International Journal of Engineering
Year:2024 | Volume:37 | Issue:9|Papers Count:15

Due to yearly uneven rainfall distribution, exsiccation of water availability, specifically during the summer in the agricultural field in Saurashtra, is a usual scenario. Saurashtra region's land formation contains Basalt rock. The area is categorized under the semi-arid region. Hence, the Groundwater condition was observed to be poor as the self-characteristic of land for water-storage capacity for the Basalt is negligible within its strata. Mining Engineering and Groundwater Improvement Techniques were incorporated to find a way out of water stress conditions. An experimental study was carried out for mass blasting with the permission of the Government body and an expert team of mining explosions at the study area within the catchment of check dams and river beds. A group containing five borewells was blasted at a 27, 18, and 9 m depth, respectively. In each borehole, 15 blasts of 2.76 unit explosion were exploded together by sealing a cover of sand over it. The impact assessment of 30 wells was monitored Post-blast within the range of 400m of the Bore Blasting Technique (BBT) performed. As a result, a rise in water level up to 19m has been recorded near the streamline of BBT performed. In addition, the movement of the water was diverted from its parental aquifer to the observed dug wells. Hence, it is concluded that channelizing the groundwater can be possible by incorporating BBT in the basalt region, and identical improvement can also possible interms of fractured basalt rock and availability of water in the basalt region.

To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member.

Membrane technology is known as one of the most efficient and extensive methods for oily wastewater treatment. In this research, polyvinyl chloride (PVC)-based mixed matrix membranes containing Uio66-NH2 nanoparticles and modified halloysite nanotubes (HNTs) were prepared using the phase inversion method. The synthesized membranes were characterized by field emission scanning electron microscope (FESEM), Fourier transform infrared (FTIR) and contact angle measurement analysis. Then, the effect of these nanoparticles was investigated for oil/water emulsion separation in the ultrafiltration process. To evaluate the prepared membranes, pure water flux, mean pore size, and oil separation ultrafiltration tests were performed. The results exhibited that addition of HNTs to the casting solution enhanced the pure water flux about up to 4 folds. Overall experimental results showed that due to the uniform distribution of halloysite nanotubes in sample 2, water contact angle decreased from 81° to 72°. UF results confirmed that sample 2 had the potential of rejecting 97% of sunflower oil.

The widespread availability of large language models (LLMs) has encouraged many individuals to explore chatbot development for their business needs. However, creating chatbots for handling sensitive information, like in healthcare, can be challenging. Mistakes made by these bots when extracting information or providing health recommendations can have serious consequences. When developing a chatbot for pharmacy recommendations, it is essential for the bot to effectively extract symptom-related information and other relevant patient data and then offer recommendations for actions or medications based on that information. In this study, we proposed a straightforward and effective approach that combines regular expression templates for information extraction with forward chaining for inference to create a pharmacy recommendation chatbot called SmartFarma. In scenarios like pharmacy recommendations, we will demonstrate that the use of regular expression templates is sufficient and produces better results than some machine learning methods. Additionally, by using regular expressions, SmartFarma can be developed with transparent data handling, allowing experts to trace, monitor, and evaluate its recommendations. This research primarily focuses on the extraction of patient information. Our model, as proposed, achieved an impressive score of 81.54%, outperforming both the Biomedical Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT) models.

Indonesia is an archipelagic country with two continents and an ocean, where it produces abundant marine products and the second largest marine source in the world, such as cuttlefish. Besides, it was for food, cuttlefish also produced shells as waste. Actually, this waste has been used for animal feed but in small scale. Others are still waste and cause pollution if not handled properly. Cuttlefish shell waste (CSW) is the basic ingredient for making hydroxyapatite (HA), while it was the main bone compound. Meanwhile, HA demand in the market is very high due to bone defects. The novelty of the research was to fulfil the increasing need for HA at low price and prevent air, water, and soil pollution by using circular economic principles (reused), and it has economic value. Synthesis HA was used microwave hydrothermal method. The CSW and coral were obtained from the pet market in Yogyakarta. They were crushed and calcined to the temperature of 900oC to obtain calcium oxide (CaO). Then, CaO was synthesized by microwave hydrothermal to obtain HA powder. Synthetic HA was characterized by the Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive X-ray (EDX), and thermogravimetric analysis (TGA). The Ca/P ratio of HA CSW and coral are 1.7 and 1.87, respectively. Moreover, coral HA has higher crystallinity than CSW (48%). The TGA shows that the highest weight loss occurred in HA CSW (16.57%). The conclusion is that both CSW and coral are raw materials used to produce HA.

