Simulación de la distribución de tiempo de residencia en un tanque agitado empleando CFD

dc.contributor.advisorMartínez Riascos, Carlos Arturo
dc.contributor.authorRamírez Hermosa, Pavel Ernubis
dc.contributor.researchgroupIngeniería de Sistemas de Procesosspa
dc.date.accessioned2025-03-20T14:56:07Z
dc.date.available2025-03-20T14:56:07Z
dc.date.issued2024
dc.descriptionfotografías, graficas, tablasspa
dc.description.abstractEn este estudio se analizó la distribución del tiempo de residencia (RTD) en tanques agitados mediante el uso de simulación por dinámica de fluidos computacional (CFD). Se emplearon tanques de 1, 5 y 100 litros, bajo diferentes condiciones operativas y geométricas, evaluando el impacto de variables como la velocidad de agitación, la posición del agitador y el tiempo de retención, con el objetivo de estudiar el comportamiento fluidodinámico en estos sistemas. Las velocidades de agitación fueron 144, 246 y 399 rpm para el tanque de 1 L, de 59, 89 y 142 rpm para el tanque de 5L, y de 9, 14 y 22 rpm para el tanque de 100 L, obteniendo números de Reynolds aproximados a 8000, 12000 y 20000 respectivamente. Los resultados experimentales de RTD se obtuvieron usando pigmento rojo como trazador y un sistema de captura económico, los cuales fueron comparados con los resultados de las simulaciones CFD. La validación de los modelos numéricos mostró una alta concordancia entre los resultados simulados y experimentales, especialmente al utilizar el modelo de turbulencia k-ε estándar, con ajustes de malla que optimizaron la precisión de las simulaciones. En particular, se observó que la configuración geométrica del tanque y la velocidad de agitación influyen directamente en la eficiencia de la mezcla y en la RTD, con valores de desviación estándar menores al 5% entre los datos simulados y experimentales. Estos resultados subrayan la capacidad de CFD como herramienta precisa y eficaz para predecir el comportamiento fluidodinámico en tanques agitados, y su utilidad en el diseño y la optimización de estos sistemas en aplicaciones industriales (Texto tomado de la fuente).spa
dc.description.abstractIn this study, the residence time distribution (RTD) in stirred tanks was analyzed using computational fluid dynamics (CFD) simulation. Tanks of 1, 5 and 100 liters were used under different operating and geometric conditions, evaluating the impact of variables such as stirring speed, stirrer position and retention time, in order to study the fluid dynamic behavior in these systems. Stirring speeds were 144, 246 and 399 rpm for the 1 L tank, 59, 89 and 142 rpm for the 5 L tank, and 9, 14 and 22 rpm for the 100 L tank, obtaining Reynolds numbers of approximately 8000, 12000 and 20000 respectively. RTD experimental results were obtained using red pigment as tracer and an inexpensive capture system, which were compared with the results of CFD simulations. The validation of the numerical models showed a high agreement between simulated and experimental results, especially when using the standard k-ε turbulence model, with mesh adjustments that optimized the accuracy of the simulations. In particular, it was observed that the geometric configuration of the tank and the stirring speed directly influence the mixing efficiency and the RTD, with standard deviation values less than 5% between the simulated and experimental data. These results underline the capacity of CFD as an accurate and effective tool to predict the fluid dynamic behavior in stirred tanks, and its utility in the design and optimization of these systems in industrial applications.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Ingeniería Químicaspa
