International Journal of Engineering
Volume:37 Issue: 8, Aug 2024

Grid-tied Neutral-Point-Clamped (NPC) inverters have been widely used in various applications recently. A superior control method is required to achieve the desired performance of a grid-connected NPC inverter. Accordingly, a second-order sliding mode control (SOSMC) method, which is designed in the stationary frame, is utilized for achieving this aim in this paper. The super-twisting second-order sliding mode control is used for solving the chattering problem of conventional first-order sliding mode control (FOSMC). In comparison to control methods which are applied in rotation frame, this method is not required transformation from rotating frame to a-b-c frame and vice versa and decoupling of d and q components. The Lyapunov stability analysis is used for designing of this controller. The performance of proposed method is evaluated by simulation results implemented in MATLAB/Simulink software. The performance of the SOSMC is also compared with FOSMC. The results show that the incorporation of SOSMC can improve current reference tracking of the NPC inverter in different scenarios.

This study addresses interceptor devices to improve the hydrodynamic characteristic of autonomous unmanned surface vehicle (USV). The bulb and rectangular interceptors had molded on a planning hull. This research aims to mitigate drag impact on rectangular interceptors at high speeds. According to this study, a bulb interceptor had a better impact than a rectangular interceptor. This research is based on the finite volume method (FVM) with dynamic fluid-body interaction (DFBI), which captured the ship’s dynamic trim and sinkage. The simulation used an overset mesh technique with two domains as a donor-acceptor cell. Furthermore, numerical calculations using the Reynolds-Averaged Navier-Stokes equation and the k-turbulence model predict the turbulent flow. Grid independence studies and international towing tank conference (ITTC) recommendations have been applied to ensure simulation accuracy. This study reported that the bulb interceptor had effectiveness between 9%-25% compared to the rectangular interceptor at high speed. This research showed that the bulb interceptor had better effectiveness than the rectangular interceptor.

Electric vehicles (EVs) have become a vital solution for environmental transportation; however, challenges related to battery life and power density persist. In pursuit of enhanced EV performance and cost-effectiveness, researchers advocate for Hybrid Energy Storage Systems (HESS), integrating various Energy Storage Systems (ESS). An efficient Energy Management Strategy (EMS) is crucial for optimal power distribution within the HESS. This study introduces a real-time, simple, and practical EMS using a low-pass filter (LPF). However, the LPF lacks State of Charge (SoC) control, necessitating the addition of a SoC Limiter. The static SoC Limiter, while effective, faces challenges in predicting peak loads, leading to suboptimal power-sharing performance. To address this limitation, LPF with Adaptive SoC Limiter (LPF-ASL) is proposed. The LPF-ASL accommodates the peak load by saving some portion of supercapacitor (SC) power for peak load. In an unpredictable initial SC SoC test, LPF-ASL achieves substantial reductions in maximum battery current compared to LPF and Fuzzy Logic Control (FLC) by up to 21.30% and 21.14%, respectively. This underscores the effectiveness of LPF-ASL in optimizing battery life and enhancing power distribution within HESS-equipped EVs.

Oil spills pose significant environmental, ecological, and economic challenges worldwide. Since current remediation technologies proved inefficient in restoring marine ecosystems, this study adopted a straightforward and economical method of utilizing iron oxide magnetic nanoparticles. Maghemite (γ-Fe2O3) magnetic nanoparticles (MNPs) were synthesized using a homogeneous co-precipitation method. The colloidal dispersibility of MNPs was enhanced by applying an ethylene diamine tetra-acetic acid (EDTA) coating, resulting in a reduction of high surface energy and a subsequent decrease in nanoparticle agglomeration. MNPs were characterized using X-ray diffraction (XRD), Fourier transform infrared Spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and vibrating sample magnetometer (VSM). Removal experiments occurred at 25°C with a mass range of adsorbent (0.02-0.06 g). Oil-contaminated magnetic nanoparticles were extracted from the water surface by an external magnetic field using a neodymium magnet. The effects of both the oil API and the mass of adsorbent on gravimetric oil removal (GOR) were investigated. GORs for APIs 23, 28.4, and 40.3 were found to be 10.5±0.2-2.45±0.24, 8.96±0.18-1.15±0.06, and 5.11±015 to 1.01± 0.12g/g, respectively. Experimental results demonstrated an inverse relationship between GOR and the API value, indicating that as the API value decreased, GOR  increased, and vice versa. Furthermore, as the mass of the adsorbent material was increased (0.02-0.06g), the GOR value decreased. The results of this study suggest that EDTA-maghemite MNPs have advantageous properties, including a small nanosize, super-paramagnetic behavior, and a large surface area. These characteristics make EDTA-maghemite a suitable sorbent for removing oil spills from water surfaces.

