Reactivity and characteristics of Pd/MOF and Pd/calcinated-MOF catalysts for CO oxidation reaction: Effect of oxygen and hydrogen

For the first time, the effect of calcination process on characteristics and catalytic performances of Pd supported on different MOFs (MIL-101(Cr), NH₂-MIL-101(Cr), and HKUST-1) was evaluated. Besides, the various orders of calcination process and reduction one on Pd/MOF and Pd/calcinated-MOF were studied, and their performances in CO oxidation reaction were presented to find the effect of H₂ and O₂. Results showed that the effect of calcination and reduction processes on the catalytic activities and characteristics strongly depends on the nature of MOF. Among MIL-based catalysts, the catalyst with no calcination treatment showed the best activity. Among MNH₂-based catalysts, high activity was obtained for Pd/MNH₂, Pd/MNH₂-C, and Pd/MNH₂(RC) samples and Pd/calcinated-MNH₂was the best. Catalytic activities of HKUST-1-based catalysts decreased with calcination due to high changes in their structures. The results are useful for predicting the performance of MOFs in oxidation processes, especially reactions in which high oxygen concentrations are involved.     

Solvothermal synthesis of vanadium phosphate catalysts for n-butane oxidation

In this paper, we have developed a simple, low-cost, template-free and surfactant-free solvothermal process for synthesis of vanadyl hydrogen phosphate hemihydrate (VOHPO₄·0.5H₂O) with well defined crystal size. The synthesis was performed by reaction of VPO₄·2H₂O with an aliphatic alcohol (isobutyl alcohol, 1-pentanol, 1-hexanol, 1-heptanol or 1-decanol). This afforded well crystallized VOHPO₄·0.5H₂O by solvothermal methods at 120 °C temperature. This new method significantly reduced the preparation time and lowered production temperature (50%) of catalyst precursor (VOHPO₄·0.5H₂O) when compared to conventional hydrothermal synthesis methods. By varying the reducing agent, the solvothermal evolution process from layered tetragonal phase VOPO₄·2H₂O to orthorhombic phase VOHPO₄·0.5H₂O was observed. It was found that the length of carbon chain in an alcohol in the solvothermal condition had a great impact on chemical and physical properties of resulting catalysts. Interestingly, there was no trace of VO(H₂PO₄)₂ an impurity noted to be readily formed under solvothermal preparation condition. Therefore, this study introduces a more facile synthetic pathway to V(III) compounds. In addition, the microwave-synthesized catalysts exhibited some properties superior to those of conventionally synthesized catalyst such as better stability, crystallinity, and catalytic activity in the production of maleic anhydride. The characterization of both precursors and calcined catalysts was carried out using X-ray diffraction (XRD), inductively coupled plasma-atomic emission spectrometer (ICP-AES), N₂ physisorption, temperature programmed reduction (H₂-TPR) and scanning electron microscopy (SEM). The XRD pattern of the active catalyst prepared by this solvothermal method confirmed the presence of smaller crystal size (between 6 and 13 nm along 0 2 0 planes) of vanadium phosphate catalyst with higher specific surface area. Finally, the yield of maleic anhydride was significantly increased from 29% for conventional catalyst to 44% for the new solvothermal catalyst.     

Global minimization of multi-funnel functions using particle swarm optimization

In this paper, we consider the problem of finding the global minimum of multi-funnel-shaped functions with many local minima, which is a well-known and interesting problem in computational biology. First, the particle swarm optimization algorithms are briefly reviewed. Then, we have applied a variant of it with linear decreasing inertia weight to solve the underlying global optimization problem. Our computational experiments on several known test problems show the efficiency of the particle swarm optimization algorithm in comparison with global convex quadratic underestimator algorithms that are widely used in the literature.     

