Effect of particle shape on methanol partial oxidation in a fixed bed using CFD reactor modeling

Particle shape is one of the most important parameters in the design and optimization of fixed-bed processes. To address the impact of particle shape on methanol partial oxidation to formaldehyde over molybdate catalyst, packings of spheres, cylinders, rings, and trilobes are numerically generated. The generated packings are used to carry out resolved particle Computational Fluid Dynamics (CFD) simulations under industrial conditions. Pressure drop, voidage and velocity profiles, radial heat transfer, and local and overall conversion and selectivity results are presented. Despite their lower particle surface area, lower particle effectiveness and more uneven flow distribution than trilobes, and lower overall heat transfer coefficient than cylinders, rings had the best conversion and selectivity due to their balance between the factors. Three longer tubes of rings, rings and cylinders, and rings and trilobes are simulated and show a small gain in selectivity for the rings and trilobes.     

Synthesis of modified catalyst and stabilization of CuO/NH4‐ZSM‐5 for conversion of methanol to gasoline

In this article, the catalytic conversion of methanol to gasoline range hydrocarbons has been studied over CuO/ NH₄‐ZSM‐5(3,5,7,9%) catalysts prepared via sono‐chemistry methods. In order to improve, copper oxide can be used as a booster on NH₄‐ZSM‐5 this catalyst property. Accordingly, the conversion process of Methanol to Gasoline (MTG) was conducted under a pressure of 1 atm and temperature of 400°C by a fixed‐bed reactor on copper oxide catalysts which were prepared based on synthetic NH₄‐ZSM‐5. The synthetic catalyst was investigated by such analyses as BET, XRD, FT‐IR, and SEM. Formation of copper oxide phase and proper distribution of copper oxide were proven on the basic level of using XRD analysis. BET analysis showed the reduction in catalyst level and SEM images depicted the proper distribution of particles. The present investigation is to study the effect of CuO loading on NH4‐ZSM‐5 support for conversion of methanol to gasoline range hydrocarbons. A series of CuO/ NH4‐ZSM‐5 catalysts were prepared, characterized, and experimented for their performance on methanol conversion and hydrocarbon yield.     

A review and critique on integrated production–distribution planning models and techniques

Optimisation modelling of integrated production–distribution (P–D) plans has raised significant interest among both researchers and practitioners over the past two decades. This paper provides the readers with a comprehensive review and critique on the current P–D planning and optimisation literature. We classify the published P–D planning models into seven categories based on their degree of complexity and hence capability in addressing real-life scenarios. Summary tables highlight the main characteristics of the selected models at each category. Next, the paper reclassifies and evaluates the proposed models based on the solution techniques used. Lastly, the unaddressed areas in the current literature are highlighted, important managerial implications are proposed and directions for future research in the area are suggested.     

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported.     

Verification of two machine learning approaches for cloud masking based on reflectance of channel IR3.9 using Meteosat Second Generation over Middle East maritime

ABSTRACT

Most cold channels of Meteosat Second Generation (MSG) satellites can distinguish between the sea and ice cloud tops, except for the IR3.9 channel because of the close reflectance and radiance values of the IR3.9 channel for maritime, low-level cloud and ice cloud tops. In this article, we introduce and evaluate two machine learning methods for cloud masking of Spinning Enhanced Visible and Infrared Imager (SEVIRI) images in the day and night that use the reflectance value of the IR3.9 channel. We reached a good correlation by comparing the results of the modelled cloud masking of Meteosat satellite images with MODIS (Moderate Resolution Imaging Spectroradiometer) and CLM (Cloud Mask product of EUMETSAT) images in a way that the coefficient of determination (R²) value was 92.34%, 89.91% and 83.69%, 78.23% in the cold season and 90.17%, 87.09% and 80.37%, 76.48% in the warm season, respectively, using the CHAID (chi-squared automatic interaction detection) decision tree and RBF (radial basis function) neural network approaches.     

