Scaling Behaviour and Complexity of the Portevin-Le Chatelier Effect

The plastic deformation of dilute alloys is often accompanied by plastic instabilities due to dynamic strain aging and dislocation interaction. The repeated breakaway of dislocations from and their recapture by solute atoms leads to stress serrations and localized strain in the strain controlled tensile tests, known as the Portevin-Le Chatelier (PLC) effect. In this present work, we analyse the stress time series data of the observed PLC effect in the constant strain rate tensile tests on Al-2.5%Mg alloy for a wide range of strain rates at room temperature. The scaling behaviour of the PLC effect was studied using two complementary scaling analysis methods: the finite variance scaling method and the diffusion entropy analysis. From these analyses we could establish that in the entire span of strain rates, PLC effect showed Levy walk property. Moreover, the multiscale entropy analysis is carried out on the stress time series data observed during the PLC effect to quantify the complexity of the distinct spatiotemporal dynamical regimes. It is shown that for the static type C band, the entropy is very low for all the scales compared to the hopping type B and the propagating type A bands. The results are interpreted considering the time and length scales relevant to the effect.Comment: 35 pages, 6 figure

ASCA Discovery of an X-ray Pulsar in the Error Box of SGR1900+14

We present a 2 - 10 keV ASCA observation of the field around the soft gamma repeater SGR1900+14. One quiescent X-ray source was detected in this observation, and it was in the SGR error box. In 2 - 10 keV X-rays, its spectrum may be fit by a power law with index -2.2, and its unabsorbed flux is 9.6 x 10^-12 erg cm^-2 s^-1. We also find a clear 5.16 s period. The properties of the three well-studied soft gamma repeaters are remarkably similar to one another, and provide evidence that all of them are associated with young, strongly magnetized neutron stars in supernova remnants.Comment: Accepted for publication in the Astrophysical Journal Letter

3D multi-agent models for protein release from PLGA spherical particles with complex inner morphologies

In order to better understand and predict the release of proteins from bioerodible micro- or nanospheres, it is important to know the influences of different initial factors on the release mechanisms. Often though it is difficult to assess what exactly is at the origin of a certain dissolution profile. We propose here a new class of fine-grained multi-agent models built to incorporate increasing complexity, permitting the exploration of the role of different parameters, especially that of the internal morphology of the spheres, in the exhibited release profile. This approach, based on Monte-Carlo (MC) and Cellular Automata (CA) techniques, has permitted the testing of various assumptions and hypotheses about several experimental systems of nanospheres encapsulating proteins. Results have confirmed that this modelling approach has increased the resolution over the complexity involved, opening promising perspectives for future developments, especially complementing in vitro experimentation

Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model

[EN] Plant-wide modelling can be considered an appropriate approach to represent the current complexity in water resource recovery facilities, reproducing all known phenomena in the different process units. Nonetheless, novel processes and new treatment schemes are still being developed and need to be fully incorporated in these models. This work presents a short chronological overview of some of the most relevant plant-wide models for wastewater treatment, as well as the authors' experience in plant-wide modelling using the general model BNRM (Biological Nutrient Removal Model), illustrating the key role of general models (also known as supermodels) in the field of wastewater treatment, both for engineering and research.Seco, A.; Ruano, MV.; Ruiz-Martínez, A.; Robles Martínez, Á.; Barat, R.; Serralta Sevilla, J.; Ferrer, J. (2020). Plant-wide modelling in wastewater treatment: showcasing experiences using the Biological Nutrient Removal Model. Water Science & Technology. 81(8):1700-1714. https://doi.org/10.2166/wst.2020.056S17001714818Barat, R., Montoya, T., Seco, A., & Ferrer, J. (2011). Modelling biological and chemically induced precipitation of calcium phosphate in enhanced biological phosphorus removal systems. Water Research, 45(12), 3744-3752. doi:10.1016/j.watres.2011.04.028Barat, R., Serralta, J., Ruano, M. V., Jiménez, E., Ribes, J., Seco, A., & Ferrer, J. (2013). Biological Nutrient Removal Model No. 2 (BNRM2): a general model for wastewater treatment plants. Water Science and Technology, 67(7), 1481-1489. doi:10.2166/wst.2013.004Batstone, D. J., Hülsen, T., Mehta, C. M., & Keller, J. (2015). Platforms for energy and nutrient recovery from domestic wastewater: A review. Chemosphere, 140, 2-11. doi:10.1016/j.chemosphere.2014.10.021Borrás F. L. 2008 Técnicas microbiológicas aplicadas a la identificación y cuantificación de organismos presentes en sistemas EBPR (Microbiological Techniques Applied to Identification and Quantification of Organisms Present in EBPR Systems). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Claros, J., Jiménez, E., Aguado, D., Ferrer, J., Seco, A., & Serralta, J. (2013). Effect of pH and HNO2 concentration on the activity of ammonia-oxidizing bacteria in a partial nitritation reactor. Water Science and Technology, 67(11), 2587-2594. doi:10.2166/wst.2013.132Copp, J. B., Jeppsson, U., & Rosen, C. (2003). TOWARDS AN ASM1 – ADM1 STATE VARIABLE INTERFACE FOR PLANT-WIDE WASTEWATER TREATMENT MODELING. Proceedings of the Water Environment Federation, 2003(7), 498-510. doi:10.2175/193864703784641207Dorofeev, A. G., Nikolaev, Y. A., Kozlov, M. N., Kevbrina, M. V., Agarev, A. M., Kallistova, A. Y., & Pimenov, N. V. (2017). Modeling of anammox process with the biowin software suite. Applied Biochemistry and Microbiology, 53(1), 78-84. doi:10.1134/s0003683817010100Drewnowski, J., Zaborowska, E., & Hernandez De Vega, C. (2018). Computer Simulation in Predicting Biochemical Processes and Energy Balance at WWTPs. E3S Web of Conferences, 30, 03007. doi:10.1051/e3sconf/20183003007Durán F. 2013 Modelación matemática del tratamiento anaerobio de aguas residuales urbanas incluyendo las bacterias sulfatorreductoras. Aplicación a un biorreactor anaerobio de membranas (Mathematical Model of Urban Wastewater Anaerobic Treatment Including Sulphate Reducing Bacteria. Application to an Anaerobic Membrane Bioreactor). PhD Thesis, Universitat Politècnica de València, Valencia, Spain.Ekama, G. A. (2009). Using bioprocess stoichiometry to build a plant-wide mass balance based steady-state WWTP model. Water Research, 