International Journal of Engineering
Volume:38 Issue: 1, Jan 2025

Microgrid, as a new structure of power systems, has challenges that should be addressed; regulating the frequency and voltage and power sharing are among the most critical challenges of AC microgrids (MGs). This paper studied a novel two-level control strategy composed of a modified droop controller at the primary level and a distributed finite-time (DFT) average controller in the secondary layer to achieve more precise active and reactive power sharing along with frequency and voltage alignment by implementing this strategy in an islanded AC MG. Thus, a typical AC MG, including three distributed generation (DG) units, is simulated in MATLAB/SIMULINK software, and the proposed method is first formulated, modeled, and then simulated. For validation and better comparison, the results obtained by the proposed method were compared to the conventional droop and other distributed methods in case studies of load changes and plugging in/out of DG units. The results proved that the proposed method appropriately shared active and reactive powers among DG units. It also efficiently restored the frequency and voltages of DG units to their nominal values. Moreover, a mathematical stability analysis is obtained that proved the stability of the proposed DFT-based secondary controller.

In the oil fields of Western Siberia, the average water cut has reached 80-95%, with a relatively low oil recovery factor (RF) of 38%. This paper proposes a comprehensive approach to solving the problem of water encroachment: identifying the cause of water cut, selecting a flow deviation technology (FDT) and its composition, selecting an affected area (isolating a zone), and calculating technological and economic efficiency. To determine the reason for the high water cut in a producing well, the Chan’s graph-analytical method was used. The FDT and chemical composition were selected based on the geological and physical characteristics of the production facility. The justification of the site for conducting the FDT was based on calculations and construction of the map of residual recoverable reserves of oil using the software package "RN-KIN". The assessment of technological efficiency was conducted according to the KogalymNIPIneft method and by building a hydrodynamic model using the modelling software tNavigator.

The field of autonomous vehicles (AV) has been the subject of extensive research in recent years. It is possible that AVs could contribute greatly to the quality of daily lives if they were implemented. A safe driver model that controls autonomous vehicles is required before this can be accomplished. Reinforcement Learning (RL) is one of the methods suitable for creating these models. In these circumstances, RL agents typically perform random actions during training, which poses a safety risk when driving an AV. To address this issue, shielding has been proposed. By predicting the future state after an action has been taken and determining whether the future state is safe, this shield determines whether the action is safe. For this purpose, reachable zonotopes must be provided, so that at each planning stage, the reachable set of vehicles does not intersect with any obstacles. To this end, we propose a Safe Reinforcement Learning by Shielding-based Reachable Zonotopes (SRLSRZ) approach. It is built around Twin Delayed DDPG (TD3) and compared with it. During training and execution, shielded systems have zero collision. their efficiency is similar to or even better than TD3. A shield-based learning approach is demonstrated to be effective in enabling the agent to learn not to propose unsafe actions. Simulated results indicate that a car vehicle with an unsafe set adjacent to the area that provides the greatest reward performs better when SRLSRZ is used as compared with other methods that are currently considered to be state-of-the-art for achieving safe RL.

Facial emotions are prime characteristics of humans that reflect the attributes of a human emotional condition. Facial Emotional Recognition (FER) is a significant research area since it has several significant applications such as crime detection, security, government sectors, surveillance, etc. Many researchers have addressed FER and put efforts into improving recognition using various methods such as Deep Convolutional Neural Networks (DCNN), Local Binary Pattern Convolutional Neural Networks (LBPCNN) and Micro Expression Recognition (MER). Nevertheless, there is a dearth of better Convolutional Neural Networks (CNN) for better accuracy of FER. Various methods have already proven their accuracies on datasets such as Fer_2013, CK48 and Legend. There are many challenges such as varying positions of images, illumination and noise etc. are not resolved yet. In this direction, the proposed modified CNN model has a combination of CNN layers to train the model and achieved better training and validation accuracy on datasets. The proposed method has three-fold contributions (1) to propose an efficient CNN model for FER and (2) to train the proposed model on three standard datasets 3) to test on real-time images. The proposed model is trained on three standard datasets (Fer_2013, CK48, and Legend) and achieves significantly higher training accuracy 92.34%, 99.84%, and 89.86%, respectively as compared to previous methods. Furthermore, the model demonstrates impressive generalization ability by achieving even higher test accuracy on real-world images 91.54%, 96.27%, and 87.47%, respectively. Also, the proposed novel CNN model has addressed all current challenges of FER with low computational complexity and prediction of facial expression more efficiently.

