Significant Energy Research Articles

Methane seepage activities in the Qiongdongnan Basin since MIS2

Gas hydrates are globally acknowledged as a significant strategic alternative energy source, and there is a consensus on the necessity to enhance their exploration. However, gas hydrates are highly prone to decomposition under variations in external environmental conditions, which can result in subsea methane seepage activities. Consequently, investigating subsea methane seepage activities holds substantial theoretical and practical significance for exploring gas hydrates. This paper evaluates the history of methane seepage activities in the Qiongdongnan Basin (QDNB) by analyzing the carbon and oxygen isotopic characteristics of benthic foraminifera and the geochemical properties of pore water from gravity sediment cores at sites QH-CL4 and QH-CL40. The results indicate that since the Marine isotope stage2 (MIS2), continuous micro-methane seepage activity has been present in the QDNB, characterized by a slight negative deviation in the carbon isotopes of benthic foraminifera. Methane seepage activity intensified during 14.6 ka BP and between 19.64–23.22 ka BP. This increase is thought to be associated with rising seawater temperature during the Bølling–Allerød interstadial and declining sea level during the Last Glacial Maximum, respectively. Moreover, current geochemical characteristics of pore water reveal strong methane seepage activity, with flux as high as 28.968 mmol·m-²·a-¹. This ongoing activity has led to gas hydrate formation within shallow layers while also causing negative deviations in pore water salinity.

Role of molecular damage in crack initiation mechanisms of tough elastomers

Tough soft materials such as multiple network elastomers (MNE) or filled elastomers are typically stretchable and include significant energy dissipation mechanisms that prevent or delay crack growth. Yet most studies and fracture models focus on steady-state propagation and damage is assumed to be decoupled from the local stress and strain fields near the crack tip. We report an in situ spatial-temporally resolved 3D measurement of molecular damage in mechanophore-labeled MNE just before a crack propagates. This technique, complemented by digital image correlation, allows us to compare the spatial distribution of both damage and deformation in single network (SN) elastomers and in MNE. Compared to SN, MNE have a wide-spread damage in front of the crack and, surprisingly, delocalize strain concentration. A continuum model, where damage distribution is fully coupled to the crack tip fields, is proposed to explain these results. Additional measurements of time-dependent molecular damage during fixed grips relaxation in the presence of a crack reveal that the less localized damage distribution delays fracture initiation. The observations and exploratory modeling reveal the dynamic fracture mechanism of MNE, providing guidance for rational design of high-performance tough elastomers.

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Plasma Electrolytic Oxidation Treatment on Magnesium Rare Earth Alloy: Effect of Low Current Density

The poor corrosion resistance of magnesium and its alloys can be overcome by developing appropriate surface treatments of these materials. The article explores the impact of using a current density of 15 mA cm−2, lower than those considered so far for the plasma electrolytic oxidation treatment of the WE43 earth rare‐based magnesium alloy, on process energy consumption as well as on microstructure and corrosion properties of oxide coatings grown on the magnesium alloy. Using a low current density during the treatment certainly means significant energy savings, but also good corrosion resistance compared to the untreated alloy, as demonstrated by electrochemical analyses and a through‐hole morphology of the oxide coating, which could be useful for all the applications in which beyond good corrosion resistance a specific surface area is essential.

Optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant

This paper presents optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant. The power plant is equipped with four steam boilers using a damper-louvers airflow control system. Analysis has revealed significant energy wastage due to inefficiency and over-air supply, with the system requiring 59,904,436 kWh annually at a financial cost of N1, 093,255,884. The existing system's inefficiency results in an annual electrical energy waste of 8,502,302 kWh. This inefficiency is attributed to the Force Draft Fan (FDF) motor operating at maximum speed continuously, regardless of the steam profile or airflow requirements. This study aimed at optimizing the steam boiler airflow control system in a typical oil refinery power plant for improved power efficiency, energy conservation, and cost savings citing Port Harcourt Refinery Company (PHRC) at Alesa-Eleme, Rivers State, Nigeria. To address this, the damper-louvers system was replaced with a Variable Frequency Drive (VFD), which would adjust the motor speed to match the required airflow, thereby reducing energy wastage. The process was simulated and VFD/FDF motor analyzed in MATLAB Simulink, determining the optimal speed, airflow, and power requirements. Results obtained show that the VFD control system improves the FDF motor's power efficiency from 47.7% to 85%, with an annual energy saving of 8,502,302 kWh (8.5 GWh). This results in a total cost benefit of N478.18 million annually, representing a 43.8% cost saving. The implementation cost of the VFD infrastructure was estimated at N111, 440,000, with a payback period of approximately eleven months. The findings suggest deployment of higher FDF motor speed ratings to enhance speed control and energy efficiency in Oil refinery power plant.

Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building

This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort.

Optimization of carbon emission in an integrated machine-piece scheduling and vehicle routing problem and its solution using MOPSO and NSGAII metaheuristic algorithms

The growth of the global economy is accompanied by significant energy consumption, and greenhouse gas emissions create various problems such as global warming and environmental degradation. To protect the environment, governments are seeking to reduce carbon emissions. Production systems that operate solely based on economic factors in the workshop only consider problems such as production speed, cost, and processing time. Two aspects can be effective in saving energy and reducing emissions at the production planning level: using routing to find the shortest path for collecting workpieces to the workshop, and turning off machines with long idle times and restarting them at the appropriate time. If the workshop production problem is combined with vehicle routing, a new problem arises. According to the research conducted so far, an integrated mathematical model for production routing has not been designed in a situation where the routing is before the production workshop. In this research, this bi-objective model is introduced, and it is solved using the augmented epsilon-constraint (AEC) method. The proposed mixed-integer linear programming model of this research includes three dimensions: environmental, social (customer satisfaction), and economic simultaneously. Given the high complexity of the mathematical model, MATLAB software and MOPSO and NSGA-II algorithms were used to solve it at higher dimensions. Seven evaluation criteria were used to compare the two proposed algorithms, and the results show that the MOPSO algorithm performs better. The findings suggest that minimizing pollution may involve sacrificing on-time delivery to customers. Consequently, decision-makers must carefully weigh the trade-off between reducing environmental impact and maintaining satisfactory delivery performance, ultimately deciding on an acceptable pollution level.

Unveiling the chemical composition, antioxidant and antibacterial properties, and mechanistic insights of Convolvulus arvensis extract through molecular docking simulations

The use of herbal extracts has gained significant importance in modern health practices, making it essential to explore their properties and applications in detail. This study investigates the effect of ultrasound on the extraction yield of Convolvulus arvensis leaves, with a focus on its chemical composition, antioxidant properties, cytotoxicity, antimicrobial activities, and mechanism of action. Antimicrobial activity was determined using minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), well diffusion agar, and disk diffusion agar assays. The antioxidant properties were assessed through ferric reducing antioxidant power (FRAP) and DPPH free radical scavenging assays. High Performance Liquid Chromatography (HPLC) and Fourier transform infrared (FTIR) spectroscopy were utilized to identify the chemical composition and functional groups present in the extract. The findings revealed that ultrasound-assisted extraction exhibited significantly higher extraction efficiency (p < 0.05). The extract displayed significant antimicrobial activity against Listeria innocua, Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus. The total phenolic and total flavonoid contents of the extract were measured at 328.85 ± 0.046 (mg GAE/100 g) and 226.23 ± 0.043 (mg QE/100 g), respectively. Moreover, the extract demonstrated notable radical scavenging activity against DPPH, with a value of 37.40 ± 0.111 (g/100 g). The FRAP assay revealed that the antioxidant activity of C. arvensis extract was quantified at 23.796 ± 0.045 μmol Fe²⁺. The highest amount of phenolic and alkaloid compounds in the extract was related to β-Phellandrene (341.52 µg/mL) and Swainsonine (360.24 µg/mL), respectively. Furthermore, the in vitro cytotoxic activity of the extract was evaluated against the Caco-2 cell line, with the IC50 value determined to be 26.55 mg/mL. The results of the docking study demonstrated that Ferulic acid displayed a significant binding energy of −6.2 kcal/mol, suggesting a robust interaction with the target enzyme and the potential to act as a modulator of glutathione peroxidases. The findings of the present study revealed that the alcoholic extract of C. arvensis possesses proper antioxidant and antimicrobial activities. Therefore, it has been used as a food preservative. However, further medical and clinical researches are required before any application in human health.

