDs in the literature, and experimental determination of CTD is at best a challenging task. The goal in this study was to determine whether computational fluid dynamics (CFD) software (FLUENT 4 and MixSim) could be used to characterize the CTD in a single-impeller mixing tank. To accomplish this, CFD software was used to simulate flow fields in three different mixing tanks by meshing the tanks with a grid of elements and solving the Navier-Stokes equations using the kappa-epsilon turbulence model. Tracer particles were released from a reference zone within the simulated flow fields, particle trajectories were simulated for 30 s, and the time taken for these tracer particles to return to the reference zone was calculated. CTDs determined by experimental measurement, which showed distinct features (log-normal, bimodal, and unimodal), were compared with CTDs determined using CFD simulation. Reproducing the signal processing procedures used in each of the experiments, CFD simulations captured the characteristic features of the experimentally measured CTDs. The CFD data suggests new signal processing procedures that predict unimodal CTDs for all three tanks. Nonideal mixing in many fermentation processes can lead to concentration gradients in nutrients, oxygen, and pH, among others. These gradients are likely to influence cellular behavior, growth, or yield of the fermentation process. Frequency of exposure to these gradients can be defined by the circulation time distribution (CTD). There are few examples of CTDs in the literature, and experimental determination of CTD is at best a challenging task. The goal in this study was to determine whether computational fluid dynamics (CFD) software (FLUENT 4 and MixSim) could be used to characterize the CTD in a single-impeller mixing tank. To accomplish this, CFD software was used to simulate flow fields in three different mixing tanks by meshing the tanks with a grid of elements and solving the Navier--Stokes equations using the Kappa - member of turbulence model. Tracer particles were released from a reference zone within the simulated flow fields, particle trajectories were simulated for 30 s, and the time taken for these tracer particles to return to the reference zone was calculated. CTDs determined by experimental measurement, which showed distinct features (log-normal, bimodal, and unimodal), were compared with CTDs determined using CFD simulation. Reproducing the signal processing procedures used in each of the experiments, CFD simulations captured the characteristic features of the experimentally measured CTDs. The CFD data suggests new signal processing procedures that predict unimodal CTDs for all three tanks. |
| Author | Davidson, Kyle M. Marten, Mark R. Sushil, Shrinivasan Eggleton, Charles D. |
| Author_xml | – sequence: 1 givenname: Kyle M. surname: Davidson fullname: Davidson, Kyle M. organization: Department of Mechanical Engineering, 1000 Hilltop Circle, University of Maryland, Baltimore County (UMBC), Baltimore, Maryland 21250 – sequence: 2 givenname: Shrinivasan surname: Sushil fullname: Sushil, Shrinivasan organization: Department of Mechanical Engineering, 1000 Hilltop Circle, University of Maryland, Baltimore County (UMBC), Baltimore, Maryland 21250 – sequence: 3 givenname: Charles D. surname: Eggleton fullname: Eggleton, Charles D. email: eggleton@umbc.edu organization: Department of Mechanical Engineering, 1000 Hilltop Circle, University of Maryland, Baltimore County (UMBC), Baltimore, Maryland 21250 – sequence: 4 givenname: Mark R. surname: Marten fullname: Marten, Mark R. email: marten@umbc.edu organization: Department of Chemical and Biochemical Engineering, 1000 Hilltop Circle, University of Maryland, Baltimore County (UMBC), Baltimore, Maryland 21250 |
