Measuring volume fractions of a three-phase flow without separation utilizing an approach based on artificial intelligence and capacitive sensors.

Many different kind of fluids in a wide variety of industries exist, such as two-phase and three-phase. Various combinations of them can be expected and gas-oil-water is one of the most common flows. Measuring the volume fraction of phases without separation is vital in many aspects, one of which is financial issues. Many methods are utilized to ascertain the volumetric proportion of each phase. Sensors based on measuring capacity are so popular because this kind of sensor operates seamlessly and autonomously without necessitating any form of segregation or disruption for measuring in the process. Besides, at the present moment, Artificial intelligence (AI) can be nominated as the most useful tool in several fields, and metering is no exception. Also, three main type of regimes can be found which are annular, stratified, and homogeneous. In this paper, volume fractions in a gas-oil-water three-phase homogeneous regime are measured. To accomplish this objective, an Artificial Neural Network (ANN) and a capacitance-based sensor are utilized. To train the presented network, an optimized sensor was implemented in the COMSOL Multiphysics software and after doing a lot of simulations, 231 different data are produced. Among all obtained results, 70 percent of them (161 data) are awarded to the train data, and the rest of them (70 data) are considered for the test data. This investigation proposes a new intelligent metering system based on the Multilayer Perceptron network (MLP) that can estimate a three-phase water-oil-gas fluid's water volume fraction precisely with a very low error. The obtained Mean Absolute Error (MAE) is equal to 1.66. This dedicates the presented predicting method's considerable accuracy. Moreover, this study was confined to homogeneous regime and cannot measure void fractions of other fluid types and this can be considered for future works. Besides, temperature and pressure changes which highly temper relative permittivity and density of the liquid inside the pipe can be considered for another future idea.

Application of frequency characteristic extraction in increasing the accuracy of X-ray based thickness gauges used for aluminum alloys employing GMDH neural network.

Radiation-based gauges have been widely utilized in the industry as a dependable, non-destructive method of measuring metal layer thickness. It is only possible to trust the conventional radiation thickness meter when the material's composition is known in advance. Thickness measurement errors are to be anticipated in contexts like rolled metal factories, where the real component of the material could diverge greatly from the stated composition. An X-ray-based device was suggested in this study to measure aluminum sheet thickness and identify the type of its alloys. Transmission and backscattered X-ray energy were recorded using two sodium iodide detectors while a 150 kV X-ray tube in the described detection system was operated. Aluminum layers of varying thicknesses (2-45 mm) and alloys (1050, 3105, 5052, and 6061) were simulated to be placed between the X-ray source and the transmission detector. The development of radiation-based systems used the MCNP code as a very powerful framework to imitate the detecting architecture and the spectra acquired by the detectors. The recorded signals were transferred to the frequency domain using the Fourier transform, and the frequency characteristics were extracted from them. Two GMDH neural networks were trained using these characteristics: one to identify the alloy type and another to determine the aluminum layer's thickness. The classifier network had a 92.2% success rate in identifying the alloy type, while the predictive network had a 1.9% error rate in determining the thickness of the aluminum layer. By extracting important characteristics and using powerful neural networks, this study was able to improve the precision with which aluminum layer thickness was measured and correctly identify the alloy type. The suggested method is used to determine the thickness of aluminum and its alloy sheets and may also be applied to other metals.

Acoustic Wave Reflection in Water Affects Underwater Wireless Sensor Networks.

The phenomenon of acoustic wave reflection off fluid-solid surfaces is the focus of this research. This research aims to measure the effect of material physical qualities on oblique incidence acoustic attenuation across a large frequency range. To construct the extensive comparison shown in the supporting documentation, reflection coefficient curves were generated by carefully adjusting the porousness and permeability of the poroelastic solid. The next stage in determining its acoustic response is to determine the pseudo-Brewster angle shift and the reflection coefficient minimum dip for the previously indicated attenuation permutations. This circumstance is made possible by modeling and studying the reflection and absorption of acoustic plane waves encountering half-space and two-layer surfaces. For this purpose, both viscous and thermal losses are taken into account. According to the research findings, the propagation medium has a significant impact on the form of the curve that represents the reflection coefficient, whereas the effects of permeability, porosity, and driving frequency are relatively less significant to the pseudo-Brewster angle and curve minima, respectively. This research additionally found that as permeability and porosity increase, the pseudo-Brewster angle shifts to the left (proportionally to porosity increase) until it reaches a limiting value of 73.4 degrees, and that the reflection coefficient curves for each level of porosity exhibit a greater angular dependence, with an overall decrease in magnitude at all incident angles. These findings are given within the framework of the investigation (in proportion to the increase in porosity). The study concluded that when permeability declined, the angular dependence of frequency-dependent attenuation reduced, resulting in iso-porous curves. The study also discovered that the matrix porosity largely affected the angular dependency of the viscous losses in the range of 1.4 × 10-14 m2 permeability.

Multiple Site Dissimilarities of Herbaceous Species Due to Coal Fly Ash Dumping Based Soil Heavy Metal Toxication.

The present study analyzes the determinants and patterns of the regional, local, and differential plant diversity of two different sites with similar climatic but varied edaphic factors. This research was undertaken to study the plant diversity and population structure as a consequence of variation in the soil quality between two biotopes: Guru Ghasidas Vishwavidyalaya in Koni (site-I) and National Thermal Power Corporation in Sipat (site-II). The soil of site-I was found to be fertile and showed rich vegetation. On the other hand, the soil of site II was found to be contaminated with heavy metals, which impacts the flora of the region. The ecology of both sites was studied, and their quantitative and qualitative aspects were compared and contrasted. The abundance, density, and richness of the plants in site II were fairly lower than in site-I, which was confirmed by utilizing Simpson's and Shannon's diversity indices. Many of the species collected from site II were heavy metal accumulators and could also serve as indicators of heavy metal toxicity.

Structural and Functional Characterization of Novel Phosphotyrosine Phosphatase Protein from Drosophila melanogaster (Pupal Retina).

A novel pair of protein tyrosine phosphatases in Drosophila melanogaster (pupal retina) has been identified. Phosphotyrosyl protein phosphatases (PTPs) are structurally diverse enzymes increasingly recognized as having a fundamental role in cellular processes including effects on metabolism, cell proliferation, and differentiation. This study presents identification of novel sequences of PTPs and their comparative homology modeling from Drosophila melanogaster (Dr-PTPs) and complexation with the potent inhibitor HEPES. The 3D structure was predicted based on sequence homology with bovine heart low molecular weight PTPs (Bh-PTPs). The sequence homologies are approximately 50% identical to each other and to low molecular weight protein tyrosine phosphatases (PTPs) in other species. Comparison of the 3D structures of Bh-PTPs and Dr-PTPs (primo-2) reveals a remarkable similarity having a four stranded central parallel ß sheet with flanking α helices on both sides, showing two right handed ß-α-ß motifs. The inhibitor shows similar binding features as seen in other PTPs. The study also highlights the key catalytic residues important for target recognition and PTPs' activation. The structure guided studies of both proteins clearly reveal a common mechanism of action and inhibitor binding at the active site and will be expected to contribute toward the basic understanding of functional association of this enzyme with other molecules.