Web Server Based Electrical Control System Analysis for Smart Buildings

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios

Artificial Neural Network to Predict Curvature Light Shelf Design Related Daylighting Optimization on Office Spaces

Energy Optimization in building design field now has been revolutionized due to AI and machine learning applications. Leveraging daylight to reduce artificial lighting consumption holds promise for significant energy savings, yet the nonlinear nature of daylight patterns poses challenges in prediction and optimization. This study proposes a novel approach to automated light shelf design using machine learning algorithms, specifically artificial neural networks (ANNs) such as recurrent neural networks (RNN) by long short term memory (LSTM), to accelerate daylighting simulation and optimization processes. The methodology employed two distinct approaches: Firstly, we employed the theoretical-analytical approach to explore methods for utilizing machine learning in natural lighting and light shelf parameters. Second, the practical and applied method involved creating a predictive model for designing the light shelf using appropriate AI and ML techniques. This model is based on an office geometry model at the El Arish weather file in Egypt, four-dimensional training room models with three internal zones-oriented south. Rhinoceros and Grasshopper, two parametric simulation tools, are used to normalize and optimize light shelf parameters like geometry cross-section, curvature surface, width, height position, depth, tilt angle, and reflectance materials. Then, the Galapagos plug-in and Colibri2 are used for dataset creation and optimization. The results demonstrate that automated light shelf operation has a significant impact on internal daylighting quality. RNNs enable rapid prediction of optimization models, reducing time consumption in the early design phase. ML facilitates decision-making by generating evaluative criteria from user-selected design options. RNNs were classified as good and bad and used LSTM to enhance prediction accuracy for efficient illuminance values metric in zones 1 and 2. Challenges include increasing the simulation procedure's efficiency. The results of model accuracy reached 99%. Hence, future research should prioritize resolving the previously identified concerns. In conclusion, this study underscores the importance of ML-driven approaches in early design phases to optimize building energy consumption and pave the way for sustainable architectural practices.

Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

The increasing global demand for renewable energy has propelled the adoption of photovoltaic systems as a key component of sustainable energy infrastructure. Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.

Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Undetected photovoltaic system faults can lead to significant energy losses, often exceeding 10%, necessitating efficient fault detection and diagnosis. Artificial intelligence, particularly machine learning and deep learning, offers promising solutions for real-time, high-volume fault detection and complex pattern recognition in PV systems. This research analyzes various PV fault detection studies, examining their objectives, methods, results, and the prevalence of ML/DL approaches. The analysis highlights the application of both classical ML algorithms, such as K-Nearest Neighbors and Random Forest, and advanced DL models, including Convolutional Neural Networks, for PV fault diagnosis.

Correlation The Level of Nutritional Knowledge, Breakfast Consumption Patterns With Nutritional Status of Age Students 10-12 MI Dakwatul Khoiriyah Kediri

Children aged 10–12 years need significant energy due to their rapid growth and active lifestyles. Their nutritional status is influenced by nutritional knowledge and breakfast habits. To examine the relationship between nutritional knowledge, breakfast habits, and nutritional status of students aged 10–12 years at MI Dakwatul Khoiriyah Kediri. A cross-sectional study with 40 students using questionnaires for nutritional knowledge, SQ-FFQ interviews to evaluate breakfast habits, and BMI for age index to determine nutritional status. Spearman correlation tests were used for analysis. The study found that 62.5% had low nutritional knowledge, 60% had infrequent breakfast habits, and 65% were malnourished. There is a significant relationship between the level of nutritional knowledge and nutritional status (p=0.000, r=0.688) and between the habit of rarely eating breakfast and poor nutritional status (p=0.000, r=0.812). There is a significant relationship between nutritional knowledge, breakfast habits, and nutritional status of students aged 10–12 years at MI Dakwatul Khoiriyah Kediri. Students should have daily breakfast to meet nutritional needs and improve nutritional status.

