Characteristic and Catalytic for Mordenite and Zsm-5 Reaction in Produce Hydrocarbons

Conversion of methanol to the use of the treated mordenite and ZSM-5 studied in this description. Mordenite catalyst activity which showed a decrease in activity without modified quickly. After hydrothermally dealuminated mordenite done and acidification with HCl, it turns out that longer life of the catalyst thus obtained. Furthermore treated mordenite showed high selectivity to olefin formation. Acidity is measured with predictably by Spectrophotometer Infra Red observations of pyridine adsorbed, also signal of the adsorbed NO. Number of sites was reduced in the presence of acid dealumination treatment. There is no longer detectable acidity found in mordenite which has dealuminated well. Pore volume measured by the adsorption of toluene. The better stability and selectivity of the catalyst in forming various olefins in the use of the treated mordenite discussed here in terms of acidity and shape selectivity

Selective hydrogenation of 1,5,9-cyclododecatriene in up- and down-flow fixed-bed reactors: experimental observations and modeling

The performance of trickle and flooded-bed reactors has been investigated and compared for an exothermic multi-step catalytic reaction. Selective hydrogenation of cyclododecatriene over Pd/Al2O3 has been studied in both up- and down-flow modes of operation in the same pilot reactor. In the down-flow mode, hot spots and runaway could not be avoided without diluting both catalyst bed and liquid reactant. With this diluted system, the up-flow reactor leads to a higher productivity and a much better selectivity. A non-isothermal plug-flow reactor model predicts the performances of the up-flow reactor satisfactorily, but is found to be unsuitable to the case of a trickle-bed reactor. In the latter case, the productivity was underestimated, when complete wetting of catalyst particles was assumed. On the other hand, when partial wetting effect was incorporated, the calculated selectivity was always much higher than that observed actually in a trickle bed, due to heterogeneities of liquid velocity and partial wetting (poorly irrigated zones

A 24-GHz SiGe Phased-Array Receiver—LO Phase-Shifting Approach

A local-oscillator phase-shifting approach is introduced to implement a fully integrated 24-GHz phased-array receiver using an SiGe technology. Sixteen phases of the local oscillator are generated in one oscillator core, resulting in a raw beam-forming accuracy of 4 bits. These phases are distributed to all eight receiving paths of the array by a symmetric network. The appropriate phase for each path is selected using high-frequency analog multiplexers. The raw beam-steering resolution of the array is better than 10 [degrees] for a forward-looking angle, while the array spatial selectivity, without any amplitude correction, is better than 20 dB. The overall gain of the array is 61 dB, while the array improves the input signal-to-noise ratio by 9 dB

Multi-electrode nerve cuff recording - model analysis of the effects of finite cuff length

The effect of finite cuff length on the signals recorded by electrodes at different positions along the nerve was analysed in a model study. Relations were derived using a one-dimensional model. These were evaluated in a more realistic axially symmetric 3D model. This evaluation indicated that the cuff appeared shorter because of edge effects at the beginning and end of the cuff. The method for velocity selective filtering introduced by Donaldson was subsequently analysed. In this method, velocity selective filtering is achieved by summing the signals of subsequent tripoles after applying time shifts tuned to a certain conduction velocity. It was also found that the optimum electrode distance for a given cuff length for maximum summed RMS of symmetrical tripoles in the cuff is larger than when evaluating peak-peak amplitudes of single fibre action potentials. Velocity selective filtering yields better selectivity when using symmetrical tripoles, but may yield larger signal RMS when using the wider asymmetrical tripoles, potentially allowing for shorter cuffs. It is speculated that application of a multi-electrode reference may improve velocity selectivity for asymmetrical tripoles

Why Are Deep Representations Good Perceptual Quality Features?

