Study of the influence of operational conditions and hollow-fiber diameter on the ultrafiltration performance of a secondary treatment effluent

Secondary treatment effluents from municipal wastewater treatment plants (MWWTP) must achieve high water quality standards for their reuse in agriculture. To achieve these standards, ultrafiltration (UF) process, which is economically feasible, is carried out. However, UF has a drawback, membrane fouling, which causes operating difficulties and an increment of the operating cost. In order to minimize this phenomenon, it is important to determine the best operational conditions. Wastewater samples provided by MWWTP have a lot of variability in their composition due to factors such as temperature, efficiency of the secondary treatment, etc. Besides, the soluble microbial products of the secondary effluent are dependent on the type of the biological treatment implemented and its operating conditions. A model wastewater feed solution was prepared consisting of 15 mg/L of bovine serum albumin and 5.5 mg/L of dextran. In this research, UF tests were performed with the optimal simulated wastewater using two membranes UFCM5 Norit X-flow® hollow-fiber: one of them with a fiber diameter of 1.5 mm and the other one with a fiber diameter of 0.8 mm. The operational conditions, which influence membrane fouling, were varied in the range of 62 100 kPa for transmembrane pressure (TMP) and in the range of 0.8 1.2 m/s for cross-flow velocity (CFV). The best operational conditions were selected in terms of higher permeate flux. The highest permeate flux was obtained for the membrane of 0.8 mm and the lower energy consumption was achieved at a CFV of 1.2 m/s and a TMP of 62 kPa.Torà Grau, M.; Soler Cabezas, JL.; Vincent Vela, MC.; Mendoza Roca, JA.; Martínez Francisco, FJ. (2015). Study of the influence of operational conditions and hollow-fiber diameter on the ultrafiltration performance of a secondary treatment effluent. Desalination and Water Treatment. 1-7. doi:10.1080/19443994.2015.1118887S1

Ultrafiltration fouling trend simulation of a municipal wastewater treatment plant effluent with model wastewater