Bremsstrahlung radiation power in fusion plasmas revisited: towards accurate analytical fitting

Abstract In fusion plasmas, where electron temperatures $T_e$ range from keV to hundreds of keV, Bremsstrahlung radiation constitutes a significant energy loss mechanism. While various thermal average fitting formulas exist in the literature, their accuracy is limited, particularly for $T_e \leq 20$ keV with error $&gt;10\%$. Additionally, non-relativistic fitting formulas become invalid for $T_e \geq 50$ keV. The accurate calculation of Bremsstrahlung radiation is important for fusion gain study, especially of advanced fuels fusion. In this work, we develop new but still simple fitting formulas that are valid for electron temperatures ranging from small ($\lesssim0.1$ keV) to extreme relativistic range, with errors of less than 1\% even for high atomic number ions (e.g., $Z=30$), which could be sufficient for fusion plasma applications. Both electron-ion (e-i) and electron-electron (e-e) Bremsstrahlung radiations are considered. Both polynomial fitting with truncated ($t=k_BT_e/m_ec^2\leq10$) and one-fit-all formulas are provided. Additionally, we offer fitting formulas for e-i and e-e specifically for electron energies, which could prove useful in non-Maxwellian Bremsstrahlung radiation studies.

Metal Replacement in Aeronautics Applications Using Two‐Dimensional Multifunctional Composites

Carbon‐based composites have replaced metals in many structural components of modern airplanes; however, functional applications still require metal conductors and devices. Here, the production and integration in small‐scale airplane parts of electric devices based on metal‐free layered composites of graphene, boron nitride, and polymers are described. Thanks to their two‐dimensional structure, these materials feature excellent tunable thermoelectrical properties and high flexibility. We describe the production and performance of heaters and ice sensors installed on scaled NACA‐0012 airfoils to avoid ice formation, to remove ice or to detect the ice on the outer skin of the airfoil. We demonstrate that we can tune the electrical conductivity of the composite heating element from 105 to 103 S m−1 that is preserved even after 1000 bending cycles. The heaters successfully achieve anti‐icing and deicing in representative icing conditions and can sustain high‐power densities as required for electrothermal ice‐protection systems in aviation. The composite material can be readapted into a sensor able to detect ice formation in real time in an icing wind tunnel. Moreover, the possibility to use the heaters developed as heating molds to cure composite materials by out‐of‐oven curing, with a significant energy saving potential, is demonstrated.

Resonant Solitary States in Complex Networks

Abstract Partially synchronized solitary states occur frequently when a synchronized system of networked oscillators with inertia is perturbed locally. Several asymptotic states of different frequencies can coexist at the same node.&amp;#xD;Here, we reveal the mechanism behind this multistability: additional solitary frequencies arise from the coupling between network modes and the solitary oscillator's frequency, leading to significant energy transfer. This can cause the solitary node's frequency to resonate with a Laplacian eigenvalue. We analyze which network structures enable this resonance and explain longstanding numerical observations.&amp;#xD;Another solitary state that is known in the literature is characterized by the effective decoupling of the synchronized network and the solitary node at the natural frequency. Our framework unifies the description of solitary states near and far from resonance, allowing to predict the behavior of complex networks from their topology.

Investigating the Effects of PCM-Integrated Walls on Thermal Performance for UK Residential Buildings of Different Typologies

Phase change materials (PCMs) can improve the thermal performance of building facades. The integration position of a PCM in the facades is influenced by multiple factors including the material properties of the PCM, building types, and the internal and external conditions of a building. However, this has not been a focus within the UK dwelling stock, where many dwellings are not thermally protected. This paper, therefore, presents a numerical study with the aid of building simulation that comparatively analysed the thermal performance between four typical UK dwelling types (semi-detached house, terraced house, detached house, and apartment) situated in North East England. The PCM was implemented into the external wall of the dwellings with the positions altered to determine the most effective position. It was determined that the PCM positioned internally was the most effective for all the dwelling types. These results demonstrated that the PCM being implemented in the apartment, semi-detached, and terraced houses had only marginal heat loss reductions (by 8%, 14%, and 8%, respectively) in comparison with that of the detached house (by 30%). It was also found that the large external wall area of the detached house acted as significant thermal energy storage, which was capable of offsetting heat transmission and stabilising indoor thermal conditions. In summary, this paper contributes to the matters concerning the effect of PCMs on indoor thermal performance in dwellings of different typologies in the UK.

