Designing biomass supply chains within planetary boundaries

The design of sustainable supply chains, which recently emerged as an active area of research in process systems engineering, is vital to ensure sustainable development. Despite past and ongoing efforts, the available methods often overlook impacts beyond climate change or incorporate them via standard life cycle assessment metrics that are hard to interpret from an absolute sustainability viewpoint. We here address the design of biomass supply chains considering critical ecological limits of the Earth—planetary boundaries—which should never be surpassed by anthropogenic activities. Our method relies on a mixed-integer linear program that incorporates a planetary boundaries-based damage model to quantify absolute sustainability precisely. We apply this approach to the sugarcane-to-ethanol industry in Argentina, identifying the optimal combination of technologies and network layout that minimize the impact on these ecological boundaries. Our framework can find applications in a wide range of supply chain problems related to chemicals and fuels production, energy systems, and agriculture planning.     

Influence of different reactor types on Nannochloropsis oculata microalgae culture for lipids and fatty acid production

Greenhouse gases emitted into the atmosphere by burning of fossil fuels cause global warming. One option is obtaining biodiesel. Nannochloropsis oculata was cultured under different light intensities and reactors at 25°C for 21 days with f/2 medium to assess their effects on cell density, lipid, and fatty acids (FAs). N. oculata improved cell density on fed-batch glass tubular reactor (7 L) at 200 μmol E m⁻² s⁻¹, yielding 3.5 × 10⁸ cells ml⁻¹, followed by fed-batch Erlenmeyer flask (1 L) at 650 μmol E m⁻² s⁻¹ with 1.7 × 10⁸ cells ml⁻¹. The highest total lipid contents (% g lipid × g dry biomass⁻¹) were 44.4 ± 0.8% for the reactor (1 L) at 650 μmol E m⁻² s⁻¹ and 35.2 ± 0.2% for the tubular reactor (7 L) at 200 μmol E m⁻² s⁻¹, until twice as high compared with the control culture (Erlenmeyer flask 1 L, 80 μmol E m⁻² s⁻¹) with 21.2 ± 1%. Comparing the total lipid content at 200 μmol E m⁻² s⁻¹, tubular reactor (7 L) and reactor 1 L achieved 35.2 ± 0.2% and 28.3 ± 1%, respectively, indicating the effect of shape reactor. The FAs were affected by high light intensity, decreasing SFAs to 2.5%, and increased monounsaturated fatty acids + polyunsaturated fatty acids to 2.5%. PUFAs (20:5n-3) and (20:4n-3) were affected by reactor shape, decreasing by half in the tubular reactor. In the best culture, fed-batch tubular reactor (7 L) at 200 μmol E m⁻² s⁻¹ contains major FAs (16:0; 38.06 ± 0.16%), (16:1n-7; 30.74 ± 0.58%), and (18:1n-9; 17.15 ± 0.91%).     

Biodegradability and improved mechanical performance of polyhydroxyalkanoates/agave fiber biocomposites compatibilized by different strategies

In this work, biocomposites made of polyhydroxyalkanoates (PHA) with natural fibers were produced via compression molding. In particular, polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV) were reinforced with 20 wt% of agave fibers. Different compatibilization strategies were investigated to improve the fiber-matrix interaction: fiber surface treatment in PHA solution, fiber surface treatment in maleated PHA solution, fiber propionylation, and extrusion with maleated PHA. The biocomposites were characterized in terms of morphology, mechanical properties, water absorption, and biodegradability by CO₂ production tracking. In general, fiber propionylation was the best strategy for mechanical properties enhancement and water uptake decreasing. Biocomposites with propionylated fibers showed improved flexural strength (170% for PHB and 84% for PHBV). The flexural modulus was also enhanced with propionylated fibers up to 19% and 18% compared to uncompatibilized biocomposites (PHB and PHBV, respectively). Tensile strength increased by 16% (PHB) and 14% (PHBV), and the water absorption was reduced using propionylated fibers going from 6.6% to 4.4% compared with biocomposites with untreated fibers. Most importantly, the impact strength was also improved for all biocomposites by up to 96% compared with the neat PHA matrices. Finally, it was found that the compatibilization did not negatively modify the PHA biodegradability.     

Influence of deposited amine-functionalized Si-MCM-41 in polyacrylonitrile electrospun membranes applied for separation of water in oil emulsions

Among several oil/water emulsion separation technologies, the utilization of nanoparticle-decorated membranes with diverse functionalities has received considerable attention in recent years, particularly if the antifouling capacity can be improved. In this article, we propose a new membrane based on surface-hydrolyzed polyacrylonitrile electrospun membranes and/or decorated with amine-functionalized Si-MCM-41 nanoparticles to be used as oil/water emulsion separation treatment and to determine their antifouling ability. X-Ray photoelectron spectrometry, attenuated total reflectance Fourier transform infrared spectroscopy, and toluidine blue O assay, scanning electron microscopy, contact angle measurements for oil under water and thermogravimetry were used for characterizing the membranes and an assay of permeability was developed to quantify the diffusion of oil molecules across the electrospun membrane. The electrospun and/or decorated membranes showed an underwater oleophobic wettability, which can separate oil-in-water emulsions with 87% separation efficiency, results of fouling experiments, evaluated in terms of rejection and flux recovery ratio, exhibited good antifouling ability, but the membrane decoration process did not lead to superior outcomes compared with undecorated membranes.     