Secondary treatment effluents from Municipal Wastewater Treatment Plants require tertiary treatments to be reused in agriculture. Among tertiary treatment technologies, ultrafiltration has been proven to be a reliable reclamation process. Nevertheless this technique has an important disadvantage: membrane fouling. This phenomenon causes decline in permeate flux with time and increases the operational costs. Due to the fact that secondary effluents from Municipal Wastewater Treatment Plants contain a large amount of different compounds and that there is certain variability in their composition, the use of a simplified model wastewater consisting of only few compounds may help to simulate better the ultrafiltration fouling trend. The main secondary treatment effluent components responsible for fouling membrane during ultrafiltration tests are extracellular polymeric substances. These substances are mainly composed of proteins and polysaccharides, thus they are commonly used to prepare model wastewaters. This work consisted in two parts. Firstly, a model wastewater was selected among different model solutions mimicking secondary treatment effluent. Secondly, ultrafiltration behaviour of the selected model solution was compared with the behaviour of the secondary effluent in the ultrafiltration tests at different cross-flow velocities and transmembrane pressures. The membrane used in the ultrafiltration tests was UFCM5 Norit X-flow® hollow-fiber. To prepare model wastewaters, three parameters (proteins and carbohydrates concentrations and chemical oxygen demand) were considered. The model wastewater that represented the best the fouling trend of the secondary treatment effluent had a composition of 15 mg/l of bovine serum albumin and 5.5 mg/l of dextranThe authors wish to gratefully acknowledge the financial support of the Generalitat Valenciana through the project "Ayudas para la realizacion de proyectos I+D para grupos de investigacion emergentes GV/2013."Tora Grau, M.; Soler Cabezas, JL.; Vincent Vela, MC.; Mendoza Roca, JA.; Martínez Francisco, FJ. (2015). Ultrafiltration fouling trend simulation of a municipal wastewater treatment plant effluent with model wastewater. Desalination and Water Treatment. 1-9. doi:10.1080/19443994.2014.999714S19Qin, J.-J., Oo, M. H., Lee, H., & Kolkman, R. (2004). Dead-end ultrafiltration for pretreatment of RO in reclamation of municipal wastewater effluent. Journal of Membrane Science, 243(1-2), 107-113. doi:10.1016/j.memsci.2004.06.010Arévalo, J., Garralón, G., Plaza, F., Moreno, B., Pérez, J., & Gómez, M. Á. (2009). Wastewater reuse after treatment by tertiary ultrafiltration and a membrane bioreactor (MBR): a comparative study. Desalination, 243(1-3), 32-41. doi:10.1016/j.desal.2008.04.013Katsoufidou, K., Yiantsios, S. G., & Karabelas, A. J. (2008). An experimental study of UF membrane fouling by humic acid and sodium alginate solutions: the effect of backwashing on flux recovery. Desalination, 220(1-3), 214-227. doi:10.1016/j.desal.2007.02.038Muthukumaran, S., Nguyen, D. A., & Baskaran, K. (2011). Performance evaluation of different ultrafiltration membranes for the reclamation and reuse of secondary effluent. Desalination, 279(1-3), 383-389. doi:10.1016/j.desal.2011.06.040Henderson, R. K., Subhi, N., Antony, A., Khan, S. J., Murphy, K. R., Leslie, G. L., … Le-Clech, P. (2011). Evaluation of effluent organic matter fouling in ultrafiltration treatment using advanced organic characterisation techniques. Journal of Membrane Science, 382(1-2), 50-59. doi:10.1016/j.memsci.2011.07.041Muthukumaran, S., Jegatheesan, J. V., & Baskaran, K. (2013). Comparison of fouling mechanisms in low-pressure membrane (MF/UF) filtration of secondary effluent. Desalination and Water Treatment, 52(4-6), 650-662. doi:10.1080/19443994.2013.826324Yu, C.-H., Fang, L.-C., Lateef, S. K., Wu, C.-H., & Lin, C.-F. (2010). Enzymatic treatment for controlling irreversible membrane fouling in cross-flow humic acid-fed ultrafiltration. Journal of Hazardous Materials, 177(1-3), 1153-1158. doi:10.1016/j.jhazmat.2010.01.022Gao, W., Liang, H., Ma, J., Han, M., Chen, Z., Han, Z., & Li, G. (2011). Membrane fouling control in ultrafiltration technology for drinking water production: A review. Desalination, 272(1-3), 1-8. doi:10.1016/j.desal.2011.01.051Kaya, Y., Barlas, H., & Arayici, S. (2011). Evaluation of fouling mechanisms in the nanofiltration of solutions with high anionic and nonionic surfactant contents using a resistance-in-series model. Journal of Membrane Science, 367(1-2), 45-54. doi:10.1016/j.memsci.2010.10.037Delgado, S., Dı́az, F., Vera, L., Dı́az, R., & Elmaleh, S. (2004). Modelling hollow-fibre ultrafiltration of biologically treated wastewater with and without gas sparging. Journal of Membrane Science, 228(1), 55-63. doi:10.1016/j.memsci.2003.09.011Fan, L., Nguyen, T., Roddick, F. A., & Harris, J. L. (2008). Low-pressure membrane filtration