Estimation of gross calorific value of coals using artificial neural networks

The gross calorific value (GCV) is an important property defining the energy content and thereby efficiency of fuels, such as coals. There exist a number of correlations for estimating the GCV of a coal sample based upon its proximate and/or ultimate analyses. These correlations are mainly linear in character although there are indications that the relationship between the GCV and a few constituents of the proximate and ultimate analyses could be nonlinear. Accordingly, in this paper a total of seven nonlinear models have been developed using the artificial neural networks (ANN) methodology for the estimation of GCV with a special focus on Indian coals. The comprehensive ANN model developed here uses all the major constituents of the proximate and ultimate analyses as inputs while the remaining six sub-models use different combinations of the constituents of the stated analyses. It has been found that the GCV prediction accuracy of all the models is excellent with the comprehensive model being the most accurate GCV predictor. Also, the performance of the ANN models has been found to be consistently better than that of their linear counterparts. Additionally, a sensitivity analysis of the comprehensive ANN model has been performed to identify the important model inputs, which significantly affect the GCV. The ANN-based modeling approach illustrated in this paper is sufficiently general and thus can be gainfully extended for estimating the GCV of a wide spectrum of solid, liquid and gaseous fuels.     

Synthesis of nanocrystalline hydroxyapatite using surfactant template systems: Role of templates in controlling morphology

Hydroxyapatite (HA) nanopowder was synthesized by reverse microemulsion technique using calcium nitrate and phosphoric acid as starting materials in aqueous phase. Cyclohexane, hexane, and isooctane were used as organic solvents, and Dioctyl sulfosuccinate sodium salt (AOT), dodecyl phosphate (DP), NP5 (poly(oxyethylene)₅ nonylphenol ether), and NP12 (poly(oxyethylene)₁₂ nonylphenol ether) as surfactants to make the emulsion. Effect of synthesis parameters, such as type of surfactant, aqueous to organic ratio (A/O), pH and temperature on powder characteristics were studied. It was found that the surfactant templates played a significant role in regulating the morphology of the nanoparticle. Hydroxyapatite nanoparticle of different morphologies such as spherical, needle shape or rod-like were obtained by adjusting the conditions of the emulsion system. Synthesized powder was characterized using X-ray diffraction (XRD), BET surface area and transmission electron microscopy (TEM). Phase pure HA nanopowder with highest surface area of 121 m²/g were prepared by this technique using NP5 as a surfactant. Densification studies showed that this nanoparticle can give about 98% of their theoretical density. In vitro bioactivity of the dense HA compacts was confirmed by excellent apatite layer formation after 21 days in SBF solution. Cell material interaction study showed good cell attachment and after 5 days cells were proliferated on HA compacts in OPC1 cell culture medium. The results imply this to be a versatile approach for making hydroxyapatite nanocrystals with controlled morphology and excellent biocompatibility.     

Taguchi analysis of surface modification technique using W-Cu powder metallurgy sintered tools in EDM and characterization of the deposited layer

The present study describes an experimental research on surface modification during electrical discharge machining (EDM) by depositing a hard layer over the work surface of C-40 grade plain carbon steel using specially prepared powder metallurgy compact tools. The investigated process parameters were composition, compaction pressure, sintering temperature, pulse on-time, peak?Ccurrent setting, and duty factor. Measurements of deposited layer thickness, mass transfer rate, tool wear rate, surface roughness and microhardness were undertaken on the EDM-ed specimens. Different studies like X-ray diffraction, optical microscopy, scanning electron microscopy, and energy dispersive X-ray spectroscopy were carried out to ascertain the characteristics of the deposited layer on the work surface. These analyses confirmed the presence of the tool materials in the work surface layer. At first, an L-16 orthogonal array was applied as Taguchi DOE technique and the ANOVA was done to study the effects of pertinent process parameters. An optimum condition was achieved using overall evaluation criteria. Later on, a detailed study was carried out to get a smooth and regular deposition of material. The characterization of the deposited layer is presented. The deposited layer with a wide range of average layer thickness of 3?C785???m, enriched with tool materials (W and Cu) and with the formation of tungsten carbide, and having microhardness of 9.81?C12.75?GPa at the hardest zone was successfully deposited over the work surface.     