The demand for various types of zeolites in the oil and gas industry is associated with their high ion exchange and adsorption properties. This study is aimed to identify the regularities of the process of obtaining zeolite precursors from waste silica gel and industrial aluminium hydroxide since water glass and aluminate solution are the initial hydrogel components for the hydrothermal synthesis of zeolites. In addition, the process of hydrothermal synthesis of zeolites was investigated, namely, the effect of the molar ratio of SiO2:Al2O3 between hydrogel components on the structure and type of zeolite produced was determined. Identification of phases and study of the morphology of initial substances and obtained samples were carried out using X-ray diffraction and scanning electron microscopy methods. For the sample representing the monophase of LTA zeolite, the value of ion exchange capacity for Ca2+ ion and the particle size of the main fraction were determined to be 550 mEq/100g and less than 10 μm, respectively.

Peanut shell (PS) waste and plastic waste are abundant in Indonesia and have the potential to pollute the environment. The limited availability of fossil fuels also makes all parties look for other energy sources. This research aims to find a solution to this problem. The effect of adding low density polyethylene (LDPE), polypropylene (PP), and biodegradable plastic to briquettes made from peanut shell waste was investigated in this research. Briquettes were made using starch adhesive and a compaction pressure of 70 N/m2 using a laboratory scale piston. The compression test is essential because, during the stacking process, the briquettes must withstand loads from external pressure, which significantly influences the quality of the briquettes during storage and transportation. This research found that adding biodegradable plastic increased the modulus of elasticity and compressive strength of briquettes, namely 20 MPa and 4.45 MPa, compared to briquettes without plastic. The highest calorific value of briquettes in this study was obtained from the addition of PP plastic, amounting to 6185 cal/g. Based on the quality of the briquettes, the lowest moisture content was obtained from the addition of LDPE plastic at 7.71%, and the lowest ash content was obtained from the addition of biodegradable plastic at 4.01%, the lowest volatile content was obtained from the addition of PP plastic amounting to 15.71%. The most considerable fixed carbon content was obtained from adding PP plastic, amounting to 80.25%.

This paper introduces a novel approach that adeptly navigates this trade-off, significantly enhancing the deployment efficiency of remote healthcare systems. The existing methodologies in remote healthcare networks typically face challenges in balancing robust security measures with the need for high-speed data transmission. This model meticulously selects from a pool of encryption methods — AES, RSA, ECC, DSA, Blowfish, TwoFish — and hashing methods — Argon2, SHA1, SHA256, SHA512, MD5, Bcrypt — to tailor a solution that upholds high security while enhancing speed. The rationale behind employing GCN lies in its ability to efficiently handle the complex, non-linear relationships among different encryption and hashing techniques, while Deep Dyna Q Learning dynamically adjusts hyperparameters to optimize for speed without compromising security.The results were compelling, showcasing an 8.5% improvement in energy efficiency, a 4.9% increase in speed, an 8.3% rise in throughput, a 5.9% enhancement in packet delivery ratio, and a 3.9% boost in communication consistency compared to existing methods. Notably, this enhanced performance was maintained even under various security threats, including Sybil, masquerading, spoofing, and spying attacks.

This paper presents the problem of workover operations in the production wells. Liquid hydrocarbons recovery involves many operations, and production wells workover and servicing are essential parts of this process. Most of the well intervention operations require preliminary well killing that includes generating a hydrostatic back pressure with a column of fluid required density to eliminate any reservoir fluid inflow and gas surges. Preliminary well killing ensures safety of both crew members and production zone. This work focuses on a specialized additive for workover fluids developed by authors aiming to create and stabilize inverse emulsions. The results of conducted research have shown that the application of special additives not only significantly reduces clay swelling (from 25% to 3.1% in the case of a 5% NaCl solution) but also prevents sudden changes in wettability. These findings lead to conclusion that the use of special formulations results in preserving reservoir permeability by maintaining the size of pores and fractures. Simultaneously, it prevents the formation from a water blockage that could impact fluid flow paths. In case of using kill fluids based on inverse emulsions the permeability decreased 1.5 times less than in case of using water-based fluids.