of secondary effluent in water reuse: Pre-treatment for fouling reduction. Journal of Membrane Science, 320(1-2), 135-142. doi:10.1016/j.memsci.2008.03.058Xiao, D., Li, W., Chou, S., Wang, R., & Tang, C. Y. (2012). A modeling investigation on optimizing the design of forward osmosis hollow fiber modules. Journal of Membrane Science, 392-393, 76-87. doi:10.1016/j.memsci.2011.12.006Zator, M., Ferrando, M., López, F., & Güell, C. (2007). Membrane fouling characterization by confocal microscopy during filtration of BSA/dextran mixtures. Journal of Membrane Science, 301(1-2), 57-66. doi:10.1016/j.memsci.2007.05.038Nataraj, S., Schomäcker, R., Kraume, M., Mishra, I. M., & Drews, A. (2008). Analyses of polysaccharide fouling mechanisms during crossflow membrane filtration. Journal of Membrane Science, 308(1-2), 152-161. doi:10.1016/j.memsci.2007.09.060Nguyen, S. T., & Roddick, F. A. (2011). Chemical cleaning of ultrafiltration membrane fouled by an activated sludge effluent. Desalination and Water Treatment, 34(1-3), 94-99. doi:10.5004/dwt.2011.2790Xiao, K., Wang, X., Huang, X., Waite, T. D., & Wen, X. (2009). Analysis of polysaccharide, protein and humic acid retention by microfiltration membranes using Thomas’ dynamic adsorption model. Journal of Membrane Science, 342(1-2), 22-34. doi:10.1016/j.memsci.2009.06.016Hwang, K.-J., & Chiang, Y.-C. (2014). Comparisons of membrane fouling and separation efficiency in protein/polysaccharide cross-flow microfiltration using membranes with different morphologies. Separation and Purification Technology, 125, 74-82. doi:10.1016/j.seppur.2014.01.041Yamamura, H., Okimoto, K., Kimura, K., & Watanabe, Y. (2014). Hydrophilic fraction of natural organic matter causing irreversible fouling of microfiltration and ultrafiltration membranes. Water Research, 54, 123-136. doi:10.1016/j.watres.2014.01.024Nigam, M. O., Bansal, B., & Chen, X. D. (2008). Fouling and cleaning of whey protein concentrate fouled ultrafiltration membranes. Desalination, 218(1-3), 313-322. doi:10.1016/j.desal.2007.02.027MOUROUZIDISMOUROUZIS, S., & KARABELAS, A. (2006). Whey protein fouling of microfiltration ceramic membranes—Pressure effects. Journal of Membrane Science, 282(1-2), 124-132. doi:10.1016/j.memsci.2006.05.012Carić, M. Đ., Milanović, S. D., Krstić, D. M., & Tekić, M. N. (2000). Fouling of inorganic membranes by adsorption of whey proteins. Journal of Membrane Science, 165(1), 83-88. doi:10.1016/s0376-7388(99)00221-5Tasselli, F., Cassano, A., & Drioli, E. (2007). Ultrafiltration of kiwifruit juice using modified poly(ether ether ketone) hollow fibre membranes. Separation and Purification Technology, 57(1), 94-102. doi:10.1016/j.seppur.2007.03.007Vincent-Vela, M.-C., Álvarez-Blanco, S., Lora-García, J., & Bergantiños-Rodríguez, E. (2009). Estimation of the gel layer concentration in ultrafiltration: Comparison of different methods. Desalination and Water Treatment, 3(1-3), 157-161. doi:10.5004/dwt.2009.454Valiño, V., San Román, M. F., Ibáñez, R., Benito, J. M., Escudero, I., & Ortiz, I. (2014). Accurate determination of key surface properties that determine the efficient separation of bovine milk BSA and LF proteins. Separation and Purification Technology, 135, 145-157. doi:10.1016/j.seppur.2014.07.051Luck, P. J., Vardhanabhuti, B., Yong, Y. H., Laundon, T., Barbano, D. M., & Foegeding, E. A. (2013). Comparison of functional properties of 34% and 80% whey protein and milk serum protein concentrates. Journal of Dairy Science, 96(9), 5522-5531. doi:10.3168/jds.2013-6617Marcos, B., Moresoli, C., Skorepova, J., & Vaughan, B. (2009). CFD modeling of a transient hollow fiber ultrafiltration system for protein concentration. Journal of Membrane Science, 337(1-2), 136-144. doi:10.1016/j.memsci.2009.03.036Chung, T.-S., Qin, J.-J., & Gu, J. (2000). Effect of shear rate within the spinneret on morphology, separation performance and mechanical properties of ultrafiltration polyethersulfone hollow fiber membranes. Chemical Engineering Science, 55(6), 1077-1091. doi:10.1016/s0009-2509(99)00371-1Salahi, A., Mohammadi, T., Rahmat Pour, A., & Rekabdar, F. (2009). Oily wastewater treatment using ultrafiltration. Desalination and Water Treatment, 6(1-3), 289-298. doi:10.5004/dwt.2009.480Janssen, A. N., van Agtmaal, J., van den Broek, W. B. P., de Koning, J., Menkveld, H. W. H., Schrotter, J.-C., … van der Graaf, J. H. J. M. (2008). Monitoring of SUR to control and enhance the performance of dead-end ultrafiltration installations treating wwtp effluent. Desalination, 231(1-3), 99-107. doi:10.1016/j.desal.2007.10.024Torà-Grau, M., Soler-Cabezas, J. L., Vincent-Vela, M. C., Mendoza-Roca, J. A., & Martínez-Francisco, F. J. (2014). Comparison of different model solutions to simulate membrane fouling in the ultrafiltration of a secondary effluent from a municipal wastewater treatment plant. Desalination and Water Treatment, 1-7. doi:10.1080/19443994.2014.93986

