Data-driven modelling of micro and ultra - filtration processes

dc.contributor.advisorPrado-Rubio, Oscar Andrés
dc.contributor.advisorGrisales Diaz, Victor Hugo
dc.contributor.authorLopez-Murillo, Luis Humberto
dc.contributor.orcidLopez-Murillo, Luis Humberto [0000-0001-9973-1871]spa
dc.contributor.researchgroupGrupo de Investigación en Aplicación de Nuevas Tecnologías (GIANT)spa
dc.date.accessioned2023-01-23T17:09:52Z
dc.date.available2023-01-23T17:09:52Z
dc.date.issued2022
dc.descriptiongraficas, tablasspa
dc.description.abstractThe microfiltration (MF) and ultrafiltration (UF) processes are widely used in several industrial and research fields, and different enterprises have emerged to develop enhancements and new designs of such technologies. Nevertheless, some drawbacks related to process operation, namely concentration polarization and fouling, keep membranes from spreading in all industrial sectors. Concentration polarization and fouling are the main problems in MF and UF to be managed in order to design a separation process. Dynamic operation strategies are used to mitigate adverse effects of polarization and fouling and improve the separation performance. Nevertheless, there is a balance among the operational conditions to reach the desired effects. In this research, two hybrid mathematical models are developed and tuned to represent the concentration polarization phenomena in dynamic UF of dextran T500. Such models yield an adjusted determination coefficient of 0.9185 and 0.9626, respectively, and can predict the concentration at the membrane surface, the flux and the observed rejection. The results display the intensifying effect of dynamic operation by decreasing the Molecular Weight Cut-Off (MWCO) of the membrane up to 74 times without reducing the flux. The experimental data from literature and herein developed hybrid models provide system insights for membrane systems design where the selectivity can be enhanced and tunned according to operating conditions rather than the membrane pore size. The best hybrid mathematical model is used to explore the UF system under dynamic operation at different scenarios aiming to provide further system understanding. With this focus, a sensitivity analysis is accomplished in order to evaluate the separation performance in terms of flux and rejection factor as a function of input variables: backshock time (BS), time between backshocks (TBBS), dextran bulk concentration (Cb). The sensitivity analysis allows finding operational regions where high fluxes can be achieved while keeping acceptable rejection factor. Aiming to highlight the advantages of applying dynamic operation instead of conventional filtration, a comparative analysis is performed between a membrane with low MWCO under conventional cross-flow operation and a membrane with high MWCO under dynamic operation. Concentration polarization effect is analyzed and explained by concentration polarization modulus. This modulus is defined as the ratio between concentration at the membrane surface and the bulk concentration. Values as high as 160 for this modulus have a negative impact on selectivity, while values close or lower than 34 improve separation. Average flux can be enhanced up to 43.8 % with BS = 1 s and TBBS = 5 s. With respect to the comparative analysis, membrane cost savings reach values around 50 % by operating a membrane of high MWCO under dynamic conditions. Mathematical modeling in dynamic ultrafiltration is a key tool, from a process system engineering perspective, to assess the separation performance under different operating conditions. The hybrid mathematical model developed in this research allows optimization of operation through sensitivity analysis, and allows designing of the separation process given a definite concentration target, in the context of dextran ultrafiltration. (Texto tomado de la fuente)eng