The prediction of a Sudden Cardiac Death (SCD) long enough before its occurrence is vital for cases outside the hospital. This study investigate the effect of the simultaneous application of Electrocardiogram (ECG) and Heart Rate Variability (HRV) signals in the SCD prediction 60 minutes before its incidence. To do so, first, the SCD prediction was performed in each of the one-minute intervals by different groups of linear and nonlinear ECG and HRV features using the Support Vector Machine (SVM) classifier. The results showed that the best accuracy for SCD prediction was 91.23%. Next, all features were ranked locally in each of the one-minute intervals before the incidence of the death using the Minimum Redundancy and Maximum Relevancy (MRMR) method. Then, the SCD was predicted by applying four top local features from the ECG and HRV signals in each one-minute interval an hour before the death, showing a mean accuracy and sensitivity of 99% and 98.76%, respectively. Finally, by selecting the four most effective features according to the number of times they have been chosen in all one-minute intervals, the mean accuracy and sensitivity of SCD prediction were calculated at 96.15% and 95.07%, respectively. Additionally, since there is a similarity between the ECG signal of the pre-SCD and the Congestive Heart Failure (CHF), the classification of the Normal, CHF, and pre-SCD was performed, indicating a mean accuracy of 79.7%; it was also discovered that the Normal data could be separated from the SCD and CHF data with higher accuracy.

Several studies have been reported about the importance of excavator positioning on open pit. It is essential to study and evaluate the slope stability of non-metallic deposits to understand how various factors affect it. In this work, the relationship between slope stability and the positioning of excavators in non-metallic deposits (as sand and peat) was investigated in order to increase the safety and efficiency of excavators during excavation processes. A two-stage method is proposed. A model of excavator on the pit slope was designed and imported to ''Rocscience Slide 2D'' software with taking all slope parameters constant, except for the slope angle, excavator positioning on the slope and pressure of excavator on slope (NGP). After that the factor of safety value was collected using the Spencer Method. Moving forward with the Two-Level, Three-Factor Full Factorial Design at the Design Expert software to study the effect of each factor on the response variable (FoS), as well as the effects of interactions between factors.  The control of NGP value can reduce slope stability in average of 5 to 30% and slope angle can increase FoS by up to 5 to 15%. The study found that rational excavator positioning can significantly reduce the risk of slope failure and improve the overall productivity of the excavation process in non-metallic material deposits. Furthermore, the information obtained could be useful for the development of excavator artificial intelligence systems to control the excavator positioning on open-pit slope and minimize the effect of factor different factor on slope stability.

In this study, a comprehensive methodology for assessing water quality that integrates heavy metal pollution indices with advanced machine learning techniques was proposed, which includes Support Vector Machine (SVM), Decision Trees (DT), Random Forest, and XGBoostHcritical indicators of contamination of heavy metals. In this approach, models achieved exceptional accuracy rates. SVM demonstrates 88.57% accuracy, DT achieves 91.96% and Random Forest further enhances predictive capabilities with an accuracy of 94.11%. Notably, with an accuracy of 93.28%, XGBoost also makes a substantial contribution. This innovative approach enables real-time monitoring and proactive management of water resources, offering a robust tool for addressing environmental difficulties brought on by heavy metal pollution. By integrating machine learning algorithms, we provide insights into water quality dynamics, aiding in early detection and mitigation of contamination risks. Moreover, the inclusion of Random Forest enhances model robustness and adaptability across diverse environmental settings. This work underscores the importance of leveraging data-driven methodologies to safeguard environmental health and ensure sustainable water management practices. By combining indices with advanced machine learning techniques, we offer a scalable and effective solution for addressing water contamination challenges, thereby contributing to Improved efforts to protect the environment and better outcomes for public health.

The purpose of the study is aimed to determine the prospects for the development of operational control and monitoring systems, including a system to monitor the stress-strain state of a drill string. The communication channel between sensors installed in the drill string (DS) or bottom hole assembly (BHA) and wellhead is an integral part of a potential monitoring and operational control system. This article is an overview of scientific achievements in the field of communication channels used in drilling and downhole operations. Special attention is paid to fiber-optic telemetry, which is often not included in the classification of Measurement While Drilling (MWD) systems. Two main directions for the development of monitoring systems are identified. In the short term it is proposed to apply monitoring systems based on analytical models with a combination of indirect parameter analysis, still requiring high bandwidth communication channels, hence the relevance of hybrid channels. In the long term, it is proposed to use wired communication channels with a distributed network of sensors along the drill string. This paper can be useful for researchers in the field of communication channel development, as it reviews recent scientific achievements and identifies the main unsolved problems of the channels used in oil and gas field development.