Comparison of different model solutions to simulate membrane fouling in the ultrafiltration of a secondary effluent from a municipal wastewater treatment plant

The quality of the secondary treatment effluent (STE) from a municipal wastewater treatment plant (MWWTP) is not good enough for some applications such as agriculture. Membrane ultrafiltration (UF) has been proven to be a reliable tertiary treatment to achieve the needed water quality. The productivity of the UF processes depends on the membrane fouling. The aim of this work is to prepare a model wastewater that could mimic the fouling trend of a STE wastewater from a MWWTP. Several model wastewaters consisting of different proteins and carbohydrates were used in the UF experiments. UF was also performed with a STE. The membrane used in the UF tests was a UFCM5 from Norit X-flow® hydrophilic polyethersulfone/polyvinylpyrrolidone blend hollow-fiber UF membrane of 200 KDa molecular weight cut-off with a fiber diameter of 1.5 mm. Membrane configuration was inside-out. UF tests with model wastewater and STE wastewater were compared. The results showed that the best model wastewater, which represents the fouling trend of STE wastewater is the model wastewater whose composition is 15 mg/l of bovine serum albumin and 5.5 mg/l of dextran.The authors of this work wish to gratefully acknowledge the financial support from the Generalitat Valenciana through the program "Ayudas para la realizacion de proyectos I+D para grupos de investigacion emergentes GV/2013."Tora Grau, M.; Soler Cabezas, JL.; Vincent Vela, MC.; Mendoza Roca, JA.; Martínez Francisco, FJ. (2014). Comparison of different model solutions to simulate membrane fouling in the ultrafiltration of a secondary effluent from a municipal wastewater treatment plant. Desalination and Water Treatment. 1-7. https://doi.org/10.1080/19443994.2014.939865S17Delgado, S., Dı́az, F., Vera, L., Dı́az, R., & Elmaleh, S. (2004). Modelling hollow-fibre ultrafiltration of biologically treated wastewater with and without gas sparging. Journal of Membrane Science, 228(1), 55-63. doi:10.1016/j.memsci.2003.09.011Qin, J.-J., Oo, M. H., Lee, H., & Kolkman, R. (2004). Dead-end ultrafiltration for pretreatment of RO in reclamation of municipal wastewater effluent. Journal of Membrane Science, 243(1-2), 107-113. doi:10.1016/j.memsci.2004.06.010Konieczny, K. (1998). Disinfection of surface and ground waters with polymeric ultrafiltration membranes. Desalination, 119(1-3), 251-258. doi:10.1016/s0011-9164(98)00166-0Madaeni, S. S., Fane, A. G., & Grohmann, G. S. (1995). Virus removal from water and wastewater using membranes. Journal of Membrane Science, 102, 65-75. doi:10.1016/0376-7388(94)00252-tArnal Arnal, J. M., Sancho Fernández, M., Martín Verdú, G., & Lora García, J. (2001). Design of a membrane facility for water potabilization and its application to Third World countries. Desalination, 137(1-3), 63-69. doi:10.1016/s0011-9164(01)00205-3Arévalo, J., Garralón, G., Plaza, F., Moreno, B., Pérez, J., & Gómez, M. Á. (2009). Wastewater reuse after treatment by tertiary ultrafiltration and a membrane bioreactor (MBR): a comparative study. Desalination, 243(1-3), 32-41. doi:10.1016/j.desal.2008.04.013Katsoufidou, K., Yiantsios, S. G., & Karabelas, A. J. (2008). An experimental study of UF membrane fouling by humic acid and sodium alginate solutions: the effect of backwashing on flux recovery. Desalination, 220(1-3), 214-227. doi:10.1016/j.desal.2007.02.038Muthukumaran, S., Nguyen, D. A., & Baskaran, K. (2011). Performance evaluation of different ultrafiltration membranes for the reclamation and reuse of secondary effluent. Desalination, 279(1-3), 383-389. doi:10.1016/j.desal.2011.06.040Henderson, R. K., Subhi, N., Antony, A., Khan, S. J., Murphy, K. R., Leslie, G. L., … Le-Clech, P. (2011). Evaluation of effluent organic matter fouling in ultrafiltration treatment using advanced organic characterisation techniques. Journal of Membrane Science, 382(1-2), 50-59. doi:10.1016/j.memsci.2011.07.041Fan, L., Nguyen, T., Roddick, F. A., & Harris, J. L. (2008). Low-pressure membrane filtration of secondary effluent in water reuse: Pre-treatment for fouling reduction. Journal of Membrane Science, 320(1-2), 135-142. doi:10.1016/j.memsci.2008.03.058Xiao, D., Li, W., Chou, S., Wang, R., & Tang, C. Y. (2012). A modeling investigation on optimizing the design of forward osmosis hollow fiber modules. Journal of Membrane Science, 392-393, 76-87. doi:10.1016/j.memsci.2011.12.006Kaya, Y., Barlas, H., & Arayici, S. (2011). Evaluation of fouling mechanisms in the nanofiltration of solutions with high anionic and nonionic surfactant contents using a resistance-in-series model. Journal of Membrane Science, 367(1-2), 45-54. doi:10.1016/j.memsci.2010.10.037Yu, C.-H., Fang, L.-C., Lateef, S. K., Wu, C.-H., & Lin, C.