Eng, Volume 6, Issue 1 (January 2025) – 20 articles

Despite their significant environmental benefits, solar photovoltaic (PV) systems are susceptible to malfunctions and performance degradation. This paper addresses detecting and diagnosing faults from a dataset representing a 250 kW PV power plant with three types of faults. A comprehensive dataset analysis is conducted to improve the dataset quality and uncover intricate relationships between features and the target variable. By introducing novel feature importance averaging techniques, a two-phase fault detection and diagnosis framework employing tree-based models is proposed to identify faults from normal cases and diagnose the fault type. An ensemble of six tree-based classifiers, including decision trees, random forest, Stochastic Gradient Boosting, LightGBM, CatBoost, and Extra Trees, is trained in both phases. The results show 100% accuracy in the first phase, particularly with the Extra Trees classifier. In the second phase, Extra Trees, XGBoost, LightGBM, and CatBoost achieve similar accuracy, with Extra Trees demonstrating superior training and convergence speed. This study then incorporates Explainable Artificial Intelligence (XAI), utilizing LIME and SHAP analyzers to validate the research findings. The results highlight the superiority of the proposed approach over others, solidifying its position as an innovative and effective solution for fault detection and diagnosis in PV systems. Full article

Proton exchange membrane fuel cells (PEMFCs), as a clean energy technology, show remarkable potential for a wide range of applications. However, high altitude regions pose significant challenges for PEMFC system operation due to thin air and low oxygen partial pressure. Existing logic judgement-based controls exhibit defects such as poor robustness and poor adaptability, which seriously restrict PEMFC system operation. In order to address this issue, this paper puts forth an intelligent control of a PEMFC system air compressor (AC) and back pressure valve (BPV) using an asynchronous advantage actor-critic (A3C) algorithm and systematically compares it with the logic judgement-based control. The application of an A3C-based control under three distinct high altitude test conditions demonstrated a notable enhancement in dynamic responsiveness, with an improvement of up to 40% compared to the results for the logic judgement-based control. Additionally, an improvement of 5.8% in electrical efficiency was observed. The results demonstrate that the A3C-based control displays significant robustness and control precision in response to altitude alterations. Full article

Lubricant friction modifier additives are used on lower viscosity engine oils to mitigate boundary friction. This work presents the development of a graphene-based material as an oil friction modifier additive, from formulation to actual vehicle tests. The graphene material was initially characterized using scanning electron microscopy (SEM) and Raman spectroscopy, which revealed the predominance of graphene nanoplatelets (GNPs) with an average of nine layers. After functionalization, two GNP additive variants were initially mixed with a fully formulated SAE 0W-20 engine oil and tribologically evaluated using reciprocating sliding tests at 40 and 120 °C and Hertzian pressure up to 1.2 GPa when both variants presented friction reduction. Then, the GNP additive variant with better performance was evaluated in a vehicle emission test using a fully formulated 5W-20 SAE oil as a reference. The addition of 0.1% of GNPs reduced fuel consumption by 2.6% in urban conditions and 0.8% in highway ones. The urban test cycle was FTP75 and higher benefits of the GNP additive occurred especially on the test start, when the engine and oil were still cold and on test portions where the vehicle speed was lower. Full article

This paper explores the historical development of conventional plastics, tracing their evolution from early forms to their pervasive use in modern society. Its observations include the rise of mass plastic production during World War II and the post-war development, showcasing plastics’ economic and societal impact. The environmental repercussions of plastic pollution have led to increased global awareness and calls for sustainable alternatives. The emergence of bioplastics is investigated, including their classification, properties, applications, and challenges in scaling. This paper emphasises the urgency of adopting bioplastics for a sustainable future and discusses efforts towards homogenisation and standardisation across global markets. Full article