43(8), 2101-2120. doi:10.1016/j.watres.2009.01.036EPA 2006 User's manual version 4.03 2006. Available from: https://www.epa.gov/ceam/minteqa2-equilibrium-speciation-model (accessed July 2019).Fernández-Arévalo, T., Lizarralde, I., Fdz-Polanco, F., Pérez-Elvira, S. I., Garrido, J. M., Puig, S., … Ayesa, E. (2017). Quantitative assessment of energy and resource recovery in wastewater treatment plants based on plant-wide simulations. Water Research, 118, 272-288. doi:10.1016/j.watres.2017.04.001Ferrer, J., Seco, A., Serralta, J., Ribes, J., Manga, J., Asensi, E., … Llavador, F. (2008). DESASS: A software tool for designing, simulating and optimising WWTPs. Environmental Modelling & Software, 23(1), 19-26. doi:10.1016/j.envsoft.2007.04.005Ferrer J., Seco A., Ruano M. V., Ribes J., Serralta J., Gómez T., Robles A. 2011 LoDif BioControl® Control Software, Intellectual Property. Main Institution: Universitat de València; Universitat Politècnica de València.Flores-Alsina, X., Corominas, L., Snip, L., & Vanrolleghem, P. A. (2011). Including greenhouse gas emissions during benchmarking of wastewater treatment plant control strategies. Water Research, 45(16), 4700-4710. doi:10.1016/j.watres.2011.04.040Flores-Alsina, X., Arnell, M., Amerlinck, Y., Corominas, L., Gernaey, K. V., Guo, L., … Jeppsson, U. (2014). Balancing effluent quality, economic cost and greenhouse gas emissions during the evaluation of (plant-wide) control/operational strategies in WWTPs. Science of The Total Environment, 466-467, 616-624. doi:10.1016/j.scitotenv.2013.07.046Flores-Alsina, X., Kazadi Mbamba, C., Solon, K., Vrecko, D., Tait, S., Batstone, D. J., … Gernaey, K. V. (2015). A plant-wide aqueous phase chemistry module describing pH variations and ion speciation/pairing in wastewater treatment process models. Water Research, 85, 255-265. doi:10.1016/j.watres.2015.07.014Ge, Z. (2017). Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemometrics and Intelligent Laboratory Systems, 171, 16-25. doi:10.1016/j.chemolab.2017.09.021Grau, P., de Gracia, M., Vanrolleghem, P. A., & Ayesa, E. (2007). A new plant-wide modelling methodology for WWTPs. Water Research, 41(19), 4357-4372. doi:10.1016/j.watres.2007.06.019Grau, P., Copp, J., Vanrolleghem, P. A., Takács, I., & Ayesa, E. (2009). A comparative analysis of different approaches for integrated WWTP modelling. Water Science and Technology, 59(1), 141-147. doi:10.2166/wst.2009.589Henze M., Gujer W., Mino T., van Loosdrecht M. C. M. 2000 Activated Sludge Models ASM1, ASM2, ASM2d and ASM3. IWA Scientific and Technical Report No.9. IWA Publishing, London, UK.Jeppsson, U., & Pons, M.-N. (2004). The COST benchmark simulation model—current state and future perspective. Control Engineering Practice, 12(3), 299-304. doi:10.1016/j.conengprac.2003.07.001Jeppsson, U., Rosen, C., Alex, J., Copp, J., Gernaey, K. V., Pons, M.-N., & Vanrolleghem, P. A. (2006). Towards a benchmark simulation model for plant-wide control strategy performance evaluation of WWTPs. Water Science and Technology, 53(1), 287-295. doi:10.2166/wst.2006.031Ji, X., Liu, Y., Zhang, J., Huang, D., Zhou, P., & Zheng, Z. (2018). Development of model simulation based on BioWin and dynamic analyses on advanced nitrate nitrogen removal in deep bed denitrification filter. Bioprocess and Biosystems Engineering, 42(2), 199-212. doi:10.1007/s00449-018-2025-xJiménez, E., Giménez, J. B., Ruano, M. V., Ferrer, J., & Serralta, J. (2011). Effect of pH and nitrite concentration on nitrite oxidation rate. Bioresource Technology, 102(19), 8741-8747. doi:10.1016/j.biortech.2011.07.092Jiménez, E., Giménez, J. B., Seco, A., Ferrer, J., & Serralta, J. (2012). Effect