dc.description.researchareaSimulación de Procesosspa
dc.description.sponsorshipproyecto “Estrategia para escalamiento de biorreactores usando análisis de Fluido Dinámica Computacional”, parte de la “Convocatoria para el Apoyo a Proyectos de Investigación, Creación Artística e Innovación de la Sede Bogotá de la Universidad Nacional de Colombia – 2020”, con código QUIPU 202010032347spa
dc.format.extentxvi, 125 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/87701
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.facultyFacultad de Ingenieríaspa
dc.publisher.placeBogotá, Colombiaspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Químicaspa
dc.relation.referencesAbdulmouti, H. (2013). Particle imaging velocimetry (PIV) technique: principles and applications, review. Yanbu Journal of Engineering and Science, 6, 35-65.spa
dc.relation.referencesAbdulwahab, M.R., Ali, Y.H., Habeeb, F.J., Borhana, A.A., Abdelrhman, A.M. & Al-Obaidi, S.M.A. (2020). A Review in Particle Image Velocimetry Techniques. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 65(2), 213-229.spa
dc.relation.referencesAdamczyk, W. P., Klimanek, A., Białecki, R. A., Węcel, G., Kozołub, P., & Czakiert, T. (2014). Comparison of the standard Euler–Euler and hybrid Euler–Lagrange approaches for modeling particle transport in a pilot-scale circulating fluidized bed.Particuology,15, 129–137. https://doi.org/10.1016/j.partic.2013.06.008spa
dc.relation.referencesAdrian, R.J. & Westerweel, J. (2011) Particle Image Velocimetry. Cambridge University Press, Cambridge.spa
dc.relation.referencesAubin, J., Fletcher, D. F., & Xuereb, C. (2004). Modeling turbulent flow in stirred tanks with CFD: the influence of the modeling approach, turbulence model and numerical scheme.Experimental Thermal and Fluid Science,28(5), 431–445. https://doi.org/10.1016/j.expthermflusci.2003.04.001spa
dc.relation.referencesBakker, R. J. (2003). Package FLUIDS 1. Computer programs for analysis of fluid inclusion data and for modelling bulk fluid properties. Chemical Geology, 194(1-3), 3–23. https://doi.org/10.1016/s0009-2541(02)00268-1spa
dc.relation.referencesBöhm, L., Hohl, L., Bliatsiou, C., & Kraume, M. (2019). Multiphase Stirred Tank Bioreactors – New Geometrical Concepts and Scale‐up Approaches. Chemie Ingenieur Technik, 91(12), 1724–1746. https://doi.org/10.1002/cite.201900165spa
dc.relation.referencesBrouyère, S., Dassargues, A., Therrien, R., & Sudicky, E. (2024). Modelling of dual porosity media: comparisons of different techniques and evaluation of the impact on plume transport simulations. ModelCARE’99: Calibration and Reliability in Groundwater Modelling. https://hdl.handle.net/2268/2803spa
dc.relation.referencesWen, C.Y. & Fan, L.T. (1975). Models for Flow System and Chemical Reactors. Dekker, New York.spa
dc.relation.referencesCameron, S. (2011). PIV algorithms for open-channel turbulence research: Accuracy, resolution and limitations. Journal of Hydro-Environment Research, 5(4), 247–262. https://doi.org/10.1016/j.jher.2010.12.006spa
dc.relation.referencesChen, S., & Gu, H. (2019). CFD simulation and analysis of reactor integral hydraulic tests. Annals of Nuclear Energy, 135, 106962–106962. https://doi.org/10.1016/j.anucene.2019.106962spa
dc.relation.referencesChitale, S.K., Jadhav, P.N., Dhoble, S.S., Dhokpande, S. & Ingole, P. (2022). Study of Residence Time Distribution in Chemical Industry A Review. International Journal of Scientific Research in Science and Technology, 9(1), 47-55.spa