Multi-objective optimization of functionally graded materials,thickness and aspect ratio in micro-beams embedded in an elastic medium

Optimal design of micron-scale beams as a general case is an important problem for development of micro-electromechanical devices. For various applications, the mechanical parameters such as mass, maximum deflection and stress, natural frequency and buckling load are considered in strategies of micro-manufacturing technologies. However, all parameters are not of equal importance in each operating condition but multi-objective optimization is able to select optimal states of micro-beams which have desirable performances in various micro-electromechanical devices. This paper provides optimal states of design variables including thickness, distribution parameter of functionally graded materials, and aspect ratio in simply supported FG micro-beams resting on the elastic foundation using analytical solutions. The elastic medium is assumed to be as a two-layered foundation including a shear layer and a linear normal layer. Also, the size effect on the mechanical parameters is considered using the modified strain gradient theory and non-dominated sorting genetic algorithm-II is employed to optimization procedure. The target functions are defined such that the maximum deflection, maximum stress and mass must be minimized while natural frequency and critical buckling load must be maximized. The optimum patterns of FG micro-beams are presented for exponential and power-law FGMs and the effect of theory type and elastic foundation discussed in details. Findings indicate that the elastic foundation coefficients and internal length scale parameters of materials have the significant influences on the distribution of design variables. It is seen that the optimum values of inhomogeneity parameter and aspect ratio for E-FG micro-beams predicted by the modified strain gradient theory are larger than those of the classical continuum theory. Also, the multi-objective optimization is able to improve the normalized values of mass, maximum deflection, buckling load and natural frequency of P-FG micro-beams.     

Information fusion-based meta-classification predictive modeling for ETF performance

This study is aimed at determining the future share net inflows and outflows of Exchange Traded Funds (ETFs). The relationship between net flows is closely related to investor perception of the future and past performance of mutual funds. The net flows for Exchange Traded Funds are expected to be less related to overall fund performance, but rather based on the characteristics of the fund that make it attractive to an individual investor. In order to explore the relationship between investor’s perception of ETFs and subsequent net flows, this study is designed to shed light on the multifaceted linkages between fund characteristics and net flows. A meta-classification predictive modeling approach is designed for the use of large data sets. Then its implementation and results are discussed. A thorough selection of fifteen attributes from each fund, which are the most likely contributors to fund inflows and outflows, is deployed in the analyses. The large data set calls for the use of a robust systematic approach to identifying the attributes of the funds that best predict future inflows and outflows of the fund. The predictive performance of the proposed decision analytic methodology was assessed via the 10-fold cross validation, which yielded very promising results.     

A Dynamic Load-Balancing Parallel Search for Enumerative Robot Path Planning

We present a parallel formulation for enumerative search in high dimensional spaces and apply it to planning paths for a 6-dof manipulator robot. Participating processors perform local A* search towards the goal configuration. To exploit all the processors at their maximum capacity at all times, a dynamic load-balancing scheme matches idle and busy processors for load transfer. For comparison purposes, we have also implemented an existing parallel static load-balancing formulation based on regular domain decomposition. Both methods achieved almost linear speed-up in our experiments. The two methods follow different search strategies in parallel and the implementation of the existing method (with tuned space decomposition) was more time efficient on average. However, the planning time of that method is highly dependent on the distribution of the search space among the processors and its tuned decomposition varies for different obstacle placements. Empirical selection of the space decomposition parameters for the existing method does not guarantee minimal planning time in all environments and leads to slower planning than our dynamic load-balancing method in some cases. The performance of the developed dynamic method is independent of the obstacle placements and the method can achieve consistent speed-up in all environments.     

Fuzzy sliding-mode control of a human arm in the sagittal plane with optimal trajectory

Patients with spinal cord injuries cannot move their limbs using their intact muscles. A suitable controller can be used to move their arms by employing the functional electrical stimulation method. In this article, a fuzzy exponential sliding-mode controller is designed to move a musculoskeletal human arm model to track an optimal trajectory in the sagittal plane. This optimal arm trajectory is obtained by developing a policy for the central nervous system. In order to specify the optimal trajectory between two points, two dynamic and static optimal criteria are applied simultaneously. The first dynamic objective function is defined to minimize the joint torques, and the second static optimization is offered to minimize the muscle forces at each moment. In addition, fuzzy logic is used to tune the sliding-surface parameter to enable an appropriate tracking performance. Simulation results are evaluated and compared with experimental data for upward and downward movements of the human arm.