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Natural gas hydrates, with their efficient and clean energy characteristics, are deemed a significant pillar within the future energy sector, and their resource quantification and development have a profound impact on the transformation of global energy structure. However, how to accurately identify gas hydrate reservoirs (GHRs) is currently a hot research topic. This study explores the logging identification method of marine GHRs based on machine learning (ML) according to the logging data of the International Ocean Drilling Program (IODP) Expedition 311. This article selects six ML methods, including Gaussian process classification (GPC), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR). The internal relationship between logging data and hydrate reservoir is analyzed through six ML algorithms. The results show that the constructed ML model performs well in gas hydrate reservoir identification. Among them, RF has the highest accuracy, precision, recall, and harmonic mean of precision and recall (F1 score), all of which are above 0.90. With an area under curve (AUC) of nearly 1 for RF, it is confirmed that ML technology is effective in this area. Research has shown that ML provides an alternative method for quickly and efficiently identifying GHRs based on well logging data and also offers a scientific foundation and technical backup for the future prospecting and mining of natural gas hydrates. Full article

With the acceleration of the oilfield development process during the high water content period, the contradiction between the increase in energy consumption and the decrease in the energy efficiency of the gathering and transportation system has become increasingly obvious. This paper develops a grey relational analysis model using a combination of AHP and EWM. Based on the characteristics of light oil production, a four-level evaluation indicator system is developed. Based on game theory, AHP can provide subjective weights, the EWM can provide objective weights, and subjective and objective combinations are used for a more reasonable assignment. Concurrently, the 0.05 distinguishing coefficient and the ideal reference values are selected as the GRA reference sequence to evaluate the energy consumption of the gathering and transportation system as a whole and each subsystem. The analysis of a light oil block indicates significant room for improvement in the energy efficiency correlation across the system. Taking the central processing station as an example, the grey relational degree of electricity consumption per unit of injected water is measured at 0.12, marking it as the weakest link in the system. This study supports efficiency enhancement by identifying energy consumption bottlenecks within the system. Full article

The requirement for energy transition through the residential sector has increased research on the dissemination of solar thermal power systems in this area. Parabolic Trough Collector (PTC), as one of the matured solar technologies for thermal power generation, has shown huge potential in meeting demands for heating and domestic hot water systems. In this experimental study, several small-scale PTCs have been developed with four alternative absorber shapes: a simple cylindrical absorber, a spiral absorber, and two different configurations of a sinusoidal absorber to examine their performance under domestic application (non-evacuated and non-tracking). The study aims to analyze the applicability of such systems to be used as a water-heating source in buildings and compare the performance of the proposed configurations in terms of thermal efficiency to find the most appropriate design. The experimental results revealed that the simple shape provides a minimum average thermal efficiency of 24%, while the maximum thermal efficiency of 32% is obtained with the spiral shape. Studying various orientations of the sinusoidal shape revealed that thermal efficiencies of 30% and 20% could be achieved using the parallel and the perpendicular shapes, respectively. Finally, a concise economic and environmental analysis is performed to study the proposed systems as solutions for domestic water heating applications, which highlights the suitability of PTCs for integration with future sustainable buildings. Full article

The rapid development of autonomous driving technology has been a key driver for the emergence of driverless delivery vehicles. To promote wider adoption, it is essential to address consumers’ concerns about safety and reliability, leverage psychological factors, and implement supportive policies that encourage technology adoption while ensuring public safety and privacy. Therefore, it is necessary to explain and predict consumers’ behavior and intention to adopt driverless delivery vehicles. To this end, this study extends the Technology Acceptance Model (TAM) to include technological complexity and perceived trust. This study evaluates the model by applying necessary condition analysis (NCA) and partial least squares structural equation modeling (PLS-SEM) to analyze data from 579 respondents from Jiangsu Province, China. This study explores the sustainability implications of autonomous delivery vehicles, highlighting their potential to reduce environmental impact and promote a more sustainable transportation system. The outcomes indicate that perceived ease of use (PEU), attitude, perceived trust, technological complexity (TECOM), and perceived usefulness (PU) are significant determinants and necessary conditions of consumers’ intention to adopt driverless delivery vehicles. Perceived trust and TECOM had a significant and indirect influence on consumers’ intention to adopt driverless delivery vehicles via PU and PEU. Perceived trust and technological complexity had a substantial impact on consumers’ adoption intention of driverless delivery vehicles. The study recommends that managers work closely with regulators to ensure their technologies meet all local standards and regulations. It also recommends its potential to reduce carbon emissions, improve energy efficiency, and contribute to a more sustainable transportation system. Full article