dc.relation.referencesDagadu, C.P.K., Stegowski, Z., Sogbey, B.J.A.Y., & Adzaklo, S.Y. (2015). Mixing Analysis in a Stirred Tank Using Computational Fluid Dynamics. Journal of Applied Mathematics and Physics, 03(06), 637–642. https://doi.org/10.4236/jamp.2015.36076spa
dc.relation.referencesDantec Dynamics. (n.d.). Precision Measurement Systems & Sensors. https://www.dantecdynamics.com/components/synchronizers/spa
dc.relation.referencesDanckwerts. P.V. (1981). The definition and measurement of some characteristics of mixtures.Elsevier EBooks, 268–287. https://doi.org/10.1016/b978-0-08-026250-5.50050-2spa
dc.relation.referencesDing, J., Wang, X., Zhou, X.F., Ren, N.Q., & Guo, W.Q. (2010). CFD optimization of continuous stirred-tank (CSTR) reactor for biohydrogen production. Bioresource Technology, 101(18), 7005–7013. https://doi.org/10.1016/j.biortech.2010.03.14spa
dc.relation.referencesMarshall, E.M. & Bakker, A. (2002). Computational Fluid Mixing. Fluent Inc., 10 Cavendish Court, USAspa
dc.relation.referencesFletcher, D.F. (2022) The future of computational fluid dynamics (CFD) simulation in the chemical process industries. Chemical Engineering Research and Design, 187. doi.org/10.1016/j.cherd.2022.09.021spa
dc.relation.referencesFogler, H.S. (1999) Elements of Chemical Reaction Engineering. Prentice Hall of India, New Delhi.spa
dc.relation.referencesPapageorgakis, G.C. & Assanis, D.N. (1999). Comparison of linear and nonlinear rng-based k- epsilon models for incompressible turbulent flows. Numerical Heat Transfer, Part B: Fundamentals, 35(1), 1–22. https://doi.org/10.1080/104077999275983spa
dc.relation.referencesGillissen, J.J.J. & Van den Akker, H.E.A. (2012). Direct numerical simulation of the turbulent flow in a baffled tank driven by a Rushton turbine. AIChE Journal, 58(12), 3878–3890. https://doi.org/10.1002/aic.13762spa
dc.relation.referencesGnatowska, R., Sosnowski, M. & Václav Uruba. (2017). CFD modelling and PIV experimental validation of flow fields in urban environments. E3S Web of Conferences, 14, 01034–01034. https://doi.org/10.1051/e3sconf/20171401034spa
dc.relation.referencesHadad, T. & Gurka, R. (2013). Effects of particle size, concentration and surface coating on turbulent flow properties obtained using PIV/PTV. Experimental Thermal and Fluid Science, 45, 203–212. https://doi.org/10.1016/j.expthermflusci.2012.11.006spa
dc.relation.referencesHand, D.P., Entwistle, J.D., Maier, R.R.J., Kuhn, A., Greated, C.A., & Jones, J.D.C. (1999). Fibre optic beam delivery system for high peak power laser PIV illumination. Measurement Science and Technology, 10(3), 239–245. https://doi.org/10.1088/0957-0233/10/3/021spa
dc.relation.referencesHossain, S., Hossain, I., Pramanik, S. & Ahamed, J. U. (2017). Analyzing the Turbulent Flow Characteristics by Utilizing k-? Turbulence Model. European Journal of Engineering and Technology Research, 2(11), 28–34. https://doi.org/10.24018/ejeng.2017.2.11.510spa
dc.relation.referencesJiang, J., Wu, J., Poncin, S. & Li, H.Z. (2016). Effect of hydrodynamic shear on biogas production and granule characteristics in a continuous stirred tank reactor. Process Biochemistry, 51(3), 345–351. doi: 10.1016/j.procbio.2015.12.014spa
dc.relation.referencesJordan, W. K., & March, R. P. (1953). Studies on Overholding in High-Temperature Short-Time Pasteurizers Operated on Water.Journal of Dairy Science,36(6), 614–619. https://doi.org/10.3168/jds.s0022-0302(53)91537-9spa
