Synthesis gas production from various biomass feedstocks

The decomposition of five different biomass samples was studied in a horizontal laboratory reactor. The samples consisted of esparto grass, straw, Posidonea Oceanic seaweed, waste from urban and agricultural pruning and waste from forest pruning. Both pyrolysis in inert atmosphere and combustion in the presence of oxygen were studied. Different heating rates were used by varying the input speed. Major gas compounds were analyzed. The experimental results show that the amount of CO formed is lower in less dense species. It is also found that there is an increase of hydrocarbons formed at increasing feeding rates, in particular methane, while there is a decrease in the production of hydrogen.

Gas production during the pyrolysis and gasification of biological and physico-chemical sludges from oil refinery

Pyrolysis and gasification of two different sludges coming from a Spanish refinery have been performed at different experimental conditions. A physico-chemical (PC) and a biological (BIO) sludge have been studied. Runs at different heating rates (approx. 4 and 10 K/s) and with different contact time between gases and decomposed sludge have been performed. In general, the ratio H2/CO is higher in pyrolytic runs. The highest ratio is obtained in the pyrolysis at low heating rate and parallel flow, using both sludges. The maximum emission of CO, i.e. the worst combustion conditions, is given in the runs where contact time is minimized and at high heating rates.

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.

Refinery Nonconventional Feedstocks: Influence of the Coprocessing of Vacuum Gas Oil and Low Density Polyethylene in Fluid Catalytic Cracking Unit on Full Range Gasoline Composition

Gasoline coming from refinery fluid catalytic cracking (FCC) unit is a major contributor to the total commercial grade gasoline pool. The contents of the FCC gasoline are primarily paraffins, naphthenes, olefins, aromatics, and undesirables such as sulfur and sulfur containing compounds in low quantities. The proportions of these components in the FCC gasoline invariable determine its quality as well as the performance of the associated downstream units. The increasing demand for cleaner and lighter fuels significantly influences the need not only for novel processing technologies but also for alternative refinery and petrochemical feedstocks. Current and future clean gasoline requirements include increased isoparaffins contents, reduced olefin contents, reduced aromatics, reduced benzene, and reduced sulfur contents. The present study is aimed at investigating the effect of processing an unconventional refinery feedstock, composed of blend of vacuum gas oil (VGO) and low density polyethylene (LDPE) on FCC full range gasoline yields and compositional spectrum including its paraffins, isoparaffins, olefins, napthenes, and aromatics contents distribution within a range of operating variables of temperature (500–700 °C) and catalyst-feed oil ratio (CFR 5–10) using spent equilibrium FCC Y-zeolite based catalyst in a FCC pilot plant operated at the University of Alicante’s Research Institute of Chemical Process Engineering (RICPE). The coprocessing of the oil-polymer blend led to the production of gasoline with very similar yields and compositions as those obtained from the base oil, albeit, in some cases, the contribution of the feed polymer content as well as the processing variables on the gasoline compositional spectrum were appreciated. Carbon content analysis showed a higher fraction of the C9–C12 compounds at all catalyst rates employed and for both feedstocks. The gasoline’s paraffinicity, olefinicity, and degrees of branching of the paraffins and olefins were also affected in various degrees by the scale of operating severity. In the majority of the cases, the gasoline aromatics tended toward the decrease as the reactor temperature was increased. While the paraffins and iso-paraffins gasoline contents were relatively stable at around 5 % wt, the olefin contents on the other hand generally increased with increase in the FCC reactor temperature.

Development of a novel pyrolysis-gas chromatography/mass spectrometry method for the analysis of poly(lactic acid) thermal degradation products

Thermal degradation of PLA is a complex process since it comprises many simultaneous reactions. The use of analytical techniques, such as differential scanning calorimetry (DSC) and thermogravimetry (TGA), yields useful information but a more sensitive analytical technique would be necessary to identify and quantify the PLA degradation products. In this work the thermal degradation of PLA at high temperatures was studied by using a pyrolyzer coupled to a gas chromatograph with mass spectrometry detection (Py-GC/MS). Pyrolysis conditions (temperature and time) were optimized in order to obtain an adequate chromatographic separation of the compounds formed during heating. The best resolution of chromatographic peaks was obtained by pyrolyzing the material from room temperature to 600 °C during 0.5 s. These conditions allowed identifying and quantifying the major compounds produced during the PLA thermal degradation in inert atmosphere. The strategy followed to select these operation parameters was by using sequential pyrolysis based on the adaptation of mathematical models. By application of this strategy it was demonstrated that PLA is degraded at high temperatures by following a non-linear behaviour. The application of logistic and Boltzmann models leads to good fittings to the experimental results, despite the Boltzmann model provided the best approach to calculate the time at which 50% of PLA was degraded. In conclusion, the Boltzmann method can be applied as a tool for simulating the PLA thermal degradation.