dc.description.abstractLos procesos de microfiltración (MF) y ultrafiltración (UF) se utilizan ampliamente en varios campos industriales y de investigación, y han surgido diferentes empresas para desarrollar mejoras y nuevos diseños de dichas tecnologı́as. Sin embargo, algunos inconvenientes re- lacionados con la operación del proceso, a saber, la polarización de la concentración y el ensuciamiento, impiden que el uso de las membranas se extienda en todos los sectores indus- triales. La polarización de la concentración y el ensuciamiento son los principales problemas en MF y UF que deben gestionarse para diseñar un proceso de separación. Las estrategias de operación dinámica se utilizan para mitigar los efectos adversos de la polarización y el ensuciamiento y mejorar el rendimiento de la separación. No obstante, existe un equilibrio entre las condiciones operativas para alcanzar los efectos deseados. En esta investigación, se desarrollan y ajustan dos modelos matemáticos hı́bridos para representar los fenómenos de polarización de la concentración en la UF dinámica de dextrano T500. Dichos modelos arro- jan un coeficiente de determinación ajustado de 0.9185 y 0.9626, respectivamente, y pueden predecir la concentración en la superficie de la membrana, el flujo y el rechazo observado. Los resultados muestran el efecto intensificador de la operación dinámica al disminuir el MWCO de la membrana hasta 74 veces sin reducir el flujo. Los datos experimentales de la literatura y los modelos hı́bridos desarrollados en este documento brindan información sobre el sistema para el diseño de sistemas de membrana donde la selectividad se puede mejorar y ajustar de acuerdo con las condiciones operativas en lugar del tamaño de poro de la membrana. El mejor modelo matemático hı́brido se utiliza para explorar el sistema de UF en funcionamiento dinámico en diferentes escenarios con el objetivo de proporcionar una mayor comprensión del sistema. Con este enfoque, se realiza un análisis de sensibilidad para evaluar el desempeño de la separación en términos de flujo y factor de rechazo en función de las variables de entrada: duración del backshock (BS), tiempo entre backshocks (TBBS) y concentración de dextrano (Cb). El análisis de sensibilidad permite encontrar regiones ope- rativas donde se pueden lograr flujos elevados manteniendo un factor de rechazo aceptable. Con el objetivo de resaltar las ventajas de aplicar la operación dinámica en lugar de la filtra- ción convencional, se realiza un análisis comparativo entre una membrana con bajo MWCO en operación convencional de flujo cruzado y una membrana con alto MWCO en operación dinámica. El efecto de la polarización de la concentración se analiza y explica mediante el módulo de polarización. Este módulo se define como la relación entre la concentración en la superficie de la membrana y la concentración en el seno del fluido. Valores tan altos como 160 para este módulo tienen un impacto negativo en la selectividad, mientras que valores cercanos o inferiores a 34 mejoran la separación. El flujo medio puede aumentarse hasta un 43,8 % con BS = 1 s y TBBS = 5 s. Con respecto al análisis comparativo, los ahorros en costos de membrana alcanzan valores en torno al 50 % al operar una membrana de alto MW- CO en condiciones dinámicas. El modelado matemático en ultrafiltración dinámica es una herramienta clave, desde la perspectiva de la ingenierı́a de sistemas de procesos, para evaluar el rendimiento de la separación en diferentes condiciones operativas. El modelo matemático hı́brido desarrollado en esta investigación permite la optimización de la operación a través del análisis de sensibilidad y permite diseñar el proceso de separación dado un objetivo de concentración definido, en el contexto de la ultrafiltración de dextrano.spa
dc.description.curricularareaQuímica Y Procesosspa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Ingeniería Químicaspa
dc.format.extentv, 113 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/83070
dc.language.isoengspa
dc.publisherUuniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.facultyFacultad de Ingeniería y Arquitecturaspa
dc.publisher.placeManizales, Colombiaspa
dc.publisher.programManizales - Ingeniería y Arquitectura - Maestría en Ingeniería - Ingeniería Químicaspa
dc.relation.referencesAbels, C., Carstensen, F., and Wessling, M. (2013). Membrane processes in biorefinery applications. Journal of Membrane Science, 444:285–317.spa
dc.relation.referencesAssociation, A. W. W. (2005). Microfiltration and Ultrafiltration Membranes for Drinking Water, volume 53. American Water Works Association.spa
dc.relation.referencesAzevedo, C. R., D´ıaz, V. G., Prado-Rubio, O. A., Willis, M. J., Pr´eat, V., Oliveira, R., and Stosch, M. (2019). Hybrid semiparametric modeling: A modular process systems engineering approach for the integration of available knowledge sources.spa
