A new approach to designing reduced scale thermal-hydraulic experiments

Reduced scale experiments are often employed in engineering because they are much cheaper than real scale testing. Unfortunately, though, it is difficult to design a thermal-hydraulic circuit or equipment in reduced scale capable of reproducing, both accurately and simultaneously, all the physical phenomena that occur in real scale and operating conditions. This paper presents a methodology to designing thermal-hydraulic experiments in reduced scale based on setting up a constrained optimization problem that is solved using genetic algorithms (GAs). In order to demonstrate the application of the methodology proposed, we performed some investigations in the design of a heater aimed to simulate the transport of heat and momentum in the core of a pressurized water reactor (PWR) at 100% of nominal power and non-accident operating conditions. The results obtained show that the proposed methodology is a promising approach for designing reduced scale experiments.     

Preparation of polypropylene‐based nanocomposites using nanosized MCM‐41 as support and in situ polymerization

MCM‐41 nanoparticles were used for preparing nanocomposites through the in situ polymerization of propylene. The performance of the catalytic system and the final properties of the materials obtained are highly dependent on the methodology used for impregnation of the catalyst onto the support particles, and therefore an optimization study for the impregnation methodology of the catalyst (Me₂Si(Ind)₂ZrCl₂) was carried out. Two different methodologies were used; the results in terms of catalytic activity and polymer molecular masses indicated that the most promising one involved the pre‐activation of the catalyst with the cocatalyst, methylaluminoxane, followed by impregnation onto the MCM‐41 nanoparticles. Thus, an optimized route for the preparation of polypropylene nanocomposites achieving significant improvements in catalyst activity was developed. The nanocomposite materials were characterized by GPC, TGA and DSC. The dispersion state and the size of the nanoparticles incorporated in the polypropylene matrix were investigated by transmission electron microcopy. Additionally, this methodology allows simultaneous control of the desired amount of support and the concentration of catalyst to be used in the in situ polymerization. © 2015 Society of Chemical Industry     

Influence of mesostasis in volcanic rocks on the alkali-aggregate reaction

Mesostasis material present in the interstices of volcanic rocks is the main cause of the alkali-aggregate reaction (AAR) in concretes made with these rock aggregates. Mesostasis often is referred to as volcanic glass, because it has amorphous features when analyzed by optical microscopy. However, this study demonstrates that mesostasis in the interstitials of volcanic rocks most often consists of micro to cryptocrystalline mineral phases of quartz, feldspars, and clays. Mesostasis has been identified as having different characteristics, and, thus, this new characterization calls for a re-evaluation of their influence on the reactivity of the volcanic rocks. The main purpose of this study is to correlate the characteristics of mesostasis with the AAR in mortar bars containing basalts and rhyolites.     

A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features

The need to leverage knowledge through data mining has driven enterprises in a demand for more data. However, there is a gap between the availability of data and the application of extracted knowledge for improving decision support. In fact, more data do not necessarily imply better predictive data-driven marketing models, since it is often the case that the problem domain requires a deeper characterization. Aiming at such characterization, we propose a framework drawn on three feature selection strategies, where the goal is to unveil novel features that can effectively increase the value of data by providing a richer characterization of the problem domain. Such strategies involve encompassing context (e.g., social and economic variables), evaluating past history, and disaggregate the main problem into smaller but interesting subproblems. The framework is evaluated through an empirical analysis for a real bank telemarketing application, with the results proving the benefits of such approach, as the area under the receiver operating characteristic curve increased with each stage, improving previous model in terms of predictive performance.

     

Generalized Hermitian Radial Basis Functions Implicits from polygonal mesh constraints

In this work we investigate a generalized interpolation approach using radial basis functions to reconstruct implicit surfaces from polygonal meshes. With this method, the user can define with great flexibility three sets of constraint interpolants: points, normals, and tangents; allowing to balance computational complexity, precision, and feature modeling. Furthermore, this flexibility makes possible to avoid untrustworthy information, such as normals estimated on triangles with bad aspect ratio. We present results of the method for applications related to the problem of modeling 2D curves from polygons and 3D surfaces from polygonal meshes. We also apply the method to problems involving subdivision surfaces and front-tracking of moving boundaries. Finally, as our technique generalizes the recently proposed HRBF Implicits technique, comparisons with this approach are also conducted.     

Improving the performance of fuzzy rules-based forecasters through application of FCM algorithm

Prediction models based on artificial intelligence techniques have been widely used in Time Series Forecasting in several areas. They are often fuzzy models or neural networks. This paper describes the development of neural and fuzzy models for forecasting time series of practical examples, and shows the comparisons of results between models, including the results of statistical modeling. The use of data clustering algorithms like Fuzzy C-Means is considered in fuzzy models.     

One‐Step Formulation of Protein Microparticles with Tailored Properties: Hard Templating at Soft Conditions

Formulation of therapeutic proteins into particulate forms is a main strategy for site‐specific and prolonged protein delivery as well as for protection against degradation. Precise control over protein particle size, dispersity, purity, as well as mild preparation conditions and minimal processing steps are highly desirable. It is, however, hard to fit all these criteria with conventional preparation techniques. Here a one‐step hard‐templating synthesis of microparticles composed of functional, non‐denatured protein is reported. The method is based on filling porous CaCO₃ microtemplates with the protein near to its isoelectric point (pI) followed by pH‐ or EDTA‐mediated dissolution of the tempplates. In principle, a wide variety of proteins can be converted into microparticles using this approach. The main requirement is an overlap of the protein insolubility and a template solubility for a certain parameter (here pH or EDTA). Here the formulation of insulin particles is studied in detail and it is shown that particles consisting of high molecular weight protein (catalase) can also be prepared. In this context, the synthesis of CaCO₃ templates with controlled size, the mechanism of the protein microparticle formation and mechanical properties of the microparticles are discussed. For the first time, the fabrication of mesoporous monodispersed CaCO₃ microtemplates with identical porocity but tuned diameter from 3 to 20 μm is demonstrated. The protein particle diameter can be adjusted by choosing the appropriate template size that is critical for successful pulmonary delivery of insulin. As a first step towards insulin delivery, the in vitro release of insulin at physiological conditions is studied.     

Gain scheduling adaptive control applied to a particular mixer-settler equipment

The mixer-settler based on phase inversion (MSBPI) is a system used for treatment of wastewaters in order to reduce the content of oils and greases from wastewaters produced in oil industry. Its operation principle combines mixing, extraction and separation by decantation in a unique process. In particular, for control purpose, the level of the interface between organic solvent and water, in the separation section, is a critical variable to guarantee performance and security during operation of the MSBPI. In this work, an image-based detector to monitor the organic solvent–water interface level is proposed and a feedback control loop for the process is presented. Initially, a conventional controller (PID) was implemented and, subsequently, a strategy of gain scheduling adaptive control was developed in order to improve the process response in closed-loop. The experimental results showed that the adaptive control presented an efficient performance, even when disturbances of significant magnitude were applied to the process.