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Developing cost-effective and stable materials for the electrocatalysis of hydrogen evolution reaction (HER) remains challenging. In this study, efficient catalysts for HER were synthesized by integrating the cobalt and molybdenum oxides via electrodeposition, followed by subsequent sulfurization of the as-prepared oxides using chemical vapor deposition (CVD). This methodology allowed the incorporation of both cobalt and molybdenum components into the catalyst in a single step. The as-synthesized CoSx@MoSx/MoOx-based catalysts exhibited excellent hydrogen production performance in acidic media owing to the presence of Co-S and Mo-S bonds in the hybrid structure. Particularly, CoSx@MoSx/MoOx(90@360) and MoSx@CoOx(180@180) displayed the best HER performances with low overpotentials of 80 mV and 150 mV, respectively. The catalysts were highly stable, with their stability preserved for over 1000 cycles with marginal reduction in overall efficiency. Therefore, these findings suggest the potential of CoSx@MoSx/MoOx and MoSx@CoOx composites as ideal candidates for developing low-cost catalysts for electrochemical hydrogen production. [ABSTRACT FROM AUTHOR]

Integrating more active components into a catalyst material could facilitate the development of multifunctional electrocatalysts for energy conversion and storage applications. In this study, we developed a multifunctional electrocatalyst, namely, Pd alloyed with Co-Fe deposited on N-doped mesoporous carbon derived from tissue paper (Pd@Co-Fe/N-TDC). The synergism in Pd@Co-Fe/N-TDC, stemming from the interatomic alloy between Pd and Co-Fe, N-doped mesoporous carbon with defective surfaces, distribution of polyhedral Pd nanoparticles, and strong metal-support interfacial interaction, resulted in significantly high electrocatalytic performance for both oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Pd@Co-Fe/N-TDC was found to be an efficient bifunctional oxygen electrocatalyst, and this was evidenced by a high onset potential (1.01 V) and kinetic current density (2.6 mA/cm2) for the ORR and by a low overpotential (296 mV) and a low Tafel slope value (38 mV/dec) for the OER, along with a small Δ E of 736 mV. The catalyst also exhibited high durability for both ORR and OER, even after 10000 and 5000 cycles, respectively. Theoretical assessment provides an insight into the synergism of active metal sites in Pd@Co-Fe/N-TDC, which showed its potential for use as a non-Pt electrocatalyst for energy applications. [ABSTRACT FROM AUTHOR]

The variable pressure source system is widely used in renewable energy recovery scenarios. However, the instability of the new energy input and the nonlinearity of the power generation system can lead to problems such as more difficult tracking and control of the maximum power point and poor power generation quality. To address the problem of lower efficiency under varying inputs, which is prevalent in renewable energy generation, this paper establishes an energy recovery system model based on power conversion rectifier topology, designs a speed current double closed‐loop control strategy, proposes an online variable‐step maximum efficiency point tracking method based on experience curves, and tests the overall energy recovery effect through system simulation and comparative experiments. The simulation results show that the maximum efficiency point tracking method proposed in this paper reduces the number of optimization searches by 50% and improves optimization speed. The experiment results show that, under the drastic changes of the input, the method proposed in this paper could reduce the total harmonic distortion to 5.65%, improve the energy recovery efficiency by 10.976%, and reduce the fluctuation ratio of the voltage to 2.43%. This study can provide an important reference for the collection and utilization of new energy sources. [ABSTRACT FROM AUTHOR]