of pH, substrate and free nitrous acid concentrations on ammonium oxidation rate. Bioresource Technology, 124, 478-484. doi:10.1016/j.biortech.2012.07.079Kazadi Mbamba, C., Flores-Alsina, X., John Batstone, D., & Tait, S. (2016). Validation of a plant-wide phosphorus modelling approach with minerals precipitation in a full-scale WWTP. Water Research, 100, 169-183. doi:10.1016/j.watres.2016.05.003Kazadi Mbamba, C., Lindblom, E., Flores-Alsina, X., Tait, S., Anderson, S., Saagi, R., … Jeppsson, U. (2019). Plant-wide model-based analysis of iron dosage strategies for chemical phosphorus removal in wastewater treatment systems. Water Research, 155, 12-25. doi:10.1016/j.watres.2019.01.048Liu, Y., Peng, L., Ngo, H. H., Guo, W., Wang, D., Pan, Y., … Ni, B.-J. (2016). Evaluation of Nitrous Oxide Emission from Sulfide- and Sulfur-Based Autotrophic Denitrification Processes. Environmental Science & Technology, 50(17), 9407-9415. doi:10.1021/acs.est.6b02202Lizarralde, I., Fernández-Arévalo, T., Brouckaert, C., Vanrolleghem, P., Ikumi, D. S., Ekama, G. A., … Grau, P. (2015). A new general methodology for incorporating physico-chemical transformations into multi-phase wastewater treatment process models. Water Research, 74, 239-256. doi:10.1016/j.watres.2015.01.031Lizarralde, I., Fernández-Arévalo, T., Manas, A., Ayesa, E., & Grau, P. (2019). Model-based opti mization of phosphorus management strategies in Sur WWTP, Madrid. Water Research, 153, 39-52. doi:10.1016/j.watres.2018.12.056Maere, T., Verrecht, B., Moerenhout, S., Judd, S., & Nopens, I. (2011). BSM-MBR: A benchmark simulation model to compare control and operational strategies for membrane bioreactors. Water Research, 45(6), 2181-2190. doi:10.1016/j.watres.2011.01.006Mannina, G., Ekama, G., Caniani, D., Cosenza, A., Esposito, G., Gori, R., … Olsson, G. (2016). Greenhouse gases from wastewater treatment — A review of modelling tools. Science of The Total Environment, 551-552, 254-270. doi:10.1016/j.scitotenv.2016.01.163Martí, N., Barat, R., Seco, A., Pastor, L., & Bouzas, A. (2017). Sludge management modeling to enhance P-recovery as struvite in wastewater treatment plants. Journal of Environmental Management, 196, 340-346. doi:10.1016/j.jenvman.2016.12.074Moretti, P., Choubert, J.-M., Canler, J.-P., Buffière, P., Pétrimaux, O., & Lessard, P. (2017). Dynamic modeling of nitrogen removal for a three-stage integrated fixed-film activated sludge process treating municipal wastewater. Bioprocess and Biosystems Engineering, 41(2), 237-247. doi:10.1007/s00449-017-1862-3Nagy, J., Kaljunen, J., & Toth, A. J. (2019). Nitrogen recovery from wastewater and human urine with hydrophobic gas separation membrane: experiments and modelling. Chemical Papers, 73(8), 1903-1915. doi:10.1007/s11696-019-00740-xNewhart, K. B., Holloway, R. W., Hering, A. S., & Cath, T. Y. (2019). Data-driven performance analyses of wastewater treatment plants: A review. Water Research, 157, 498-513. doi:10.1016/j.watres.2019.03.030Nopens, I., Batstone, D. J., Copp, J. B., Jeppsson, U., Volcke, E., Alex, J., & Vanrolleghem, P. A. (2009). An ASM/ADM model interface for dynamic plant-wide simulation. Water Research, 43(7), 1913-1923. doi:10.1016/j.watres.2009.01.012Nopens, I., Benedetti, L., Jeppsson, U., Pons, M.