Virtual porous materials to predict the air void topology and hydraulic conductivity of asphalt roads

This paper investigates the effects of air void topology on hydraulic conductivity in asphalt mixtures with porosity in the range 14%–31%. Virtual asphalt pore networks were generated using the Intersected Stacked Air voids (ISA) method, with its parameters being automatically adjusted by the means of a differential evolution optimisation algorithm, and then 3D printed using transparent resin. Permeability tests were conducted on the resin samples to understand the effects of pore topology on hydraulic conductivity. Moreover, the pore networks generated virtually were compared to real asphalt pore networks captured via X-ray Computed Tomography (CT) scans. The optimised ISA method was able to generate realistic 3D pore networks corresponding to those seen in asphalt mixtures in term of visual, topological, statistical and air void shape properties. It was found that, in the range of porous asphalt materials investigated in this research, the high dispersion in hydraulic conductivity at constant air void content is a function of the average air void diameter. Finally, the relationship between average void diameter and the maximum aggregate size and gradation in porous asphalt materials was investigated

Evaluación de proyectos: Estudio de factibilidad para la creación de Hotel Isgab en el Municipio de Managua en el año 2015

El presente informe de Seminario de Graduación denominado “Estudio de Factibilidad para la Creación del Hotel ISGAB en el Municipio de Managua en el año 2015”, se elaboró con las finalidades fundamentales de Conocer las Generalidades de la Evaluación de un Proyecto, Describir la Evaluación de un Proyecto, Presentar los Métodos de la Evaluación Financiera y Elaborar un Estudio de Factibilidad para la Creación del Hotel ISGAB en el Municipio de Managua en el año 2015. Un proyecto de inversión es una propuesta que surge como resultado de estudio que la sustenta y está conformada por un conjunto determinado con acciones con el fin de lograr ciertos objetivos. El propósito de un proyecto de inversión es poder generar ganancias o beneficio adicionales a los inversionistas que lo promueven y como resultado de este, también se verán beneficiado los grupos o poblaciones a quienes va dirigido. Proyecto de inversión privada y pública. La evaluación del proyecto es un instrumento o herramienta que genera información, permitiendo emitir un juicio sobre la conveniencia y confiabilidad de la estimación preliminar del beneficio que genera el proyecto en estudio

Guest-induced structural deformation in Cu-based metal-organic framework upon hydrocarbon adsorption

In a world where capture and separation processes represent above 10% of global energy consumption, novel porous materials, such as Metal-Organic Frameworks (MOFs) used in adsorption-based processes are a promising alternative to dethrone the high-energy-demanding distillation. Shape and size tailor-made pores in combination with Lewis acidic sites can enhance the adsorbate-adsorbent interactions. Understanding the underlying mechanisms of adsorption is essential to designing and optimizing capture and separation processes. Herein, we analyze the adsorption behaviour of light hydrocarbons (methane, ethane, ethylene, propane, and propylene) in two synthesized copper-based MOFs, Cu-MOF-74 and URJC-1. The experimental and computational adsorption curves reveal a limited effect of the exposed metal centers on the olefins. The lower interaction Cu-olefin is also reflected in the calculated enthalpy of adsorption and binding geometries. Moreover, the diamond-shaped pores' deformation upon external stimuli is first reported in URJC-1. This phenomenon is highlighted as the key to understanding the adsorbent's responsive mechanisms and potential in future industrial applications.</p

Hydrothermal stability and catalytic performance of desilicated highly siliceous zeolites ZSM-5

