Valorization of fines from construction and demolition wastes

La economía circular es un concepto y un nuevo paradigma, ha sido la base de las normas y leyes que sustentan las nuevas prácticas sobre el manejo de residuos en los países que pertenecen a la Unión Europea. Concepto que hoy en día cobra más fuerza, desencadenado por la crisis climática que se ha convertido en un hecho y una realidad inquietante. Por ello, para 2020 en Catalunya, los gestores de residuos tenían previsto valorizar hasta el 75% de los residuos de construcción y demolición (RCD). Siendo el sector de la construcción, un sector que no solo produce residuos en las actividades de construcción y demolición, sino también en la producción de sus principales materias primas. El cemento Portland fabricado causa el 8% de las emisiones mundiales de dióxido de carbono, con una emisión total de CO2 de más de 2,5 Gt. Producido principalmente por la calcinación de la piedra caliza en la producción de Clinker. El último informe entregado por el sector de la gestión de residuos en 2021, mostró que se ha valorizado el 61,4% de los residuos de construcción y demolición para 2020. Valor muy por debajo del objetivo principal acordado. Así que, en la necesidad de mejorar este desempeño, reducir la huella de carbono y frenar eventualmente la alta explotación de minerales industriales como la caliza. El presente artículo se centra en la caracterización y valorización de dos residuos de árido fino procedentes del proceso de reciclado de los residuos de construcción y demolición. El árido fino es tratado en las plantas de reciclaje y forma un limo que no ha despertado interés económico en el último tiempo. Debido a esto, se han estudiado las propiedades térmicas (DTA/TG), plásticas, químicas (XRF) y mineralógicas (XRD y SEM/EDS) de estos materiales para evaluar su potencial uso como reemplazo parcial del cemento Portland. Demostrando que los materiales tienen una alta reactividad debido principalmente al rol del gel C-S-H una vez que es tratado térmicamente a 900°C.Circular economy is a concept and a new paradigm, it has been the base of the norms and laws that sustain the new practices on waste management in the countries that belong the European Union. Concept that is stronger nowadays, triggered by the climate crisis that is a fact and disturbing reality. Due to this, by 2020 the waste management sector had planned to valorise up to 75% of the construction and demolition waste(C&DW). The construction is a sector that not only produce waste on the construction and demolition activities, but also in the production of its main raw material. Portland cement manufactured cause 8% of the worldwide carbon dioxide emissions, with a total CO2 emission of more than 2.5 Gt. Mainly produced by the calcination of the limestone in the clinker production. The last report given by the waste management sector in 2021, showed that it has valorised 61,4% of the construction and demolition waste by 2020, value way below the main objective accorded. So, in the need to improve this performance, reduce the carbon footprint and slow down, or hopefully eventually stop the high exploitation of industrial minerals as limestone. The present article focuses on the characterization and valorisation of two different fine aggregate waste that are product of the recycling process of the construction and demolition waste. The fine aggregate is treated in the recycling plants and forms a silt that has had no economic interest. Due this, the thermal (DTA/TG), plastic, chemical (XRF) and mineralogic (XRD and SEM/EDS) properties of this materials has been studied to assess their potential use as partial replacement of Portland cement. Showing that the materials has high reactivity mostly due the roll of the C-S-H gel once it is thermally treated at 900°C. If at least 30% of replacement is carried out, the reduction of the C&DW stockpiled will allow that 77% of the C&DW be valorised. Bringing with it a reduction close to 451.000 tons of CO2

Processing-structure-property relationships of biopolyester/zinc oxide fibrous scaffolds engineered by centrifugal spinning

This study addresses the processing of nonwoven fibrous materials obtained by centrifugal spinning method, namely Forcespinning; a high yield and low production cost technique little explored in this field. Poly(D, L-lactic acid) (PDLLA) and poly(3-hydroxybutyrate) (PHB) were used as matrices and reinforced with zinc oxide nanoparticles (n-ZnO). The morphology, mechanical, and thermal performance of the developed composites were analyzed as well as the antibacterial effect of n-ZnO. Fibrous materials with n-ZnO concentrations of 1, 3, and 5 wt. % for PDLLA and 1 and 3 wt. % for PHB were evaluated. The results showed that the incorporation of n-ZnO produces an increase in the viscosity of the precursor solutions for both polymeric systems, which caused an increase in the average fiber diameter, though the morphology was not affected, obtaining mostly long, continuous, and homogenous fibers. In addition, a decrease in thermal stability was observed to a greater extent in PDLLA systems. Regarding the mechanical performance, optimal properties were obtained at a concentration of 3 and 1 wt. % of n-ZnO for PDLLA and PHB, respectively. Antibacterial studies showed that PHB with 1 and 3 wt. % of n-ZnO effectively combat strains of E. coli and S. aureus, presenting 100% of strain growth inhibition. In the case of PDLLA, a higher n-ZnO concentration (5 wt. %) was required to reach a strain growth inhibition above 97%. Finally, cell viability tests demonstrated that the designed fibrous mats support cell proliferation, indicating their potential for use as scaffolds in bone tissue regeneration