-N., Alex, J., Copp, J. B., … Vanrolleghem, P. A. (2010). Benchmark Simulation Model No 2: finalisation of plant layout and default control strategy. Water Science and Technology, 62(9), 1967-1974. doi:10.2166/wst.2010.044Ontiveros, G. A., & Campanella, E. A. (2013). Environmental performance of biological nutrient removal processes from a life cycle perspective. Bioresource Technology, 150, 506-512. doi:10.1016/j.biortech.2013.08.059Penya-Roja, J. M., Seco, A., Ferrer, J., & Serralta, J. (2002). Calibration and Validation of Activated Sludge Model No.2d for Spanish Municipal Wastewater. Environmental Technology, 23(8), 849-862. doi:10.1080/09593332308618360Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). A plant-wide energy model for wastewater treatment plants: application to anaerobic membrane bioreactor technology. Environmental Technology, 37(18), 2298-2315. doi:10.1080/09593330.2016.1148903Pretel, R., Robles, A., Ruano, M. V., Seco, A., & Ferrer, J. (2016). Economic and environmental sustainability of submerged anaerobic MBR-based (AnMBR-based) technology as compared to aerobic-based technologies for moderate-/high-loaded urban wastewater treatment. Journal of Environmental Management, 166, 45-54. doi:10.1016/j.jenvman.2015.10.004Rehman, U., Audenaert, W., Amerlinck, Y., Maere, T., Arnaldos, M., & Nopens, I. (2017). How well-mixed is well mixed? Hydrodynamic-biokinetic model integration in an aerated tank of a full-scale water resource recovery facility. Water Science and Technology, 76(8), 1950-1965. doi:10.2166/wst.2017.330Rieger L., Gillot S., Langergraber G., Ohtsuki T., Shaw A., Takacs I., Winkler S. 2012 Guidelines for Using Activated Sludge Models Scientific and Technical report No. 21. EWA Task Group on Good Modelling Practice. IWA Publishing Volume 11.Robles, A., Ruano, M. V., Ribes, J., Seco, A., & Ferrer, J. (2014). Model-based automatic tuning of a filtration control system for submerged anaerobic membrane bioreactors (AnMBR). Journal of Membrane Science, 465, 14-26. doi:10.1016/j.memsci.2014.04.012Robles, A., Capson-Tojo, G., Ruano, M. V., Seco, A., & Ferrer, J. (2018). Real-time optimization of the key filtration parameters in an AnMBR: Urban wastewater mono-digestion vs. co-digestion with domestic food waste. Waste Management, 80, 299-309. doi:10.1016/j.wasman.2018.09.031Ruano, M. V., Serralta, J., Ribes, J., Garcia-Usach, F., Bouzas, A., Barat, R., … Ferrer, J. (2012). Application of the general model ‘Biological Nutrient Removal Model No. 1’ to upgrade two full-scale WWTPs. Environmental Technology, 33(9), 1005-1012. doi:10.1080/09593330.2011.604877Seco, A., Ribes, J., Serralta, J., & Ferrer, J. (2004). Biological nutrient removal model No.1 (BNRM1). Water Science and Technology, 50(6), 69-70. doi:10.2166/wst.2004.0361Serralta, J., Ferrer, J., Borrás, L., & Seco, A. (2004). An extension of ASM2d including pH calculation. Water Research, 38(19), 4029-4038. doi:10.1016/j.watres.2004.07.009Shoener, B. D., Schramm, S. M., Béline, F., Bernard, O., Martínez, C., Plósz, B. G., … Guest, J. S. (2019). Microalgae and cyanobacteria modeling in water resource recovery facilities: A critical review. Water Research X, 2, 100024. doi:10.1016/j.wroa.2018.100024Solon, K., Flores-Alsina, X., Kazadi Mbamba, C., Ikumi, D., Volcke, E. I. P., Vaneeckhaute, C., … Jeppsson, U. (2017). Plant-wide modelling of phosphorus transformations in wastewater treatment systems: Impacts of control and operational strategies. Water Research, 113, 97-110. doi:10.1016/j.watres.2017.02.007Solon, K., Jia, M., & Volcke, E. I. P. (2019). Process schemes for future energy-positive water resource recovery facilities. Water Science and Technology, 79(9), 1808-1820. doi:10.2166/wst.2019.183Vanrolleghem, P. A., Rosen, C., Zaher, U., Copp, J., Benedetti, L., Ayesa, E., & Jeppsson, U. (2005). Continuity-based interfacing of models for wastewater systems described by Petersen matrices. Water Science and Technology, 52(1-2), 493-500. doi:10.2166/wst.2005.055