Highly siliceous zeolites, namely MFI type have attracted the great attention due to their higher hydrothermal stability, higher selectivity to organic compounds, and often better catalytic properties in comparison with Al-rich zeolites. The native zeolite (Si/Al = 164) and its desilicated analogues were deeply characterized with regard to their structural and textural properties by X-ray diffraction, low temperature adsorption of nitrogen and solid-state Al-27 MAS NMR. Their acidic properties were evaluated in quantitative IR studies. Finally, the catalytic performance of desilicated zeolites ZSM-5 was evaluated in the cracking of n-decane, 1,3,5-tri-iso-propylbenzene and vacuum gas oil. In this article, it is shown that high silica zeolites prepared by NaOH and NaOH&TBAOH leaching presented good hydrothermal stability with only slightly lower resistance when comparing to native steamed zeolite. The mesoporosity was preserved after the steaming treatment. The influence of the generated mesoporosity on the higher activity was evidenced in both 1,3,5-tri-isopropylbenzene and diesel oil cracking of steamed hierarchical zeolites. In spite of their lowered acidity, the mesopores system benefited the diffusion of the bulky molecule and finally provided higher activity of hierarchical zeolites. (C) 2016 Elsevier Inc. All rights reserved.This work was financed by Grant No. 2015/18/E/ST4/00191 from the National Science Centre - Poland. F. Rey and J. Martinez-Triguero thank for the support of the Spanish Government-MINECO through "Severo Ochoa" (SEV 2012-0267), MAT2015-71842-P and CTQ2015-68951-C3-1-R.Tarach, KA.; Martínez-Triguero, J.; Rey Garcia, F.; Góra-Marek, K. (2016). Hydrothermal stability and catalytic performance of desilicated highly siliceous zeolites ZSM-5. Journal of Catalysis. 339:256-259. https://doi.org/10.1016/j.jcat.2016.04.023S25625933

SEG-SSC: a framework based on synthetic examples generation for self-labeled semi-supervised classification

Self-labeled techniques are semi-supervised classification methods that address the shortage of labeled examples via a self-learning process based on supervised models. They progressively classify unlabeled data and use them to modify the hypothesis learned from labeled samples. Most relevant proposals are currently inspired by boosting schemes to iteratively enlarge the labeled set. Despite their effectiveness, these methods are constrained by the number of labeled examples and their distribution, which in many cases is sparse and scattered. The aim of this work is to design a framework, named SEG-SSC, to improve the classification performance of any given self-labeled method by using synthetic labeled data. These are generated via an oversampling technique and a positioning adjustment model that use both labeled and unlabeled examples as reference. Next, these examples are incorporated in the main stages of the self-labeling process. The principal aspects of the proposed framework are: (a) introducing diversity to the multiple classifiers used by using more (new) labeled data, (b) fulfilling labeled data distribution with the aid of unlabeled data, and (c) being applicable to any kind of self-labeled method. In our empirical studies, we have applied this scheme to four recent self-labeled methods, testing their capabilities with a large number of data sets. We show that this framework significantly improves the classification capabilities of self-labeled techniques

Continuous and embedded solutions for SHM of concrete structures using changing electrical potential in self-sensing cement-based composites

Interest in the concept of self-sensing structural materials has grown in recent years due to its potential to enable continuous low-cost monitoring of next-generation smart-structures. The development of cement-based smart sensors appears particularly well suited for monitoring applications due to their numerous possible field applications, their ease of use and long-term stability. Additionally, cement-based sensors offer a unique opportunity for structural health monitoring of civil structures because of their compatibility with new or existing infrastructure. Particularly, the addition of conductive carbon nanofillers into a cementitious matrix provides a self-sensing structural material with piezoresistive characteristics sensitive to deformations. The strain-sensing ability is achieved by correlating the external loads with the variation of specific electrical parameters, such as the electrical resistance or impedance. Selection of the correct electrical parameter for measurement to correlate with features of interest is required for the condition assessment task. In this paper, we investigate the potential of using altering electrical potential in cement-based materials doped with carbon nanotubes to measure strain and detect damage in concrete structures. Experimental validation is conducted on small-scale specimens including a steel-reinforced beam of conductive cement paste. Comparisons are made with constant electrical potential and current methods commonly found in the literature. Experimental results demonstrate the ability of the changing electrical potential at detecting features important for assessing the condition of a structure. Conference Pre

Mesopore-modified mordenites as catalysts for catalytic pyrolysis of biomass and cracking of vacuum gasoil processes