The use of photovoltaic-static compensators (PV-STATCOM) to improve the system voltage profile has recently received much attention. This paper proposes coordinated control of multiple PV-STATCOM systems using fuzzy logic. The main feature of the proposed controller is that it can be operated in different modes: full-PV, partial-STATCOM and full-STATCOM in both capacitive and inductive compensation. In this strategy, when a disturbance occurs, depending on its severity, one or more PV-STATCOM systems go to the partial-STATCOM or full-STATCOM modes to optimize the reactive power compensation at the load bus. Then, after fixing the problem, the system returns to the full-PV power generation mode. To evaluate the system performance, different operating conditions were simulated with EMTDC/PSCAD software. The results confirm that the proposed fuzzy controller can improve the voltage profile dynamics.

The most significant change in the structure of Portland cement at temperatures above 110°C is a decrease in strength. There are several additives that enhance the cement's resistance to structural failure at high temperatures, such as metallurgical slags, quartz, and carbon nanomaterials. One of the emerging approaches is the utilization of mineral wool impregnated with carbon nanotubes. The relatively small particle size of mineral wool enables the filling of capillary micropores with carbon nanotubes, thereby increasing the overall structural integrity of the cement. This study investigates the impact of adding mineral wool saturated with carbon nanotubes (25%) to Saudi G-class cement. Six cement paste specimens were prepared with mineral wool contents ranging from 0 to 25%. The chemical composition of the cement was analyzed using X-ray fluorescence, the physical composition was evaluated via X-ray diffraction, testing of the cement slurry samples was conducted in accordance with ISO 10426-2:2003, tensile strength testing of the specimens was performed using the Brazilian method as per ASTM D 3967-08, and permeability testing was carried out according to ISO 10426-2:2003. The properties were assessed under elevated thermal conditions of 300°C. After curing for 7 and 28 days, these cement stones underwent testing over a temperature range from 25 to 300°C. The research findings indicate that the optimal content is 10-15% mineral wool containing 25% carbon nanotubes (resulting in a carbon nanotube concentration in the cement matrix of 2.5-3.75%). Results obtained after 28 days show high compressive strength values exceeding 50 MPa, which is 5-23% higher than that of the additive-free cement at 25°C and 5.7-6.2 times higher at 300°C. Additionally, tensile strength values are higher, being 1.4 times greater than the additive-free cement at 25°C and 3.7-3.9 times higher at 300°C. Moreover, low permeability values of the cementitious material were achieved, with permeability ranging from 2 to 3 mD*10-3 for mineral wool concentrations of 5 to 15%, representing a 2-2.5 times reduction compared to critical values, thereby ensuring good inter-column insulation.

Using externally bonded (EB) fibre-reinforced composites for the enhancement of reinforced concrete (RC) structures had been addressed in extensive research throughout recent years. Despite the numerous works on the flexural, shear, and axial strengthening of reinforced concrete members, there are not many works on the torsional strengthening. As a result, information about how reinforced RC members under torsion behave in EB composites tends to be rather limited. In this research, the torsional strengthening of RC beams using EB composites will be thoroughly analyzed and reviewed. After a thoroughly literature review, a database of experimental tests is established, containing beams made of EB Fiber Reinforced Polymer (FRP). To ascertain the effectiveness of the strengthening system, the geometric and mechanical features of RC beams, composite kinds, and casing configurations were assessed, along with the investigation of several failure modes for failure beams. It was concluded that, according to the experimental data, the torsional strength of RC beams can be improved by using FRCM composites and EB FRP. A torsional strength increase of the database beams ranged from 0% to 178%, with an average of 51%. Fully wrapped beams showed the greatest increase in torsional strength.