This article presents a framework of using MEMS sensors to investigate unsteady flow speeds of a flapping wing or the new concept of sensors on flapping wings (SOFWs). Based on the implemented self-heating flow sensor using U18 complementary metal–oxide–semiconductor (CMOS) MEMS foundry provided by the Taiwan Semiconductor Research Institute (TSRI), the compact sensing region of the flow sensor was incorporated for in situ diagnostics of biomimetic flapping issues. The sensitivity of the CMOS MEMS flow sensor, packaged with a parylene coating of 10 μm thick to prolong the lifetime, was observed as −3.24 mV/V/(m/s), which was below the flow speed of 6 m/s. A comprehensive investigation was conducted on integrating CMOS MEMS flow sensors on the leading edge of the mean aerodynamic chord (m.a.c.) of the flexible 70-cm-span flapping wings. The interpreted flow speed signals were checked and demonstrated similar behavior with the (net) thrust force exerted on the flapping wing, as measured in the wind tunnel experiments using the force gauge. The experimental results confirm that the in situ measurements using the concept of SOFWs can be useful for measuring the aerodynamic forces of flapping wings effectively, and it can also serve for future potential applications. Full article

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·${\text{m}}^{-3}$, 162.3 Bq·${\text{m}}^{-3}$, and 150.8 Bq·${\text{m}}^{-3}$, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·${\text{m}}^{-3}$. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment. Full article

Nowadays, the increase in sustainable investments, especially when it comes to energy efficiency in buildings, has been recognised as an important pillar towards reductions in energy consumption. In this context, there is a need for efficient and user-friendly digital tools that can support decision-making procedures for all involved parties in the energy efficiency value chain. The scope of this paper is to present a high-level architecture and system design of the energy efficiency marketplace developed within the framework of the ENERGATE project, an EU-funded initiative aiming to assist Building Owners, Project Implementors, and Financial Institutions to collaborate and execute energy-efficient building renovations. To this end, structured data through predefined information entries will be collected. This will facilitate the interactions between heterogenous stakeholders and contribute to the standardisation of processes. The paper focuses on the functional description of the system design of the ENERGATE platform by defining the architecture, components, modules, interfaces, and structured data, as well as highlighting the requirements of potential platform users and design principles to meet the necessary requirements while ensuring security, reliability, and effectiveness. Full article

The supercritical water gasification (SCWG) and carbon dioxide gasification of agro-industrial and urban waste residues—Coffee Husk, Eucalyptus Biochar, Energy Sugarcane, and Refuse-Derived Fuel (RDF)—were studied using TeS^® v.2 software, which employs a non-stoichiometric thermodynamic model to minimize Gibbs free energy and predict equilibrium compositions. The effects of temperature (873.15–1273.15 K), pressure (220–260 bar), biomass feed (18–69%), and gasifying agents on hydrogen and methane formation were analyzed. Higher temperatures and biomass feed percentages favored hydrogen production, while lower temperatures increased methane formation. At 1273.15 K, RDF showed the highest hydrogen yield in SCWG, rising from 0.43 to 1.42 mol, followed by Energy Sugarcane (0.39 to 1.23 mol), Coffee Husk (0.34 to 0.74 mol), and Eucalyptus Biochar (0.33 to 0.62 mol). In CO₂ gasification, hydrogen yields were lower but followed a similar trend. At 873.15 K, RDF also exhibited the highest methane increase in SCWG, from 0.14 to 0.91 mol, followed by Energy Sugarcane (0.12 to 0.65 mol), Coffee Husk (0.11 to 0.36 mol), and Eucalyptus Biochar (0.11 to 0.29 mol). Methane formation in CO₂ gasification was significantly lower, with RDF increasing from 0.0035 to 0.35 mol, followed by Energy Sugarcane (0.0024 to 0.24 mol), Coffee Husk (0.0002 to 0.058 mol), and Eucalyptus Biochar (0.0002 to 0.028 mol). On the other hand, a slight increase in hydrogen formation was observed as pressure decreased, while the opposite effect was observed for methane formation, with a small increase in its production as pressure increased. The impact of pressure change on the equilibrium compositions was not as significant as the effect observed by varying temperature; this behavior was observed in both gasification processes studied. Additionally, the behavior of the H₂/CO molar ratio for each biomass in the studied gasification processes was analyzed to assess the potential uses of the produced syngas. It was observed that the SCWG resulted in significantly higher H₂/CO molar ratios compared to CO₂ gasification. Full article