This study investigates the long-term impact of insulation degradation on building heating energy consumption, with a focus on extruded polystyrene (XPS) insulation. Year-by-year degradation in thermal transmittance was derived from long-term experimental data and applied to prototypical energy models of multifamily apartment buildings and office buildings. Simulations were performed using both Actual Meteorological Year (AMY) and Typical Meteorological Year (TMY) data for six cities representing Korea’s major climate zones. The results showed that insulation degradation led to a significant increase in heating energy consumption from 23.2% to 34.9% in AMY simulations and 23.5% to 36.2% in TMY simulations for multifamily apartment buildings over 15 years. The difference between the AMY and TMY estimates was within 4%, demonstrating the reliability of TMY for long-term performance assessments. Notably, the southern and Jeju zones exhibited higher sensitivity to degradation due to their relaxed insulation standards and lower initial thermal performance. Office buildings were less affected, with increases below 8%, attributed to smaller envelope areas and higher internal heat gains. These findings highlight the need for zone-specific insulation standards and differentiated energy-saving design strategies by building type to ensure long-term energy efficiency. Full article

Nd–Fe–B permanent magnets are vital for numerous key technologies in strategic sectors such as renewable energy production, e-mobility, defense, and aerospace. Accordingly, the demand for rare earth elements (REEs) enormously increases in parallel to a significant uncertainty in their supply. Thus, research and innovative studies are focus on the investigation of sustainable solutions to the problem and a closed-loop value chain. The present study is based on two benign-by-design approaches aimed at decreasing the recycling loop span by preparing standardized batches of EoL Nd–Fe–B materials to be treated separately depending on their properties, as well as using mechanochemical method for waste processing. The previously reported benefits of both direct recycling and mechanochemistry include significant improvements in processing metrics, such as energy use, ecological impact, technology simplification, and cost reduction. Waste-sintered Nd–Fe–B magnets from motorbikes were collected, precisely sorted, selected, and pre-treated. The study presents a protocol of resource-efficient recycling through mechanochemical processing of non-oxidized sintered EoL magnets, involving the extraction of Nd₂Fe₁₄B magnetic grains and refining the material’s microstructure and particle size after 120 min of high-energy ball milling in a zirconia reactor. The recycled material preserves the main Nd₂Fe₁₄B magnetic phase, while an anisotropic particle shape and formation of a thin Nd/REE-rich layer on the grain surface were achieved. Full article

Multipurpose heat pumps are devices able to provide simultaneously heating and cooling requirements. These devices concurrently provide useful thermal energy at condenser and evaporator with a single electrical energy input, potentially achieving energy savings as heat-recovery and co-generative technology. Despite their potential contribution to the energy transition goals as both renewable and energy-efficient technology, their use is not yet widespread. An application example for multipurpose heat pumps is air handlers, where cooling and reheat coils are classically fed by separate thermal generators (i.e., boiler, heat pumps, and chillers). This research aims at presenting the energy potential of multipurpose heat pumps as thermal generators of air handler units, comparing their performances with a classic separate configuration. A museum in the Mediterranean climate is selected as a reference case, as indoor temperature and relative humidity must be continuously controlled by cold and hot coils. The thermal loads at building and air handler level are evaluated through TRNSYS 17 and MATLAB 2022b, through specific dynamic models developed according to manufacturer’s data. An integrated building-HVAC simulation, on the cooling season with a one-hour timestep, demonstrates the advantages of the proposed technology. Indeed, the heating load is almost entirely provided by recovering energy at the condenser, and a 22% energy saving is obtained compared to classic separate generators. Furthermore, a sensitivity analysis confirms that the multipurpose heat pump outperforms separate generation systems across different climates and related loads, with consistently better energy performance due to its adaptability to varying heating and cooling demands. Full article

