Eng, Volume 5, Issue 2 (June 2024) – 10 articles

The integration of renewable energy sources, such as wind and solar, into co-located hybrid power plants (HPPs) has gained significant attention as an innovative solution to address the intermittency and variability inherent in renewable systems among plant developers because of advancements in technology, economies of scale, and government policies. However, it is essential to examine different challenges and aspects during the development of a major work on large-scale hybrid plants. This includes the need for optimization, sizing, energy management, and a control strategy. Hence, this research offers a thorough examination of the present state of co-located utility-scale wind–solar-based HPPs, with a specific emphasis on the problems related to their sizing, optimization, and energy management and control strategies. The authors developed a review approach that includes compiling a database of articles, formulating inclusion and exclusion criteria, and conducting comprehensive analyses. This review highlights the limited number of peer-reviewed studies on utility-scale HPPs, indicating the need for further research, particularly in comparative studies. The integration of machine learning, artificial intelligence, and advanced optimization algorithms for real-time decision-making is highlighted as a potential avenue for addressing complex energy management challenges. The insights provided in this manuscript will be valuable for researchers aiming to further explore HPPs, contributing to the development of a cleaner, economically viable, efficient, and reliable power system. Full article

The increasing demand for deep excavations in construction projects emphasizes the necessity of robust support structures to ensure safety and stability. Support structures are critical in stabilizing excavation pits, with a primary focus on enhancing their bearing capacity. This paper employs finite element modeling techniques to conduct a numerical analysis of nails and helical anchors’ bearing capacity. To reinforce the stability of pit walls, selecting an appropriate method for guard structure construction is imperative. The chosen method should efficiently redistribute forces induced by soil mass weight, displacements, and potential loads in the pit vicinity to the ground. Various techniques, including trusses, piles, cross-bracing systems, nailing, and anchorage systems, are utilized for this purpose. The study evaluates numerical models for two guard structure configurations: nailing systems and helical anchorage. It examines the impact of parameters such as displacement, helical helix count, helix diameter variations, and the integration of nailing systems with helices. Comparative analyses are conducted, including displacement comparisons between different nailing systems and helical anchor systems, along with laboratory-sampled data. The research yields significant insights, with a notable finding highlighting the superior performance of helical bracings compared to nailing systems. The conclusions drawn from this study provide specific outcomes that contribute valuable knowledge to the field of deep excavation support structures, guiding future design and implementation practices. Full article

Various techniques have been employed to detect damage in civil engineering structures. Apart from the model-based approach, which demands the frequent updating of its corresponding finite element method (FEM)-built model, data-driven methods have gained prominence. Environmental and operational effects significantly affect damage detection due to the presence of damage-related trends in their analyses. Time-domain approaches such as autoregression and metrics such as the Mahalanobis squared distance have been utilized to mitigate these effects. In the realm of machine learning (ML) models, their effectiveness relies heavily on the type and quality of the extracted features, making this aspect a focal point of attention. The objective of this work is therefore to deploy and observe potential feature extraction approaches used as input in training fully data-driven damage detection machine learning models. The most damage-sensitive segment (MDSS) feature extraction technique, which potentially treats signals under multiple conditions, is also proposed and deployed. It identifies potential segments for each feature coefficient under a defined criterion. Therefore, 680 signals, each consisting of 8192 data points, are recorded using accelerometer sensors at the Los Alamos National Laboratory in the USA. The data are obtained from a three-story 3D building frame and are utilized in this research for a mainly data-driven damage detection task. Three approaches are implemented to replace four missing signals with the generated ones. In this paper, multiple fast Fourier and wavelet-transformed features are employed to evaluate their performance. Most importantly, a power spectral density (PSD)-based feature extraction approach that considers the maximum variability criterion to identify the most sensitive segments is developed and implemented. The performance of the MDSS selection technique, proposed in this work, surpasses that of all 18 trained neural networks (NN) and recurrent neural network (RNN) models, achieving more than 80% prediction accuracy on an unseen prediction dataset. It also significantly reduces the feature dimension. Furthermore, a sensitivity analysis is conducted on signal segmentation, overlapping, the treatment of a training dataset imbalance, and principal component analysis (PCA) implementation across various combinations of features. Binary and multiclass classification models are employed to primarily detect and additionally locate and identify the severity class of the damage. The collaborative approach of feature extraction and machine learning models effectively addresses the impact of environmental and operational effects (EOFs), suppressing their influences on the damage detection process. Full article