New developments in cold spray based on higher gas and particle temperatures

In cold spraying, bonding is associated with shear instabilities caused by high strain rate deformation during the impact. It is well known that bonding occurs when the impact velocity of an impacting particle exceeds a critical value. This critical velocity depends not only on the type of spray material, but also on the powder quality, the particle size, and the particle impact temperature. Up to now, optimization of cold spraying mainly focused on increasing the particle velocity. The new approach presented in this contribution demonstrates capabilities to reduce critical velocities by well-tuned powder sizes and particle impact temperatures. A newly designed temperature control unit was implemented to a conventional cold spray system and various spray experiments with different powder size cuts were performed to verify results from calculations. Microstructures and mechanical strength of coatings demonstrate that the coating quality can be significantly improved by using well-tuned powder sizes and higher process gas temperatures. The presented optimization strategy, using copper as an example, can be transferred to a variety of spray materials and thus, should boost the development of the cold spray technology with respect to the coating quality. This article was originally published inBuilding on 100 Years of Success, Proceedings of the 2006 International Thermal Spray Conference (Seattle, WA), May 15–18, 2006, B.R. Marple, M.M. Hyland, Y.-Ch. Lau, R.S. Lima, and J. Voyer, Ed., ASM International, Materials Park, OH, 2006.     

Effect of the marine environment on reinforced concrete durability in Iberoamerican countries: DURACON project/CYTED

This work presents some of the results from the project: “Effect of the environment on reinforcement durability” (DURACON) in its first two-years period, which investigates the influence of urban and marine meteorochemical parameters on the performance of reinforced concrete structures. The results presented in this investigation are from 21 marine test sites only (no urban environments are included), distributed among 11 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Mexico, Spain, Uruguay, Portugal and Venezuela). The environment was evaluated using ISO Standard 9223 and the concrete was characterized by measuring compressive strength, elastic modulus, total and effective porosity, chloride permeability according to ASTM standards, as well as the effective porosity and resistance to water absorption using the Fagerlund method. To that effect, concrete specimens (with and without reinforcement) were prepared for electrochemical and physical/mechanical/chemical tests using the existing materials in each participating country, following strict procedures which enabled the preparation of similar concrete samples. Two water/cement (w/c) ratios (0.45 and 0.65) were selected, where the concrete with 0.45 w/c ratio had to have a minimum cement content of 400 kg/m³ and the one with 0.65 w/c ratio a compressive strength of 210 kg/cm². Type I Portland cement, siliceous sand, and crushed rock as coarse aggregates (13-mm maximum nominal size) were used. After a one-year exposure, the results of the corrosion potentiality and probability analysis of the reinforcement in the different test stations showed that, for marine atmospheres, the most aggressive environment to induce steel corrosion was at Portugal’s Cabo Raso station, and the least aggressive one was at Chile’s Valparaíso station. These results are comparable with the ones found using electrochemical measurements, after a two-year exposure.     

Fuzzy Structure of Complex Belief Systems: Fuzzy Relations and Fuzzy Belief Sets

In previous work, Nescolarde-Selva and Usó-Doménech (2014a Nescolarde-Selva, J. and J. L. Usó-Doménech. “Semiotic Vision of Ideologies.” Found Sci 19 (2014a): 263–282.[Crossref], [Web of Science ®] , [Google Scholar], b) discussed the theory that complex belief systems have a topological structure. In this article it is suggested that this structure is also fuzzy. We introduce the concepts of fuzzy sets in the context of beliefs (substantive and derived), and between derived beliefs themselves. Also introduced are the concepts of fuzzy covering and fuzzy invariance and the relationships between them.     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

A novel methodology to predict urban traffic congestion with ensemble learning

Urban traffic congestion prediction is a very hot topic due to the environmental and economical impacts that currently implies. In this sense, to be able to predict bottlenecks and to provide alternatives to the circulation of vehicles becomes an essential task for traffic management. A novel methodology, based on ensembles of machine learning algorithms, is proposed to predict traffic congestion in this paper. In particular, a set of seven algorithms of machine learning has been selected to prove their effectiveness in the traffic congestion prediction. Since all the seven algorithms are able to address supervised classification, the methodology has been developed to be used as a binary classification problem. Thus, collected data from sensors located at the Spanish city of Seville are analyzed and models reaching up to 83 % are generated.