-F. (2010). Enzymatic treatment for controlling irreversible membrane fouling in cross-flow humic acid-fed ultrafiltration. Journal of Hazardous Materials, 177(1-3), 1153-1158. doi:10.1016/j.jhazmat.2010.01.022Gao, W., Liang, H., Ma, J., Han, M., Chen, Z., Han, Z., & Li, G. (2011). Membrane fouling control in ultrafiltration technology for drinking water production: A review. Desalination, 272(1-3), 1-8. doi:10.1016/j.desal.2011.01.051Amin Saad, M. (2004). Early discovery of RO membrane fouling and real-time monitoring of plant performance for optimizing cost of water. Desalination, 165, 183-191. doi:10.1016/j.desal.2004.06.021Jayalakshmi, A., Rajesh, S., & Mohan, D. (2012). Fouling propensity and separation efficiency of epoxidated polyethersulfone incorporated cellulose acetate ultrafiltration membrane in the retention of proteins. Applied Surface Science, 258(24), 9770-9781. doi:10.1016/j.apsusc.2012.06.028Qu, F., Liang, H., Wang, Z., Wang, H., Yu, H., & Li, G. (2012). Ultrafiltration membrane fouling by extracellular organic matters (EOM) of Microcystis aeruginosa in stationary phase: Influences of interfacial characteristics of foulants and fouling mechanisms. Water Research, 46(5), 1490-1500. doi:10.1016/j.watres.2011.11.051Wang, C., Li, Q., Tang, H., Yan, D., Zhou, W., Xing, J., & Wan, Y. (2012). Membrane fouling mechanism in ultrafiltration of succinic acid fermentation broth. Bioresource Technology, 116, 366-371. doi:10.1016/j.biortech.2012.03.099Nataraj, S., Schomäcker, R., Kraume, M., Mishra, I. M., & Drews, A. (2008). Analyses of polysaccharide fouling mechanisms during crossflow membrane filtration. Journal of Membrane Science, 308(1-2), 152-161. doi:10.1016/j.memsci.2007.09.060Zator, M., Ferrando, M., López, F., & Güell, C. (2007). Membrane fouling characterization by confocal microscopy during filtration of BSA/dextran mixtures. Journal of Membrane Science, 301(1-2), 57-66. doi:10.1016/j.memsci.2007.05.038Xiao, K., Wang, X., Huang, X., Waite, T. D., & Wen, X. (2009). Analysis of polysaccharide, protein and humic acid retention by microfiltration membranes using Thomas’ dynamic adsorption model. Journal of Membrane Science, 342(1-2), 22-34. doi:10.1016/j.memsci.2009.06.016Nigam, M. O., Bansal, B., & Chen, X. D. (2008). Fouling and cleaning of whey protein concentrate fouled ultrafiltration membranes. Desalination, 218(1-3), 313-322. doi:10.1016/j.desal.2007.02.027MOUROUZIDISMOUROUZIS, S., & KARABELAS, A. (2006). Whey protein fouling of microfiltration ceramic membranes—Pressure effects. Journal of Membrane Science, 282(1-2), 124-132. doi:10.1016/j.memsci.2006.05.012Carić, M. Đ., Milanović, S. D., Krstić, D. M., & Tekić, M. N. (2000). Fouling of inorganic membranes by adsorption of whey proteins. Journal of Membrane Science, 165(1), 83-88. doi:10.1016/s0376-7388(99)00221-5Tasselli, F., Cassano, A., & Drioli, E. (2007). Ultrafiltration of kiwifruit juice using modified poly(ether ether ketone) hollow fibre membranes. Separation and Purification Technology, 57(1), 94-102. doi:10.1016/j.seppur.2007.03.007Hao, Y., Moriya, A., Maruyama, T., Ohmukai, Y., & Matsuyama, H. (2011). Effect of metal ions on humic acid fouling of hollow fiber ultrafiltration membrane. Journal of Membrane Science, 376(1-2), 247-253. doi:10.1016/j.memsci.2011.04.035Marcos, B., Moresoli, C., Skorepova, J., & Vaughan, B. (2009). CFD modeling of a transient hollow fiber ultrafiltration system for protein concentration. Journal of Membrane Science, 337(1-2), 136-144. doi:10.1016/j.memsci.2009.03.036Chung, T.-S., Qin, J.-J., & Gu, J. (2000). Effect of shear rate within the spinneret on morphology, separation performance and mechanical properties of ultrafiltration polyethersulfone hollow fiber membranes. Chemical Engineering Science, 55(6), 1077-1091. doi:10.1016/s0009-2509(99)00371-1Nguyen, T.-A., Yoshikawa, S., Karasu, K., & Ookawara, S. (2012). A simple combination model for filtrate flux in cross-flow ultrafiltration of protein suspension. Journal of Membrane Science, 403-404, 84-93. doi:10.1016/j.memsci.2012.02.026Domínguez Chabaliná, L., Rodríguez Pastor, M., & Rico, D. P. (2013). Characterization of soluble and bound EPS obtained from 2 submerged membrane bioreactors by 3D-EEM and HPSEC. Talanta, 115, 706-712. doi:10.1016/j.talanta.2013.05.062Viebke, C. (2000). Determination of molecular mass distribution of κ-carrageenan and xanthan using asymmetrical flow field-flow fractionation. Food Hydrocolloids, 14(3), 265-270. doi:10.1016/s0268-005x(99)00066-1Kelly, S. T., & Zydney, A. L. (1995). Mechanisms for BSA fouling during microfiltration. Journal of Membrane Science, 107(1-2), 115-127. doi:10.1016/0376-7388(95)00108-oHwang, K.-J., & Sz, P.-Y. (2011). Membrane fouling mechanism and concentration effect in cross-flow microfiltration of BSA/dextran mixtures. Chemical Engineering Journal, 166(2), 669-677. doi:10.1016/j.cej.2010.11.04