Accurate settlement forecasting is essential for preventing severe structural and infrastructure damage. This paper investigates predicting tunneling-induced ground settlements using machine learning models. Empirical methods for estimating settlements are often imprecise and site-specific. Developing novel, accurate prediction methods is critical to avoid catastrophic damage. The umbrella arch method constrains deformations for initial stability before installing primary support. This study develops machine learning models to forecast settlements solely from umbrella arch parameters, disregarding soil properties. Multilayer perceptron artificial neural networks (MLP-ANN) and support vector regression (SVR) are applied. Results demonstrate machine learning outperforms empirical methods. The MLP-ANN surpasses SVR, with R2 of 0.98 and 0.92, respectively. Strong correlation is observed between umbrella arch configuration and settlements. The suggested approach effectively estimates surface displacements lacking mechanical properties. Overall, this study supports machine learning, specifically MLP-ANN, as an efficient, reliable alternative to empirical methods for predicting tunneling-induced ground settlements from umbrella arch design.

In modern horticulture, the grape industry across the globe has been coping with the issue of grape crop diseases. The detection of grape leaf diseases using automated methods can greatly assist farmers in mitigating yield losses and ensuring sustainability. However, existing systems face hurdles while handling grape leaf images at the farm level, and these models fail to generalize well on un-seen images. This study proposes the development of a well-curated real-time dataset of grape leaf images assimilated through field visits in the study area in India. This designed dataset is further used to train convolutional neural network models to accurately identify and classify grape leaves as either diseased or healthy. The potential of transfer learning using CNN models like VGG, ResNet, Inception, and Xception is assessed on the curated dataset. Our findings indicate that ResNet50V2 outperformed the other models in accurately identifying and classifying grape leaf diseases. Using transfer learning, existing weights (pre-trained) and learned features were utilized for further training and fine-tuning the CNN models on our curated dataset.  The results of the proposed approach are compared with existing automated grape leaf disease identification techniques. It is observed that the proposed approach, which is on a real-time grape leaf image dataset, provides the highest accuracy among others. Further, this study provides a well-curated dataset of on-field grape leaf images in the Indian context, which can serve as a benchmark for future research. This study shows that deep learning techniques can aid farmers in identifying grape leaf diseases early.

Reducing the speed of the vehicle can reduce the risk and severity of the collision. Automatic means of vehicle traffic operation control, such as automatic speed cameras, is one of the main methods to solve traffic violations such as speeding. This study takes the actual operation of the automated hardware complex for vehicle traffic control in the Russian Federation as the research objective, and discusses the impact of the application of automated hardware complex for vehicle traffic control on traffic safety. In the process of building the automated traffic flow monitoring system of urban agglomeration, it is necessary to form a set of methodology to evaluate the hardware complex of traffic infrastructure. According to the research results of the influence of vehicle traffic automation and control system on road safety, a model, method and algorithm were developed to evaluate the efficiency of automated hardware complex for vehicle traffic control. The relevant factors introduced into the intelligent transportation system for the overall evaluation of the application efficiency of the vehicle traffic operation control automation hardware complex were determined. A life cycle model of automated hardware complex for vehicle traffic operation control was established to evaluate its operation efficiency in the development stage of intelligent transportation system, and on this basis, the maneuverability standard of urban agglomeration traffic monitoring system management is determined. The results show that the number of hardware complexes used in vehicle traffic operation control has a positive impact on the road traffic accident rate. It is suggested that the standard of one hardware complex for every 6.5 thousand registered vehicles in this area should be used as an indicator of the equipment level of the integrated automated system for vehicle traffic control in this area.

During reservoir development process, the permeability of near wellbore zone decreases, and filtration characteristics of productive formations deteriorate due to anthropogenic impact for the primary and secondary improved oil recovery process and other technological operations. To restore and improve the productivity of wells in heterogeneous low-permeability terrigenous reservoirs, classic acid treatments are recommended. Aggressive secondary sedimentation or in other words, precipitation occurs during acid treatments in terrigenous reservoirs, confirming the effectiveness of putting wells into operation through the proposed chemical composition as the cleaning and stimulating operations. Therefore, a comprehensive approach and methods of impact on the near wellbore zone are necessary, as these are multifactorial processes. This research is focused on developing effective technologies to improve technical means or compositions that restore well productivity by decolmatizing the formation and substantiating effective chemical compositions. The results of this work show that the proposed method and chemical reagent highly improved the permeability of near wellbore zone and as a result increased the productivity index of the well.