We address the challenges of limescale deposition and its management in urban construction sites, specifically within the Sumayil North project in Tel Aviv. Jet grouting, a method increasingly favored over conventional dewatering techniques for its minimal environmental impact and efficiency, is scrutinized for its unintended consequences on groundwater chemistry, particularly in relation to limescale formation. Our investigation centers on a dual approach: dissecting the geochemical dynamics leading to limescale deposition following jet grouting operations, and evaluating a remedial acid injection strategy implemented to counteract this phenomenon. We identify the critical factors influencing aquifer water chemistry through a detailed hydro-chemical analysis encompassing the Pleistocene Coastal Aquifer’s dynamics. The study reveals that the interaction between grout components and aquifer water significantly alters groundwater pH, driving the precipitation of calcium carbonate. The subsequent implementation of a sulfuric acid injection regimen successfully mitigated limescale accumulation, restoring pumping efficiency and neutralizing pH levels. We propose a workflow to manage and prevent limescale, emphasizing preemptive measures like custom grout compositions and controlled dewatering, with strict post-intervention groundwater monitoring. This approach balances operational efficiency, infrastructure integrity, and environmental stewardship in urban construction projects interfacing with sensitive aquifer systems. Full article

Microorganisms (MOs) are prominent in ecological functioning and balance. The rhizosphere is considered one of the most diverse ecosystems on Earth and serves as a breeding spot for many MOs. Rhizosphere microbial diversity changes according to plant species, genotype, and the nature of the soil. The current study reports the possible use of bacteria isolated from the rhizosphere of Azadirachta indica for synthesizing silver nanoparticles (AgNPs). The physicochemical characterization and antibacterial activity of these green synthesized AgNPs are also reported. The gene (16S rRNA) sequence of bacteria isolated from the rhizosphere showed a maximum similarity of 99.25% with Bacillus subtilis. After incubation, the colorless reaction mixture transformed to brown, which indicates the formation of AgNPs, and UV-vis spectral analysis also confirmed the biosynthesis of AgNPs. Compared to lower temperatures, the efficiency of AgNP synthesis was high at the higher temperature. The scanning electron microscope image demonstrated spherical-shaped AgNPs with sizes ranging from 18 to 21 nm. Energy-dispersive X-ray analysis established the elemental analysis of synthesized AgNPs. The synthesized AgNPs showed strong bactericidal properties against pathogenic bacteria Klebsiella pneumonia, Pseudomonas aeruginosa, Escherichia coli, and methicillin-resistant Staphylococcus aureus. Full article

The highly crystalline ZnAl layered double hydroxides (ZnAl-NO₃-LDHs) are utilized for the potential transformation into mixed metal oxides (MMOs) through thermal decomposition and used further for the photodegradation of phenol to assess the influence of calcination on ZnAl-LDHs with enhanced photoactivity. The structure, composition, and morphological evolution of ZnAl-LDHs to ZnO-based MMO nanocomposites, which are composed of ZnO and ZnAl₂O₄, after calcination at different temperatures (400–600 °C), are all thoroughly examined in this work. The final ZnO and ZnAl₂O₄-based nanocomposites showed enhanced photocatalytic activity. The findings demonstrated that calcining ZnAl-LDHs from 400 to 600 °C increased the specific surface area and also enhanced the interlayer spacing of d₀₀₃ while the transformation of LDHs into ZnO/ZnAl₂O₄ nanocomposites through calcining the ZnAl-LDH precursor at 600 °C showed significant photocatalytic properties, leading to complete mineralization of phenol under UV irradiation. Full article

The oil and gas industry faces significant challenges due to wear on drilling motor components, such as thrust pins and inserts. These components are critical to the efficiency and reliability of drilling operations, yet are susceptible to wear, leading to significant economic losses, operational downtime, and safety risks. Despite previous research on wear-resistant materials and surface treatments, gaps exist in understanding the unique properties of thrust pins and inserts. The aim of this study is to enhance mechanical system performance by characterizing the wear resistance of these components. Through chemical analysis, hardness assessments, and metallographic examinations, the study seeks to identify specific alloys and microstructures conducive to wear resistance. Key findings reveal that AISI 9314 thrust pins exhibit superior wear resistance with a tempered martensite microstructure and a hardness of 41 HRc, whereas AISI 9310 inserts are less resistant, with a hardness of 35 HRc. The research employs advanced techniques, including a pin-on-disc tribometer, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and profilometry, to evaluate wear behavior, visualize wear patterns, analyze elemental composition, and quantify material loss and surface roughness. Our findings demonstrate that optimizing the material selection can significantly enhance the durability and efficiency of drilling motors. This has profound implications for the oil and gas industry, offering pathways to reduce maintenance costs, improve operational efficiency, and contribute to environmental sustainability by optimizing energy consumption and minimizing the carbon footprint of drilling operations. Full article

The problem addressed in this article consists of the motion control of a quadrotor affected by model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning, is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow for an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor built from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on. Full article

This study examines the practicality and limitations of using a FANUC CRX-10 iA/l collaborative robot to assemble a product component, highlighting the trade-offs between increased robotization and reduced manual intervention. Through a detailed case study in the i-Labs laboratory, critical factors affecting precision assembly such as station layout, tooling design and robot programming are discussed. The findings highlight the benefits of robots for nonstop operation, freeing up human operators for higher value tasks despite longer cycle times. In addition, the paper advocates further research into reliable gripping of small components, a current challenge for robotics. The work contributes to open science by sharing partial results and methods that could inform future problem solving in robotic assembly. Full article