The emergence of connected and automated vehicles (CAVs) offers promising opportunities to enhance traffic control and improve overall transportation system performance. However, the complexity and dynamic nature of modern traffic networks pose significant challenges for traditional routing methods. To achieve optimal vehicle routing and support sustainable mobility, more adaptive and intelligent strategies are needed. Among recent advancements, model-based deep reinforcement learning has shown exceptional potential in solving complex decision-making problems across various domains. Leveraging this capability, the present study applies a model-based deep reinforcement learning approach to address the energy-efficient routing problem in a simulated CAV environment. The routes recommended by the algorithm are compared to the shortest route calculated by traffic simulation software. The simulation results show a significant improvement in energy efficiency when the vehicle follows the routes suggested by the learning algorithm, even when the vehicle is subjected to new traffic scenarios. In addition, a comparison of the model-based agent with a conventional model-free reinforcement learning agent across varied traffic conditions demonstrates the robustness of the model-based algorithm. This work represents the first application of a model-based deep reinforcement learning algorithm to the energy-efficient routing problem for CAVs. This work also showcases a novel application of the foundational algorithm AlphaGo Zero. Full article

Investigating the microstructural damage and mechanical properties of coal under deep mine hygrothermal conditions is essential for ensuring the safe and efficient extraction of coal resources. In this study, X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and nanoindentation techniques were employed to examine the surface morphology and microscale mechanical properties of coal samples exposed to four environmental conditions, initial, humidified, heated, and coupled hygrothermal, under a peak indentation load of 70 mN. The results indicate that humidification led to the formation of dissolution pores and localized surface softening, resulting in a 15.9% increase in the peak indentation depth and reductions in the hardness and elastic modulus by 29.53% and 17.14%, respectively. Heating caused localized disintegration and the collapse of the coal surface, accompanied by surface hardening, with a slight 0.4% decrease in the peak indentation depth and increases in hardness and the elastic modulus by 1.32% and 1.56%, respectively. Under the coupled hygrothermal condition, numerous fine dissolution pores and microcracks developed on the coal surface, and the mechanical properties exhibited intermediate values between those observed in the humidified and heated states. Notably, the elevated temperature suppressed the moisture penetration into the coal matrix to some extent in the hygrothermal environment. A positive correlation was found between the hardness and elastic modulus, independent of the coal sample condition. The mineralogical composition significantly influenced the microscale mechanical behavior, with hard quartz minerals corresponding to lower peak indentation depths and a higher hardness, whereas soft kaolinite showed the opposite trend. Full article

This article is devoted to the study of the possibilities for improvAzing the quality of energy management systems adopted at open-pit mining enterprises in the Russian Federation. The main idea of the work is to apply stochastic boundary value analysis methods using the production function for individual and integral estimates of the performance of energy-consuming objects when performing various types of technological work. It is shown that mining enterprises are experiencing problems in the field of rational energy consumption due to the lack of strictly formalized ways to determine the frontiers of the efficiency value of the parameter of specific energy consumption (SEC). A justification is given for the need to apply stochastic frontier analysis (SFA) methods and use the Cobb–Douglas production function to account for the nonlinearity and stochasticity of the operating conditions of energy-consuming mining objects. The results of a statistical analysis of the data on the operation of EKG-10 excavators at operating enterprises in Siberia are presented, as well as an assessment of their energy efficiency using the adopted approach based on planning the target value of SEC. The results of computational experiments on constructing an energy efficiency model using the SFA/Cobb–Douglas function for various data segmentation options are presented. Computational experiments have been conducted to compare variants based on the Cobb–Douglas production function and translog function with semi-normal and exponential distribution forms for the same data set. A comparative assessment is given of the approaches to the complex analysis of activities adopted at enterprises and proposed in this study, characterizing potential hidden energy losses in the range from 4.53% to 20.73%. Full article

Real-time mining (RTM) has become increasingly significant in response to the growing need for sustainable mineral resource extraction, driven by global population growth and technological progress. This innovative approach addresses critical challenges, such as declining ore grades, deeper and less accessible deposits, and rising energy costs, by integrating advanced online grade monitoring, data analysis, and process optimization. By employing real-time grade control, dynamic mine planning, and production optimization, it enhances the efficiency of resource extraction while minimizing environmental and social impacts. Originally proposed about a decade ago, RTM has gained attention for its potential to revolutionize the industry. This review examines recent advancements in closed-loop concepts, emphasizing the integration of advanced sensors and data analytics to enable continuous monitoring and adaptive decision making across the mining value chain. It highlights the role of online sensor technologies in providing high-resolution data for process optimization and evaluates various mining optimization techniques. The paper also explores data assimilation methods, such as Kalman filters and artificial intelligence (AI), showcasing their ability to continuously update models and reduce operational uncertainties. Ultimately, it proposes a comprehensive framework for adaptive, data-driven mining operations that promote sustainable development, enhance profitability, and improve decision-making capabilities. Full article