To calculate the losses of distribution feeders, this paper uses an iterative method that is limited to restricted measurements. The approach presented in this paper uses bill data in addition to output information from a very small number of real-time measurements located on the secondary side of distribution transformers. This method attempts to estimate the load of distribution transformers injected into LV feeders. Energy losses for LV feeders are evaluated by first estimating the power and periodic energy injected to each of the LV feeders and then subtracting the total consumption bills from these estimated values. By using this method, the amount of energy loss is estimated. In this article, a new method called iterative power factor adjustment method is considered as a potential method for estimating losses. The power factor can be increased by repeatedly using evolutionary algorithms and including capacitors in the system. In order to reduce system losses and increase network effectiveness. In this paper, a new method for examining and evaluating Non-Technical Losses (NTL) is proposed. This method considers load estimation and limited measurement to place high priority feeders.

This research article presents a novel approach to Maximum Power Point Tracking (MPPT) for photovoltaic systems, employing a modified bacterial foraging algorithm with dynamically adjustable mutation rates. This method is specifically tailored to address the challenges presented by partial shading conditions, ensuring efficient and rapid tracking of the MPP while preventing local optima entrapment. To evaluate the performance of this innovative technique, a comparative analysis is conducted against the original bacterial foraging algorithm and the grey wolf optimization algorithm, both commonly employed in MPPT applications. The modified algorithm incorporates a unique strategy that dynamically adapts mutation rates based on the algorithm's convergence behavior, enhancing the tracking accuracy from 81.31% to 89.39%. To validate the effectiveness of the proposed technique, extensive simulations are carried out using MATLAB Simulink, considering various partial shading scenarios commonly encountered in practical photovoltaic applications. It's worth noting that the shading scenario data were extracted from the NASA Worldwide Prediction of Energy website, specifically from the city of Ain El Ibel Djelfa irradiance records. The simulation results unequivocally demonstrate the superiority of the modified bacterial foraging MPPT technique over both algorithms in terms of tracking efficiency (0.4s to 0.9s) and robustness under partial shading conditions. The findings of this research offer valuable insights into the potential advantages of employing a modified bacterial foraging approach for MPPT applications. This innovative techniques with its ability significantly enhance its performance in real-world scenarios involving partial shading, positioning it as a promising choice for optimizing photovoltaic system efficiency and power output.

Acceptable seismic performance, ease and low cost in design and implementation are advantages of passive dampers, but fixed performance parameters corresponding to the type and amount of input energy reduce their efficiency. In this research, a new two-level passive damper in rigid connections with variable stiffness, strength, and energy absorption capacity is introduced and its seismic performance in 5, 10, and 15-story steel frames is evaluated with nonlinear dynamic analysis using SAP2000 software. The results show that, despite the different dynamic parameters in the selected seismic records, such as the frequency content and duration of ground motions, the performance of the structures under all earthquakes has improved significantly, which confirms the effectiveness of the proposed damper in rigid connections on improving the seismic performance structures. Besides, results prove the proposed damper effectiveness on decreasing the structural response such as maximum displacement and base shear. The average displacements reduced by 61%, 51% and 16% compared to those of BSEEP-4ES connections for the 5, 10 and 15-story frames, respectively. Besides, maximum base shear forces reduced by average of 29% and 15% compared to those of BSEEP-4ES connections for the 5 and 10-story frames, respectively.

Drilling muds play the essential role the drilling process as they perform many functions, such as the removing of drill cuttings and controlling well pressure. Water-based drilling muds, through the use of more environmentally friendly additives, can meet increased environmental requirements. Many researchers have studied the effects of different natural materials on drilling mud to find environmentally friendly and effective drilling mud materials. The popularity of using waste materials is mainly due to their cost and favourable environmental impact. Performed literature review showed that the rheological and filtration characteristics of water-based drilling muds are enhanced with the addition of leaves of various plants and trees. This article presents investigations of clay-free drilling mud with the addition of birch and aspen leaf powder. This is relevant in the autumn period, since fallen leaves are solid municipal waste and should be disposed of by removal to landfills. The obtained data indicate the possibility of using leaf powder in the drilling mud composition. Addition of leaf powder to the base mud increases the gel strength and plastic viscosity of the mud.  In an extent reduces the yield point and filtration index. Rheological parameters increase by 10-30% and filtration index decreases up to 15-17%.