Accurate crop classification is crucial for agricultural monitoring and decision-making. Remote sensing's ultimate goal is the precise extraction and classification of crops. Based on a cloud platform, the study area of Guntur district, Andhra Pradesh India, presents a multi-crop classification approach using Sentinel-2 satellite imagery and machine learning techniques. The study area encompasses a diverse agricultural region with three major crop types. After pre-processing, spectral and textural features were extracted. It compares the traditional four machine learning algorithms employed, adding the NDVI, NDBI, MNDWI, and BSI vegetation indices for multi-crop classification enhances accuracy, and offers diverse and complementary information. The overall classification accuracy achieved 95%, with individual crop accuracies ranging from 85 to 96%. The scalable and simple classification method proposed in this research gives full play to the advantages of cloud platforms in data and operation, and the traditional machine learning compared with other algorithms can effectively improve the classification accuracy, and individual areas of crop production are calculated. The results underscored the reliability of GEE-based crop mapping in the region, demonstrating a satisfactory level of classification accuracy, surpassing 97% across distinct time intervals in overall accuracy values, Kappa coefficients, and F1-Score.

This study introduces a biosensor employing a dielectric-modulated dual material gate tunnel field effect transistor (DM-DMG TFET) in 10 nanometer technology. To detect diverse bio molecules, the biosensor incorporates a nano gap cavity formed through gate overlap on the drain side. The variation in ambipolar current serves as the sensing parameter, influenced by altering the dielectric constants of immobilized bio molecules within the nano cavity. In this paper, the simulation results of biosensor employing a dielectric-modulated dual material gate tunnel field effect transistor with different dielectric constants imposed in nano cavity. A comprehensive examination of the biosensor's performance is conducted, exploring various positions and filling factors of bio molecules within the nano cavity region. This analysis involves a thorough investigation into the device's performance, considering a range of parameters such as drain current, electric field distribution, variations in surface potential, configurations of energy bands, behaviors of carrier concentration, and the Ion/Ioff ratio. The simulations are done using the Silvaco TCAD Atlas tool.

Today, with the growth of the population and increasing trend in the use of electrical energy, the importance of the reliability and stability of the power grid has increased. The ever-increasing development of the power grid and subsequent blackouts of the power grid can lead to serious problems in the daily life and economy of a country. In addition to economic damages, power losses in the power network can lead to dissatisfaction and decreased consumer confidence in the power grid. This research has been carried out to check the application of the genetic algorithm to calculate reliability indices including SAIFI, SAIDI, etc., and its impact on enhancing the reliability of the standard IEEE 33 and 69 bus distribution networks. Additionally, this study explores the GA effectiveness in minimizing both active and reactive power losses. The simulation results in MATLAB, show the constructive effect of applying the GA, shedding light on its potential to optimize the distribution network reliability and minimize power losses, offering valuable insights for power system optimization and reliability improvement.

A great deal of research has been conducted on the performance of granular columns under vertical loads. However, in some situations, the movement of the soil mass can lead to lateral deformations and, as a result, shear stresses in the soil and columns. The primary objective of the present study is to numerically investigate the shear performance of soft clay soil improved with a single Ordinary Granular Column (OGC) in the direct shear test using a hybrid Discrete Element-Finite Difference Method (DEM-FDM). The numerical modeling method was first validated by simulating a direct shear test conducted previously on a soft clay-OGC composite in the laboratory. Afterward, an extensive parametric study was conducted to determine how various factors affect the shear strength of clay-OGC composites. According to the results, increasing the area replacement ratio from 15 to 35% can increase the peak shear strength of clay-OGC composites in the direct shear test by up to two times, depending on the level of applied normal stress. The micro-scale results also indicated that the surface roughness of soil particles in OGC has a greater effect on the shear strength of clay-OGC composites than their angularity. Furthermore, the results showed that the equivalent friction angle of clay-OGC composites should be calculated based on the residual friction angle of granular soil used in OGC.

Wireless sensor networks contain of many sensors that can serve as powerful tools for data collection in environments. A key challenge in these networks is the limited lifetime of sensor batteries. Ideally, all nodes would exhaust their energy simultaneously or through regular scheduling, maximizing the lifetime. Consequently, the primary concern is achieving optimal energy utilization to extend the network's lifetime over a logical duration. Depleting the batteries of the sensors means stopping the operation of the network, because it is practically impossible to replace the batteries of thousands of nodes. To address this issue, the low energy adaptive clustering hierarchical (LEACH) protocol has been widely recognized as one of the prominent solutions for clustering WSNs. However, the random selection of cluster heads in each round under the LEACH protocol fails to guarantee proper convergence. To overcome this limitation, this paper proposes a refined approach by utilizing a genetic algorithm and a novel objective function that incorporates various factors, including energy level and distance. The algorithm employs chromosomes to represent CHs and facilitates the selection of cluster nodes. Notably, the proposed algorithm dynamically performs clustering, meaning that clustering is conducted iteratively, considering identifying dead nodes. By leveraging this approach, the algorithm significantly enhances the clustering quality, ultimately leading to an increased network lifetime. To validate its effectiveness, it is compared with LEACH, LEACH_E and LEACH_EX algorithms, demonstrating its superior capabilities. On average, the proposed algorithm has more alive nodes in the network, and the remaining energy is at least 11% higher than the best other algorithms.