dc.relation.referencesJordan, W. K., & Holland, R. F. (1953). STUDIES ON THERMAL METHODS OF MEASURING THE HOLDING TIME IN HIGH-TEMPERATURE SHORT-TIME PASTEURIZERS.Journal of Milk and Food Technology,16(1), 15–25. https://doi.org/10.4315/0022-2747-16.1.15spa
dc.relation.referencesJordan, W. K., Holland, R. F., & White, J. C. (1949). THE DETERMINATION OF THE HOLDING TIME IN HIGH-TEMPERATURE, SHORT-TIME PASTEURIZING UNITS.Journal of Milk and Food Technology,12(2), 87–92. https://doi.org/10.4315/0022-2747-12.2.87spa
dc.relation.referencesKhapre, A., Rajavathsavia, D. & Munshi, B. (2016). Study on residence time distribution of CSTR using CFD. Indian Journal of Chemical Technology, 23, 114-120spa
dc.relation.referencesKamla, Y., Ameur, H., Karas, A. & Arab, M.I. (2019). Performance of new designed anchor impellers in stirred tanks. Chemical Papers, 74(3), 779–785. https://doi.org/10.1007/s11696- 019-00902-xspa
dc.relation.referencesKeane, R.D. (1994). Correlation Methods of PIV Analysis. Springer EBooks, 271–289. https://doi.org/10.1007/978-1-4899-1271-8_13spa
dc.relation.referencesKumaresan, T., & Joshi, J. B. (2006). Effect of impeller design on the flow pattern and mixing in stirred tanks.Chemical Engineering Journal,115(3), 173–193. https://doi.org/10.1016/j.cej.2005.10.002spa
dc.relation.referencesLane, G.L. (2017). Improving the accuracy of CFD predictions of turbulence in a tank stirred by a hydrofoil impeller. Chemical Engineering Science, 169, 188–211. https://doi.org/10.1016/j.ces.2017.03.061spa
dc.relation.referencesLevenspiel, O. (1999) Chemical Reaction Engineering. 3rd Edition. John Wiley & Sons, New York, 54. http://dx.doi.org/10.1021/ie990488gspa
dc.relation.referencesLi, L. & Xu, B. (2022). CFD simulation of hydrodynamics characteristics in a tank with forward- reverse rotating impeller. Journal of the Taiwan Institute of Chemical Engineers, 131, 104174. https://doi.org/10.1016/j.jtice.2021.104174spa
dc.relation.referencesLintz, H. & Weber, W. (1980). The study of mixing in a continuous stirred tank reactor using an autocatalytic reaction. Chemical Engineering Science, 35, 203-208 DOI:10.1016/0009- 2509(80)80088-1.spa
dc.relation.referencesLu, W.M., Wu, H.Z. & Ju, M.Y. (1997). Effects of baffle design on the liquid mixing in an aerated stirred tank with standard Rushton turbine impellers. Chemical Engineering Science, 52(21- 22), 3843–3851. https://doi.org/10.1016/s0009-2509(97)88929-4spa
dc.relation.referencesMaji, S. & Sahu, A.K. (2021). Stirred tank simulation using Partially-Averaged Navier-Stokes turbulence model. SN Applied Sciences, 3(5). https://doi.org/10.1007/s42452-021-04488-6spa
dc.relation.referencesMatzke, M., Behrens, C., Niklas Jongebloed, Steins, D., Ulbricht, M. & Schultz, H. J. (2022). Investigation and Visualization of Flow Fields in Stirred Tank Reactors Using a Fluorescence Tracer Method. Chemie Ingenieur Technik, 94(8), 1131–1140. https://doi.org/10.1002/cite.202200006spa
dc.relation.referencesMavros, P. (2001). Flow Visualization in Stirred Vessels. Chemical Engineering Research and Design, 79(2), 113–127. https://doi.org/10.1205/02638760151095926spa
dc.relation.referencesMeier, W., Boxx, I., Stöhr, M. & Carter, C.D. (2010). Laser-based investigations in gas turbine model combustors. 49(4), 865–882. https://doi.org/10.1007/s00348-010-0889-xspa
dc.relation.referencesMittal, G. & Kikugawa, R.I. (2021). Computational fluid dynamics simulation of a stirred tank reactor. Materials Today: Proceedings, 46(20), 11015-11019.spa