dc.relation.referencesBacchin, P., Aimar, P., and Field, R. (2006). Critical and sustainable fluxes: Theory, experiments and applications. Journal of Membrane Science, 281:42–69.spa
dc.relation.referencesBaker, R. W. (2012). Membrane Technology and Applications. John Wiley & Sons, 3 edition.spa
dc.relation.referencesBakhshayeshi, M., Kanani, D. M., Mehta, A., van Reis, R., Kuriyel, R., Jackson, N., and Zydney, A. L. (2011). Dextran sieving test for characterization of virus filtration membranes. Journal of Membrane Science, 379:239–248.spa
dc.relation.referencesBeier, S. P. and Jonsson, G. (2009). Critical flux determination by flux-stepping. AIChE Journal, 56:1739–1747.spa
dc.relation.referencesCalabro, V. and Basile, A. (2011). 1 - fundamental membrane processes, science and engineering.spa
dc.relation.referencesCapannelli, G., Vigo, F., and Munari, S. (1983). Ultrafiltration membranes — characterization methods. Journal of Membrane Science, 15:289–313.spa
dc.relation.referencesCharcosset, C. (2006). Membrane processes in biotechnology: An overview. Biotechnology Advances, 24:482–492.spa
dc.relation.referencesDiaz, V. H. G., Prado-Rubio, O. A., Willis, M. J., and von Stosch, M. (2017). Dynamic hybrid model for ultrafiltration membrane processes.spa
dc.relation.referencesEscobar, I. and der Bruggen, B. V. (2011). Modern Applications in Membrane Science and Technology, volume 1078. American Chemical Society.spa
dc.relation.referencesField, R. W. and Pearce, G. K. (2011). Critical, sustainable and threshold fluxes for membrane filtration with water industry applications. Advances in Colloid and Interface Science, 164:38–44.spa
dc.relation.referencesHangos, K. M. and Cameron, I. T. (2001). 1 - the role of models in process systems engineering.spa
dc.relation.referencesHillis, P. (2000). Membrane case studies, past present and future.spa
dc.relation.referencesHughes, D. and Field, R. W. (2006). Crossflow filtration of washed and unwashed yeast suspensions at constant shear under nominally sub-critical conditions. Journal of Membrane Science, 280:89–98.spa
dc.relation.referencesJonsson, G. E. and Rubio, O. A. P. (2011). Modeling and operation of dynamic membrane processes. International Congress on Membranes and Membrane Processes : ICOM 2011; Conference date: 01-01-2011.spa
dc.relation.referencesKeil, F. J. (2018). Process intensification. Reviews in Chemical Engineering, 34:135–200.spa
dc.relation.referencesKennedy, M. D., Kamanyi, J., Rodrguez, S. G. S., Lee, N. H., Schippers, J. C., and Amy, G. (2008). Water treatment by microfiltration and ultrafiltration.spa
dc.relation.referencesKnops, F. N. M. and Franklin, B. (2000). Ultrafiltration for 90 mld cryptosporidium and giardia-free drinking water. a case study for the yorkshire water keldgate plant.spa
dc.relation.referencesLeos, J. Z. and Zydney, A. L. (2017). Microfiltration and Ultrafiltration. Routledge.spa
dc.relation.referencesMulder, M. (1996). Basic Principles of Membrane Technology. Springer Netherlands.spa
dc.relation.referencesNunes, S. P. and Peinemann, K.-V. (2006). Membrane technology: in the chemical industry. John Wiley & Sons.spa
dc.relation.referencesPabby, A. K., Rizvi, S. S. H., and Requena, A. M. S. (2008). Handbook of membrane separations: chemical, pharmaceutical, food, and biotechnological applications. CRC press.spa
dc.relation.referencesPeinemann, K.-V. and Nunes, S. P. (2010). Membranes for water treatment. John Wiley & Sons.spa
dc.relation.referencesPistikopoulos, E. N., Barbosa-Povoa, A., Lee, J. H., Misener, R., Mitsos, A., Reklaitis, G. V., Venkatasubramanian, V., You, F., and Gani, R. (2021a). Process systems engineering – the generation next? Computers & Chemical Engineering, 147:107252.spa
dc.relation.referencesPistikopoulos, E. N., Tian, Y., and Bindlish, R. (2021b). Operability and control in process intensification and modular design: Challenges and opportunities. AIChE Journal, 67.spa
dc.relation.referencesPonce-Ortega, J. M., Al-Thubaiti, M. M., and El-Halwagi, M. M. (2012). Process intensification: New understanding and systematic approach. Chemical Engineering and Processing: Process Intensification, 53:63–75.spa
dc.relation.referencesPopham, N. (2019). Resin infusion for the manufacture of large composite structures.spa
dc.relation.referencesPrado-Rubio, O. A., Fontalvo, J., and Woodley, J. M. (2019). 8. conception, design, and development of intensified hybrid-bioprocesses.spa