[EN] Mesopore-modified mordenite zeolitic materials with different Si/Al ratios have been repared and tested in the biomass pyrolysis and catalytic cracking of vacuum gasoil. Alkaline treatment was carried out to generate mesoporosity. Severity of alkaline treatment was found to be of paramount importance to tune the generated mesoporosity, while it significantly affected the crystallinity of treated mordenites. It was moreover observed that the alkaline treatment selectively extracted Si decreasing the Si/Al ratio of treated samples. Catalytic activity of parent and alkaline treated mordenites was studied in the pyrolysis of biomass. All zeolitic based materials produced less amounts of bio-oil but of better quality (lowering the oxygen content from &#8764;40% to as much as 21%) as compared to the non-catalytic pyrolysis experiments. On the other hand, it was found that the combination of mesopore formation and high surface area after alkaline treatment of the mordenite with a high Si/Al ratio resulted in the enhancement of its catalytic activity, despite the reduction of its acidity. The increment of the decarboxylation and dehydration reactions, combined with a reduction of carbon deposition on the catalyst, resulted in a remarkable decrease in the oxygen content in the organic fraction and therefore, resulted in a superior quality liquid product. Alkaline treated mordenites were additionally acid treated targeting dealumination and removal of the extra framework debris, thus generating mesopore-modified mordenite samples with stronger acid sites and higher total acidity, as candidate catalysts for catalytic cracking of vacuum gasoil. Desilicated and especially desilicated and dealuminated mordenites exhibited the highest activity and selectivity towards LCO with the best olefinicity in gases and higher bottoms conversion. Therefore, an optimized desilicated dealuminated mordenite additive could be an interesting candidate as a component of the FCC catalyst for a high LCO yield.The financial support of this work by the ACENET COMMON INITIATIVE HECABIO: "HEterogeneous CAtalysis for the Conversion of Solid BIOmass into Renewable Fuels and Chemicals" Project ACE.07.026 is gratefully acknowledged.Stefanidis, S.; Kalogiannis, K.; Iliopoulou, EF.; Lappas, AA.; Martínez Triguero, LJ.; Navarro Ruiz, MT.; Chica, A.... (2013). Mesopore-modified mordenites as catalysts for catalytic pyrolysis of biomass and cracking of vacuum gasoil processes. Green Chemistry. 15(6):1647-1658. doi:10.1039/c3gc40161hS1647165815

Transforming big data into smart data: An insight on the use of the k-nearest neighbors algorithm to obtain quality data

The k-nearest neighbours algorithm is characterised as a simple yet effective data mining technique. The main drawback of this technique appears when massive amounts of data -likely to contain noise and imperfections - are involved, turning this algorithm into an imprecise and especially inefficient technique. These disadvantages have been subject of research for many years, and among others approaches, data preprocessing techniques such as instance reduction or missing values imputation have targeted these weaknesses. As a result, these issues have turned out as strengths and the k-nearest neighbours rule has become a core algorithm to identify and correct imperfect data, removing noisy and redundant samples, or imputing missing values, transforming Big Data into Smart Data - which is data of sufficient quality to expect a good outcome from any data mining algorithm. The role of this smart data gleaning algorithm in a supervised learning context will be investigated. This will include a brief overview of Smart Data, current and future trends for the k-nearest neighbour algorithm in the Big Data context, and the existing data preprocessing techniques based on this algorithm. We present the emerging big data-ready versions of these algorithms and develop some new methods to cope with Big Data. We carry out a thorough experimental analysis in a series of big datasets that provide guidelines as to how to use the k-nearest neighbour algorithm to obtain Smart/Quality Data for a high quality data mining process. Moreover, multiple Spark Packages have been developed including all the Smart Data algorithms analysed

A taxonomic look at instance-based stream classifiers

Large numbers of data streams are today generated in many fields. A key challenge when learning from such streams is the problem of concept drift. Many methods, including many prototype methods, have been proposed in recent years to address this problem. This paper presents a refined taxonomy of instance selection and generation methods for the classification of data streams subject to concept drift. The taxonomy allows discrimination among a large number of methods which pre-existing taxonomies for offline instance selection methods did not distinguish. This makes possible a valuable new perspective on experimental results, and provides a framework for discussion of the concepts behind different algorithm-design approaches. We review a selection of modern algorithms for the purpose of illustrating the distinctions made by the taxonomy. We present the results of a numerical experiment which examined the performance of a number of representative methods on both synthetic and real-world data sets with and without concept drift, and discuss the implications for the directions of future research in light of the taxonomy. On the basis of the experimental results, we are able to give recommendations for the experimental evaluation of algorithms which may be proposed in the future.project RPG-2015-188 funded by The Leverhulme Trust, UK, and TIN 2015-67534-P from the Spanish Ministry of Economy and Competitiveness. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 731593