dc.relation.referencesMoin, P. & Mahesh, K. (1998). Direct numerical simulation: A Tool in Turbulence Research. Annual Review of Fluid Mechanics, 30(1), 539–578. https://doi.org/10.1146/annurev.fluid.30.1.539spa
dc.relation.referencesMonaldi, A.C., Romero, G.G., Cabrera, C.M., Blanc, A.V. & Alanís, E.E. (2016). Rolling Shutter Effect aberration compensation in Digital Holographic Microscopy. Optic Communications, 366, 94–98. https://doi.org/10.1016/j.optcom.2015.12.048spa
dc.relation.referencesNadal-Rey, G., McClure, D.D., Kavanagh, J.M., Cassells, B. Cornelissen, S., Fletcher, D.F. & Gernaey, K.V. (2022). Computational fluid dynamics modelling of hydrodynamics, mixing and oxygen transfer in industrial bioreactors with Newtonian broths. Biochemical Engineering Journal, 177.spa
dc.relation.referencesNassauer, J., & Kessler, H. G. (1980). Physikalische Beschreibung der Verweilzeit‐Verteilung in einemLeitungs‐system. Chemie-Ingenieur-Technik, 52(5), 450. https://doi.org/10.1002/cite.330520524spa
dc.relation.referencesNechita, M.T., Suditu, G.D., Puițel, A.C. & Drăgoi, E.N. (2023). Residence Time Distribution: Literature Survey, Functions, Mathematical Modeling, and Case Study—Diagnosis for a Photochemical Reactor. Processes, 11(12), 3420. https://doi.org/10.3390/pr11123420spa
dc.relation.referencesOchieng, A., Onyango & Kiriamiti, K. (2010). Experimental measurement and computational fluid dynamics simulation of mixing in a stirred tank: a review. South African Journal of Science, 105(11/12). https://doi.org/10.4102/sajs.v105i11/12.139spa
dc.relation.referencesOrszag, S.A. (1969). Representation of Isotropic Turbulence by Scalar Functions. Studies in Applied Mathematics, 48(3), 275–279. https://doi.org/10.1002/sapm1969483275spa
dc.relation.referencesPatel, V.C., Rodi, W. & Scheuerer, G. (1985). Turbulence models for near-wall and low Reynolds number flows - A review. AIAA Journal, 23(9), 1308–1319. https://doi.org/10.2514/3.9086spa
dc.relation.referencesPaul, E.L., Atiemo-Obeng, V.A. & Kresta, S.M. (Eds.). (2003). Handbook of Industrial Mixing. John Wiley & Sons, Inc. https://doi.org/10.1002/0471451452spa
dc.relation.referencesPieralisi, I., Montante, G. & Paglianti, A. (2016). Prediction of fluid dynamic instabilities of low liquid height-to-tank diameter ratio stirred tanks. Chemical Engineering Journal, 295, 336–346. https://doi.org/10.1016/j.cej.2016.03.026spa
dc.relation.referencesPinheiro Torres, A., Oliveira, F.A.R., Baptista, P. N. & Oliveira, J. C. (1993). Evaluation of conventional and new residence time distribution models for the description of tubular aseptic processing systems. In 3rd Conference of Food Engineering (CoFE’93), postTerc No. 13.11, Chicago, Illinois, 21-24 February 1993spa
dc.relation.referencesPinheiroTorres, A. Oliveira, F.A.R. &Fortuna, S.P. (1993). Residence time distribution of liquids in a continuous tubular thermal processing system part I: Relating RTD to processing conditions. Journal of Food Engineering, 35(2), 147-163. https://doi.org/10.1016/S0260-8774(98)00007- 7spa
dc.relation.referencesPlasari, E., David, R. & Villermaux, J. (1977). Phenomena in residence time distributions of mechanically and jet-stirred reactors in the liquid phase. Chemical Engineering Science, 32(9), 1121–1124. https://doi.org/10.1016/0009-2509(77)80155-3spa
dc.relation.referencesPoynton, C. A. (2012).Digital video and HD : algorithms and interfaces. Morgan Kaufmannspa
dc.relation.referencesPriyadi, K., Lu, C.T. & Sutanto, H. (2019). Optimization of impeller design for stirred tank using computational fluid dynamics. IOP Conference Series: Materials Science and Engineering, 567, 012032. https://doi.org/10.1088/1757-899x/567/1/012032spa