dc.relation.referencesPrado-Rubio, O. A., Morales-Rodrıguez, R., Andrade-Santacoloma, P., and HernandezEscoto, H. (2016). Process intensification in biotechnology applications.spa
dc.relation.referencesPrado-Rubio, O. A. and von Stosch, M. (2017). Towards sustainable flux determination for dynamic ultrafiltration through multivariable system identification.spa
dc.relation.referencesRosinha, I. P. (2011). High frequency backshock effect on ultrafiltration of selected polysaccharides. master thesis. universidade técnica de lisboa & technical university of denmark.spa
dc.relation.referencesSchrotter, J.-C. and Bozkaya-Schrotter, B. (2010). Current and emerging membrane processes for water treatment.spa
dc.relation.referencesScott, K. (1995). Introduction to membrane separations.spa
dc.relation.referencesSikdar, S. K. and Criscuoli, A. (2017). Sustainability and how membrane technologies in water treatment can be a contributor.spa
dc.relation.referencesSingh, R. (2015). Water and membrane treatment.spa
dc.relation.referencesSkiborowski, M. (2018). Process synthesis and design methods for process intensification. Current Opinion in Chemical Engineering, 22:216–225.spa
dc.relation.referencesStankiewicz, A. and Moulijn, J. A. (2018). Re-Engineering the Chemical Processing Plant. CRC Press.spa
dc.relation.referencesStarbard, N. (2009). Beverage Industry Microfiltration. Wiley.spa
dc.relation.referencesTian, Y., Demirel, S. E., Hasan, M. F., and Pistikopoulos, E. N. (2018). An overview of process systems engineering approaches for process intensification: State of the art. Chemical Engineering and Processing - Process Intensification, 133:160–210.spa
dc.relation.referencesUnited-Nations (2015). Sustainable development goals.spa
dc.relation.referencesWagner, J. (2001). Membrane Filtration Handbook: Practical Tips and Hints. Osmonics.spa
dc.relation.referencesWei, P., Cheng, L.-H., Zhang, L., Xu, X.-H., lin Chen, H., and jie Gao, C. (2014). A review of membrane technology for bioethanol production. Renewable and Sustainable Energy Reviews, 30:388–400.spa
dc.relation.referencesWHO (2006). Guidelines for drinking-water quality.spa
dc.relation.referencesWickramasinghe, S. R., Bower, S. E., Chen, Z., Mukherjee, A., and Husson, S. M. (2009). Relating the pore size distribution of ultrafiltration membranes to dextran rejection. Journal of Membrane Science, 340:1–8.spa
dc.relation.referencesYehl, C. J. and Zydney, A. L. (2021). Characterization of dextran transport and molecular weight cutoff (mwco) of large pore size hollow fiber ultrafiltration membranes. Journal of Membrane Science, 622:119025.spa
dc.relation.referencesZendehboudi, S., Rezaei, N., and Lohi, A. (2018). Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review. Applied Energy, 228:2539–2566.spa
dc.relation.referencesZhang, P. (2010). Industrial control system simulation routines.spa
dc.relation.referencesZydney, A. L. and Xenopoulos, A. (2007). Improving dextran tests for ultrafiltration membranes: Effect of device format. Journal of Membrane Science, 291:180–190.spa
dc.relation.referencesCengel, Y. A. and Ghajar, A. J. (2020). Heat and Mass Transfer: Fundamentals and Applications. McGraw-Hill Education.spa
dc.relation.referencesAzevedo, C. R., Dıaz, V. G., Prado-Rubio, O. A., Willis, M. J., Preat, V., Oliveira, R., and Stosch, M. (2019). Hybrid Semiparametric Modeling: A Modular Process Systems Engineering Approach for the Integration of Available Knowledge Sources. In Syst. Eng. Fourth Ind. Revolut., number January 2020, pages 345–373. Wiley.spa
dc.relation.referencesBaker, R. W. (2012). Membrane Technology and Applications. John Wiley & Sons, 3 edition.spa
dc.relation.referencesBakhshayeshi, M., Kanani, D. M., Mehta, A., van Reis, R., Kuriyel, R., Jackson, N., and Zydney, A. L. (2011a). Dextran sieving test for characterization of virus filtration membranes. J. Memb. Sci., 379(1-2):239–248.spa
dc.relation.referencesBakhshayeshi, M., Zhou, H., Olsen, C., Yuan, W., and Zydney, A. L. (2011b). Understanding dextran retention data for hollow fiber ultrafiltration membranes. J. Memb. Sci., 385- 386:243–250.spa
dc.relation.referencesBasedow, A. M. and Ebert, K. H. (1979). Production, characterization, and solution properties of dextran fractions of narrow molecular weight distributions. J. Polym. Sci. Polym. Symp., 66(1):101–115.spa
dc.relation.referencesBird, R. B., Stewart, W. E., and Lightfoot, E. N. (2002). Transport phenomena. J. Wiley, 2nd, wiley edition.spa