The increasing environmental consideration and the growth of instability of the energy market call for methods that can process the organic fraction of municipal solid waste (OFMSW) into fuel instead of disposal. Due to the pyrolysis and gasification that are efficient procedures to achieve valuable products, gasification of OFMSW with different particle sizes and compositions was carried out in the presence of Ni and Ni-Ce-loaded Y-zeolite in a multizone tubular kiln reactor. During the experiments, 500°C was applied in the first reactor zone, while the 2nd zone was at an elevated temperature of 600°C or 900°C in the presence of steam. The combined pyrolysis and gasification experiments were also carried out without a catalyst with the same operating conditions. The feedstock was collected from the organic fraction of a Hungarian mechanical-biological treatment (MBT) plant and was separated into four different fractions based on particle size: <1 cm dry biomass and fine particles, 1-2 cm paper-rich, 2-6 cm paper and plastic-rich feedstocks, and <6 cm mixture of these fractions. During the experimental work, product yields; gaseous product composition; the ratio of H2/CO, CO2/CO, and CH4/CO; and lower heating value were determined in the function of feedstock composition, the applied temperature, and catalysts. It was found that the hydrogen content and H2/CO ratio of gaseous products were increased due to catalyst application and temperature elevation in the 2nd reactor zone. The addition of Ce to the Ni/Y-zeolite catalyst was advantageous in the case of hydrogen formation at a lower temperature (600°C). The hydrogen and methane content of products obtained from the catalytic pyrolysis-gasification of paper and plastic-rich OFMSWs on elevated temperatures were higher, which increased the lower heating value of those products. Based on the elemental analysis of the obtained solid residues, it was found that paper/plastic-rich feedstock released hydrogen and carbon with a higher extent. 1-2 cm feedstock-related solid residues had the highest H/C ratio which caused a 12.5-12.8 MJ/kg gross heating value. As a result, combined pyrolysis and gasification appear to be an efficient method to attain valuable outputs from OFMSW not only in gas but also in solid products. [ABSTRACT FROM AUTHOR]

Energy harvesting technology, as an emerging energy technology, has contributed outstandingly to the application of online monitoring sensors in power systems. The accurate parameter identification of the harvesters is crucial for their power supply applications. This work aims to investigate the parameter identification of harvesters with complex equivalent circuit models, here taking a current transformer as an example. However, previous studies focused on optimizing algorithms rather than data sources. This paper demonstrates a vector impedance quantum genetic algorithm (QGA) parameter identification method to identify the amplitude and phase information of the impedance responses. By comparing the results of the proposed method with the genetic algorithm and PSO based on impedance responses and QGA based on load resistance responses, it is proved that the proposed vector impedance QGA identification method is optimal in terms of accuracy, speed, and robustness. The root mean square errors of the output voltage and the phase difference with the primary current for the proposed method are 5.884 × 10−5 V and 4.473 × 10−4 ms. Moreover, the practical applicability of this method is validated, demonstrating its effectiveness in real‐life and industrial settings. The proposed identification method in this paper changes the source data so that the sample data are small in volume and extensive in information, which enables faster and more accurate parameter identification. This study provides a new idea for parameter identification researches. [ABSTRACT FROM AUTHOR]

Today's societies need a sustainable supply of energy more than ever, whereas unseen events challenge the electric power supply. The coordinated operation of a power‐gas‐transportation system can decrease the effects of such faults in the distribution level. This paper proposes a model predictive control approach for dynamic load restoration in an integrated energy system. The coordination of electric and gas networks besides the mobile energy storage (MES) units provides a novel resilient alternative to serve active and reactive loads. First, the location and time of drastic events are identified as the initialization phase in this paper using a vulnerability analysis by a master–slave problem to find the weak points of the electric grid. Then, a rolling horizon model predictive control‐based approach is proposed to make corrective decisions to serve loads. Meanwhile, a new linearization approach for the Weymouth equation has been proposed based on the binary expansion method. The proposed linear resiliency‐oriented scheduling problem is tested on IEEE 33‐bus and IEEE 69‐bus integrated with a seven‐node gas distribution grid and a six‐station transportation railway. The analysis revealed that the coordination between electric and gas resources minimizes the not‐supplied load; MES units act as certain emergency tools to serve the critical loads. [ABSTRACT FROM AUTHOR]

Energy consumption has risen much more while the economy has expanded so quickly. As a result, significant environmental contamination is created, which causes public worry. Under this background, enhancing energy efficiency and lowering pollution is particularly crucial for the fulfillment of sustainable economic development. Efficiency evaluation for energy and environment is an effective measure to achieve both energy saving and emission reduction as well as sustainable social development. In this paper, energy and environmental efficiency consists of energy utilization efficiency and pollution treatment efficiency. An improved two‐stage DEA model is proposed, which takes into account how stochastic and environmental factors affect decision‐making units (DMUs). Finally, the author analyses the panel data of 15 cities in Southwest China from 2010 to 2018, which proves the feasibility and effectiveness of this study. The results show Southwest China's poor energy and environmental efficiency as well as the stark differences among its cities. The scientific value revealed by this paper is as follows: (1) for the study of energy and environmental efficiency, this paper is more targeted at the differences between cities and (2) using an improved two‐stage DEA model to consider the effects of stochastic factors on DMUs. [ABSTRACT FROM AUTHOR]