Today, the machining of heat-resistant alloys based on triple, quad, or penta equilibria high-entropy alloy systems of elements (ternary, quaternary, quinary iron-, titanium-, or nickel-rich alloys), including dual-phase by Gibb’s phase rule, steels of the austenite class, and nickel- and titanium-based alloys, are highly relevant for the airspace and aviation industry, especially for the production of gas turbine engines. Cutting tools in contact with those alloys should withstand intensive mechanical and thermal loads (tense state of 1.38·10⁸–1.54·10⁸ N/m², temperature up to 900–1200 °C). The most spread material for those tools is cutting ceramics based on oxides, nitrides of the transition and post-transition metals, and metalloids. This work considers the wear resistance of the cutting insert of silicon nitride with two unique development coatings — titanium–zirconium nitride coating (Ti,Zr)N and complex quad nitride coating with TiN content up to 70% (Ti,Al,Cr,Si)N with a thickness of 3.8–4.0 µm on which microtextures were produced by the assisted electric discharge machining with the electrode-tool of ø0.25 mm. The microtextures were three parallel microgrooves of R0.13^+0.02 mm at a depth of 0.025₋₀.₀₅. The operational life was increased by ~1.33 when the failure criterion in turning nickel alloy was 0.4 mm. Full article

The presented paper analyzes the impact of limestone dust accumulation on photovoltaic (PV) panel performance, focusing on the specific surrounding conditions near quarries. The results from the performed field measurements show that high concentrations of limestone dust accumulate significantly faster in these areas, and a hard layer is formed in the presence of moisture. This layer of dust is resistant to removal, even in moderate precipitation and winds with speeds between 6 and 9 m/s, making it a significant problem for the long-term performance of the systems. The analysis revealed that the lack of systematic cleaning of the panels leads to a drop in efficiency of over 20%, with this loss pointedly limiting the return on investment. This study highlights the need for innovative maintenance approaches, such as regular cleaning, use of special coatings and adapting designs to specific environmental conditions. This is essential for the development of strategies to manage, maintain and improve PV systems in areas with high levels of dust pollution. Full article

The perception of animal emotions is key to enhancing veterinary practice, human–animal interactions, and protecting domesticated species’ welfare. This study presents a unique emotion classification deep learning-based approach for pet animals. The actual and emotional status of dogs and cats have been classified using a modified EfficientNetB5 model. Utilizing a dataset of images classified into four different emotion categories—angry, sad, happy, and neutral—the model incorporates sophisticated feature extraction methods, such as Dense Residual Blocks and Squeeze-and-Excitation (SE) blocks, to improve the focus on important emotional indicators. The basis of the second strategy is EfficientNetB5, which is known for providing an optimal balance in terms of accuracy and processing capabilities. The model exhibited robust generalization abilities for the subtle identification of emotional states, achieving 98.2% accuracy in training and 91.24% during validation on a separate dataset. These encouraging outcomes support the model’s promise for real-time emotion detection applications and demonstrate its adaptability for wider application in ongoing pet monitoring systems. The dataset will be enlarged, model performance will be enhanced for more species, and real-time capabilities will be developed for real-world implementation. Full article

In this paper, a review of the advances in brushless synchronous motors is presented because there has been an increasing interest in advanced motor control and to address the weaknesses of conventional motor control. The traditional motor control strategies, for example, proportional plus integral controllers (PIs), are simple and easy to maintain. On the contrary, they require accurate tuning and are affected by motor parameter variations. To address these challenges and many others (power factors, torque ripple, current limit, voltage limit, speed limit), advanced control methods are required to enhance the performance of the motor drive control. The advanced control techniques include model predictive control, slide mode control, reinforcement learning, and fuzzy logic control. This paper provides a comprehensive review of advances in control methods and addresses the challenges and limitations associated with their practical application. Full article

The use of segmented stator and rotor designs in AC electric machine construction offers several significant advantages, including a high-copper fill factor, increased torque density, improved field-weakening performance, simplified manufacturing processes, and enhanced mechanical strength. Additionally, segmented designs allow for the incorporation of oriented steel—either partially or fully—which exhibits excellent magnetic properties in the rolling direction, resulting in more efficient machine performance. However, lamination segmentation also introduces challenges. Parasitic air gaps between segments and an increased number of cut edges in the assembled stack can alter the magnetic properties of the machine, potentially leading to degraded performance. Furthermore, the use of oriented steel remains complex, as its highly nonlinear magnetic properties vary depending on the direction of the magnetic flux. This paper reviews the widely adopted stator and rotor segmentation techniques available in the literature, discussing their potential benefits and limitations. It also covers key aspects such as popular manufacturing approaches, the impact of segmentation on machine performance, advanced finite-element analysis (FEA) techniques for numerical modeling, and experimental methods for evaluating the performance of segmented stator and rotor constructions in AC machines. By addressing these areas, this work provides a comprehensive resource for machine designers seeking to develop AC machines with segmented stators and rotors. Full article

