Biochar supported manganese based catalyst for low-temperature selective catalytic reduction of nitric oxide

Clean Technologies and Environmental Policy - This research work aims to study the performance of biochar-supported manganese-based catalysts for conversion of NOx in the selective catalytic...     

Prediction of critical total drawdown in sand production from gas wells: Machine learning approach

Sand production is a critical issue in petroleum wells. The critical total drawdown (CTD) is an essential indicator of the onset of sand production. Although some models are available for CTD prediction, most of them are proven to lack accuracy or use commercial software. Furthermore, the previous correlations have not studied the trend analysis to verify the correct relationships between the parameters. Therefore, this study aims to build accurate and robust models for predicting CTD using response surface methodology (RSM) and support vector machine (SVM). The RSM is utilized to obtain the equation without using any software. The SVM model is an alternative method to predict the CTD with higher accuracy. This study used 23 datasets to develop the proposed models. The CTD is a strong function of the total vertical depth, cohesive strength, effective overburden vertical stress, and transit time with correlation coefficients (R) of 0.968, 0.963, 0.918, and −0.813. Different statistical methods, that is, analysis of variance (ANOVA), F-statistics test, fit statistics, and diagnostics plots, have shown that the RSM correlation has high accuracy and is more robust than correlations reported in the literature. Moreover, trend analysis has proven that the proposed models ideally follow the correct trend. The RSM correlation decreased the average absolute percent relative error (AAPRE) by 12.7% compared to all published correlations' AAPRE of 22.6%–30.4%. The SVM model has shown the lowest AAPRE of 6.1%, with the highest R of 0.995. The effects of all independent variables on the CTD are displayed in three-dimensional plots and showed significant interactions.     

Material Advancements in Fabrication of Mixed‐Matrix Membranes

The mixed‐matrix membrane (MMM) is a new membrane material for gas separation and plays a vital role for the advancement of current membrane‐based separation technology. Blending between inorganic fillers like carbon molecular sieves, zeolite, metal oxides, silica and silica nanoparticles, carbon nanotubes, zeolitic imidazolate framework, metal organic framework, and glassy and rubbery polymers etc. is possible. Due to mechanical, thermal, and chemical stability, these membranes achieve high permeability and selectivity as compared to pure polymeric materials. Despite of these advantages, the MMM performances are still below industrial expectations because of membrane defects and related processing problems as well as the nonuniform dispersion of fillers in MMMs. Material selection for organic and inorganic phases, preparation techniques, material advancements, and performance of MMMs are discussed. Issues and challenges faced during MMM synthesis as well as problem solutions are highlighted.     

Structural, physical, magnetic and electrical properties of La-substituted W-type hexagonal ferrites

Lanthanum doped W-type hexaferrites BaZn₂La_xFe_16−xO₂₇ (x = 0, 0.2, 0.4, 0.6, 0.8, 1.0) were synthesized by co-precipitation and sintered at 1320 °C. The X-ray diffraction reveals W-type hexagonal structure with few traces of secondary phase. The decrease in grain size as a function of La-concentration is attributed to the fact that La acts as a grain inhibitor. The saturation magnetization and remanance decrease due to spin canting on B-sites. The increase in coercivity follows 1/r behavior where r is the radius of grain. The DC resistivity was observed to increase from 0.59 × 10⁷ to 8.42 × 10⁷ Ω cm with increasing La-contents due to the unavailability of Fe³⁺ ions. This enhancement in resistivity makes these materials promising candidates for use at high frequencies in order to reduce eddy current losses.     

Effect of nano-silica on the chemical durability and mechanical performance of fly ash based geopolymer concrete

In this study, the effect of nano silica on the short term severe durability performance of fly ash based geopolymer concrete (GPC) specimens was investigated. Four types of GPC were produced with two types of low calcium fly ashes (FAI and FAII) with and without nano silica, and ordinary Portland cement concrete (OPC) concrete was also cast for reference. For the geopolymerization process, the alkaline activator has selected a mixture of sodium silicate solution (Na₂SiO₃) and sodium hydroxide solution (NaOH) with a ratio (Na₂SiO₃/ NaOH) of 2.5. Main objectives of the study were to investigate the effect of usability or replaceability of nano silica-based low calcium fly ash based geopolymer concretes instead of OPC concrete in structural applications and make a contribution to standardization process of the fly ash based geopolymer concrete. To achieve the goals, four types of geopolymer and OPC concretes were subjected to sulfuric acid (H₂SO₄), magnesium sulfate (MgSO₄) and seawater (NaCl) solutions with concentrations of 5%, 5%, and 3.5%, respectively. Visual appearances and weight changes of the concretes under chemical environments were utilized for durability aspects. Compressive, splitting tensile and flexural strength tests were also performed on specimens to evaluate the mechanical performance under chemical environments. Results indicated that FAGPC concretes showed superior performance than OPC concrete under chemical attacks due to low calcium content. Amongst the chemical environments, sulfuric acid (H₂SO₄) was found to be the most dangerous environment for all concrete types. In addition, nano silica (NS) addition to FAGPC specimens improved both durability and residual mechanical strength due to the lower porosity and more dense structure. The FAIIGPC specimens including nano silica showed the superior mechanical performance under chemical environment.     