Recently, intermediate feature maps of pre-trained convolutional neural networks have shown significant perceptual quality improvements, when they are used in the loss function for training new networks. It is believed that these features are better at encoding the perceptual quality and provide more efficient representations of input images compared to other perceptual metrics such as SSIM and PSNR. However, there have been no systematic studies to determine the underlying reason. Due to the lack of such an analysis, it is not possible to evaluate the performance of a particular set of features or to improve the perceptual quality even more by carefully selecting a subset of features from a pre-trained CNN. This work shows that the capabilities of pre-trained deep CNN features in optimizing the perceptual quality are correlated with their success in capturing basic human visual perception characteristics. In particular, we focus our analysis on fundamental aspects of human perception, such as the contrast sensitivity and orientation selectivity. We introduce two new formulations to measure the frequency and orientation selectivity of the features learned by convolutional layers for evaluating deep features learned by widely-used deep CNNs such as VGG-16. We demonstrate that the pre-trained CNN features which receive higher scores are better at predicting human quality judgment. Furthermore, we show the possibility of using our method to select deep features to form a new loss function, which improves the image reconstruction quality for the well-known single-image super-resolution problem.Comment: To be presented at ECCV 202

Investigating the selectivity of weed harrowing with new methods

In six field experiments it was investigated whether row spacing, timing, direction and orientation of post-emergence weed harrowing in spring barley influenced the selectivity and whether it is important that increasing intensities of harrowing are generated either by increasing number of passes or increasing driving speed. Selectivity was defined as the relationship between crop burial in soil immediately after treatment and weed control. To estimate crop burial, digital image analysis was used in order to make the estimations objective. The study showed that narrow row spacing decreased selectivity in a late growth stage (21) whereas row spacing in the range of 5.3 cm to 24 cm had no effects in an early growth stage (12). Harrowing across rows decreased selectivity in one out of two experiments. Whether repeated passes with the harrowing were carried out in the same orientation along the rows or in alternative orientations forth and back was unimportant. There were indications that high driving speed decreases selectivity and that repeated passes with low driving speed are better than single treatments with high driving speed. Impacts on selectivity, however, were small and only significant at high degrees of weed control. Timing had no significant impact on selectivity

Adsorption-desorption noise can be used for improving selectivity

Small chemical sensors are subjected to adsorption-desorption fluctuations which usually considered as noise contaminating useful signal. Based on temporal properties of this noise, it is shown that it can be made useful if proper processed. Namely, the signal, which characterizes the total amount of adsorbed analyte, should be subjected to a kind of amplitude discrimination (or level crossing discrimination) with certain threshold. When the amount is equal or above the threshold, the result of discrimination is standard dc signal, otherwise it is zero. Analytes are applied at low concentration: the mean adsorbed amount is below the threshold. The threshold is achieved from time to time thanking to the fluctuations. The signal after discrimination is averaged over a time window and used as the output of the whole device. Selectivity of this device is compared with that of its primary adsorbing sites, based on explicit description of the threshold-crossing statistics. It is concluded that the whole sensor may have much better selectivity than do its individual adsorbing sites.Comment: 10 pages, 3 figures, 2 table

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

The application of a hybrid framework based on the combination, artificial neural network-genetic algorithm (ANN-GA), for n-thymol synthesis modeling and optimization has been developed. The effects of molar ratio propylene/cresol (X1), catalyst mass (X2) and temperature (X3) on n-thymol selectivity Y1 and m-cresol conversion Y2 were studied. A 3-8-2 ANN model was found to be very suitable for reaction modeling. The multiobjective optimization, led to optimal operating conditions (0.55 ≤X1≤0.77; 1.773 g ≤ X2 ≤1.86 g; 289.74 °C ≤ X3 ≤291.33 °C) representing good solutions for obtaining high n-thymol selectivity and high m-cresol conversion. This optimal zone corresponded to n-thymol selectivity and m-cresol conversion ranging respectively in the interval [79.3; 79.5]% and [13.4 %; 23.7]%. These results were better than those obtained with a sequential method based on experimental design for which, optimum conditions led to n-thymol selectivity and m-cresol conversion values respectively equal to 67%and 11%. The hybrid method ANN-GA showed its ability to solve complex problems with a good fitting