In the study of heat insulation properties of porous materials, simulation methods that account for both the structure of the porous material and the processes of heat and mass transfer occurring therein are widely employed. In this paper, the effective thermal conductivity of such a heterogeneous system is determined based on the methods of generalized conductivity and reduction to a unit cell. The analysis of heat and mass transfer processes in a multicomponent system is conducted by reducing it to a binary system comprising a solid skeleton and pore space. The thermal conductivity component within the pore space is dependent upon the temperature and structural characteristics of the material, the tortuosity of the pores, and the narrowing of the pore channels. This is typically characterized by a parameter known as the diffusion resistance factor, μ. This factor is an important characteristic of various heat-insulating and construction materials, and thus the development of engineering methods for calculating this parameter is a matter of urgency. In this study, a methodology for calculating μ is proposed. This methodology involves the use of a unit cell comprising a porous material with an interpenetrating structure. The unit cell is separated into two sections: one impermeable to diffusing vapor current streams (termed "adiabatic splitting") and the other perpendicular to vapor flow and exhibiting the same concentration value on its surface (termed "isothermal splitting"). The "combined" method of splitting, which employs sections of both types, yielded the most robust correlation with experimental data. Experimental studies of the thermal conductivity (λ) of wet porous materials have demonstrated that at a specific temperature, the value of λ is independent of moisture content. This indicates that in this instance, the thermal conductivity of the vapor-air mixture within the pore space is equivalent to the thermal conductivity of the liquid phase, particularly that of water.This fact enables the determination of μ for a porous material through the measurement of its thermal conductivity at varying humidity and temperature levels. The λ of the test samples was determined by means of a comparative λ-calorimeter, which has been specifically designed to study the heat-conducting properties of dispersed materials within the range from 0.04 to 80 W/(m·K). The practical consequence of the proposed studies is to guarantee energy savings and a reduction in heat losses when utilising or processing composite materials, which are multicomponent and inhomogeneous systems. Examples of such materials include composite building materials and oil-bearing rocks.

Early detection of skin lesions is essential for the success of treatment depending on the earliest possible detection of skin cancer lesions. Segmentation of skin cancer lesions is one of the most important early steps. In this regard, classic U-Net which is based on deep neural networks is the most popular architecture for medical image segmentation. However, the classic U-Net architecture lacks certain aspects. In this approach, we proposed a lightweight model designed to minimize memory usage in the deeper network layers and to reduce training and testing time. We achieved this by leveraging Multi-Level Blocks, which exclusively utilized 3x3 convolution operations. Additionally, we have utilized multiple convolutions to facilitate the transfer of information from the encoding to the decoding stage. This approach aims to minimize the semantic gap between the two stages. We have termed this information transfer path the encoder-decoder path. Our method has demonstrated outstanding performance in key metrics when tested on the PH2 dataset and has shown superior performance in terms of Accuracy and Jaccard Index on the ISIC-2017 dataset compared to the latest methods reported in existing publications. The Multi-Path U-Net method effectively recognizes and precisely segments complex features such as weak boundaries, shape, and color irregularities, and multi-part lesions with diverse color intensities.

The scope of use of solar power plants in the world is constantly expanding. Currently, significant progress is due to cheaper technologies and quality growth of it. This article presents justification for the possibility of efficient using of photovoltaic (PV) installations in oil industry for dewaxing of oil well. The study involves predicting the formation and melting of wax deposits over the course of a year, taking into account the electricity generated from the PV installation with optimal established parameters of the system. Based on the modeling results, the thickness of the paraffin wall did not exceed the permissible 9 mm with a tubing diameter of 63 mm when using only PV system as energy source. The achieved result made it possible to justify the possibility of an implementation of the PV system without additional sources of electricity (including energy storage systems). Such configuration is achieved by choosing the optimal angle of inclination of the PV panels of 60 , at which there is no downtime of the well. For the oil well flow rate from 80 t/s to 3 t/s, the power of the PV installation ranged from 40 kW to 90 kW. Such a concept will not be equally relevant to all parts of the world, but in general it will make a smooth transition to clean and safe energy. Introducing such autonomous systems is to increase the environmental safety and reliability of the energy sector with the aim of creating prerequisites for the implementation of sustainable development goals.

