TiO2–BaTiO3 nanocomposite for electron capture in dye‐sensitized solar cells

Different compositions of TiO₂–BaTiO₃ nanocomposites are synthesized with various weight ratios for dye‐sensitized solar cell (DSSC) applications. TiO₂ and BaTiO₃ nanoparticles (NPs) are synthesized by sol‐gel and solvothermal methods, respectively and are employed as the photoanode electrodes. BaTiO₃ NPs have pure cubic perovskite crystal structure with an average size of 20‐40 nm, while TiO₂ NPs show pure anatase phase with 15‐30 nm size. The power conversion efficiency (PCE) enhancement of the cells is first attained by controlling the thickness of the films for light harvesting improvement. The fabricated DSSC composed of pure BaTiO₃ NPs with an optimal thickness of 25 μm shows efficiency of 6.83%, whereas that made of pure TiO₂ NPs with 14 μm thickness has cell efficiency of 7.24%. Further improvement of cell efficiency is achieved by preparation of binary oxide nanocomposites using TiO₂ and BaTiO₃ NPs with various weight ratios. The highest PCE of 9.40% is obtained for the nanocomposite with TiO₂:BaTiO₃=85:15 (wt%). The enhancement is assigned to less recombination of photo‐generated electrons and higher incident photon to current conversion yield as a result of rapid charge collection and higher dye sensitization.     

Characterization of a New Structured Packing by Computational Fluid Dynamics

The novel wire gauze structured packing PACK‐1300Y with high specific surface area was characterized by computational fluid dynamics. The main features of PACK‐1300Y were investigated including the dry and wet pressure drop as well as the height equivalent to a theoretical plate (HETP). Moreover, the flow structure of this packing was described via numerical simulations. To evaluate the amount of HETP and dry and wet pressure drop, 3D computational fluid dynamic modeling with respect to the Eulerian‐Eulerian multiphase approach was applied. The average relative errors were determined between the findings achieved from computational fluid dynamic simulation and experimental findings for mass transfer efficiency and wet and dry pressure drop, respectively.     

Effect of milling time and clay content on the thermal stability of polyethylene‐clay nanocomposite

The aim of the paper was to investigate the thermal properties of polyethylene (PE)‐clay nanocomposites prepared via the high energy ball milling (HEBM) method. The structure and morphology of the nanocomposites were examined by transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). Thermal stability of all milled samples, including pure PE and PE‐clay nanocomposites was measured by thermal gravimetric analysis (TGA). The effect of milling time and clay contents on the thermal stability of PE was investigated. The results show that the thermal stability of PE promotes with increasing milling time. Clay has two opposed functions in the thermal stability of the nanocomposite, the first one is the barrier effect to improve the thermal stability, and another one is the catalysis effect leading to a decrease of the thermal stability. J. VINYL ADDIT. TECHNOL., 22:285–292, 2016. © 2014 Society of Plastics Engineers     

A pore network study of evaporation from the surface of a drying non‐hygroscopic porous medium

The phenomena occurring at the surface of a porous medium during drying in the capillary regime are investigated by pore network simulations. The impact of the formation of wet and dry patches at the surface on the drying rate is studied. The simulations indicate an edge effect characterized by a noticeable variation of saturation in a thin layer adjacent to the porous surface. Also, the results indicate a significant nonlocal equilibrium effect at the surface. The simulation results are exploited to test Schlünder's classical model which offers a simple closure relationship between the evaporation rate and the degree of occupancy of the surface by the liquid. In addition to new insights into the surface phenomena, the results open up new prospects for improving the continuum models of the drying process. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1435–1447, 2018     

Preventive maintenance and replacement scheduling for repairable and maintainable systems using dynamic programming

This paper presents mathematical models and a solution approach to determine the optimal preventive maintenance schedules for a repairable and maintainable series system of components with an increasing rate of occurrence of failure (ROCOF). The maintenance planning horizon has been divided into discrete and equally-sized periods and in each period, three possible actions for each component (maintain it, replace it, or do nothing) have been considered. The optimal decisions for each component in each period are investigated such that the objectives and the requirements of the system can be achieved. In particular, the cases of minimizing total cost subject to a constraint on system reliability, and maximizing system reliability subject to a budgetary constraint on overall cost have been modeled. As the optimization methodology, dynamic programming combined with branch-and-bound method is utilized and the effectiveness of the approach is presented through the use of a numerical example. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.     

A robust digital image watermarking technique using lifting wavelet transform and firefly algorithm

In this paper, an optimized and robust digital image watermarking technique based on lifting wavelet transform (LWT) and firefly algorithm is proposed. LWT is newer and faster generation of former wavelet transforms and firefly algorithm is an efficient optimizing algorithms. In current technique, base image decomposed by LWT into 4 sub bands then the first sub band separated into non overlapping blocks. After that blocks are sorted in order of descending based on standard derivation of each block. Selecting suitable blocks for special embedding process seems to be an optimization problem due to existence of a trade-off between imperceptibility and robustness. Firefly algorithm used to solve this trade-off while selecting primary blocks causes high robustness and low imperceptibility and vice versa. For improving security, Arnold transform applied to watermark and achieved scrambled image bits used as condition for embedding process. The proposed technique evaluated by variety of attacks like additive noise, average filter, median filter, sharpening filter and some other geometric and non-geometric attacks and experimental results showed its good imperceptibility and high robustness.     

Topology optimization for the seismic design of truss-like structures

A practical optimization method is applied to design nonlinear truss-like structures subjected to seismic excitation. To achieve minimum weight design, inefficient material is gradually shifted from strong parts to weak parts of a structure until a state of uniform deformation prevails. By considering different truss structures, effects of seismic excitation, target ductility and buckling of the compression members on optimum topology are investigated. It is shown that the proposed method could lead to 60% less structural weight compared to optimization methods based on elastic behavior and equivalent static loads, and is efficient at controlling performance parameters under a design earthquake.     

A hybrid computer simulation-artificial neural network algorithm for optimisation of dispatching rule selection in stochastic job shop scheduling problems

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature.     

Investigation of the Effect of Tungsten Substitution on Microstructure and Abrasive Wear Performance of In Situ VC-Reinforced High-Manganese Austenitic Steel Matrix Composite

Particulate VC-reinforced high-manganese austenitic steel matrix composites with different vanadium and tungsten contents were synthesized by conventional alloying and casting route. Microstructural characterizations showed that the composites processed by in situ precipitation of the reinforcements were composed of V₈C₇ particulates distributed in an austenitic matrix. It was observed that addition of tungsten to austenite increases work-hardening rate of subsurface layer during pin-on disk wear test. The maximum abrasive wear resistance was achieved at tungsten content equal to 2 wt pct. However, excessive addition of tungsten promoted the formation of W₃C phase and reduced the abrasive wear resistance because of decrease in distribution homogeneity and volume fraction of the reinforcing VC particles.     

Adaptive linear discriminant analysis for online feature extraction

In this paper, we present new adaptive algorithms for the computation of the square root of the inverse covariance matrix. In contrast to the current similar methods, these new algorithms are obtained from an explicit cost function that is introduced for the first time. The new adaptive algorithms are used in a cascade form with a well-known adaptive principal component analysis to construct linear discriminant features. The adaptive nature and fast convergence rate of the new adaptive linear discriminant analysis algorithms make them appropriate for online pattern recognition applications. All adaptive algorithms discussed in this paper are trained simultaneously using a sequence of random data. Experimental results using the synthetic and real multiclass, multidimensional input data demonstrate the effectiveness of the new adaptive algorithms to extract the optimal features for the purpose of classification.