Semisolid structure for M2 high speed steel prepared by cooling slope

Effects of cooling slope angle and the temperature of molten metal on the globular structure of M2 high speed steel after holding at the semisolid state have been investigated. The globular structure was achieved by pouring the molten metal at 1595 °C on the ceramic cooling slope with the length of 200 mm and the angle of 25°. The globular structure of M2 high speed steel in the form of rolled–annealed and as cast condition after holding at semisolid state has been achieved. The size of globular grains of cooling slope sample was smaller than that of the rolled–annealed and as cast samples. Solid particles of rolled–annealed sample after holding at semisolid state had better roundness compared with cooling slope sample. Dissolution of carbides in the austenite phase at grain boundaries leads to formation of globular particles in the semisolid state. MC-type and M₆C-type eutectic carbides reprecipitate during cooling cycle along grain boundaries.     

Synthesis and process modeling of mesoporous γ-Al2O3 granules using the biopolymer chitosan via experimental design

In this study, biopolymer chitosan is presented as a template for synthesizing and shaping the mesoporous γ-Al₂O₃ macrospheres. This porous γ-Al₂O₃ granule has a high surface area (310 m²/g), high pore volume (.6148 cm³/g), and pore diameter between 2 and 10 nm. The full factorial design based on a mathematical model was implemented to study the acid concentration, chitosan amount, ammonia concentration, and aging time affecting the responses (Brunauer–Emmett–Teller surface area and pore volume). Predicted responses were found to be in satisfactory agreement with experimental values (R² = .9580 and .9109, respectively). The adequacy of the model was examined by analyzing the residual distribution plots and Pareto graph. X-ray diffraction, scanning electron microscopy (SEM), thermogravimetric analysis, and N₂ adsorption/desorption techniques are employed to characterize the structure of the prepared γ-alumina sample.     

Design of low-power SAR ADCs using hybrid DACs

In the past decade, successive approximation register (SAR) analog-to-digital converter (ADC) has become a popular topology in a wide range of resolutions and sampling rates. This paper investigates methods to improve the energy-and-area efficiency of the SAR ADCs by focusing on the design of the internal digital-to-analog converter (DAC). Different hybrid resistive–capacitive DACs are studied in detail. It is shown that more than an order of magnitude improvement in energy efficiency of the DAC is achievable. The conditions for such an improvement are discussed.     

Prediction of hydrate equilibrium conditions using k-nearest neighbor algorithm to CO2 capture

Gas or clathrate hydrates are an important issue when they form in the oil and gas pipelines. Since the determination of the hydrate formation temperature and pressure is very difficult experimentally for every gas system and it is impossible in terms of cost and time approximately, mathematical models can be useful tools to overcome these difficulties. In this study, k-nearest neighbor model was used to predict the equilibrium conditions of hydrate formation in absorption and separation of carbon dioxide from flue gas mixture, containing carbon dioxide and nitrogen. At the training phase, temperature and composition data of nitrogen and carbon dioxide in the flue gas mixture at equilibrium conditions and the equilibrium pressures of hydrate formation were used as input and output, respectively. The error percentage less than 0.38% indicates the high accuracy of the proposed model. In this study, 80%, 85%, and 90% of the training data are examined for three numbers of nearest. For three numbers of used nearest (k = 1, k = 2 and k = 3), the value of k = 1 leads to the lowest error; so, it is selected as the best nearest in the presented model.     

Large deformation of thermally bonded random fibrous networks: microstructural changes and damage

A mechanical behaviour of random fibrous networks is predominantly governed by their microstructure. This study examines the effect of microstructure on macroscopic deformation and failure behaviour of random fibrous networks and its practical implication for optimisation of its structure by using finite-element simulations. A subroutine-based parametric modelling approach—a tool to develop and characterise random fibrous networks—is also presented. Here, a thermally bonded polypropylene nonwoven fabric is used as a model system. Its microstructure is incorporated into the model by explicit introduction of fibres according to their orientation distribution in the fabric. The model accounts for main deformation and damage mechanisms experimentally observed and provides the meso- and macro-level responses of the fabric. The suggested microstructure-based approach identifies and quantifies the spread of stresses and strains in fibres of the network as well as its structural evolution during deformation and damage. Its simulations also predict a continuous shift in the distribution of stresses due to structural evolution and progressive failure of fibres.