In this work, electron beam welds between Cu and Al plates were formed using different power modes, namely 1800 W, 2400 W, and 3000 W. The structure, microhardness, and tensile strength of the raw materials and the weld seams were studied. The low power of the electron beam resulted in the improper penetration and insufficient depth of the weld seam. The low power resulted in high cooling rates, which hindered the nucleation of the copper and aluminum particles. A number of intermetallic compounds (IMCs) were formed, including the metastable Cu₉Al₄ one. An increase in the power of the electron beam reduced the cooling rate and increased the miscibility between the materials. This resulted in the formation of a mostly homogeneous structure comprising an αAl solid solution and dendritic eutectic CuAl₂ intermetallic compounds. A preferred crystallographic orientation of the aluminum phase was detected regarding the sample prepared using a power of 3000 W, forming a specific texture towards the {111} family of crystallographic planes, which is the closest-packed structure. This plane characterizes the highest chemical activity and the highest plasticity. As a result, this sample exhibited the best chemical bonding between the IMCs and the aluminum matrix and the best microhardness and tensile test values. Full article

Roads are critical components of infrastructure, and assessing their quality is essential to ensure the safe transport of people and goods, which in turn supports economic prosperity. Various factors, such as subsurface conditions, moisture content, and temperature, influence road performance and can degrade their efficiency as transportation networks. While surface road defects can often be identified through visual inspection, information about subsurface extensions, their impact on structural integrity, and potential risks remain concealed. This study aimed to perform a comparative analysis of dielectric permittivity (ε) using time-lapse Ground Penetrating Radar (GPR) measurements on pre- and post-renovated road sections. This study also sought to evaluate the effectiveness of this approach for road assessment and to employ forward modeling for a deeper understanding of road defects and their associated hazards. Results revealed that the pre-renovated road section exhibited significant fluctuations in dielectric values, ranging from 3.13 to 15.9. In contrast, the post-renovated section showed consistent values within a narrow range of 5 to 6.6. Different crack types were classified, and the mean ε for each visually identified crack type was calculated. Despite the higher frequency of transverse cracks compared to other defects, longitudinal cracks exhibited the highest mean dielectric value (~10.3), while alligator cracks had the lowest (~8.33). Numerical simulations facilitated accurate interpretation of the defects identified in the road section, providing insights into their nature and associated risks. The methodology used for crack classification and numerical simulation can be applied to other road sections globally, offering a standardized approach to road assessment and maintenance planning. Full article

Fault detection and classification in transmission lines are critical for maintaining the reliability and stability of electrical power systems. Quick and accurate fault detection allows for timely intervention, minimizing equipment damage, and reducing downtime. This study addresses the challenge of effective fault classification, particularly when dealing with smaller, more practical datasets. Initially, the study examined the performance of conventional machine learning algorithms on a comprehensive dataset of 7681 samples, demonstrating high accuracy owing to the inherent symmetry of sinusoidal voltage and current signals. However, the true efficacy of these algorithms was evaluated by minimizing the dataset to 231 training samples, with the remainder being used for testing. A novel Multi-Target Ensemble Classifier was developed to improve classification accuracy. The proposed algorithm achieved an impressive overall accuracy of 0.829165, outperforming traditional methods, including the K-Nearest Neighbors Classifier, support vector classification, random forest classifier, decision tree classifier, AdaBoost classifier, gradient boosting classifier, and Gaussian NB. This research highlights the importance of efficient fault classification techniques in power systems and proposes a superior solution in the form of a multitarget ensemble classifier. Full article