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| Cites_doi | 10.1002/bit.260400207 10.1007/s004490050427 10.1115/1.3098990 |
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| References | Barneveld, J. v.; Smit, W.; Oosterhuis, N. M. G.; Pragt, H. J. Measuring the liquid circulation time in a large gas-liquid contactor by means of a radio pill. 2. Circulation time distribution.Ind.Eng.Chem. Res. 1987, 26, 2192-2195. Roberts, R. M.; Gray, M. R.; Thompson, B.; Kresta, S. M. The effect of impeller and tank geometry on circulation time distributions in stirred tanks. Trans.Inst.Chem. Eng. 1995, 73A, 78-86. Jaworski, Z.; Dyster, K. N.; Nienow, A. W. The effect of size, location and pumping direction of pitched blade turbine impellers on flow patterns: LDA measurements and CFD predictions. Trans. Inst.Chem. Eng. 2001, 79A, 887-893. Nienow, AW. Hydrodynamics of stirred bioreactors. Appl. Mech. Rev. 1998, 51, 3-31. Montante, G.; Micale, G.; Magelli, F.; Brucato, A. Experiments and CFD predictions of solid particle distribution in a vessel agitated with four pitched blade turbines. Trans.Inst.Chem. Eng. 2001, 79A, 1005-1010 Namdev, P. K.; Yegneswaran, P. K.; Thompson, B. G.; Gray, M. R. Experimental simulation of large-scale bioreactor environments using a Monte Carlo method. Can.J.Chem. Eng. 1991, 69, 513-519. Baldyga, J.; Henczka, M.; Makowski, L. Effects of mixing on parallel chemical reactions in a continuous-flow stirred-tank reactor. Trans.Inst.Chem. Eng. 2001, 79A, 895-900. Funahashi, H.; Harada, H.; Taguchi, H.; Yoshida, T. Circulation time distribution and volume of mixing regions in highly viscous xanthan gum solution in a stirred vessel. J.Chem.Eng. Jpn. 1987, 20, 277-282. Li, Z. J.; Shukla, V.; Pedersen, A. G.; Wenger, K. S.; Fordyce, A. P.; Marten, M. R. Effects of increased impeller power in a production-scale Aspergillus oryzae fermentation.Biotechnol. Prog. 2002, 18, 437-444. Mukataka, S.; Kataoka, H.; Takahashi, J. Circulation time degree of fluid exchange between upper and lower circulation regions in a stirred vessel with a dual impeller. J.Ferment. Technol. 1981, 59, 303-307. Meng, J. S.; Fox, R. O. Validation of CFD simulations of a stirred tank using particle image velocimetry data. Can.J.Chem. Eng. 1998, 76, 611-625. FLUENT Manual; Fluent Inc.: Lebanon, NH, 1997. Ferziger, J. H.; Perić, M. Computational Methods for Fluid Dynamics; Springer: New York, 1997; pp 271-282. Bryant, J. The characterization of mixing in fermentors. Adv.Biochem. Eng. 1977, 5, 101-123. Namdev, P. K.; Thompson, B. G.; Gray, M. R. Effect of feed zone in fed-batch fermentations of Saccharomyces cerevisiae. Biotechnol. Bioeng. 1992, 40, 235-246. Bakker, A.; Van den Akker, H. E. A. Single phase flow in stirred reactors. Trans.Inst.Chem. Eng. 1994, 72A, 583-593. Bylund, F.; Collet, E.; Enfors, S. O.; Larsson, G. Substrate gradient formation in the large-scale bioreactor lowers cell yield and increases byproduct formation. Bioprocess Eng. 1998, 18, 171-180. Mann, U.; Crosby, E. J. Cycle time distribution in circulating systems. Chem. Eng. Sci. 1973, 28, 623-627. 1998; 18 1995; 73A 1987; 20 1991; 69 2002; 18 1973; 28 1997 1981; 59 1994; 72A 2001; 79A 1998; 51 1998; 76 1977; 5 1992; 40 1987; 26 Montante G. (e_1_2_5_17_2) 2001; 79 e_1_2_5_9_2 (e_1_2_5_24_2) 1997 e_1_2_5_15_2 e_1_2_5_7_2 e_1_2_5_10_2 e_1_2_5_22_2 e_1_2_5_6_2 Roberts R. M. (e_1_2_5_14_2) 1995; 73 e_1_2_5_21_2 Barneveld J. v. (e_1_2_5_11_2) 1987; 26 Jaworski Z. (e_1_2_5_20_2) 2001; 79 Ferziger J. H. (e_1_2_5_23_2) 1997 Li Z. J. (e_1_2_5_4_2) 2002; 18 Bryant J. (e_1_2_5_2_2) 1977; 5 Meng J. S. (e_1_2_5_19_2) 1998; 76 Namdev P. K. (e_1_2_5_3_2) 1991; 69 Mann U. (e_1_2_5_8_2) 1973; 28 Mukataka S. (e_1_2_5_12_2) 1981; 59 Baldyga J. (e_1_2_5_16_2) 2001; 79 Bakker A. (e_1_2_5_18_2) 1994; 72 Marten M. R. (e_1_2_5_5_2) 1997 Funahashi H. (e_1_2_5_13_2) 1987; 20 |