dc.relation.referencesRaffel, M., Willert, C., Scarano, F., Kähler, C.J., Wereley, S. & Kompenhans, J. (2018). Particle Image Velocimetry. https://doi.org/10.1007/978-3-319-68852-7spa
dc.relation.referencesRajavathsavai, D., Khapre, A. & Munshi, B. (2014). Study of mixing behavior of cstr using CFD. Brazilian Journal of Chemical Engineering, 31(1), 119–129. https://doi.org/10.1590/s0104- 66322014000100012spa
dc.relation.referencesRamírez-Gómez, R., García-Cortés, D., Martínez-de Jesús, G., González-Brambila, M.M., Alonso, A., Martínez-Delgadillo, S.A. & Ramírez-Muñoz, J. (2015). Performance Evaluation of Two High-Shear Impellers in an Unbaffled Stirred Tank. Chemical Engineering & Technology, 38(9), 1519–1529. https://doi.org/10.1002/ceat.201400792spa
dc.relation.referencesRaschi, M., Mut, F., Byrne, G., Putman, C.M., Tateshima, S., Viñuela, F., Tanoue, T., Tanishita, K. & Cebral, J.R. (2011). CFD and PIV analysis of hemodynamics in a growing intracranial aneurysm. International Journal for Numerical Methods in Biomedical Engineering, 28(2), 214–228. https://doi.org/10.1002/cnm.1459spa
dc.relation.referencesRazavi, S. M., Román-Ospino, A. D., Bhalode, P., Scicolone, J., Callegari, G., Dubey, A., Koolivand, A., Krull, S., Tian, G., Xu, X., O’Connor, T., Ierapetritou, M., & Muzzio, F. (2023). Selection of an appropriate tracer to measure the residence time distribution (RTD) of continuous powder blending operations.Powder Technology,429, 118864. https://doi.org/10.1016/j.powtec.2023.118864spa
dc.relation.referencesRieger, F. & Ditl, P. (1994). Suspension of solid particles. Chemical Engineering Science, 49(14), 2219–2227. https://doi.org/10.1016/0009-2509(94)e0029-pspa
dc.relation.referencesRodrigues, A.E. (2021). Residence time distribution (RTD) revisited. Chemical Engineering Science, 230, 116188. https://doi.org/10.1016/j.ces.2020.116188spa
dc.relation.referencesRudniak, L., Machniewski, P.M., Milewska, A. & Molga, E. (2004). CFD modelling of stirred tank chemical reactors: homogeneous and heterogeneous reaction systems. Chemical Engineering Science, 59(22-23), 5233–5239. https://doi.org/10.1016/j.ces.2004.09.014spa
dc.relation.referencesScarano, F. (2013). Tomographic PIV: principles and practice. Measurement Science and Technology, 24(1), 012001–012001. https://doi.org/10.1088/0957-0233/24/1/012001spa
dc.relation.referencesSoloff, S.M., Adrian, R.J. & Liu, Z.C. (1997). Distortion compensation for generalized stereoscopic particle image velocimetry. 8(12), 1441–1454. https://doi.org/10.1088/0957-0233/8/12/008spa
dc.relation.referencesSommer, A.E., Rox, H., Shi, P., Eckert, K. & Rzehak, R. (2021). Solid-liquid flow in stirred tanks: “CFD-grade” experimental investigation. Chemical Engineering Science, 245, 116743. https://doi.org/10.1016/j.ces.2021.116743spa
dc.relation.referencesTahry, S.H. E. (1983). k-epsilon equation for compressible reciprocating engine flows. Journal of Energy, 7(4), 345–353. https://doi.org/10.2514/3.48086spa
dc.relation.referencesTamburini, A., Cipollina, A., Micale, G., Brucato, A. & Ciofalo, M. (2011). CFD simulations of dense solid–liquid suspensions in baffled stirred tanks: Prediction of suspension curves. Chemical Engineering Journal, 178, 324–341. https://doi.org/10.1016/j.cej.2011.10.016spa
dc.relation.referencesTawk, R., Ghannam, B. & Nemer, M. (2019). Topology optimization of heat and mass transfer problems in two fluids—one solid domains. Numerical Heat Transfer, Part B: Fundamentals,76(3), 130–151. https://doi.org/10.1080/10407790.2019.1644919spa
