One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.

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Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

This paper investigates the physical and mechanical properties of bighorns of Deccani breed sheep native from Karnataka, India. The exhaustive work comprises two cases. First, rehydrated (wet) and ambient (dry) conditions, and second, the horn coupons were selected for longitudinal and lateral (transverse) directions. More than seventy-two samples were subjected to a test for physical and mechanical property extraction. Further, twenty-four samples were subjected to physical property testing, which included density and moisture absorption tests. At the same time, mechanical testing included analysis of the stress state dependence with the horn keratin tested under tension, compression, and flexural loading. The mechanical properties include the elastic modulus, yield strength, ultimate strength, failure strain, compressive strength, flexural strength, flexural modulus, and hardness. The results showed anisotropy and depended highly on the presence of water content more than coupon orientation. Wet conditioned specimens had a significant loss in mechanical properties compared with dry specimens. The observed outcomes were shown at par with results for yield strength of 53.5 ± 6.5 MPa (which is better than its peers) and a maximum compressive stress of 557.7 ± 5 MPa (highest among peers). Young’s modulus 6.5 ± 0.5 GPa and a density equivalent to a biopolymer of 1.2 g/cc are expected to be the lightest among its peers; flexural strength 168.75 MPa, with lowest failure strain percentage of 6.5 ± 0.5 and Rockwell hardness value of 60 HRB, seem best in the class of this category. Simulation study identified a suitable application area based on impact and fatigue analysis. Overall, the exhaustive experimental work provided many opportunities to use this new material in various diversified applications in the future.

Investigation of Mechanical and Physical Properties of Big Sheep Horn as an Alternative Biomaterial for Structural Applications

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent.

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Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime

In this paper a novel ladder resonator is introduced to reduce mutual coupling effect in the patch antenna array structure. Applied patch antennas are operating at 2.45 GHz frequency, which specially used for MIMO (multiple input multiple output) systems. The edge-to-edge distance between two microstrip patch antennas is 0.05 λ. The proposed ladder resonator impressively blocks the surface current between two patch antennas at the operating frequency, which results in mutual effect reduction. The designed configuration has been analyzed, simulated and measured. Scattering parameters with and without of proposed resonator has been investigated. The result shows that, the proposed configuration increases isolation between two microstrip patch antennas about 44 dB. 

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Mutual Coupling Reduction in Microstrip Patch Antenna Arrays Using Simple Microstrip Resonator

Genetic algorithm is widely used in multi-objective mechanical structure optimization. In this paper, a genetic algorithm-based optimization method for ladle refractory lining structure is proposed. First, the parametric finite element model of the new ladle refractory lining is established by using ANSYS Workbench software. The refractory lining is mainly composed of insulating layer, permanent layer and working layer. Secondly, a mathematical model for multi-objective optimization is established to reveal the functional relationship between the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell, the total mass of the ladle and the structural parameters of the ladle refractory lining. Genetic algorithm translates the optimization process of ladle refractory lining into natural evolution and selection. The optimization results show that, compared with the unoptimized ladle refractory lining structure (insulation layer thickness of 0 mm, permanent layer thickness of 81 mm, and working layer thickness of 152 mm), the refractory lining with insulation layer thickness of 8.02 mm, permanent layer thickness of 76.20 mm, and working layer thickness of 148.61 mm has the best thermal insulation performance and longer service life within the variation of ladle refractory lining structure parameters. Finally, the results of the optimization are verified and analyzed in this paper. The study found that by optimizing the design of the ladle refractory lining, the maximum equivalent force on the ladle lining, the maximum temperature on the ladle shell and the ladle mass were reduced. The thermal insulation performance and the lightweight performance of the ladle are improved, which is very important for improving the service life of the ladle.

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Multi-Objective Optimization Design of Ladle Refractory Lining Based on Genetic Algorithm

Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.

Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas-Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries.

