196 resultados para Portfolio optimization


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This paper investigated the influence of three micro electrodischarge milling process parameters, which were feed rate, capacitance, and voltage. The response variables were average surface roughness (R a ), maximum peak-to-valley roughness height (R y ), tool wear ratio (TWR), and material removal rate (MRR). Statistical models of these output responses were developed using three-level full factorial design of experiment. The developed models were used for multiple-response optimization by desirability function approach to obtain minimum R a , R y , TWR, and maximum MRR. Maximum desirability was found to be 88%. The optimized values of R a , R y , TWR, and MRR were 0.04, 0.34 μm, 0.044, and 0.08 mg min−1, respectively for 4.79 μm s−1 feed rate, 0.1 nF capacitance, and 80 V voltage. Optimized machining parameters were used in verification experiments, where the responses were found very close to the predicted values.

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The scale of BT's operations necessitates the use of very large scale computing systems, and the storage and management of large volumes of data. Customer product portfolios are an important form of data which can be difficult to store in a space efficient way. The difficulties arise from the inherently structured form of product portfolios, and the fact that they change over time as customers add or remove products. This paper introduces a new data-modelling abstraction called the List_Tree. It has been designed specifically to support the efficient storage and manipulation of customer product portfolios, but may also prove useful in other applications with similar general requirements.

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Renewable energy generation is expected to continue to increase globally due to renewable energy targets and obligations to reduce greenhouse gas emissions. Some renewable energy sources are variable power sources, for example wind, wave and solar. Energy storage technologies can manage the issues associated with variable renewable generation and align non-dispatchable renewable energy generation with load demands. Energy storage technologies can play different roles in each of the step of the electric power supply chain. Moreover, large scale energy storage systems can act as renewable energy integrators by smoothing the variability. Compressed air energy storage is one such technology. This paper examines the impacts of a compressed air energy storage facility in a pool based wholesale electricity market in a power system with a large renewable energy portfolio.

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Traditionally, the optimization of a turbomachinery engine casing for tip clearance has involved either twodimensional transient thermomechanical simulations or three-dimensional mechanical simulations. This paper illustrates that three-dimensional transient whole-engine thermomechanical simulations can be used within tip clearance optimizations and that the efficiency of such optimizations can be improved when a multifidelity surrogate modeling approach is employed. These simulations are employed in conjunction with a rotor suboptimization using surrogate models of rotor-dynamics performance, stress, mass and transient displacements, and an engine parameterization.

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Alkali activated binders, based on ash and slag, also known as geopolymers, can play a key role in reducing the carbon footprint of the construction sector by replacing ordinary Portland cement in some concretes. Since 1970s, research effort has been ongoing in many research institutions. In this study, pulverized fuel ash (PFA) from a UK power plant, ground granulated blast furnace slag (GGBS) and combinations of the two have been investigated as geopolymer binders for concrete applications. Activators used were sodium hydroxide and sodium silicate solutions. Mortars with sand/binder ratio of 2.75 with several PFA and GGBS combinations have been mixed and tested. The optimization of alkali dosage (defined as the Na2O/binder mass ratio) and modulus (defined as the Na2O/SiO2 mass ratio) resulted in strengths in excess of 70 MPa for tested mortars. Setting time and workability have been considered for the identification of the best combination of PFA/GGBS and alkali activator dosage for different precast concrete products. Geopolymer concrete building blocks have been replicated in laboratory and a real scale factory trial has been successfully carried out. Ongoing microstructural characterization is aiming to identify reaction products arising from PFA/GGBS combinations.

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Hydrous cerium oxide (HCO) was synthesized by intercalation of solutions of cerium(III) nitrate and sodium hydroxide and evaluated as an adsorbent for the removal of hexavalent chromium from aqueous solutions. Simple batch experiments and a 25 factorial experimental design were employed to screen the variables affecting Cr(VI) removal efficiency. The effects of the process variables; solution pH, initial Cr(VI) concentration, temperature, adsorbent dose and ionic strength were examined. Using the experimental results, a linear mathematical model representing the influence of the different variables and their interactions was obtained. Analysis of variance (ANOVA) demonstrated that Cr(VI) adsorption significantly increases with decreased solution pH, initial concentration and amount of adsorbent used (dose), but slightly decreased with an increase in temperature and ionic strength. The optimization study indicates 99% as the maximum removal at pH 2, 20 °C, 1.923 mM of metal concentration and a sorbent dose of 4 g/dm3. At these optimal conditions, Langmuir, Freundlich and Redlich–Peterson isotherm models were obtained. The maximum adsorption capacity of Cr(VI) adsorbed by HCO was 0.828 mmol/g, calculated by the Langmuir isotherm model. Desorption of chromium indicated that the HCO adsorbent can be regenerated using NaOH solution 0.1 M (up to 85%). The adsorption interactions between the surface sites of HCO and the Cr(VI) ions were found to be a combined effect of both anion exchange and surface complexation with the formation of an inner-sphere complex.

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Recent advances in hardware development coupled with the rapid adoption and broad applicability of cloud computing have introduced widespread heterogeneity in data centers, significantly complicating the management of cloud applications and data center resources. This paper presents the CACTOS approach to cloud infrastructure automation and optimization, which addresses heterogeneity through a combination of in-depth analysis of application behavior with insights from commercial cloud providers. The aim of the approach is threefold: to model applications and data center resources, to simulate applications and resources for planning and operation, and to optimize application deployment and resource use in an autonomic manner. The approach is based on case studies from the areas of business analytics, enterprise applications, and scientific computing.

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Light emitted from metal/oxide/metal tunnel junctions can originate from the slow-mode surface plasmon polariton supported in the oxide interface region. The effective radiative decay of this mode is constrained by competition with heavy intrinsic damping and by the need to scatter from very small scale surface roughness; the latter requirement arises from the mode's low phase velocity and the usual momentum conservation condition in the scattering process. Computational analysis of conventional devices shows that the desirable goals of decreased intrinsic damping and increased phase velocity are influenced, in order of priority, by the thickness and dielectric function of the oxide layer, the type of metal chosen for each conducting electrode, and temperature. Realizable devices supporting an optimized slow-mode plasmon polariton are suggested. Essentially these consist of thin metal electrodes separated by a dielectric layer which acts as a very thin (a few nm) electron tunneling barrier but a relatively thick (several 10's of nm) optically lossless region. (C) 1995 American Institute of Physics.