| References_xml | – reference: FLUENT Manual; Fluent Inc.: Lebanon, NH, 1997. – reference: Ferziger, J. H.; Perić, M. Computational Methods for Fluid Dynamics; Springer: New York, 1997; pp 271-282. – reference: Mukataka, S.; Kataoka, H.; Takahashi, J. Circulation time degree of fluid exchange between upper and lower circulation regions in a stirred vessel with a dual impeller. J.Ferment. Technol. 1981, 59, 303-307. – reference: Namdev, P. K.; Yegneswaran, P. K.; Thompson, B. G.; Gray, M. R. Experimental simulation of large-scale bioreactor environments using a Monte Carlo method. Can.J.Chem. Eng. 1991, 69, 513-519. – reference: Montante, G.; Micale, G.; Magelli, F.; Brucato, A. Experiments and CFD predictions of solid particle distribution in a vessel agitated with four pitched blade turbines. Trans.Inst.Chem. Eng. 2001, 79A, 1005-1010 – reference: Mann, U.; Crosby, E. J. Cycle time distribution in circulating systems. Chem. Eng. Sci. 1973, 28, 623-627. – reference: Bylund, F.; Collet, E.; Enfors, S. O.; Larsson, G. Substrate gradient formation in the large-scale bioreactor lowers cell yield and increases byproduct formation. Bioprocess Eng. 1998, 18, 171-180. – reference: Namdev, P. K.; Thompson, B. G.; Gray, M. R. Effect of feed zone in fed-batch fermentations of Saccharomyces cerevisiae. Biotechnol. Bioeng. 1992, 40, 235-246. – reference: Barneveld, J. v.; Smit, W.; Oosterhuis, N. M. G.; Pragt, H. J. Measuring the liquid circulation time in a large gas-liquid contactor by means of a radio pill. 2. Circulation time distribution.Ind.Eng.Chem. Res. 1987, 26, 2192-2195. – reference: Bakker, A.; Van den Akker, H. E. A. Single phase flow in stirred reactors. Trans.Inst.Chem. Eng. 1994, 72A, 583-593. – reference: Funahashi, H.; Harada, H.; Taguchi, H.; Yoshida, T. Circulation time distribution and volume of mixing regions in highly viscous xanthan gum solution in a stirred vessel. J.Chem.Eng. Jpn. 1987, 20, 277-282. – reference: Bryant, J. The characterization of mixing in fermentors. Adv.Biochem. Eng. 1977, 5, 101-123. – reference: Nienow, AW. Hydrodynamics of stirred bioreactors. Appl. Mech. Rev. 1998, 51, 3-31. – reference: Meng, J. S.; Fox, R. O. Validation of CFD simulations of a stirred tank using particle image velocimetry data. Can.J.Chem. Eng. 1998, 76, 611-625. – reference: Li, Z. J.; Shukla, V.; Pedersen, A. G.; Wenger, K. S.; Fordyce, A. P.; Marten, M. R. Effects of increased impeller power in a production-scale Aspergillus oryzae fermentation.Biotechnol. Prog. 2002, 18, 437-444. – reference: Jaworski, Z.; Dyster, K. N.; Nienow, A. W. The effect of size, location and pumping direction of pitched blade turbine impellers on flow patterns: LDA measurements and CFD predictions. Trans. Inst.Chem. Eng. 2001, 79A, 887-893. – reference: Roberts, R. M.; Gray, M. R.; Thompson, B.; Kresta, S. M. 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| SubjectTerms | Algorithms Biological and medical sciences Bioreactors Biotechnology Computer Simulation Fundamental and applied biological sciences. Psychology Methods. Procedures. Technologies Models, Theoretical Motion Rheology - methods Software Stress, Mechanical Various methods and equipments Viscosity |
| Title | Using Computational Fluid Dynamics Software to Estimate Circulation Time Distributions in Bioreactors |
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