This study discusses the acoustic property variations by introducing new absorption materials. An experimental set up established to simulate virtually as well as experimentally with various biodegradable, biocompatible and naturally absorbable materials for a truck cabin of carrier capacity 3.5Tonnes. The materials were embedded on the sidewall and base of the cabin covering the entire cabin not leaving any air pockets. Experimental and simulation study was carried out for six materials and in the later part a combination of these materials also carried out to see through the possible optimal cases providing due Weightage to cost and weight. A sound quality index study being made to see with each case of material how the sound pressure level affects the rider and the subordinates seating in a cabin. A detailed simulation study carried out to understand the material behavioral characteristics along with convergence study using ANSYS Workbench. The exhaustive work emphasis on percentage error study with simulation and experimental work and proposes an optimal material from sound pressure level and rider comfort aspects.

A study of sound pressure level (SPL) inside the truck cabin for new acoustic materials: An experimental and FEA approach

BACKGROUND Occupational tasks involve awkward upper limb postures, especially movement of forearm with repetitive combined gripping and torqueing exertions, which may lead to development of WMSDs. From the literature survey it was observed that there was a lack of studies focussed on the combined effect of torque and grip exertions on forearm discomfort. OBJECTIVE The present study was to investigate the effects of grip force, stroke rotation and frequency of exertions on discomfort and Electromyography (EMG) activities of the forearm muscles in a repetitive torqueing task. METHOD Twenty-seven male participants volunteered in this study. The participants performed repetitive exertions for a 5 minutes duration for each combination of the different levels of stroke rotation, grip force and frequency of exertions. Three levels of stroke rotation, three levels of grip force and three levels of frequency of exertion were chosen as independent variables. Therefore a 3 × 3 customized factorial design was used for the experiment for each level of grip force. Hence, the study was divided into three groups on the basis of grip force (50N, 70N and 90N). RESULTS The ANOVA showed that stroke rotation and frequency of exertion were significant on discomfort. Further Students Newmann test (SNK) revealed that discomfort was increased with increasing stroke rotation and frequency of exertion. The multivariate analysis of variances (MANOVA) performed on EMG data instead of ANOVA because EMG activities of five muscles simultaneously were recorded. The Results found that extensor muscles were more fatigued in torqueing with gripping task. CONCLUSIONS It was found that stroke rotation for the torqueing tasks must be kept below 45°. It was concluded that it is important to control stroke rotation to improve performance of repetitive torqueing activity.

The effect of grip force, stroke rotation and frequency on discomfort for a torqueing tasks.

Astrocyte cells form the largest cell population in the brain and can influence neuron behavior. These cells provide appropriate feedback control in regulating neuronal activities in the Central Nervous System (CNS). This paper presents a set of equations as a model to describe the interactions between neurons and astrocyte. A VHDL–AMS-based tripartite synapse model that includes a pre-synaptic neuron, the synaptic terminal, a post-synaptic neuron, and an astrocyte cell is presented. In this model, the astrocyte acts as a controller module for neurons and can regulates the spiking activity of them. Simulation results show that by regulating the coupling coefficients of astrocytes, spiking frequency of neurons can be reduced and the activity of neuronal cells is modulated.

Controlling Effects of Astrocyte on Neuron Behavior in Tripartite Synapse Using VHDL–AMS

The best way to have a safer workplace without any injuries, illnesses, and incidents during the work is to identify, assess, and prevent risks of musculoskeletal disorders. The aim of the present study is to identify and assess the risk of work-related musculoskeletal disorders (WMSDs) among the workers who are working in sewing area of garment factory in KSA. Several visits to the factory were conducted to understand and identify the risks of WMSDs during work. A survey was developed on the observed risks of WMSDs and distributed amongst 39 workers. The results were analyzed, which showed that 84.6% of workers reported pain in their body parts. Most of the workers reported that upper back, lower back, and neck were highly affected body parts during sewing task. To prevent these hazards, suggestions were given to management which includes workplace re-designing and providing comfortable chairs/tables.

Farheen Bano

Papers

Application of Wavelet Feature Extraction and Artificial Neural Networks for Improving the Performance of Gas-Liquid Two-Phase Flow Meters Used in Oil and Petrochemical Industries.

A study of sound pressure level (SPL) inside the truck cabin for new acoustic materials: An experimental and FEA approach

The effect of grip force, stroke rotation and frequency on discomfort for a torqueing tasks.

Controlling Effects of Astrocyte on Neuron Behavior in Tripartite Synapse Using VHDL–AMS

Ergonomic Hazard Identification and Assessment of a Garment Factory in KSA—An Exploratory Study