As a critical component of the energy sector, the high‐temperature gas‐cooled reactor (HTR) system plays an important role on the road to carbon neutrality. As a flagship of the safety analysis codes, the HTR code package (HCP) has been developed to provide a comprehensive modeling and simulation platform of the HTR system under operational and accidental conditions, especially for pebble bed reactors. A variety of individual legacy HTR computer codes were integrated into a consistent code package using flexible and efficient programing techniques and standards, and the know‐how gained over decades in HTR safety studies was preserved. This paper gives a state‐of‐the‐art overview in HTR studies and presents the new flexible curtain‐based control rod system that has been recently implemented. This method involves flexible manipulation of isotope concentrations and utilizes C++ object‐oriented programing principles while incorporating the legacy codes. Comparative analyses with established codes such as Serpent and ATHLET underscore the precision and reliability of the HCP, thereby enhancing its applicability in HTR design and safety evaluations. Finally, the paper outlines prospective avenues for further advancing the HCP, underscoring its evolving role in shaping the future of HTR development and analysis. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low turbulent velocity and incomplete combustion in the combustion process of the Wankel rotary engine (WRE), and setting turbulence‐induced blade (TIB) in the combustion chamber recess is an effective means to enhance the turbulent motion and promote combustion. Carifying the optimal arrangement position of TIB can get better combustion performance. Therefore, a computational fluid dynamics (CFD) simulation model of a WRE with TIB was established in this paper. Based on the differential pressure perturbation mechanism, the influence of the arrangement position of the TIB on the flow field and combustion performance were analyzed. The results showed that when the ratio of blade arrangement distance is less than 0.66, the pressure difference between the leading and trailing of the blade is small. At this time, the in‐cylinder flow is mainly disturbed by the forced disturbance of the TIB, the increase of the turbulent velocity is not obvious, and the diffusion velocity of the flame to the rotor direction is small. When the ratio of blade arrangement distance is greater than 0.66, the pressure difference between the leading and trailing of the blade is large. At this time, the In‐cylinder flow is affected by the pressure difference flow in addition to the forced disturbance of the TIB. The turbulent velocity is effectively enhanced, and the flame velocity is fast to the rotor direction. When the ratio of blade arrangement distance is 0.66, it shows the best combustion performance, and the indicated work is the largest. Compared to the scheme without TIB, the indicated work is increased by 16%. [ABSTRACT FROM AUTHOR]

Energy poverty (EP) has emerged as a major challenge to achieving sustainable development goals, and its significance in social development has increased over time. This paper aims to analyze the spatial autocorrelation between EP and social factors on a global scale. Utilizing the panel data of 116 countries from 2012 to 2019, the Bivariate local Moran index, a representative spatial econometrics tool, has been employed to examine temporal changes and spatial differences of transboundary synergy and tradeoff relations between EP and social factors. The results indicate that EP has synergy relationships with social factors, including life expectancy at birth, access to immunization, CO2 emission, and forest area, and tradeoff relationship with social factors, such as infant mortality rate, prevalence of undernourishment, forest rents, and gender inequality. Significant spatial differences have been observed that clusters of high‐income countries, particularly those in the Global North, tend to have better energy access and are surrounded by areas with favorable social conditions, and clusters of lower‐income countries, especially those in South Africa and Southeast Asia, have lower energy access and are surrounded by areas with more severe social conditions. The robustness analysis has been conducted to verify the reliability of the results. The spatial imbalance of findings offers robust evidence by emphasizing the importance of key areas, such as Southeast Asia and South Africa, that should be prioritized to take essential policy measures to address the EP and social issues. [ABSTRACT FROM AUTHOR]

