New thermosets obtained by cationic copolymerization of DGEBA with γ‐caprolactone with improvement in the shrinkage. II. Time–temperature–transformation (TTT) cure diagram

Mixtures of diglycidylether of bisphenol A (DGEBA) with different proportions of γ‐caprolactone (γ‐CL) were cured with ytterbium triflate as initiator. The curing was studied with differential scanning calorimetry (DSC) and thermo mechanical analysis (TMA). The results are presented in the form of a time–temperature–transformation diagram. The kinetic analysis was performed by means of the isoconversional integral procedure and the kinetic model was also determined using the Coats–Redfern method. Gelation was determined by means of combined experiences of DSC and TMA. The relationship between the glass transition temperature (T_g) and the degree of conversion α was determined by DSC. Using the isoconversional lines and the T_g‐α relationship, the vitrificacion curve was obtained. The methodology developed makes it possible to obtain the TTT diagram using only no‐isothermal experiments with equivalent results to those using classical isothermal procedures. The addition of γ‐CL accelerates the curing and reduces the shrinkage after gelation and consequently the internal stresses in the material. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

Lipolytic and oxidative changes in 'Chorizo' during ripening

Changes in fats during the ripening of 'chorizo'-a Spanish dry sausage- elaborated by traditional and industrial processes have been studied. Total free fatty acids (FFA), carbonyls, volatile fatty acids (acetic acid), TBA number and individual FFA were determined. A marked increase of total free fatty acids was observed, although an increase of rancidity was not detected. Except in one of the batches studied in which a rise in carbonyls was reported, these compounds remained unchanged during the ripening in the rest of the batches analysed. No significant change occurred in the proportions of different free fatty acids during ripening in both types of 'chorizo'. There was a marked increase of acetic acid during the first days of ripening followed by a slow fall in the ultimate stages.     

Thermodynamic properties of ni nitrides and phase stability in the Ni-N system

The thermodynamics of the Ni-N system is poorly known from experiments, and there is a need of information on the stability of the various nitride phases and the Ni-N phase diagram. This kind of information has been obtained by us, by combining the few measurements available with predictions, based on recently reported regularities in bonding properties and vibrational entropy of 3d transition metal compounds. A calculated Ni-N phase diagram is presented. A certain range of homogeneity for the hexagonal nitride phase is obtained, which is comparable to that of other 3d transition metal-nitrogen systems. The question of the possible existence of a stable Ni₄N phase is examined. According to our results, Ni₄N is metastable in the Ni-N system.     

The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets

Drought diagnosis and forecasting are fundamental issues regarding hydrological management in Spain, where recurrent water scarcity periods are normal. Land-surface models (LSMs) could provide relevant information for water managers on how drought conditions evolve. Here, we explore the usefulness of LSMs driven by atmospheric analyses with different resolutions and accuracies in simulating drought and its propagation to precipitation, soil moisture and streamflow through the system. We perform simulations for the 1980-2014 period with SASER (5 km resolution) and LEAFHYDRO (2.5 km resolution), which are forced by the Spanish SAFRAN dataset (at 5km and 30km resolutions), and the global eartH2Observe datasets at 0.25 degrees (including the MSWEP precipitation dataset). We produce standardized indices for precipitation (SPI), soil moisture (SSMI) and streamflow (SSI). The results show that the model structure uncertainty remains an important issue in current generation large-scale hydrological simulations based on LSMs. This is true for both the SSMI and SSI. The differences between the simulated SSMI and SSI are large, and the propagation scales for drought regarding both soil moisture and streamflow are overly dependent on the model structure. Forcing datasets have an impact on the uncertainty of the results but, in general, this impact is not as large as the uncertainty due to model formulation. Concerning the global products, the precipitation product that includes satellite observations (MSWEP) represents a large improvement compared with the product that does not.

     

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.     

Mechanochemical tuning of molecular weight distribution of styrene homopolymers as postpolymerization modification in solvent-free solid-state

Mechanochemical degradation by planetary ball milling (PM) is used for postpolymerization modification of styrene homopolymers (PS). A complete factorial design was chosen to study the effect of radical scavengers, milling time, initial molecular weight, and revolution radius (R_p), on the shape of molecular weight distributions (MWDs) of PS. Size-exclusion chromatography analysis shows the feasibility of fine-tuning MWD of PS at up to 40% conversion. Distributions ranged from unimodal to bimodal in a PM with R_p = 150 mm at different stage of milling, whereas in a PM with R_p of 60.8 mm the adjustment of unimodal distributions is achieved. Initial polydispersity is more important to develop bimodal distributions when compared with initial molecular weight. Fourier transform infrared and X-ray electron spectrometry analysis show some suppression of PS degradation and complete oxidation inhibition of macromolecular radicals with the incorporation of radical scavengers, which we considered as additional aids when adjusting the MWDs.     

Online identification of appliances from power consumption data collected by smart meters

The efficient use of resources is a matter of great concern in today’s society, especially in the energy sector. Although the main strategy to decrease energy use has long been focused on supply, over the last few years, there has been a shift to the demand side. Under this new line of action, demand-side management networks have emerged and extended from the household level to larger installations, with the appearance of the concepts of Smart Grids and even Smart Cities. The extended use of Smart Meters for measuring residential electricity consumption facilitates the creation of such intelligent environments. In this context, this article proposes a system which extracts value from the collected consumer information to identify the appliances belonging to that smart environment by means of machine learning techniques. Considering the large amount of information that would be handled when millions of homes were sending data, big data technology has been used. An experiment to evaluate the classification method was carried out with seven devices and three different configurations. The results are also reported, achieving promising results, with recognition rates of 75 % after 1 h of training and 100 % after 4 h.