Evaluation of Process Capability in Gas Carburizing Process to Achieve Quality through Limit Design Concept

NUMEROUS INDUSTRIAL APPLICATIONS requirea hard wear-resistant surface called the case,and arelatively soft,tough inside called the core.There arefive principal methods of case hardening:?Carburizing?Nitriding?Cyaniding or carbonitriding?Flame hardening?Induction hardeningThe first three methods change the chemicalcomposition,carburizing by the addition of carbon,nitriding by the addition of nitrogen and cyaniding bythe addition of both carbon and nitrogen.The last twomethods do n…     

Evaluation of Crop Models for Simulating and Optimizing Deficit Irrigation Systems in Arid and Semi-arid Countries Under Climate Variability

The variability of fresh water availability in arid and semi-arid countries poses a serious challenge to farmers to cope with when depending on irrigation for crop growing. This has shifted the focus onto improving irrigation management and water productivity (WP) through controlled deficit irrigation (DI). DI can be conceived as a strategy to deal with these challenges but more knowledge on risks and chances of this strategy is urgently needed. The availability of simulation models that can reliably predict crop yield under the influence of soil, atmosphere, irrigation, and agricultural management practices is a prerequisite for deriving reliable and effective deficit irrigation strategies. In this context, this article discusses the performance of the crop models CropWat, PILOTE, Daisy, and APSIM when being part of a stochastic simulation-based approach to improve WP by focusing primarily on the impact of climate variability. The stochastic framework consists of: (i) a weather generator for simulating regional impacts of climate variability; (ii) a tailor-made evolutionary optimization algorithm for optimal irrigation scheduling with limited water supply; and (iii) the above mentioned models for simulating water transport and crop growth in a sound manner. The results present stochastic crop water production functions (SCWPFs) that can be used as basic tools for assessing the impact on the risk for the potential yield due to water stress and climate variability. Example simulations from India, Malawi, France and Oman are presented and the suitability of these crop models to be employed in a framework for optimizing WP is evaluated.     

Thermal and structural properties of spray pyrolysed CdS thin film

Using photo acoustic technique, the thermal properties of CdS thin films grown by spray pyrolysis are measured. Thermal diffusivity and conductivity in these films decrease at least two orders compared with bulk. These results are compared with our study on nano CdS and the other available literature. The comparison is good. The dependence of thermal diffusivity on the thickness of the layer or the size of the particles on the glass substrate are analysed from the present measurement and discussed. The dependence of thermal diffusivity on the thickness of the layer on the glass substrate is discussed.     

Polyaniline–13X zeolite composite‐supported platinum electrocatalysts for direct methanol fuel cell applications

We successfully synthesized 13X zeolite using a hydrothermal method. Then, composites of polyaniline (PANI) with 13X zeolite and PANI–13X with platinum were prepared by chemical oxidative polymerization and chemical reduction, respectively. Field emission scanning electron microscopy, X‐ray diffraction, Raman spectroscopy and Brunauer–Emmett–Teller techniques were used to characterize the PANI–Pt and PANI–Pt–13X composites. Further, the electrocatalytic activity towards methanol oxidation of the synthesized catalysts was explored using cyclic voltammetry in 1 mol L^?1 CH₃OH + 0.5 mol L^?1 H₂SO₄ solution. From the obtained results, PANI–Pt–13X shows superior performance compared to PANI–Pt towards methanol oxidation and electrical conductivity. Hence, the 13X zeolite‐incorporated PANI–Pt composite could be an efficient catalyst for direct methanol fuel cell applications. © 2019 Society of Chemical Industry     

Optimization of supercritical CO2 extraction of dried Helichrysum italicum flowers by response surface methodology: GC-MS profiles of the extracts and essential oil

Supercritical (SC) CO₂ extraction of dried Immortelle flowers was performed at different process parameters. The optimal extraction conditions related to the yield were determined by response surface methodology (yield 4.09% at 20 MPa and 52°C). The extracts were analyzed by GC-MS and tremetone derivatives dominated: bitalin A, 12-acetoxytremetone, gnaphaliol, 1-[2-(2-methyl-2,3-dihydroxypropyl)-2,3-dihydro-1-benzofuran-5-yl]ethanone, isobutyl bitalin A, and 1-[2-(acetylprop-1-en-2-yl)-3-hydroxy-2,3-dihydro-1-benzofuran-5-yl]ethanone. Striking differences were found among the essential oil and SC-CO₂ extracts composition. The major oil compounds were γ-curcumene, α-pinene, β-selinene, α-selinene, and limonene. Mono- and sesquiterpene were found among minor constituents of the extracts. Neryl acetate was present in the extracts and essential oil.     

Moisture sorption isotherms of two varieties of millet

Moisture sorption isotherms of EX-BORNO and SOSAT C88 millets at temperature range of 30–70 °C and water activity range of 0.07–0.98 were determined using the static gravimetric method. The sorption isotherms of both millet varieties decreased with increasing temperature, exhibited type II behaviour according to BET classification and hysteresis having loop size increasing with increasing temperature. This is as a result of the hard nature of the millet varieties. The moisture sorption and the data fitted well with Modified Henderson, Modified Halsey, Modified Oswin and Modified GAB models. The constants of the equations used in fitting were determined by non-linear regression analysis when the models were compared using the standard error of estimate, mean relative percent deviation, fraction explained variation and residual plots. The Modified Oswin model gave the best fit for the whole set of data. The study has provided information and data useful in large scale drying and processing of millet which have remained at the traditional level despite the importance of the drought resistance crop in poverty alleviation.     

Synthesis and magnetic properties of nano-sized Cu0.5Ni0.5Fe2O4 via citrate and aloe vera: A comparative study

In this report, Cu_0.5Ni_0.5Fe₂O₄ nanoparticles were synthesized by solgel using eco-friendly aloe vera extract and citric acid separately and their properties were discussed. Structural, Functional, morphological, magnetic properties of the samples were characterized using X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Transmission electron microscopy (TEM), Vibrating sample magnetometer (VSM). XRD exhibited the samples have single phase cubic spinel with average size of 46.4?nm and 35.45?nm. Two characteristics bands of ferrite were confirmed by FTIR. TEM indicated different morphology of the samples with some regularity. VSM data showed that higher coercivity for the sample prepared by aloe vera extract.     

Analysis on Phase Transformation (ATP) Using Computational Thermal Principles (CTP)

COMPUTER ANALYSIS is a method of computerapplication that is expected by heat treaters to yieldbetter results.Quenching is an integral part of industrialheat treatment processes for steels and provides ameans by which the mechanical properties of steel canbe controlled.During quenching,the steel is typicallycooled from above the austenizing temperature anddepending upon the temperature field developed withinthe material,different types of phase transformationsoccur which result in variet…     

Supervised learning of large perceptual organization: graphspectral partitioning and learning automata

Perceptual organization offers an elegant framework to group low-level features that are likely to come from a single object. We offer a novel strategy to adapt this grouping process to objects in a domain. Given a set of training images of objects in context, the associated learning process decides on the relative importance of the basic salient relationships such as proximity, parallelness, continuity, junctions, and common region toward segregating the objects from the background. The parameters of the grouping process are cast as probabilistic specifications of Bayesian networks that need to be learned. This learning is accomplished using a team of stochastic automata in an N-player cooperative game framework. The grouping process, which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We statistically demonstrate the robust performance of the grouping and the learning frameworks on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to form large salient groups from a set of local relations, each defined over a small number of primitives