Template-free synthesis of flower-shaped zero-valent iron nanoparticle: Role of hydroxyl group in controlling morphology and nitrate reduction

Well-dispersed single phasic flower-like zero valent iron nanoparticles have been synthesized under aerobic conditions using a facile approach without the addition of any additives or templates. The role of hydroxyl groups of polyhydroxy alcohols in controlling surface morphology of nanoparticles has been thoroughly investigated. The obtained nanoparticles have been characterized by TEM, FE-SEM, XRD and BET surface area analyzer. Electron microscopy analyses reveal that the solvent plays a pivotal role in determining the morphology of the particles. With increase in viscosity of the solvent, formations of ‘petal-like’ structures, which are joined at the center are formed. The nitrate removal efficiency of the iron nanoparticles synthesized in different solvents has been studied and it is seen that the “flower-like” iron nanoparticles were most active in the removal of nitrate. Experiments have been done by varying (i) nitrate concentrations, (ii) nanoparticle dose, and (iii) type of nanoparticles. The results conclude that highest removal efficiency (～100%) was achieved when the nanoparticle dose was 2.88 g/L, even for high nitrate concentrations up to 400 mg/L. The major highlight of this work is the fact that even though the nanoparticles synthesized in glycerol-water mixture have larger size in comparison to the other nanoparticles, still they remove the nitrates with highest efficiency.”     

Unsteady natural convection in a triangular enclosure under isothermal heating

The fluid flow and heat transfer inside a triangular enclosure due to instantaneous heating on the inclined walls are investigated using an improved scaling analysis and direct numerical simulations. The development of the unsteady natural convection boundary layer under the inclined walls may be classified into three distinct stages including a start-up stage, a transitional stage and a steady state stage, which can be clearly identified in the analytical and numerical results. A new triple-layer integral approach of scaling analysis has been considered to obtain major scaling relations of the velocity, thicknesses, Nusselt number and the flow development time of the natural convection boundary layer and verified by direct numerical simulations over a wide range of flow parameters.     

Excimer laser ablation processed ferroelectric and antiferroelectric thin films

Abstract

The dielectric and electrical properties of excimer laser ablated processed paraelectric (Ba_0.5, Sr_0.5)TiO₃, ferroelectric Bi-layered SrBi₂(Ta_0.5Nb_0.5)₂O₉, and antiferroelectric (PbZrO₃) thin films have been investigated. The effect of processing parameters on the microstructure of the films and the functional properties has been presented in detail. Some of the recent studies of stress induced effects, dielectric, hysteresis and ac and dc electrical properties have been highlighted in conjunction with microstructures of the films.     

Synthesis,characterization and hydrogen adsorption properties of metal–organic framework Al-TCBPB

In this work, a new metal–organic framework (MOF) was synthesized by using a large organic ligand 1,3,5-tris[4′-carboxy(1,1′-biphenyl)-4-yl] benzene (abbreviated as TCBPB) and aluminum as the metal that forms the secondary building unit (SBU) by solvothermal method. The MOF, named as Al-TCBPB, was characterized with pore textural properties, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Raman and FT-IR spectroscopy. Hydrogen adsorption was measured volumetrically at ambient pressure and temperatures of 77, 88 and 298 K and at high pressure (up to 9 MPa) for temperatures 77 and 298 K. Pore textural properties revealed a high BET surface area of 2311 m²/g, narrow bimodal pore widths of 11.8 Å and 20 Å and a total pore volume of 0.80 cm³/g. PXRD identified the crystal structure as monoclinic with space group c2/m. This MOF adsorbs 1.53 and 0.83 wt.% of hydrogen at 77 and 88 K, respectively, and pressures up to ambient conditions. At higher pressure of 9 MPa, it demonstrated an excess adsorption of 4.8 and 1.4 wt.% at 77 and 298 K, respectively; these high-pressure data fit well with modified Dubinin–Astakov (D–A) analytical model. The heat of adsorption values of Al-TCBPB vary between 5.9 and 4.9 kJ/mol for the hydrogen adsorption loading of 0.1–0.8 wt.% and decreases monotonically to approximately 2 kJ/mol when the adsorption loading becomes 4.8 wt%.     

Tensor scale: An analytic approach with efficient computation and applications

Scale is a widely used notion in computer vision and image understanding that evolved in the form of scale-space theory where the key idea is to represent and analyze an image at various resolutions. Recently, we introduced a notion of local morphometric scale referred to as “tensor scale” using an ellipsoidal model that yields a unified representation of structure size, orientation and anisotropy. In the previous work, tensor scale was described using a 2-D algorithmic approach and a precise analytic definition was missing. Also, the application of tensor scale in 3-D using the previous framework is not practical due to high computational complexity. In this paper, an analytic definition of tensor scale is formulated for n-dimensional (n-D) images that captures local structure size, orientation and anisotropy. Also, an efficient computational solution in 2- and 3-D using several novel differential geometric approaches is presented and the accuracy of results is experimentally examined. Also, a matrix representation of tensor scale is derived facilitating several operations including tensor field smoothing to capture larger contextual knowledge. Finally, the applications of tensor scale in image filtering and n-linear interpolation are presented and the performance of their results is examined in comparison with respective state-of-art methods. Specifically, the performance of tensor scale based image filtering is compared with gradient and Weickert’s structure tensor based diffusive filtering algorithms. Also, the performance of tensor scale based n-linear interpolation is evaluated in comparison with standard n-linear and windowed-sinc interpolation methods.