Simultaneous environmental and economic process synthesis of Isobutane Alkylation

This multidisciplinary study concerns the optimal design of processes with a view to both maximizing profit and minimizing environmental impacts. This can be achieved by a combination of traditional chemical process design methods, measurements of environmental impacts and advanced mathematical optimization techniques. More to the point, this paper presents a hybrid simulation-multiobjective optimization approach that at once optimizes the production cost and minimizes the associated environmental impacts of isobutane alkylation. This approach has also made it possible to obtain the flowsheet configurations and process variables that are needed to manufacture isooctane in a way that satisfies the above-stated double aim. The problem is formulated as a Generalized Disjunctive Programming problem and solved using state-of-the-art logic-based algorithms. It is shown, starting from existing alternatives for the process, that it is possible to systematically generate a superstructure that includes alternatives not previously considered. The optimal solution, in the form a Pareto curve, includes different structural alternatives from which the most suitable design can be selected. To evaluate the environmental impact, Life Cycle Assessment based on two different indicators is employed: Ecoindicator 99 and Global Warming Potential.

Model, Metamodel and Topology

This reply to Gash’s (Found Sci 2013) commentary on Nescolarde-Selva and Usó-Doménech (Found Sci 2013) answers the three questions raised and at the same time opens up new questions.

Reduction of haloacetonitriles and haloacetones in natural waters using ceramic nanofiltration membranes

Póster presentado en 19th International Congress of Chemical and Process Engineering, Prague, Czech Republic August 28th-September 1st, 2010.

The imprint of the crustal magnetic field on the thermal spectra and pulse profiles of isolated neutron stars

Isolated neutron stars (NSs) show a bewildering variety of astrophysical manifestations, presumably shaped by the magnetic field strength and topology at birth. Here, using state-of-the-art calculations of the coupled magnetic and thermal evolution of NSs, we compute the thermal spectra and pulse profiles expected for a variety of initial magnetic field configurations. In particular, we contrast models with purely poloidal magnetic fields to models dominated by a strong internal toroidal component. We find that, while the former displays double-peaked profiles and very low pulsed fractions, in the latter, the anisotropy in the surface temperature produced by the toroidal field often results in a single pulse profile, with pulsed fractions that can exceed the 50–60 per cent level even for perfectly isotropic local emission. We further use our theoretical results to generate simulated ‘observed’ spectra, and show that blackbody (BB) fits result in inferred radii that can be significantly smaller than the actual NS radius, even as low as ∼1–2 km for old NSs with strong internal toroidal fields and a high absorption column density along their line of sight. We compute the size of the inferred BB radius for a few representative magnetic field configurations, NS ages and magnitudes of the column density. Our theoretical results are of direct relevance to the interpretation of X-ray observations of isolated NSs, as well as to the constraints on the equation of state of dense matter through radius measurements.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

Thermal decomposition of the different particles size fractions of almond shells and olive stones. Thermal behaviour changes due to the milling processes

The aim of this study is to investigate the effect of particle size on the non-isothermal pyrolysis of almond shells (AS) and olive stones (OS) and to show possible differences in the composition of the different fractions obtained after milling and sieving. The results obtained from the study of different particle size of AS and OS samples show significant differences in the solid residue obtained and in the shape and overlapping degree of the peaks, especially with the smaller particle size. These differences can be due to different factors: (a) the amount of inorganic matter, which increases as particle size decreases, (b) heat and mass transfer processes, (c) different sample composition as a consequence of the milling process which may provoke changes in the structure and the segregation of the components (in addition to the ashes) increasingly changes the composition of the sample as the particle size decreases.

Terminology of metal–organic frameworks and coordination polymers (IUPAC Recommendations 2013)

A set of terms, definitions, and recommendations is provided for use in the classification of coordination polymers, networks, and metal–organic frameworks (MOFs). A hierarchical terminology is recommended in which the most general term is coordination polymer. Coordination networks are a subset of coordination polymers and MOFs a further subset of coordination networks. One of the criteria an MOF needs to fulfill is that it contains potential voids, but no physical measurements of porosity or other properties are demanded per se. The use of topology and topology descriptors to enhance the description of crystal structures of MOFs and 3D-coordination polymers is furthermore strongly recommended.

The Goodness of Simultaneous Fits in ISIS

In a previous work, we introduced a tool for analyzing multiple datasets simultaneously, which has been implemented into ISIS. This tool was used to fit many spectra of X-ray binaries. However, the large number of degrees of freedom and individual datasets raise an issue about a good measure for a simultaneous fit quality. We present three ways to check the goodness of these fits: we investigate the goodness of each fit in all datasets, we define a combined goodness exploiting the logical structure of a simultaneous fit, and we stack the fit residuals of all datasets to detect weak features. These tools are applied to all RXTE-spectra from GRO 1008−57, revealing calibration features that are not detected significantly in any single spectrum. Stacking the residuals from the best-fit model for the Vela X-1 and XTE J1859+083 data evidences fluorescent emission lines that would have gone undetected otherwise.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.

False Anglicisms in Legal and Business English as a Lingua Franca (ELF): A Process of Back-borrowing

False Anglicisms are words which technically are not part of the English language, but "seem" English, due to their shape or resemblance to English words. These are usually the result of new creations/coinages in other languages and/or semantic shifts. Due to the use of English as a Lingua Franca, it is becoming quite common to come across these words in English with the "re-imported" shape and meaning they bring from other languages.

Identical Location Transmission Electron Microscopy Imaging of Site-Selective Pt Nanocatalysts: Electrochemical Activation and Surface Disordering

We have employed identical location transmission electron microscopy (IL-TEM) to study changes in the shape and morphology of faceted Pt nanoparticles as a result of electrochemical cycling; a procedure typically employed for activating platinum surfaces. We find that the shape and morphology of the as-prepared hexagonal nanoparticles are rapidly degraded as a result of potential cycling up to +1.3 V. As few as 25 potential cycles are sufficient to cause significant degradation, and after about 500–1000 cycles the particles are dramatically degraded. We also see clear evidence of particle migration during potential cycling. These finding suggest that great care must be exercised in the use and study of shaped Pt nanoparticles (and related systems) as electrocatlysts, especially for the oxygen reduction reaction where high positive potentials are typically employed.

Pore size distributions derived from adsorption isotherms, immersion calorimetry, and isosteric heats: A comparative study

We compare the pore size distribution of a well-characterized activated carbon derived from model-dependent, adsorption integral equation (AIE) methods with those from model-independent, immersion calorimetry and isosteric heat analyses. The AIE approach applied to nitrogen gave a mean pore width of 0.57 nm; the CO2 distribution exhibited wider dispersion. Spherical model application to CO2 and diffusion limitations for nitrogen and argon were proposed as primary reasons for inconsistency. Immersion enthalpy revealed a sharp decrease in available area equivalent to a cut-off due to molecular exclusion when the accessible surface was assessed against probe kinetic diameter. Mean pore width was identified as 0.58 ± 0.02 nm, endorsing the underlying assumptions for the nitrogen-based AIE approach. A comparison of the zero-coverage isosteric heat of adsorption for various non-polar adsorptives by the porous test sample was compared with the same adsorptives in contact with a non-porous reference adsorbent, leading to an energy ratio or adsorption enhancement factor. A linear relationship between the energy ratio and probe kinetic diameter indicated a primary pore size at 0.59 nm. The advantage of this enthalpy, model-independent methods over AIE were due to no assumptions regarding probe molecular shape, and no assumptions for pore shape and/or connectivity.