Nickel (Ni) is an interesting candidate for corrosion protection of copper (Cu) due to its present passive area. Ni films with larger passive areas have better corrosion protection than those with smaller ones. In the present research, Ni films were produced over Cu. A barreling apparatus was employed to support the produced films in the sulphate solution. Various spinning speeds (0, 50, and 100 rpm) were used on the barrel while it was being processed. Several investigations were conducted, such as deposition rate, current efficiency, surface morphology, phase, film thickness, crystallographic orientation, and electrochemical properties. Increased spinning speed resulted in a decrease in the deposition rate, current efficiency, grain size, thickness, crystallite size, and exchange current density. Compared to a higher spinning speed, the decrease in spinning speed caused an increase in the oxygen content, surface roughness, and micro-strain. The higher speed of the barrel apparatus resulted in a lower corrosion rate Ni film of 0.147 mmpy. Moreover, the lower speed of the barrel apparatus resulted in a higher exchange current density Ni film of 0.997 A/cm².

Dedicating more attention to renewable energies and power electronic improvements results in increased direct current microgrids (DC MGs) application. However, DC MGs have some challenges with voltage adjustment and power sharing. To do so, a two-layer hierarchical control structure, including a new fully distributed secondary control strategy and conventional primary droop control method, is proposed and employed in this paper to share power and swiftly adjust the voltage accurately. Indeed, a distributed-averaging proportional-integral (DAPI) secondary control strategy is introduced. Another problem in DC MGs is the existence of constant power loads (CPLs), which may result in instability. To overcome the problems caused by CPLs, a term based on the output voltage of CPL is added to the proposed DAPI to prevent instability. The required control inputs are obtained using localized data of the DC bus and their neighbor’s secondary control inputs inspired by cooperative control. Besides, this strategy needs no knowledge of the microgrid topology, which enhances flexibility. For validating the proposed DAPI strategy in DC MGs, an islanded DC MG is simulated in the MATLAB/SIMULINK software. Comparing the results with those obtained from another existing method proves the performance of the proposed DAPI controller under different scenarios of plug-and-play, communication failure, and load changes.

This paper addresses the need for an efficient and adaptable approach to solve linear acoustic wave equations in the lattice Boltzmann method (LBM). A novel lattice-adaptive model is introduced, derived through a Chapman–Enskog analysis, which utilizes a single relationship for the equilibrium distribution function across all lattice structures. The intended derivation begins by considering a standard equilibrium distribution function with unknown coefficients. By selecting the displacement of the acoustic wave as the zero-order microscopic moment, accurate recovery of the macroscopic wave equation is ensured. Unlike existing methods, the model simplifies the complexity associated with equilibrium distribution functions and offers greater versatility. The model is validated through extensive benchmark testing on one and two-dimensional wave propagation problems. Results demonstrate excellent agreement with analytical solutions, with maximum root mean square errors of 10-3 (<0.1% error) and minimum errors of 10-6 (<0.0001% error), indicating high predictive accuracy (>99.9%). Additionally, the model exhibits second-order spatial accuracy, with the relative error norm E_2 displaying slopes close to 2, signifying a spatial accuracy of second order. The numerical simulations show a decrease in errors as the mesh size becomes more refined.

This Study is focused on suitability of industrial wastes in cement stabilized soil. The investigation is based on Unconfined compressive strength (UCS) tests. Experimental work is carried out to compare the UCS of cement stabilized soil specimens with different proportion of industrial wastes like Iron ore tailing, Quarry dust, Fly ash and Bagasse ash. The mix proportion is designed such that clay content is maintained at 10.5% for fine grained soil and density of 17.5 kN/m3. It is observed that the mix comprising industrial wastes and fiber have improved the mechanical properties compared to cement stabilized soil. Fiber addition has improved post peak behavior of soil specimen. The Scanning Electron Microscopy (SEM) microstructure images depict soil particle flocculation, leading to an increase in compressive strength and Energy Dispersive X-ray Spectroscopy (EDS) studies suggest the use of industrial wastes with natural soil helps in strengthening of soil cement stabilization, as well as to minimize the environmental pollution.

In this research, a new approach to safeguard against Loss of Field (LOF) in a synchronous generator, in the presence of a series compensation capacitor, is introduced. LOF can lead to detrimental effects on both the generator and the power network. Impedance-based negative and positive offset MHO relays are employed for the rapid recognition of LOE occurrences in the network. However, issues arise when a series compensation capacitor is integrated into the system, causing maloperation of the protection relays. The introduction of the series compensation capacitor alters the total impedance value, necessitating adjustments to the relay settings for precise functionality. The variation in the overall impedance of the system leads to the underreaching and overreaching of a relay. The study provides detailed explanations and comparisons of both types of MHO relays, along with their respective settings. A simulation is applied in DIGSILENT to assess the effect of series compensation capacitors on offset MHO (negative and positive) relay LOF protection. Ultimately, the article concludes by summarizing the pros and cons of both relay schemes.