dc.relation.referencesBorujeni, E. E., Li, Y., and Zydney, A. L. (2015). Application of periodic backpulsing to reduce membrane fouling during ultrafiltration of plasmid DNA. J. Memb. Sci., 473:102– 108.spa
dc.relation.referencesByhlin, H. and J¨onsson, A.-S. (2003). Influence of adsorption and concentration polarisation on membrane performance during ultrafiltration of a non-ionic surfactant. Desalination, 151(1):21–31.spa
dc.relation.referencesChen, H. and Kim, A. S. (2006). Prediction of permeate flux decline in crossflow membrane filtration of colloidal suspension: a radial basis function neural network approach. Desalination, 192(1-3):415–428.spa
dc.relation.referencesChen, M., Shafer-Peltier, K., Randtke, S. J., and Peltier, E. (2018). Modeling arsenic (V) removal from water by micellar enhanced ultrafiltration in the presence of competing anions. Chemosphere, 213:285–294.spa
dc.relation.referencesChew, C. M., Aroua, M., and Hussain, M. (2017). A practical hybrid modelling approach for the prediction of potential fouling parameters in ultrafiltration membrane water treatment plant. J. Ind. Eng. Chem., 45:145–155.spa
dc.relation.referencesGao, Y., Qin, J., Wang, Z., and Østerhus, S. W. (2019). Backpulsing technology applied in MF and UF processes for membrane fouling mitigation: A review. J. Memb. Sci., 587:117136.spa
dc.relation.referencesGarcıa-Molina, V., Esplugas, S., Wintgens, T., and Melin, T. (2006). Ultrafiltration of aqueous solutions containing dextran. Desalination, 188(1-3):217–227.spa
dc.relation.referencesGaspar, V. M., Moreira, A. F., de Melo-Diogo, D., Costa, E. C., Queiroz, J. A., Sousa, F., Pichon, C., and Correia, I. J. (2016). Multifunctional nanocarriers for codelivery of nucleic acids and chemotherapeutics to cancer cells. In Nanobiomaterials Med. Imaging, pages 163–207. Elsevier.spa
dc.relation.referencesGrisales Dıaz, V. H., Prado-Rubio, O. A., Willis, M. J., and von Stosch, M. (2017). Dynamic hybrid model for ultrafiltration membrane processes. pages 193–198.spa
dc.relation.referencesGrzegorzek, M. and Majewska-Nowak, K. (2018). The use of micellar-enhanced ultrafiltration (MEUF) for fluoride removal from aqueous solutions. Sep. Purif. Technol., 195:1–11.spa
dc.relation.referencesGrznarova, G., Viktorin, M., and Lang, A. (2006). Characterization of virus retentive membranes by a tailor-made dextran method. Desalination, 200(1-3):297–298.spa
dc.relation.referencesInes Pereira Rosinha (2011). High frequency backshock effect on ultrafiltration of selected polysaccharides. Master thesis. Universidade Tecnica de Lisboa & Technical University of Denmark.spa
dc.relation.referencesJonsson, A.-S., Jonsson, B., and Byhlin, H. (2006). A concentration polarization model for the ultrafiltration of nonionic surfactants. J. Colloid Interface Sci., 304(1):191–199.spa
dc.relation.referencesJonsson, G. (1980). Overview of theories for water and solute transport in9 UF/RO membranes. Desalination, 35:21–38.spa
dc.relation.referencesJonsson, G. (1984). Boundary layer phenomena during ultrafiltration of dextran and whey protein solutions. Desalination, 51(1):61–77.spa
dc.relation.referencesJonsson, G. (2008). Tuning of the cut-off curves by dynamic ultrafiltration. In Proc. Int. Conf. Membr. Membr. Process. ICOM2008, Hawaii.spa
dc.relation.referencesKrishnakumar, N., Yea, M., and Cheryan, M. (2004). Ultrafiltration of soy protein concentrate: performance and modelling of spiral and tubular polymeric modules. J. Memb. Sci., 244(1-2):235–242.spa
dc.relation.referencesKwon, B., Molek, J., and Zydney, A. (2008). Ultrafiltration of PEGylated proteins: Fouling and concentration polarization effects. J. Memb. Sci., 319(1-2):206–213.spa
dc.relation.referencesMacedo, A., Duarte, E., and Pinho, M. (2011). The role of concentration polarization in ultrafiltration of ovine cheese whey. J. Memb. Sci., 381(1-2):34–40.spa
dc.relation.referencesNeggaz, Y., Vargas, M. L., Dris, A. O., Riera, F., and Alvarez, R. (2007). A combination of serial resistances and concentration polarization models along the membrane in ultrafiltration of pectin and albumin solutions. Sep. Purif. Technol., 54(1):18–27.spa
dc.relation.referencesPeinemann, K.-V. and Nunes, S. P. (2010). Membranes for water treatment. John Wiley & Sons.spa