Accurately forecasting electricity demand is crucial for maintaining the balance between supply and demand of electric energy in real‐time, ensuring the reliability and cost‐efficiency of power system operations. The integration of numerous active loads and distributed renewable energy sources into the grid has led to increased load variability, rendering the traditional point forecasting approach inadequate for meeting the evolving needs of the power system. Probabilistic forecasting, which predicts the complete probability distribution of loads and provides more extensive information on load uncertainty, has emerged as a key solution to address these challenges. The long short‐term memory (LSTM) model, known for its strong performance in modeling long series, is commonly utilized in load forecasting. Therefore, this study focuses on short‐term electric load probability forecasting for users in a specific park in Yantai. We propose a short‐term load probability forecasting model based on integrated feature selection (IFS), genetic algorithm (GA) optimization of LSTM, and quantile regression (QR), referred to as the IFS‐GA‐QRLSTM model. Initially, the integrated feature selection method is employed to identify the most influential factors affecting electric load, optimizing the model's input features and reducing data redundancy. To address the subjective nature of parameter selection in the LSTM model, we use a GA to optimize model parameters. The combination of optimized LSTM with QR enables direct generation of quantile load predictions, which are further used in kernel density estimation to construct the probability density distribution. We compare the proposed method with five basic models, QRLSTM, IFS‐QRCNN, IFS‐QRRNN, IFS‐QRLSTM, and IFS‐QRGRU, for point prediction, interval prediction, and probability prediction. Experimental results demonstrate that the proposed method in this paper exhibits better prediction performance, smaller prediction errors, and greater effectiveness compared to the aforementioned models. [ABSTRACT FROM AUTHOR]

This paper reviews the current state of microbial fuel cell (MFC) technology for energy generation. It begins by exploring clean energy alternatives, focusing on waste‐to‐energy solutions, and introduces the concept, applications, and advantages of MFCs. The biochemical processes within MFCs are explained, highlighting how microorganisms metabolize substrates through glycolysis, the Krebs cycle, and the electron transport chain to generate electrons. These electrons flow through an external circuit and combine with protons and oxygen at the cathode to produce water or reduced forms of nitrogen and sulfur. This paper also analyzes 10 key parameters affecting MFC performance: coulombic efficiency, pH, temperature, substrates, organic loading rate, electrode potential, open circuit voltage, treatment efficiency, organic removal rate, and hydraulic retention time. Recent advancements in MFC technology are also discussed, including innovations in reactor configuration and scaling, the development of new membrane materials like earthen and ceramic, and improvements in wastewater treatment methods. The advancements also extend to genetic engineering techniques to enhance microbial efficiency and component modifications, such as the use of carbon‐based nanomaterials and metal catalysts for improved performance, innovations in proton transfer membranes, and mediator‐less MFCs utilizing metal‐reducing bacteria. Challenges facing MFC technology, such as cost, scalability, and environmental sensitivity, are mentioned. The paper concludes with future directions, including the use of advanced materials, integration with wastewater treatment infrastructure, and the potential for nutrient recovery and chemical synthesis. This comprehensive review aims to provide knowledge into optimizing MFCs for sustainable energy generation and environmental benefits. [ABSTRACT FROM AUTHOR]