dc.relation.referencesTchobanoglous, G., Stensel, H., Tsuchihashi, R. & Burton, F. (2013). Wastewater Engineering Treatment and Resource Recovery. McGraw-Hill Education, New York. Research Publishing. https://www.scirp.org/reference/referencespapers?referenceid=2800902spa
dc.relation.referencesThipdech, A., Prasertsit, K. & Photaworn, S. (2024). Enhancing biodiesel production in stirred tank reactors through the implementation of a baffle array: Creating a reactor with unique characteristics. Bioresource Technology Reports, 25, 101748. https://doi.org/10.1016/j.biteb.2023.101748spa
dc.relation.referencesTibbitt, M. (2002). Practical training on Residence Time Distribution. Institute of Process Engineeringspa
dc.relation.referencesvan Overbrüggen, T., Klaas, M., Soria, J. & Wolfgang S. (2016). Experimental analysis of particle sizes for PIV measurements. Measurement Science and Technology, 27(9), 094009–094009. https://doi.org/10.1088/0957-0233/27/9/094009spa
dc.relation.referencesTorotwa, I. & Ji, C. (2018). A Study of the Mixing Performance of Different Impeller Designs in Stirred Vessels Using Computational Fluid Dynamics. Designs 2. 1:10. https://doi.org/10.3390/designs2010010spa
dc.relation.referencesToson, P., Doshi, P. & Jajcevic, D. (2019). Explicit Residence Time Distribution of a Generalised Cascade of Continuous Stirred Tank Reactors for a Description of Short Recirculation Time (Bypassing). Processes, 7(9), 615. https://doi.org/10.3390/pr7090615spa
dc.relation.referencesZalc, J.M., Szalai, E.S., Alvarez, M.M. & Muzzio, F.J. (2002). Using CFD to understand chaotic mixing in laminar stirred tanks. AIChE Journal, 48(10), 2124–2134. https://doi.org/10.1002/aic.690481004spa
dc.relation.referencesZhang, Z., Li, T., Chen, R., Wang, N., Wei, Y., & Wu, D. (2021). Injection characteristics and fuel- air mixing process of ammonia jets in a constant volume vessel.Fuel,304, 121408–121408. https://doi.org/10.1016/j.fuel.2021.121408spa
dc.relation.referencesZhu, H. & Jing, R. (2019). CFD Simulation Study on Mixing Experiment of Anaerobic Digestion Tank. E3S Web of Conferences, 118, 02047. https://doi.org/10.1051/e3sconf/201911802047spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseReconocimiento 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc660 - Ingeniería química::661 - Tecnología de químicos industrialesspa
dc.subject.proposalDinámica de Fluidos Computacionalspa
dc.subject.proposalVelocimetría por Imágenes de Partículasspa
dc.subject.proposalDistribución de Tiempo de Residenciaspa
dc.subject.proposalComputational Fluid Dynamicseng
dc.subject.proposalParticle Image Velocimetryeng
dc.subject.proposalResidence Time Distributioneng
dc.subject.unescoAnálisis numérico
dc.subject.unescoNumerical analysis
dc.subject.unescoDinámica de fluidos
dc.subject.unescoFluid dynamics
dc.titleSimulación de la distribución de tiempo de residencia en un tanque agitado empleando CFDspa
dc.title.translatedSimulation of residence time distribution in a stirred tank using CFDeng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentBibliotecariosspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.fundernameUniversidad Nacional de Colombiaspa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
1075281160.pdf
Tamaño:
69.25 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ingeniería - Ingeniería Química

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción: