Hydrothermal synthesis of a continuous zeolite Beta layer by optimization of time, temperature and heating rate of the precursor mixture

Highly crystalline zeolite Beta coatings in a range of Si/Al ratios of 12-23 were synthesized on a surface-modified molybdenum substrate by hydrothermal synthesis. The average thickness of the coatings was ca. 2 mu m corresponding to a coverage of 2.5 gm(-2). The coatings were obtained from a viscous Na, K, and TEAOH containing aluminosilicate precursor mixture with silica sol as reactive silicon source. A mechanism for the in situ growth of zeolite Beta coatings is proposed. According to this mechanism, the deposition of an amorphous gel layer on the substrate surface in the initial stage of the synthesis is an important step for the crystallization of continuous zeolite Beta coatings. The heating rate of the precursor mixture and the synthesis temperature were optimized to control the level of supersaturation and to stimulate the initial formation of a gel layer. At a Si/Al ratio of 23, fast heating and a temperature of 150 degrees C are required to obtain high coverage, while at a Si/Al ratio of 15, hydrothermal synthesis has to be performed with a slow initial heating rate at 140 degrees C. (c) 2007 Elsevier Inc. All rights reserved.

A methodology for sensor modeling and placement optimization to support temperature monitoring

This paper presents a modeling and optimization approach for sensor placement in a building zone that supports reliable environment monitoring. © 2012 ACM.

Development of complex classical force fields through force matching to ab initio data: Application to a room-temperature ionic liquid

Recent experimental neutron diffraction data and ab initio molecular dynamics simulation of the ionic liquid dimethylimidazolium chloride ([dmim]Cl) have provided a structural description of the system at the molecular level. However, partial radial distribution functions calculated from the latter, when compared to previous classical simulation results, highlight some limitations in the structural description offered by force fieldbased simulations. With the availability of ab initio data it is possible to improve the classical description of [dmim]Cl by using the force matching approach, and the strategy for fitting complex force fields in their original functional form is discussed. A self-consistent optimization method for the generation of classical potentials of general functional form is presented and applied, and a force field that better reproduces the observed first principles forces is obtained. When used in simulation, it predicts structural data which reproduces more faithfully that observed in the ab initio studies. Some possible refinements to the technique, its application, and the general suitability of common potential energy functions used within many ionic liquid force fields are discussed.

Surrogate-Based Optimization Using Multifidelity Models with Variable Parameterization and Corrected Space Mapping

Surrogate-based-optimization methods provide a means to achieve high-fidelity design optimization at reduced computational cost by using a high-fidelity model in combination with lower-fidelity models that are less expensive to evaluate. This paper presents a provably convergent trust-region model-management methodology for variableparameterization design models: that is, models for which the design parameters are defined over different spaces. Corrected space mapping is introduced as a method to map between the variable-parameterization design spaces. It is then used with a sequential-quadratic-programming-like trust-region method for two aerospace-related design optimization problems. Results for a wing design problem and a flapping-flight problem show that the method outperforms direct optimization in the high-fidelity space. On the wing design problem, the new method achieves 76% savings in high-fidelity function calls. On a bat-flight design problem, it achieves approximately 45% time savings, although it converges to a different local minimum than did the benchmark.

Optimization of heat transfer characteristics, flow distribution, and reaction processing for a microstructured reactor/heat-exchanger for optimal performance in platinum catalyzed ammonia oxidation

The present work is focused on the demonstration of the advantages of miniaturized reactor systems which are essential for processes where potential for considerable heat transfer intensification exists as well as for kinetic studies of highly exothermic reactions at near-isothermal conditions. The heat transfer characteristics of four different cross-flow designs of a microstructured reactor/heat-exchanger (MRHE) were studied by CFD simulation using ammonia oxidation on a platinum catalyst as a model reaction. An appropriate distribution of the nitrogen flow used as a coolant can decrease drastically the axial temperature gradient in the reaction channels. In case of a microreactor made of a highly conductive material, the temperature non-uniformity in the reactor is strongly dependent on the distance between the reaction and cooling channels. Appropriate design of a single periodic reactor/heat-exchanger unit, combined with a non-uniform inlet coolant distribution, reduces the temperature gradients in the complete reactor to less than 4degreesC, even at conditions corresponding to an adiabatic temperature rise of about 1400degreesC, which are generally not accessible in conventional reactors because of the danger of runaway reactions. To obtain the required coolant flow distribution, an optimization study was performed to acquire the particular geometry of the inlet and outlet chambers in the microreactor/heat-exchanger. The predicted temperature profiles are in good agreement with experimental data from temperature sensors located along the reactant and coolant flows. The results demonstrate the clear potential of microstructured devices as reliable instruments for kinetic research as well as for proper heat management in the case of highly exothermic reactions. (C) 2002 Elsevier Science B.V. All rights reserved.

SUPERCONDUCTING YBA2CU3O7 THIN-FILMS ON MGO BY KRF LASER ABLATION - OPTIMIZATION OF DEPOSITION PARAMETERS

The optimization of interrelated deposition parameters during deposition of in situ YBa2Cu3O7 thin films on MgO substrates by KrF laser ablation was systematically studied in a single experimental chamber. The optimum condition was found to be a substrate temperature of 720-degrees-C and a target-substrate distance of 5 cm in an oxygen partial pressure of 100 mTorr. These conditions produced films with T(c) = 87 K. The presence of YO in the plasma plume was found to be important in producing good quality films. The films were characterized by resistance-temperature measurements, energy dispersive x-ray analyses, scanning electron microscopy, and x-ray-diffraction measurements, and the physical reasons underlying film quality degradation at parameter values away from optimal are discussed.

Experimental design and batch experiments for optimization of Cr(VI) removal from aqueous solutions by hydrous cerium oxide nanoparticles

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.

OPTIMIZATION OF THE SLOW-MODE PLASMON POLARITON IN LIGHT-EMITTING TUNNEL-JUNCTIONS

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.

Advanced Modelling and Optimization of Infared Oven in Injection Stretch Blow-moulding for Energy Saving

In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with partial swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.

Arsenic(III,V) adsorption onto charred dolomite: charring optimization and batch studies

In this work, the removal of arsenic from aqueous solutions onto thermally processed dolomite is investigated. The dolomite was thermally processed (charred) at temperatures of 600, 700 and 800 degrees C for 1, 2, 4 and 8 h. Isotherm experiments were carried out on these samples over a wide pH range. A complete arsenic removal was achieved over the pH range studied when using the 800 degrees C charred dolomite. However, at this temperature, thermal degradation of the dolomite weakens its structure due to the decomposition of the magnesium carbonate, leading to a partial dissolution. For this reason, the dolomitic sorbent chosen for further investigations was the 8 h at 700 degrees C material. Isotherm studies indicated that the Langmuir model was successful in describing the process to a better extent than the Freundlich model for the As(V) adsorption on the selected charred dolomite. However, for the As(III) adsorption, the Freundlich model was more successful in describing the process. The maximum adsorption capacities of charred dolomite for arsenite and arsenate ions are 1.846 and 2.157 mg/g, respectively. It was found that both the pseudo first- and second-order kinetic models are able to describe the experimental data (R-2 > 0.980). The data suggest the charring process allows dissociation of the dolomite to calcium carbonate and magnesium oxide, which accelerates the process of arsenic oxide and arsenic carbonate precipitation. (C) 2014 Elsevier B.V. All rights reserved.

Approximate credal network updating by linear programming with applications to decision making

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.

Approximating Credal Network Inferences by Linear Programming

An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to very accurate inferences. The approach can also be specialized to classification with credal networks based on the maximality criterion. A complexity analysis for both the problem and the algorithm is reported together with numerical experiments, which confirm the good performance of the method. While the inner approximation produced by the algorithm gives rise to a classifier which might return a subset of the optimal class set, preliminary empirical results suggest that the accuracy of the optimal class set is seldom affected by the approximate probabilities