In modern production environments where perishable products are manufactured in a job shop system, machine reliability is of utmost importance, and delays during job processing are not acceptable. Therefore, it becomes crucial to consider machines maintenance activities and set upper bounds for interruptions between job operations. This paper tackels the Flexible Job Shop Scheduling Problem taking into account these factors. The study is conducted in two phases. Initially, a novel Mixed-Integer Linear Programming (MILP) model is elaborated for the problem and juxtaposed with the Benders decomposition method to assess computational efficiency. Nevertheless, owing to the elevated complexity of the problem, attaining an optimal solution for instances of realistic size poses an exceptionally challenging task using exact methods. Thus, in the second stage, a Discrete Grey Wolf Optimizer (D-GWO) as an alternative approach to solve the problem is proposed. The performance of the extended algorithms is evaluated through numerical tests. The findings indicate that for small instances, the Benders decomposition method outperforms other approaches. Nevertheless, as the instances grow in size, the efficiency of exact methods diminishes, and the Discrete Grey Wolf Optimizer (D-GWO) performs better under such conditions. Overall, this study highlights the importance of considering machines maintenance activities and interruptions in scheduling of job shop for the production of perishable products. The proposed model and Benders decomposition method in small instances, and the metaheuristic algorithm in large instances provide viable solutions.

In this subsection, a simulation model of underground pipeline leaks was created. Modeling the flow fields of overhead pipelines and underground pipelines with different soil porosity was conducted. The influence of the underground environment and soil porosity on the pipeline leakage field was analyzed. The influence of changes in the underground environment and soil porosity on the loss of kinetic energy at gas leakage was defined. Thus, the law of change in the characteristics of the sound signal of a leak on the pipe wall was obtained. Based on the results of study, it was determined the area of damage to a gas pipeline which is depended on proportionality of the diameter of the gas jet. As the gas is released from a crack in the gas pipeline and with different porosity of the pipe material, the gas diffusion occurred. As a result, the explosion zone increased which ultimately the release of gases into the environment under the influence of pressure increased.

Supporting sustainable development, contributing to reducing waste that causes environmental damage, and reducing the use of natural materials are part of preserving the environment and society. This is done by highlighting the manufacture of sustainable concrete pavement of acceptable quality and according to specifications. The authors previously produced a concrete pavement mixture with optimal properties by partially replacing the Portland cement with 55 wt.% of the ground granulated blast furnace slag (GGBFS) in addition to partially replacing the virgin aggregates with 30 wt.% of recycled aggregate from crushed rigid pavement. The goal of this research work is to produce a self-sensing rigid pavement mixture from wastes with high mechanical properties, that is better than regular concrete and less expensive. The new novel mixture has the ability to detect earlier the damages that occur to the concrete pavement so as to obtain a longer life by periodically maintaining the pavement on time. The previous mixture was improved by adding chopped steel shaving fibers with lengths ranging from 20-60 mm in four different volumetric ratios. These are 0.7%, 1%, 1.1%, and 1.2%. The results were compared with those of the basic mixture, and a decrease in workability and slump values were noticed. Moreover, significant improvements in the mechanical properties were obtained. The concrete's resistance to the applied loads increased by increasing the percentage of steel shaving in the mixtures, due to the increasing of cohesion forces within the mixture. The self-sensing capability for the developed mixtures was tested by measuring the changes in the electrical resistance under different types of mechanical loadings. The results showed that the direction of the applied load and the proportion of steel shavings affect the self-sensing properties in terms of the fractional variation in the electrical resistance (FVER, %), which highlights the importance of using steel shavings in producing smart concrete pavements from reused resources more efficiently and highly cost-effectiveness.