Ultrafiltration of municipal wastewater: study on fouling models and fouling mechanisms

Ultrafiltration (UF) with hollow fiber membranes is a proven membrane technique that can achieve high water quality standards as a tertiary treatment in municipal wastewater treatment plants. However, UF has a major drawback, membrane fouling, which causes losses of productivity and increases operation costs. Thus, the aim of this work is to model membrane fouling in the UF of a secondary treatment effluent. The tests were carried out with a model wastewater solution that consisted of bovine serum albumin and dextran. Three different transmembrane pressures and three different crossflow velocities were tested. Several fouling models available in the literature, and new models proposed, were fitted to permeate flux decline experimental data. The models studied by other authors and considered in this study were: Hermia s models (complete, intermediate, standard pore blocking and gel layer) and Belfort s model. The new models proposed in this work were: modified Belfort s model, quadratic exponential model, logarithmic inversed model, double exponential model and tangent inversed model. The fitting accuracy of the models was determined in terms of the R-squared and standard deviation. The results showed that the model that had the higher fitting accuracy was the logarithmic inversed model. Among the Hermia s models, the model that had the higher fitting accuracy was the intermediate pore blocking model. Therefore, the predominant fouling mechanism was determined and it was the intermediate pore blocking modelThe authors wish to gratefully acknowledge the financial support of the Generalitat Valenciana through the project "Ayudas para la realizacion de proyectos I+D para grupos de investigacion emergentes GV/2013".Soler Cabezas, JL.; Tora Grau, M.; Vincent Vela, MC.; Mendoza Roca, JA.; Martínez Francisco, FJ. (2014). Ultrafiltration of municipal wastewater: study on fouling models and fouling mechanisms. Desalination and Water Treatment. 1-11. doi:10.1080/19443994.2014.969320S111Gadani, V., Irwin, R., & Mandra, V. (1996). Ultrafiltration as a tertiary treatment: Joint research program on membranes. Desalination, 106(1-3), 47-53. doi:10.1016/s0011-9164(96)00091-4Illueca-Muñoz, J., Mendoza-Roca, J. A., Iborra-Clar, A., Bes-Piá, A., Fajardo-Montañana, V., Martínez-Francisco, F. J., & Bernácer-Bonora, I. (2008). Study of different alternatives of tertiary treatments for wastewater reclamation to optimize the water quality for irrigation reuse. Desalination, 222(1-3), 222-229. doi:10.1016/j.desal.2007.01.157Muthukumaran, S., Jegatheesan, J. V., & Baskaran, K. (2013). Comparison of fouling mechanisms in low-pressure membrane (MF/UF) filtration of secondary effluent. Desalination and Water Treatment, 52(4-6), 650-662. doi:10.1080/19443994.2013.826324Delgado, S., Dı́az, F., Vera, L., Dı́az, R., & Elmaleh, S. (2004). Modelling hollow-fibre ultrafiltration of biologically treated wastewater with and without gas sparging. Journal of Membrane Science, 228(1), 55-63. doi:10.1016/j.memsci.2003.09.011Qin, J.-J., Oo, M. H., Lee, H., & Kolkman, R. (2004). Dead-end ultrafiltration for pretreatment of RO in reclamation of municipal wastewater effluent. Journal of Membrane Science, 243(1-2), 107-113. doi:10.1016/j.memsci.2004.06.010Konieczny, K. (1998). Disinfection of surface and ground waters with polymeric ultrafiltration membranes. Desalination, 119(1-3), 251-258. doi:10.1016/s0011-9164(98)00166-0Madaeni, S. S., Fane, A. G., & Grohmann, G. S. (1995). Virus removal from water and wastewater using membranes. Journal of Membrane Science, 102, 65-75. doi:10.1016/0376-7388(94)00252-tArnal Arnal, J. M., Sancho Fernández, M., Martín Verdú, G., & Lora García, J. (2001). Design of a membrane facility for water potabilization and its application to Third World countries. Desalination, 137(1-3), 63-69. doi:10.1016/s0011-9164(01)00205-3Arévalo, J., Garralón, G., Plaza, F., Moreno, B., Pérez, J., & Gómez, M. Á. (2009). Wastewater reuse after treatment by tertiary ultrafiltration and a membrane bioreactor (MBR): a comparative study. Desalination, 243(1-3), 32-41. doi:10.1016/j.desal.2008.04.013Katsoufidou, K., Yiantsios, S. G., & Karabelas, A. J. (2008). An experimental study of UF membrane fouling by humic acid and sodium alginate solutions: the effect of backwashing on flux recovery. Desalination, 220(1-3), 214-227. doi:10.1016/j.desal.2007.02.038Muthukumaran, S., Nguyen, D. A., & Baskaran, K. (2011). Performance evaluation of different ultrafiltration membranes for the reclamation and reuse of secondary effluent. Desalination, 279(1-3), 383-389. doi:10.1016/j.desal.2011.06.040Henderson, R. K., Subhi, N., Antony, A., Khan, S. J., Murphy, K. 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The effect of winds on atmospheric layers of red supergiants: I. Modelling for interferometric observations

