A comparative study of CO<Subscript>2</Subscript> and CH<Subscript>4</Subscript> adsorption using activated carbon prepared from pine cone by phosphoric acid activation

Adsorption of pure carbon dioxide and methane was examined on activated carbon prepared from pine cone by chemical activation with H₃PO₄ to determine the potential for the separation of CO₂ from CH₄. The prepared adsorbent was characterized by N₂ adsorption-desorption, elemental analysis, FTIR, SEM and TEM. The equilibrium adsorption of CO₂ and CH₄ on AC was determined at 298, 308 and 318 K and pressure range of 1–16 bar. The experimental data of both gases were analyzed using Langmuir and Freundlich models. For CO₂, the Langmuir isotherm presented a perfect fit, whereas the isotherm of CH₄ was well described by Freundlich model. The selectivity of CO₂ over CH₄ by AC (CO₂: CH₄=50: 50, 298K, 5 bar), predicted by ideal adsorbed solution theory (IAST) model, was achieved at 1.68. These data demonstrated that pine cone-based AC prepared in this study can be successfully used in separation of CO₂ from CH₄.     

Optical Pattern Switching in Semiconductor Microresonators as All‐Optical Switch

In this paper, we present a spatial perturbation method to control the optical patterns in semiconductor microresonators in the far‐field configuration. We propose a fast all‐optical switch which operates at a low light level. The switching beam controls the behavior of output beams with strong intensities. The method has been applied successfully to different optical patterns such as rolls, squares, and hexagons.     

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.     

Effect of additives on the degradation of acrylamide–acrylic acid copolymer in aqueous solution

This paper presents the results of a systematic study of the effect of different additives on the degradation of acrylamide–acrylic acid copolymers at high temperature. Under conditions of current applications in enhanced oil recovery (without oxygen and in the presence of ferrous ions), the effects of sodium azide, sequestrant DTPA, and isobutanol have been separately tested. It is shown that DTPA enhances degradation when ferrous ions are present. A new process of stabilization which may be efficient in most of the conditions is proposed.     

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.