The development of oil and gas production technologies has made it possible to take a new look at the prospects of increasing the raw material base of oil in areas with developed production infrastructure. The projects of exploration and preparation for development of high-carbon low-permeability strata, primarily shale formations, occupy a special position today, as the world experience has shown their potential for oil and gas production. High-carbon shale formations are widely spread in Russia. The most significant of them include the Bazhenov Formation of Western Siberia and the Domanik sediments of the Eastern European Platform, the hydrocarbon potential of which is estimated ambiguously. The ambiguity of hydrocarbon potential estimation is related to the uneven distribution of organic matter determined by the variability of sedimentation conditions. The paper presents the results of the study of Upper Devonian sediments, based on analytical geochemical data of well cores and extracts of bitumoids from them. The analysis of the confinement of zones of increased concentration of organic matter to certain sedimentary conditions and facies zones made it possible to estimate the volume of mobile hydrocarbons in the amount of 5.3 billion tonnes preserved in the high carbonaceous strata. The obtained data on HC volumes were compared with the reservoir properties, studied using X-ray tomography, of low-permeability hydrocarbon-bearing clay-siliceous strata, which allowed us to consider the Upper Devonian carbonaceous strata as an important reserve for maintaining oil production in the future in one of the developed oil-producing regions of the European part of Russia.

This article presents data on main technological fleet of excavation-loading equipment of mining enterprises of Uzbekistan, which traditionally are consumers of open-pit rope excavators of ECG type. Uzbekistan is traditionally consumer of open-pit rope excavators of ECG type. The data on experience of operation of open-pit hydraulic excavators by mining enterprises of Uzbekistan as of 2022 is given. The methodology of determination of time of working out and productivity of excavator at development of inclined exit ledge in conditions of coal mine Angrensky (Uzbekistan) is given. The calculation of time of working of the exit ledge is made and the average operational productivity of the excavator is determined. The technology of ledge mining by longitudinal drifts with the use of mobile excavator-crushing complexes at the lateral location of the bottom-hole conveyor and the presence of a mobile interstage loader with consecutive mining operations on two horizons is presented. According to the given technological scheme the methodology of determination of the full working cycle of the complex is recommended. The estimation of excavator bucket filling coefficient at different face height and different depth of bucket penetration into the face is carried out. A mathematical model for determining the area of the digging segment of a quarry excavator is developed. The mathematical model of definition of the area of excavation volume for a single digging cycle considering the depth of bucket penetration into the face is proposed for the refined estimation of face parameters.

Banking, a vital economic pillar worldwide, thrives with effective management, aiding economic growth. Mitigating risks and addressing cost control are key challenges. Prioritizing strategies to enhance performance in both risk management and cost efficiency is crucial for the banking sector's success and economic stability. One approach is to select partners in such a way that the risk of bank insolvency and total costs are reduced, and the capital adequacy of the bank is increased. So, in this work, we first created a mathematical model to achieve the above goals in the field of banking using the approach of selecting partners. In this model, three objective functions are considered for the optimal selection of partners, two of which aim to minimize risk and cost, and the last objective is to maximize capital adequacy. To solve this multi-objective model, we implemented an integrated intelligent system. A combination of a multi-objective genetic algorithm and a neural network was used in this system. A multilayer perceptron neural network is used to calculate the nondeterministic parameters based on the data from different periods. The proposed method was evaluated using a numerical example in MATLAB software. The obtained results and their comparison with one of the classic algorithms show the superiority and reliability of this intelligent system. Using this system, the optimal partners can be selected to achieve the set goals. The most important factors in the field of risk have been identified. Then, a meta-heuristic multi-objective algorithm (NSGA-II) along with an intelligent neural network system has been used to optimally select partners. According to this intelligent system, a suitable methodology is presented along with the optimization algorithm.

This study introduces an Enhanced Autoregressive Integrated Moving Average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA 202 accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning.

This article presents the results of engine and laboratory tests with fuel before and after treatment with a heavy particle generator. The results of the research have shown that in the course of conducted research the impact of heavy particle generator on fuel during engine tests leads to an increase in its detonation resistance and a reduction in fuel consumption (up to 15%), as well as significantly improves the environmental performance of the engine. The reduction in concentration of carbon monoxide (CO) from 0.09% to 0.03%, carbon dioxide - by 6% and nitrogen oxides - by 7%. The results of research of hydrocarbon composition of fuels by GC-MS method have shown that the impact of heavy particles generator have a favorable effect on the octane number of gasoline, as well as low-temperature properties of diesel fuel. The change of engine operation parameters after the impact of heavy particles generator is established. The change can occur as a result of complex influence on fuel and engine systems, and as a result of influence on a single factor, which is the root cause of the subsequent change of parameters.