Universidad y sociedad: comunicación e integración en empresas e instituciones públicas y organizaciones no lucrativas

Depto. de Teorías y Análisis de la ComunicaciónFac. de Ciencias de la InformaciónFALSEsubmitte

The PAU survey: Estimating galaxy photometry with deep learning

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed Lumos for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artifacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10% to 2%, comparing to aperture photometry. Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with the Deepz machine learning photo-z code and the photo-z outlier rate by 20%. The photo-z improvement is lower than expected from the SNR increment, however currently the photometric calibration and outliers in the photometry seem to be its limiting factor.Comment: 20 pages, 22 figure

The PAU Survey: A Forward Modeling Approach for Narrow-band Imaging

Weak gravitational lensing is a powerful probe of the dark sector, once measurement systematic errors can be controlled. In Refregier & Amara (2014), a calibration method based on forward modeling, called MCCL, was proposed. This relies on fast image simulations (e.g., UFig; Berge et al. 2013) that capture the key features of galaxy populations and measurement effects. The MCCL approach has been used in Herbel et al. (2017) to determine the redshift distribution of cosmological galaxy samples and, in the process, the authors derived a model for the galaxy population mainly based on broad-band photometry. Here, we test this model by forward modeling the 40 narrow-band photometry given by the novel PAU Survey (PAUS). For this purpose, we apply the same forced photometric pipeline on data and simulations using Source Extractor (Bertin & Arnouts 1996). The image simulation scheme performance is assessed at the image and at the catalogues level. We find good agreement for the distribution of pixel values, the magnitudes, in the magnitude-size relation and the interband correlations. A principal component analysis is then performed, in order to derive a global comparison of the narrow-band photometry between the data and the simulations. We use a `mixing' matrix to quantify the agreement between the observed and simulated sets of Principal Components (PCs). We find good agreement, especially for the first three most significant PCs. We also compare the coefficients of the PCs decomposition. While there are slight differences for some coefficients, we find that the distributions are in good agreement. Together, our results show that the galaxy population model derived from broad-band photometry is in good overall agreement with the PAUS data. This offers good prospect for incorporating spectral information to the galaxy model by adjusting it to the PAUS narrow-band data using forward modeling.Comment: Submitted to JCAP, 28 pages, 15 figures, 3 appendice

The PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies

We present the first measurements of the projected clustering and intrinsic alignments (IA) of galaxies observed by the Physics of the Accelerating Universe Survey (PAUS). With photometry in 40 narrow optical passbands (4500 Å–8500 Å), the quality of photometric redshift estimation is σz ∼ 0.01(1 + z) for galaxies in the 19 deg2 Canada-France-Hawaii Telescope Legacy Survey W3 field, allowing us to measure the projected 3D clustering and IA for flux-limited, faint galaxies (i < 22.5) out to z ∼ 0.8. To measure two-point statistics, we developed, and tested with mock photometric redshift samples, ‘cloned’ random galaxy catalogues which can reproduce data selection functions in 3D and account for photometric redshift errors. In our fiducial colour-split analysis, we made robust null detections of IA for blue galaxies and tentative detections of radial alignments for red galaxies (∼1 − 3σ), over scales of 0.1 − 18 h−1 Mpc. The galaxy clustering correlation functions in the PAUS samples are comparable to their counterparts in a spectroscopic population from the Galaxy and Mass Assembly survey, modulo the impact of photometric redshift uncertainty which tends to flatten the blue galaxy correlation function, whilst steepening that of red galaxies. We investigate the sensitivity of our correlation function measurements to choices in the random catalogue creation and the galaxy pair-binning along the line of sight, in preparation for an optimised analysis over the full PAUS area