dc.relation.referencesPrado-Rubio, O. A. and von Stosch, M. (2017). Towards Sustainable Flux Determination for Dynamic Ultrafiltration through Multivariable System Identification. In 27th Eur. Symp. Comput. Aided Process Eng., volume 3, pages 2719–2724.spa
dc.relation.referencesPu, Y., Zou, Q., Liu, L., Han, Z., Wang, X., Wang, Q., and Chen, S. (2012). Clinical dextran purified by fractional ultrafiltration coupled with water washing. Carbohydr. Polym., 87(2):1257–1260.spa
dc.relation.referencesSahoo, G. B. and Ray, C. (2006). Predicting flux decline in crossflow membranes using artificial neural networks and genetic algorithms. J. Memb. Sci., 283(1-2):147–157.spa
dc.relation.referencesSalladini, A., Prisciandaro, M., and Barba, D. (2007). Ultrafiltration of biologically treated wastewater by using backflushing. Desalination, 207(1-3):24–34.spa
dc.relation.referencesSaltık, M. B., Ozkan, L., Jacobs, M., and van der Padt, A. (2017). Dynamic modeling of ultrafiltration membranes for whey separation processes. Comput. Chem. Eng., 99:280– 295.spa
dc.relation.referencesScott, K. (1996). Handbook of Industrial Membranes. Elsevier Science, 1st edition.spa
dc.relation.referencesShi, L., Huang, J., Zhu, L., Shi, Y., Yi, K., and Li, X. (2019). Role of concentration polarization in cross flow micellar enhanced ultrafiltration of cadmium with low surfactant concentration. Chemosphere, 237:124859.spa
dc.relation.referencesSong, E.-H., Shang, J., and Ratner, D. (2012). Polysaccharides. In Polym. Sci. A Compr. Ref., pages 137–155. Elsevier.spa
dc.relation.referencesSrijaroonrat, P., Julien, E., and Aurelle, Y. (1999). Unstable secondary oil/water emulsion treatment using ultrafiltration: fouling control by backflushing. J. Memb. Sci., 159(1- 2):11–20.spa
dc.relation.referencesStankiewicz, A. I. and Moulijn, J. A. (2000). Process intensification: transforming chemical engineering. Chem. Eng. Prog., 96(1):22–34.spa
dc.relation.referencesTkacik, G. and Michaels, S. (1991). A Rejection Profile Test for Ultrafiltration Membranes & Devices. Bio/Technology, 9(10):941–946.spa
dc.relation.referencesVerma, S. P. and Sarkar, B. (2017). Rhamnolipid based micellar-enhanced ultrafiltration for simultaneous removal of Cd(II) and phenolic compound from wastewater. Chem. Eng. J., 319:131–142.spa
dc.relation.referencesVerma, S. P. and Sarkar, B. (2018). Simultaneous removal of Cd (II) and p-cresol from wastewater by micellar-enhanced ultrafiltration using rhamnolipid: Flux decline, adsorption kinetics and isotherm studies. J. Environ. Manage., 213:217–235.spa
dc.relation.referencesVinther, F. and J¨onsson, A.-S. (2016a). Modelling of optimal back-shock frequency in hollow fibre ultrafiltration membranes I: Computational fluid dynamics. J. Memb. Sci., 506:130– 136.spa
dc.relation.referencesVinther, F. and Jonsson, A.-S. (2016b). Modelling of optimal back-shock frequency in hollowfibre ultrafiltration membranes II: Semi-analytical mathematical model. J. Memb. Sci., 506:137–143.spa
dc.relation.referencesVinther, F., Pinelo, M., Brøns, M., Jonsson, G., and Meyer, A. S. (2014a). Mathematical modelling of dextran filtration through hollow fibre membranes. Sep. Purif. Technol., 125:21–36.spa
dc.relation.referencesVinther, F., Pinelo, M., Brøns, M., Jonsson, G., and Meyer, A. S. (2014b). Predicting optimal back-shock times in ultrafiltration hollow fibre modules through path-lines. J. Memb. Sci., 470:275–293.spa
dc.relation.referencesVinther, F., Pinelo, M., Brøns, M., Jonsson, G., and Meyer, A. S. (2015). Predicting optimal back-shock times in ultrafiltration hollow fiber modules II: Effect of inlet flow and concentration dependent viscosity. J. Memb. Sci., 493:486–495.spa
dc.relation.referencesWickramasinghe, S. R., Bower, S. E., Chen, Z., Mukherjee, A., and Husson, S. M. (2009). Relating the pore size distribution of ultrafiltration membranes to dextran rejection. J. Memb. Sci., 340(1-2):1–8.spa
dc.relation.referencesWijmans, J., Nakao, S., Van Den Berg, J., Troelstra, F., and Smolders, C. (1985). Hydrodynamic resistance of concentration polarization boundary layers in ultrafiltration. J. Memb. Sci., 22(1):117–135.spa
dc.relation.referencesYee, K. W., Wiley, D. E., and Bao, J. (2009). A unified model of the time dependence of flux decline for the long-term ultrafiltration of whey. J. Memb. Sci., 332(1-2):69–80.spa
dc.relation.referencesYehl, C. J. and Zydney, A. L. (2021). Characterization of dextran transport and molecular weight cutoff (MWCO) of large pore size hollow fiber ultrafiltration membranes. J. Memb. Sci., 622:119025.spa