Wireless Sensor Networks (WSNs) serve as vital infrastructure across various domains; yet face escalating cyber threats that challenge their security and integrity. Current security approaches exhibit limitations, such as inadequate granularity in addressing node and cluster head vulnerabilities, and a lack of cohesive prevention-detection strategies. In response, this research proposes a novel two-phase security method tailored for WSNs. The first phase employs Convolutional Neural Networks (CNNs) optimized with the Emperor Penguin algorithm for precise intrusion detection at the node level. Enhanced data security is ensured through integration of the SHA256 hashing algorithm, bolstering both prevention and detection strategies. Subsequently, the second phase extends this approach to cluster heads, forming a cohesive security framework informed by the first phase's output. This comprehensive methodology not only addresses current challenges but also represents a significant leap forward in WSN security, promising robust protection against evolving threats. The obtained results indicate that the proposed method not only enhance the security of both nodes and clusterheads, but also integrating SHA256 can improve preventation without harming the detection phase.

Successes in the development of oil and gas accumulations in China's inland sedimentary basins - the Tarim, Junggarian and Sichuan basins - are attracting increasing attention to their thorough and consistent exploration. The most promising, along with traditional sites and accumulations in low-permeable "shale" strata, is the study of deep-seated complexes (with depths of more than 5 km), the oil and gas content of which is determined to a significant extent by specific geological conditions, which determined both the composition of organic matter and the primary conditions of formation of the reservoirs. One of the most diverse in terms of hydrocarbon accumulation conditions is the Junggarian basin in north-western China and specifically its pre-Lower Cretaceous formations with emphasis on the Lower Jurassic oil-and-gas bearing complex with oil-and-gas-maternal strata of high carbonaceous clayey sediments of the Badaowan Formation (Badaowan). A Detailed study of well sections located in the central part of the Junggarian basin (Che-Mogu anticlinal uplift, wells Y-1, S-1, Z-1) allowed to reconstruct the palaeogeographic setting, sedimentation conditions and reservoir distribution in the Lower Jurassic part of the section at great depths. Analyses of geological history of the basin, along with the rate of sedimentation, made it possible to identify promising zones of their preservation at great depths. Analysis of geochemical indicators (data from pyrolytic studies of organic matter and bitumoids) made it possible to clarify the prospects for oil and gas bearing capacity of the Central Depression and the Che-Mogu uplift of the Junggarian basin.

The construction of oil and gas wells involves well casing and annulus cementing. Cement stone undergoes dynamic loads during drilling, perforation or hydraulic fracturing of a productive formation, and corrosive leaching, exposure to rock pressure and temperature during operation. All this causes fractures and channels to form, through which formation fluids can migrate to upper or lower horizons, as well as to the wellhead. This paper studies a new composition of a cementing material with self-healing properties to control behind-the-casing flow by sealing the annulus and adding a water-swellable polymer (WSP) in a three-layer polyethylene glycol (PEG) shell to the cement slurry. In addition to self-healing of cement stone, the paper also considers an additive to increase the strength characteristics of cement stone with silica, which made it possible to compensate for the loss of stone strength after WSP addition. The findings of the study to determine the strength of cement stone samples, density, spread ability and setting time of cementing slurries with WSP are presented. In addition, a new method for encapsulating polymer particles in a water-soluble shell is presented.

Panic buying, characterized by consumers purchasing unusually large quantities of products in response to disasters, perceived threats, or anticipated price raises or shortages, remains a multifaceted phenomenon requiring further investigation. The COVID-19 crisis has provided a unique opportunity to conduct thorough analyses of panic buying behavior in a real-world context. Furthermore, the pandemic has underscored the importance of understanding panic buying dynamics, given its significant impact on consumer behavior and supply chain resilience. While many studies have concentrated on the psychological aspects of this phenomenon, there exists a gap in exploring its impact on products and goods. Therefore, there is a critical need to examine its effects across various product categories. In this study, we employed innovative topic modeling techniques to examine panic buying behavior and its implications during the COVID-19 crisis. Leveraging data from the X platform, our study adopts a novel approach integrating Sentence-BERT and BERTopic methodologies to identify key topics across diverse product categories. By providing insights into the outcomes of panic buying, this study contributes to a more comprehensive understanding of consumer behavior during crisis. Moreover, our findings hold considerable significance for policymakers and supply chain managers, offering insights to develop targeted interventions aimed at mitigating the impact of panic buying on supply chains and ensuring efficient resource allocation during future crises.