Context. Red supergiants (RSGs) are evolved massive stars in a stage preceding core-collapse supernova. The physical processes that trigger mass loss in their atmospheres are still not fully understood, and they remain one of the key questions in stellar astrophysics. Based on observations of α Ori, a new semi-empirical method to add a wind to hydrostatic model atmospheres of RSGs was recently developed. This method can reproduce many of the static molecular shell (or 'MOLsphere') spectral features. Aims. We used this method of adding a semi-empirical wind to a MARCS model atmosphere to compute synthetic observables, comparing the model to spatially resolved interferometric observations.We present a case study to model published interferometric data of HD 95687 and V602 Car obtained with the AMBER instrument at the Very Large Telescope Interferometer (VLTI). Methods. We computed model intensities with respect to the line-of-sight angle (μ) for different mass-loss rates, spectra, and visibilities using the radiative transfer code TURBOSPECTRUM. We were able to convolve the models to match the different spectral resolutions of the VLTI instruments, studying a wavelength range of 1.8-5 μm corresponding to the K, L, and M bands for GRAVITY and MATISSE data. The model spectra and squared visibility amplitudes were compared with the published VLTI/AMBER data. Results. The synthetic visibilities reproduce observed drops in the CO, SiO, and water layers that are not shown in visibilities based on MARCS models alone. For the case studies, we find that adding a wind onto the MARCS model with simple radiative equilibrium dramatically improves the agreement with the squared visibility amplitudes as well as the spectra, with the fit being even better when applying a steeper density profile than predicted from previous studies. Our results reproduce observed extended atmospheres up to several stellar radii. Conclusions. This paper shows the potential of our model to describe extended atmospheres in RSGs. It can reproduce the shapes of the spectra and visibilities with a better accuracy in the CO and water lines than previous models. The method can be extended to other wavelength bands for both spectroscopic and interferometric observations. We provide temperature and density stratifications that succeed, for the first time, in reproducing observed interferometric properties of RSG atmospheres