dc.relation.referencesZaidi, S. and Kumar, A. (2004). Experimental studies in the dead-end ultrafiltration of dextran: analysis of concentration polarization. Sep. Purif. Technol., 36(2):115–130.spa
dc.relation.referencesZarrintaj, P., Saeb, M. R., Jafari, S. H., and Mozafari, M. (2020). Application of compatibilized polymer blends in biomedical fields. In Compat. Polym. Blends, pages 511–537. Elsevier.spa
dc.relation.referencesZydney, A. L. and Xenopoulos, A. (2007). Improving dextran tests for ultrafiltration membranes: Effect of device format. J. Memb. Sci., 291(1-2):180–190.spa
dc.relation.referencesAbels, C., Carstensen, F., and Wessling, M. (2013). Membrane processes in biorefinery applications. Journal of Membrane Science, 444:285–317.spa
dc.relation.referencesBaker, R. W. (2012). Membrane Technology and Applications. John Wiley & Sons, 3 edition.spa
dc.relation.referencesBakhshayeshi, M., Kanani, D. M., Mehta, A., van Reis, R., Kuriyel, R., Jackson, N., and Zydney, A. L. (2011). Dextran sieving test for characterization of virus filtration membranes. Journal of Membrane Science, 379:239–248.spa
dc.relation.referencesCharcosset, C. (2006). Membrane processes in biotechnology: An overview. Biotechnology Advances, 24:482–492.spa
dc.relation.referencesChhaya, Sharma, C., Mondal, S., Majumdar, G., and De, S. (2012). Clarification of stevia extract by ultrafiltration: Selection criteria of the membrane and effects of operating conditions. Food and Bioproducts Processing, 90:525–532.spa
dc.relation.referencesDıaz-Montes, E., Yanez-Fernandez, J., and Castro-Mu˜noz, R. (2020). Microfiltrationmediated extraction of dextran produced by leuconostoc mesenteroides sf3. Food and Bioproducts Processing, 119:317–328.spa
dc.relation.referencesGaspar, V. M., Moreira, A. F., de Melo-Diogo, D., Costa, E. C., Queiroz, J. A., Sousa, F., Pichon, C., and Correia, I. J. (2016). Multifunctional nanocarriers for codelivery of nucleic acids and chemotherapeutics to cancer cells.spa
dc.relation.referencesJonsson, G. E. and Prado-Rubio, O. A. (2011). Modeling and operation of dynamic membrane processes. International Congress on Membranes and Membrane Processes : ICOM 2011 ; Conference date: 01-01-2011.spa
dc.relation.referencesKwon, B., Molek, J., and Zydney, A. (2008). Ultrafiltration of pegylated proteins: Fouling and concentration polarization effects. Journal of Membrane Science, 319:206–213.spa
dc.relation.referencesLopez-Murillo, L. H., Grisales-Dıaz, V. H., Pinelo, M., and Prado-Rubio, O. A. (2021). Ultrafiltration intensification by dynamic operation: Insights from hybrid modeling. Chemical Engineering and Processing - Process Intensification, 169:108618.spa
dc.relation.referencesMacedo, A., Duarte, E., and Pinho, M. (2011). The role of concentration polarization in ultrafiltration of ovine cheese whey. Journal of Membrane Science, 381:34–40spa
dc.relation.referencesMach, O. and Lacko, L. (1968). Density gradient in a dextran medium. Analytical Biochemistry, 22:393–397.spa
dc.relation.referencesMountzouris, K., Gilmour, S., Grandison, A., and Rastall, R. (1999). Modeling of oligodextran production in an ultrafiltration stirred-cell membrane reactor. Enzyme and Microbial Technology, 24:75–85.spa
dc.relation.referencesMountzouris, K., Gilmour, S., and Rastall, R. (2002). Continuous production of oligodextrans via controlled hydrolysis of dextran in an enzyme membrane reactor. Journal of Food Science, 67:1767–1771.spa
dc.relation.referencesNeggaz, Y., Vargas, M. L., Dris, A. O., Riera, F., and Alvarez, R. (2007). A combination of serial resistances and concentration polarization models along the membrane in ultrafiltration of pectin and albumin solutions. Separation and Purification Technology, 54:18–27.spa
dc.relation.referencesPeinemann, K.-V. and Nunes, S. P. (2010). Membranes for water treatment. John Wiley & Sons.spa
dc.relation.referencesPinelo, M., Jonsson, G., and Meyer, A. S. (2009). Membrane technology for purification of enzymatically produced oligosaccharides: Molecular and operational features affecting performance. Separation and Purification Technology, 70:1–11.spa
dc.relation.referencesPrado-Rubio, O. A., Morales-Rodrıguez, R., Andrade-Santacoloma, P., and HernandezEscoto, H. (2016). Process intensification in biotechnology applications.spa