Cationic polymerization of high-molecular-weight phthalaldehyde-butanal copolymer

Low ceiling temperature polyaldehydes are of interest for transient materials because the temperature of depolymerization can be at or below room temperature. There is interest in expanding the number of aldehydes which can be copolymerized so as to change the vapor pressure and other properties of the depolymerized products. Although fast depolymerization has been achieved with polyaldehydes, the rate of monomer evaporation after depolymerization can be controlled by incorporating lower molecular weight monomers into the polymer. High vapor pressure aliphatic aldehydes have been copolymerized with low vapor pressure and high reactivity phthalaldehyde to create stable, high molecular weight polymers with high vapor pressure. A method for measuring the depolymerization time by quartz crystal microbalance has been developed. The copolymer of phthalaldehyde and butanal improves the evaporation time for the polymer by a factor of 11. The onset of thermal decomposition of the copolymer was increased from 107 °C for the phthalaldehyde homopolymer to 141 °C for the copolymer. The tensile strength of the copolymer was 0.8–1.6 GPa. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 46921.     

Various Techniques for Preparation of Thin‐Film Composite Mixed‐Matrix Membranes for CO2 Separation

The application of thin‐film composite mixed‐matrix membranes (TFC‐MMMs) for gas separation is widely considered as an efficient separation technology. The principal methods for the preparation of TFC‐MMMs are dip‐coating, phase inversion, and interfacial polymerization comprising different types of support layers. These methods influence the CO₂ permeation over the selective and support layers. A comprehensive review is provided for capturing new details of progress achieved in developing TFC‐MMMs with detailed performance of gas separation in the previous few years. Various preparation techniques of TFC‐MMMs and their effect on the gas separation performance of the prepared membranes are described.     

Modelling of a new solar air heater through least-squares support vector machines

This paper reports on a modelling study of new solar air heater (SAH) system efficiency by using least-squares support vector machine (LS-SVM) method. In this study, a device for inserting an absorbing plate made of aluminium cans into the double-pass channel in a flat-plate SAH. A SAH system is a multi-variable system that is hard to model by conventional methods. As regards the LS-SVM, it has a superior capability for generalization, and this capability is independent on the dimensionality of the input data. In this study, a LS-SVM based method was intended to adopt SAH system for efficient modelling. For modelling, different mass flow rates in flow duct and collector types are used and then for obtaining the optimum LS-SVM parameters, such as regularization parameter, and optimum kernel function and parameters, several tests have been carried out. The performance of the proposed methodology was evaluated by using several statistical validation parameters. It is found that root mean squared error (RMSE) value is 0.0024, the coefficient of multiple determinations (R²) value is 0.9997 and coefficient of variation (cov) value is 2.1194 for the proposed radial basis function (RBF)-kernel LS-SVM method at 0.03 kg/s air mass flow rate. It is found that RMSE value is 0.0135, R² value is 0.9991 and cov value is 2.9868 for the proposed RBF-kernel LS-SVM method at 0.05 kg/s air mass flow rate. Comparison between predicted and experimental results indicates that the proposed LS-SVM model can be used for estimating the efficiency of SAHs with reasonable accuracy.     

Group sequential testing of homogeneity in genetic linkage analysis

Human genetic linkage studies have the objective of testing whether disease genes are linked to genetic markers based on family genetic data. Sometimes, these studies require many years of recruiting informative families and large amount of funds. One way to reduce the required sample size for such studies is to use sequential testing procedures. In this paper, we investigate two group sequential tests for homogeneity in binomial mixture models that are commonly used in genetic linkage analysis. We conduct Monte Carlo simulations to examine the performance of the group sequential procedures. The results show that the proposed group sequential procedures can save, on average, substantial sample size and detect linkage with almost the same power as their nonsequential counterparts.