According to the International Institute of Refrigeration (IIR), 20% of worldwide electricity consumption is for refrigeration, with domestic refrigeration appliances comprising a fifth of this demand. As the uptake of renewable energy sources for on-grid and isolated electricity supply increases, the need for mechanisms to match demand and supply better and increase power system flexibility has led to enhanced attention on demand-side management (DSM) practices to boost technology, infrastructure, and market efficiencies. Refrigeration requirements will continue to rise with development and climate change. In this work, particle swarm optimization (PSO) is used to evaluate energy saving and load factor improvement possibilities for refrigeration devices at a site in Kenya, using a combination of DSM load shifting and strategic conservation, and based on appliance temperature evolution measurements. Refrigeration energy savings of up to 18% are obtained, and the load factor is reduced. Modeling is done for a hybrid system with grid, solar PV, and battery, showing a marginal increase in solar energy supply to the load relative to the no DSM case, while the grid portion of the load supply reduces by almost 25% for DSM relative to No DSM. Full article

Water, the source of life, is undeniably essential to all living beings in nature. However, the process of industrialization has led to the pollution of water resources. Photocatalytic water treatment technology can convert solar energy into environmentally friendly and renewable chemical energy, effectively degrading organic pollutants in water. This offers a promising solution for the purification of water environments. The development of high-performance photocatalysts is crucial for photocatalytic reactions. Polyoxometalates (POMs) are anionic metal oxide clusters that come in various sizes and shapes. Their unique electronic properties, tunable structures, and photocatalytic activity make them highly promising materials for the efficient degradation of organic pollutants in water. This review summarizes the recent advances in emerging POM-based photocatalytic materials for water treatment, elaborating on their mechanisms of action. Finally, the current development prospects and the future challenges of POM-based photocatalytic materials are envisioned. Full article

The upper facilities of polar marine equipment face severe freezing risks in ice-covered regions, necessitating energy-efficient electric heat tracing design. Existing models neglect coupled environmental factors (temperature–wind–humidity), leading to the overestimation of heating power. In this paper, experiment and CFD simulation are used to study the change of convective heat transfer coefficients of electric tracing circular tube components under the polar coupling environmental conditions of wind speed of 0~8 m/s, temperature of −40~0 °C, and air relative humidity of 10~95%, and the corresponding mathematical prediction model is established. The results show that increasing the wind speed and relative humidity will both increase the convective heat transfer coefficient of the circular tube, while the temperature is inversely proportional to the convective heat transfer coefficient of the circular tube. The convective heat transfer coefficient shows an average growth rate of only 2.8–3.8% as the temperature decreases from −10 °C to −40 °C, which is significantly lower than the effects of wind speed (average growth rate 59–50%) and humidity (average growth rate 7.5–12.7%). When the wind speed exceeds 2 m/s, the growth rate of humidity’s effect on the coefficient increases from 17.82% to 33.96%. Mathematical prediction models can provide certain references for the calculation and design of reasonable heating amounts for anti-icing and de-icing of polar equipment’s circular tube components under ice-covered regions. Full article

Torque and velocity fluctuations in internal combustion engines (ICEs), particularly during idle and low-speed operation, can reduce efficiency, increase vibration, and impose mechanical stress on coupled systems. This work presents the design, simulation, and experimental validation of a passive balancing cam mechanism developed to mitigate fluctuations in single-cylinder internal combustion engines (ICEs). The system consists of a cam and a spring-loaded follower that synchronizes with the engine cycle to store and release energy, generating a compensatory torque that stabilizes rotational speed. The mechanism was implemented on a single-cylinder Honda^® engine and evaluated through simulations and laboratory tests under idle conditions. Results demonstrate a reduction in torque ripple amplitude of approximately 54% and standard deviation of 50%, as well as a decrease in angular speed fluctuation amplitude of about 43% and standard deviation of 42%, resulting in significantly smoother engine behavior. These improvements also address longstanding limitations in traditional powertrains, which often rely on heavy flywheels or electronically controlled dampers to manage rotational irregularities. Such solutions increase system complexity, weight, and energy losses. In contrast, the proposed passive mechanism offers a simpler, more efficient alternative, requiring no external control or energy input. Its effectiveness in stabilizing engine output makes it especially suited for integration into hybrid electric systems, where consistent generator performance and low mechanical noise are critical for efficient battery charging and protection of sensitive electronic components. Full article