Structure, gas-barrier properties and overall migration of poly(lactic acid) films coated with hydrogenated amorphous carbon layers

Hydrogenated amorphous carbon (a-C:H) films were grown on a poly(lactic acid) (PLA) substrate by means of a radiofrequency plasma-enhanced chemical vapour deposition (rf-PECVD) technique with different deposition times (5, 20 and 40 min). The main goal of this treatment was to increase the barrier properties of PLA, maintaining its original transparency and colour as well as controlling interactions with food simulants for packaging applications. Morphological, chemical, and mechanical properties of PLA/a-C:H systems were evaluated while permeability and overall migration tests were performed in order to determine the effect of the plasma treatment on the gas-barrier properties of PLA films and their application in food packaging. Morphological results suggested a good adhesion of the deposited layers onto the polymer surface and the samples treated for 5 and 20 min only slightly darkened the PLA film. X-ray photoelectron spectroscopy revealed that the structural properties of the carbon layer deposited onto the PLA film depend on the exposure time. PLA/a-C:H system treated for 5 min showed the highest barrier properties, while none of the studied samples exceeded the migration limit established by the current legislation, suggesting the suitability of these materials in packaging applications.

3D-based reconstruction using growing neural gas landmark: application to rapid prototyping in shoe last manufacturing

Customizing shoe manufacturing is one of the great challenges in the footwear industry. It is a production model change where design adopts not only the main role, but also the main bottleneck. It is therefore necessary to accelerate this process by improving the accuracy of current methods. Rapid prototyping techniques are based on the reuse of manufactured footwear lasts so that they can be modified with CAD systems leading rapidly to new shoe models. In this work, we present a shoe last fast reconstruction method that fits current design and manufacturing processes. The method is based on the scanning of shoe last obtaining sections and establishing a fixed number of landmarks onto those sections to reconstruct the shoe last 3D surface. Automated landmark extraction is accomplished through the use of the self-organizing network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates up to 12 times the surface reconstruction and filtering processes used by the current shoe last design software. The proposed method offers higher accuracy compared with methods with similar efficiency as voxel grid.

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

Biogas from MSW landfill: Composition and determination of chlorine content with the AOX (adsorbable organically bound halogens) technique

An exhaustive characterization of the biogas from some waste disposal facilities has been carried out. The analysis includes the main components (methane, carbon dioxide, nitrogen and oxygen) as well as trace components such as hydrogen sulphide, ammonia and VOCs (volatile organic compounds) including siloxanes and halogenated compounds. VOCs were measured by GC/MS (Gas Chromatography/Mass Spectrometry) using two different procedures: thermal desorption of the Tenax TA and Carbotrap 349 tubes and SPME (Solid Phase Micro-Extraction). A method has been established to measure the total halogen content of the biogas with the AOX (adsorbable organically bound halogens) technique. The equipment used to analyze the samples was a Total Organic Halogen Analyzer (TOX-100). Similar results were obtained when comparing the TOX (Total Organic Halogen) values with those obtained by GC/MS. The halogen content in all the samples was under 22 mg Cl/Nm3 which is below the limit of 150 mg/Nm3 proposed in the Spanish Regulations for any use of the biogas. The low chlorine content in the biogas studied, as well as the low content of other trace compounds, makes it suitable for use as a fuel for electricity generating engines.

A new surface joining technique for the design of shoe lasts

The footwear industry is a traditional craft sector, where technological advances are difficult to implement owing to the complexity of the processes being carried out, and the level of precision demanded by most of them. The shoe last joining operation is one clear example, where two halves from different lasts are put together, following a specifically traditional process, to create a new one. Existing surface joining techniques analysed in this paper are not well adapted to shoe last design and production processes, which makes their implementation in the industry difficult. This paper presents an alternative surface joining technique, inspired by the traditional work of lastmakers. This way, lastmakers will be able to easily adapt to the new tool and make the most out of their know-how. The technique is based on the use of curve networks that are created on the surfaces to be joined, instead of using discrete data. Finally, a series of joining tests are presented, in which real lasts were successfully joined using a commercial last design software. The method has shown to be valid, efficient, and feasible within the sector.

Evidence of Intracrystalline Mesostructured Porosity in Zeolites by Advanced Gas Sorption, Electron Tomography and Rotation Electron Diffraction

The small size of micropores (typically <1 nm) in zeolites causes slow diffusion of reactant and product molecules in and out of the pores and negatively impacts the product selectivity of zeolite based catalysts, for example, fluid catalytic cracking (FCC) catalysts. Size-tailored mesoporosity was introduced into commercial zeolite Y crystals by a simple surfactant-templating post-synthetic mesostructuring process. The resulting mesoporous zeolite Y showed significantly improved product selectivity in both laboratory testing and refinery trials. Advanced characterization techniques such as electron tomography, three-dimensional rotation electron diffraction, and high resolution gas adsorption coupled with hysteresis scanning and density functional theory, unambiguously revealed the intracystalline nature and connectivity of the introduced mesopores. They can be considered as molecular highways that help reactant and product molecules diffuse quickly to and away from the catalytically active sites within the zeolite crystals and, thus, shift the selectivity to favor the production of more of the valuable liquid fuels at reduced yields of coke and unconverted feed.