The paramount significance of temperature’s influence on rock engineering endeavors underscores its profound capacity to alter the physical and mechanical attributes of rocks. Among the most crucial techniques utilized to thermally induce damage and diminish the tensile resilience of rock materials are microwave irradiation and heat treatment. This research examines and compares the effects of these two modalities on the dynamic tensile characteristics of Fangshan granite (FG), including their implications under conditions of overload and dependencies on loading rate, utilizing the sophisticated Split Hopkinson Pressure Bar (SHPB) apparatus. In particular, the dynamic real tensile strength (RST) of Brazilian disc (BD) specimens was meticulously gauged and contrasted after subjecting them to microwave irradiation at a potent 6 kW for varying durations (1.5, 3.0, and 4.5 min) and heat treatment across distinct temperature thresholds (178 °C, 345 °C, and 473 °C). To enhance the precision of the measurements, an overload correction was implemented by affixing a strain gauge in close proximity to the core of the BD specimen. The conventional dynamic tensile strength exhibited a reduction of approximately 20 to 30% with the prolongation of microwave radiation time. Furthermore, an additional decrease in tensile strength was observed with the elevation of heat treatment temperatures, reaching a maximum reduction of up to 40%. This phenomenon can be attributed to the proliferation and expansion of microcracks within the rock matrix. It was noteworthy that the RTS, corrected for overloading effects, exhibited a comparable trend to the dynamic traditional tensile strength (TTS). Both were significantly correlated with the loading rate, with the dynamic tensile strength demonstrating an average decrease of approximately 25% when the loading rate was increased. Interpolation and fitting analyses were employed to investigate the effects of microwave radiation duration, heat treatment temperature, and loading rate on the dynamic tensile strength of FG samples. Furthermore, it was established that the overload ratio increased in conjunction with an increase in microwave radiation duration, heat treatment temperature, and loading rate, reaching a maximum value of 1.5. Full article

The formation of geotechnical structures on foundations composed of low-strength soils is associated with a number of risks and difficulties. Soils such as clay-salt slurries are characterized by low bearing capacity and a tendency to deform under load. In this study, a numerical simulation of the stability analysis of an embankment constructed on low-strength soils consisting of clay-salt slurries is carried out, and the study of the dependence of the stability and behavior of the embankment on the configuration of this foundation, without taking into account the embedment of rocks and with introduction of rocks into the geotechnical system, is considered. The results prove that the sloping configuration of low-strength soils greatly complicates the stability of the embankment. It is noted that the stability factor is significantly reduced under the influence of loads on low-strength soil, particularly when the geotechnical system has a configuration with slope angles of 5° and 10°, and, in addition, when rocks are embedded in low-strength soil if the underlying soil layer is a weak foundation. In view of this, the assessment of embankment stability on clay-salt slurries requires careful analysis due to a number of specific characteristics of these soils that create complex geotechnical conditions. Full article

Objectives: This study aims to evaluate the potential of lightweight concrete mixtures incorporating sustainable materials, such as nopal mucilage and aloe vera, to enhance thermal and structural performance while promoting eco-friendly construction practices. The objective is to analyze their effects on physical, mechanical, and thermal properties to optimize mixture design. Methods/Analysis: Six lightweight concrete mixtures were prepared using varying dosages of tuff, expanded clay, nopal mucilage, and aloe vera as lightweight and stabilizing agents. To assess their performance, a series of physical tests (bulk density, water absorption, and slump), mechanical tests (compressive strength), and thermal characterizations (conductivity, heat capacity, and resistivity) were conducted. Fractal analysis was employed to evaluate the structural complexity of the mixtures. Findings: The results revealed significant differences based on the materials used. Mixtures with aloe vera exhibited extreme water absorption (up to 11.472%) and varying consistency, from fluid (“spreads”) with tuff to workable with expanded clay. When combined with expanded clay, Nopal mucilage-based mixtures showed lower workability but higher compressive strengths (up to 11.447 MPa). Expanded clay increased bulk density and enhanced thermal efficiency, with mixtures incorporating aloe vera or nopal mucilage demonstrating high heat retention and structural complexity. The compressive strengths ranged from 7.343 MPa (aloe vera-tuff) to 12.207 MPa (water-tuff), highlighting the impact of stabilizing agents on mechanical performance. Novelty or Improvement: This study introduces a novel evaluation of lightweight concrete mixtures using nopal mucilage and aloe vera, focusing on their synergistic effects with lightweight aggregates such as tuff and expanded clay. The findings provide valuable insights into optimizing eco-friendly concrete mixtures with improved thermal retention, workability, and mechanical properties, offering a sustainable alternative for modern construction. Full article