dc.relation.referencesPu, Y., Zou, Q., Liu, L., Han, Z., Wang, X., Wang, Q., and Chen, S. (2012). Clinical dextran purified by fractional ultrafiltration coupled with water washing. Carbohydrate Polymers, 87:1257–1260.spa
dc.relation.referencesQi, T., Da, X., Zhang, Y., Chen, X., Cui, Z., Qiu, M., and Fan, Y. (2020). Modeling and optimal operation of intermittent feed diafiltration for refining oligodextran using nanoporous ceramic membranes. Separation and Purification Technology, 253:117491.spa
dc.relation.referencesRosinha, I. P. (2011). High frequency backshock effect on ultrafiltration of selected polysaccharides. master thesis. universidade tecnica de lisboa & technical university of denmark.spa
dc.relation.referencesSu, Z., Luo, J., Pinelo, M., and Wan, Y. (2018). Directing filtration to narrow molecular weight distribution of oligodextran in an enzymatic membrane reactor. Journal of Membrane Science, 555:268–279.spa
dc.relation.referencesTorras, C., Nabarlatz, D., Vallot, G., Montane, D., and Garcia-Valls, R. (2008). Composite polymeric membranes for process intensification: Enzymatic hydrolysis of oligodextrans. Chemical Engineering Journal, 144:259–266.spa
dc.relation.referencesVinther, F. and Jonsson, A.-S. (2016a). Modelling of optimal back-shock frequency in hollow fibre ultrafiltration membranes i: Computational fluid dynamics. Journal of Membrane Science, 506:130–136.spa
dc.relation.referencesVinther, F. and Jonsson, A.-S. (2016b). Modelling of optimal back-shock frequency in hollowfibre ultrafiltration membranes ii: Semi-analytical mathematical model. Journal of Membrane Science, 506:137–143.spa
dc.relation.referencesVinther, F., Pinelo, M., Brøns, M., Jonsson, G., and Meyer, A. S. (2014a). Mathematical modelling of dextran filtration through hollow fibre membranes. Separation and Purification Technology, 125:21–36.spa
dc.relation.referencesVinther, F., Pinelo, M., Brøns, M., Jonsson, G., and Meyer, A. S. (2014b). Predicting optimal back-shock times in ultrafiltration hollow fibre modules through path-lines. Journal of Membrane Science, 470:275–293.spa
dc.relation.referencesVinther, F., Pinelo, M., Brøns, M., Jonsson, G., and Meyer, A. S. (2015). Predicting optimal back-shock times in ultrafiltration hollow fiber modules ii: Effect of inlet flow and concentration dependent viscosity. Journal of Membrane Science, 493:486–495.spa
dc.relation.referencesWei, P., Cheng, L.-H., Zhang, L., Xu, X.-H., lin Chen, H., and jie Gao, C. (2014). A review of membrane technology for bioethanol production. Renewable and Sustainable Energy Reviews, 30:388–400.spa
dc.relation.referencesWijmans, J., Nakao, S., Berg, J. V. D., Troelstra, F., and Smolders, C. (1985). Hydrodynamic resistance of concentration polarization boundary layers in ultrafiltration. Journal of Membrane Science, 22:117–135.spa
dc.relation.referencesYehl, C. J. and Zydney, A. L. (2021). Characterization of dextran transport and molecular weight cutoff (mwco) of large pore size hollow fiber ultrafiltration membranes. Journal of Membrane Science, 622:119025.spa
dc.relation.referencesZarrintaj, P., Saeb, M. R., Jafari, S. H., and Mozafari, M. (2020). Application of compatibilized polymer blends in biomedical fields.spa
dc.relation.referencesZeynali, R., Ghasemzadeh, K., Jalilnejad, E., and Basile, A. (2020). Economic evaluation of wastewater and water treatment technologies.spa
dc.relation.referencesZuriaga-Agustı, E., Alventosa-deLara, E., Barredo-Damas, S., Alcaina-Miranda, M., IborraClar, M., and Mendoza-Roca, J. (2014). Performance of ceramic ultrafiltration membranes and fouling behavior of a dye-polysaccharide binary system. Water Research, 54:199–210.spa
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.proposalDynamic ultrafiltrationeng
dc.subject.proposalMembrane intensificationeng
dc.subject.proposalMWCO tuningeng
dc.subject.proposalHybrid modelingeng
dc.subject.proposalUltrafiltración dinámicaspa
dc.subject.proposalIntensificación de membranasspa
dc.subject.proposalModelamiento híbridospa
dc.subject.proposalMWCOspa
dc.subject.unescoModelo matemático
dc.subject.unescoMathematical models
dc.titleData-driven modelling of micro and ultra - filtration processeseng
dc.title.translatedModelamiento basado en datos de procesos de micro y ultra - filtraciónspa
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.contentImagespa
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

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