Swelling-collapse transition of self-attracting walks

We study the structural properties of self-attracting walks in d dimensions using scaling arguments and Monte Carlo simulations. We find evidence for a transition analogous to the \Theta transition of polymers. Above a critical attractive interaction u_c, the walk collapses and the exponents \nu and k, characterising the scaling with time t of the mean square end-to-end distance ~ t^{2 \nu} and the average number of visited sites ~ t^k, are universal and given by \nu=1/(d+1) and k=d/(d+1). Below u_c, the walk swells and the exponents are as with no interaction, i.e. \nu=1/2 for all d, k=1/2 for d=1 and k=1 for d >= 2. At u_c, the exponents are found to be in a different universality class.Comment: 6 pages, 5 postscript figure

Production of microalgal external organic matter in a Chlorella-dominated culture: influence of temperature and stress factors

Although microalgae are recognised to release external organic matter (EOM), little is known about this phenomenon in microalgae cultivation systems, especially on a large scale. A study on the effect of microalgae-stressing factors such as temperature, nutrient limitation and ammonium oxidising bacteria (AOB) competition in EOM production by microalgae was carried out. The results showed non-statistically significant differences in EOM production at constant temperatures of 25, 30 and 35 °C. However, when the temperature was raised from 25 to 35 °C for 4 h a day, polysaccharide production increased significantly, indicating microalgae stress. Nutrient limitation also seemed to increase EOM production. No significant differences were found in EOM production under lab conditions when the microalgae competed with AOB for ammonium uptake. However, when the EOM concentration was monitored during continuous outdoor operation of a membrane photobioreactor (MPBR) plant, nitrifying bacteria activity was likely to be responsible for the increase in EOM concentration in the culture. Other factors such as high temperatures, ammonium-depletion and low light intensities could also have induced cell deterioration and thus have influenced EOM production in the outdoor MPBR plant. Membrane fouling seemed to depend on the biomass concentration of the culture. However, under the operating conditions tested, the behaviour of fouling rate with respect to the EOM concentration was different depending on the initial membrane state

A new strategy to maximize organic matter valorization in municipalities: combination of urban wastewater with kitchen food waste and its treatment with AnMBR technology

[EN] The aim of this study was to evaluate the feasibility of treating the kitchen food waste (FW) jointly with urban wastewater (WW) in a wastewater treatment plant (WWTP) by anaerobic membrane technology (AnMBR). The experience was carried out in six different periods in an AnMBR pilot-plant for a total of 536 days, varying the SRT, HRT and the food waste penetration factor (PF) of food waste disposers. The results showed increased methane production of up to 190% at 70 days SRT, 24 hours HRT and 80% PF, compared with WW treatment only. FW COD and biodegradability were higher than in WW, so that the incorporation of FW into the treatment increases the organic load and the methane production and reduces sludge production (0.142 vs 0.614 kg VSSkg removed COD-1, at 70 days SRT, 24 hours HRT and 80% PF, as compared to WW treatment only).This research work was possible thanks to financial support from Generalitat Valenciana (project PROMETE0/2012/029) which is gratefully acknowledged. Besides, support from FCC Aqualia participation in INNPRONTA 2011 IISIS IPT-20111023 project (partially funded by The Centre for Industrial Technological Development (CDTI) and from the Spanish Ministry of Economy and Competitiveness) is gratefully acknowledged.Moñino Amorós, P.; Aguado García, D.; Barat, R.; Jiménez, E.; Giménez, J.; Seco, A.; Ferrer, J. (2017). A new strategy to maximize organic matter valorization in municipalities: combination of urban wastewater with kitchen food waste and its treatment with AnMBR technology. Waste Management. 62:274-289. https://doi.org/10.1016/j.wasman.2017.02.006S2742896

The observational legacy of preon stars - probing new physics beyond the LHC

We discuss possible ways to observationally detect the superdense cosmic objects composed of hypothetical sub-constituent fermions beneath the quark/lepton level, recently proposed by us. The characteristic mass and size of such objects depend on the compositeness scale, and their huge density cannot arise within a context of quarks and leptons alone. Their eventual observation would therefore be a direct vindication of physics beyond the standard model of particle physics, possibly far beyond the reach of the Large Hadron Collider (LHC), in a relatively simple and inexpensive manner. If relic objects of this type exist, they can possibly be detected by present and future x-ray observatories, high-frequency gravitational wave detectors, and seismological detectors. To have a realistic detection rate, i.e., to be observable, they must necessarily constitute a significant fraction of cold dark matter.Comment: 8 pages, 4 figures. Added one reference [24]. Reformulated the discussion at the end of Section II. Accepted for publication in Phys. Rev.