Seismic evaluation of plan irregular reinforced concrete building equipped with comb teeth metallic passive damper

Active and passive damping devices exhibit significant energy dissipating behavior under seismic loads and are being used to control vibration in buildings worldwide. Comb Teeth Damper (CTD) is one of the most promising recent types of passive dampers which consist of steel plates with multiple teeth designed to dissipate seismic energy by in-plane flexural yielding. In this study, the CTD was modeled and analyzed under lateral cyclic load using the finite element analysis software, ABAQUS. The first phase of this investigation was a comprehensive parametric study on CTD with two different numbers of teeth, namely 4 teeth (4 CTD) and 5 teeth (5 CTD); each model with six different thicknesses, namely 5, 10, 15, 20, 25, and 30 mm. The parametric study showed that by increasing the number of teeth, the load-carrying capacity, initial stiffness, and energy dissipation capacity have increased considerably. A similar trend was observed when the thickness of the damper was increased. From the results of the parametric study, 5CTD with 15 mm thickness damper model was identified as a suitable model for utilizing in Reinforced Concrete (RC) buildings using optimization algorithm. In the second phase, a G + 14 storey plan irregular RC building was modeled in the structural engineering software ETABS and subjected to six different strong earthquake ground motions. The non-linear time history analysis was performed on the building with and without identified CTD and found that the building equipped with dampers reduced 56% top displacement, 60% inter-storey drift, and 33% energy dissipation.

Assessing the Influence of Occupancy Factors on Energy Performance in US Small Office Buildings

Office buildings are responsible for about 35% of the total electricity in the US and over 70% of building energy consumption occurs during occupancy periods. Therefore, understanding occupancy behavior is crucial for reducing building energy consumption. However, given the stochastic nature of occupant behavior, identifying which occupancy parameters have the most impact on energy consumption poses a considerable challenge. This study aims to investigate and quantify the impact of various occupancy parameters on the energy performance of a US small-sized office building using an EnergyPlus-based nationwide energy simulation. First, dynamic occupancy schedules are created based on different occupancy parameters using an agent-based model. Next, the generated dynamic occupancy schedules are integrated into a small office building model from the Department of Energy’s prototypes. This creates a dataset of occupancy parameters and building energy performance across various climate zones. Finally, various feature selection and statistical analysis methods are applied to the generated dataset. This helps identify significant occupancy parameters and quantify their impact on building energy performance across different climate zones. According to the results of the study, buildings located in cool marine, mixed marine, and warm marine climate zones had lower total energy consumption compared to other zones. Additionally, feature selection methods identified “Occupant Density” as the primary significant variable impacting energy consumption, across all climate zones. These findings offer valuable insights into the influential occupancy parameters across various climate zones, highlighting the importance of tailoring occupancy schedules to enhance energy efficiency. They provide practical guidance that can be directly applied to optimize energy consumption and achieve significant energy savings in small office settings with different weather conditions.

Thermodynamic analysis of a novel compressed carbon dioxide energy storage coupled dry methane reforming system with integrated carbon capture

Chemical absorption CO2 capture, compressed carbon dioxide energy storage (CCES) and dry reforming of methane (DRM) can be used for continuous carbon capture, storage and utilization. However, CO2 capture is often accompanied by significant energy consumption. Considering the waste high-grade thermal energy at the exit of solar methane reforming, the article proposes a coupled system in which the thermal energy at the exit of the DRM system is used in CCES to improve the system's work capacity, and the remaining heat and the compression heat of the CCES are used for CO2 capture. The study established mathematical models of the three subsystems, performed thermodynamic analyses, and completed experiments on dry reforming of methane. The results show that the coupled system can increase the electro-electric conversion efficiency by 150.49 % reaching 220.33 % and the energy efficiency by 7.25 % reaching 77.09 %. The coupled system can save up to 43.33 % of CO2 capture heat. The DRM subsystem can utilize the higher temperature CO2 in the tail end of the CCES system, and its methane conversion efficiency and solar-fuel efficiency can be increased by 3.54 % and 3.20 % respectively to reach 58.22 % and 61.02 %. And the economic analysis found that the coupled system has better economics.