Evaluating the Potential of Polygenic Risk Score to Improve Colorectal Cancer Screening

Background: Colorectal cancer has high incidence and associ-ated mortality worldwide. Screening programs are recommended for men and women over 50. Intermediate screens such as fecal immunochemical testing (FIT) select patients for colonoscopy with suboptimal sensitivity. Additional biomarkers could improve the current scenario. Methods: We included 2,893 individuals with a positive FIT test. They were classified as cases when a high-risk lesion for colorectal cancer was detected after colonoscopy, whereas the control group comprised individuals with low-risk or no lesions. 65 colorectal cancer risk genetic variants were geno-typed. Polygenic risk score (PRS) and additive models for risk prediction incorporating sex, age, FIT value, and PRS were generated. Results: Risk score was higher in cases compared with controls [per allele OR = 1.04; 95% confidence interval (CI), 1.02-1.06; P = 65), compared with those in the first decile (<= 54; OR = 2.22; 95% CI, 1.59-3.12; P < 0.0001). The model combining sex, age, FIT value, and PRS reached the highest accuracy for identifying patients with a high-risk lesion [cross-validated area under the ROC curve (AUROC): 0.64; 95% CI, 0.62-0.66]. Conclusions: This is the first investigation analyzing PRS in a two-step colorectal cancer screening program. PRS could improve current colorectal cancer screening, most likely for higher at-risk subgroups. However, its capacity is limited to predict colorectal cancer risk status and should be complemented by additional biomarkers.Impact: PRS has capacity for risk stratification of colorectal cancer suggesting its potential for optimizing screening strategies alongside with other biomarkers

Anti-Hu antibodies activate enteric and sensory neurons.

IgG of type 1 anti-neuronal nuclear antibody (ANNA-1, anti-Hu) specificity is a serological marker of paraneoplastic neurological autoimmunity (including enteric/autonomic) usually related to small-cell lung carcinoma. We show here that IgG isolated from such sera and also affinity-purified anti-HuD label enteric neurons and cause an immediate spike discharge in enteric and visceral sensory neurons. Both labelling and activation of enteric neurons was prevented by preincubation with the HuD antigen. Activation of enteric neurons was inhibited by the nicotinic receptor antagonists hexamethonium and dihydro-β-erythroidine and reduced by the P2X antagonist pyridoxal phosphate-6-azo (benzene-2,4-disulfonic acid (PPADS) but not by the 5-HT3 antagonist tropisetron or the N-type Ca-channel blocker ω-Conotoxin GVIA. Ca(++) imaging experiments confirmed activation of enteric neurons but not enteric glia. These findings demonstrate a direct excitatory action of ANNA-1, in particular anti-HuD, on visceral sensory and enteric neurons, which involves nicotinic and P2X receptors. The results provide evidence for a novel link between nerve activation and symptom generation in patients with antibody-mediated gut dysfunction

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

Background: The aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk. Methods: We included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell’s C statistic. Results: The Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell’s C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories. Conclusions: These models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.This study was funded by grant PS09/01340 and The Spanish Network on Chronic Diseases REDISSEC (RD12/0001/0007) from the Health Research Fund (Fondo de Investigación Sanitaria) of the Spanish Ministry of Health

Helicobacter pylori Diagnostic Tests Used in Europe : Results of over 34,000 Patients from the European Registry on Helicobacter pylori Management

Funding Information: This study was funded by Richen; however, clinical data were not accessible and the company was not involved in any stage of the Hp-EuReg study (design, data collection, statistical analysis, or manuscript writing). We want to thank Richen for their support. This project was promoted and funded by the European Helicobacter and Microbiota Study Group (EHMSG), the Spanish Association of Gastroenterology (AEG) and the Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd). The Hp-EuReg was co-funded by the European Union programme HORIZON (grant agreement number 101095359) and supported by the UK Research and Innovation (grant agreement number 10058099). The Hp-EuReg was co-funded by the European Union programme EU4Health (grant agreement number 101101252). Acknowledgments We want to especially thank Sylva-Astrik Torossian for her assistance in language editing. Natalia García Morales is the first author who is acting as the submission’s guarantor. All authors approved the final version of the manuscript.Peer reviewedPublisher PD