Why Hydrogen. Nowadays, the energy industry and research laboratories are heavily working on developing ideas and methods to reduce and/or eliminate the carbon dioxide emission to control the problem of global warming. The solution relies on finding alternative green energy resources to reduce the world's dependence on conventional fossil hydrocarbon energy. Green hydrogen gas from water stands on the top of the alternative resources since it does not produce carbon dioxide when burned and in return does not add to global warming. Since seawater is the largest resource on earth, this makes the hydrogen production from seawater the most economically attractive solution. It is very important to avoid using fresh water as a source of hydrogen energy due to the dependence of all living creatures, humans, animals, plants, etc., on such very limited resources. In this paper, the applications of the proposed novel solution of green hydrogen production directly from seawater relies on solving one of the major problems, electrode corrosion. Generating Hydrogen. The direct method to produce green hydrogen from seawater is the electrolysis technique. This technique requires a direct current power supply for the positive and the negative electrodes. The outcome of the electrolysis is decomposing the saltwater to its positively charged hydrogen and sodium ions at the negative electrode and its negatively charged oxygen and chlorine ions at the positive electrode. The Real Challenge. Traditionally in the electrolysis analysis of water, both electrodes are made of metals. At the positive electrode, both the oxygen and the chlorine cause a high rate of corrosion to that electrode. This results in the interruption of the electrolysis process, bad water condition due to the corrosion deposits, and cost burden due to the need to periodically replace the corroded electrodes and/or use high-cost corrosion-resistant alloys. The Novel Solution. A low-cost but powerful solution to the corrosion problem is introduced, tested, and experimentally proved. The novelty of the solution relies on replacing the classic special high-cost metallic positive electrode with a nonmetallic, no-cost, one. The nonmetallic electrode is made of surface rock portions that have high porosity and pore volume and high permeability and pore connectivity and is saturated with saline water. Surface rocks with the above properties exhibit very low resistivity and hence conduct electricity like metallic electrodes. The voltage and the current conducted by such electrode are controlled by the electrode geometry, connectivity, and physical properties. Results and Discussions. Multiple experiments using the proposed nonmetallic high-porosity and high-permeability positive electrode are conducted and presented in this paper. The experimental work is based on testing multiple rock electrodes with different rock properties, sizes, and connectivity in comparison to the metallic ones. The comparison showed that the novel solution solved the corrosion challenges of the metallic electrodes with excellent efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a supplementary design method for a three‐power source hybrid power transmission system based on the lever method, aimed at enhancing its power performance and economic efficiency. It makes up for some defects of traditional configuration design methods. Firstly, in order to better show the connection between the two planetary rows in the composite distribution configuration, an equivalent lever model with four nodes was established. The rod model was screened based on the connection relationship between the mechanical point distribution and power source. The rod model was analyzed in combination with the motor speed and torque characteristics, and the value interval of the two mechanical points of the composite distribution rod model was obtained. Then, the four‐node rod model was split. By adding the clutch and brake to control the connection between different nodes of the planetary bank, the working mode of the configuration can be switched. The supplementary design was carried out by increasing the input distribution to compensate for the low efficiency of the composite distribution configuration when driving at low speed, and three optimal configurations were obtained. (Based on the constraints of factors such as rod length, there are finally three solutions that meet the requirements.) Finally, the vehicle dynamics model was established, and the simulation results based on the dynamic programming principle showed that compared with the typical three‐power configuration Prius+, the fuel economy of the optimal configuration was improved in different degrees. Configuration (II) was selected as the object for parameter optimization, and the results showed that the power performance of the vehicle is improved by 1.8%, and the economy is improved by 5.47%, which verifies the validity of the configuration design method proposed in this paper. [ABSTRACT FROM AUTHOR]

This comprehensive review delves into the intersection of ensemble machine learning models and interpretability techniques for biomass and waste gasification, a field crucial for sustainable energy solutions. It tackles challenges like feedstock variability and temperature control, highlighting the need for deeper understanding to optimize gasification processes. The study focuses on advanced modeling techniques like random forests and gradient boosting, alongside interpretability methods like the Shapley additive explanations and partial dependence plots, emphasizing their importance for transparency and informed decision‐making. Analyzing diverse case studies, the review explores successful applications while acknowledging challenges like overfitting and computational complexity, proposing strategies for practical and robust models. Notably, the review finds ensemble models consistently achieve high prediction accuracy (often exceeding R2 scores of 0.9) for gas composition, yield, and heating value. These models (34% of reviewed papers) are the most applied method, followed by artificial neural networks (26%). Heating value (12%) was the most studied performance metric. However, interpretability is often neglected during model development due to the complexity of techniques like permutation and Gini importance. The paper calls for dedicated research on utilizing and interpreting ensemble models, especially for co‐gasification scenarios, to unlock new insights into process synergy. Overall, this review serves as a valuable resource for researchers, practitioners, and policymakers, offering guidance for enhancing the efficiency and sustainability of biomass and waste gasification. [ABSTRACT FROM AUTHOR]

Optimal sizing and management of hybrid wind turbine-diesel-battery system for reverse osmosis seawater desalination in NEOM city is the objective of the paper. Therefore, the paper explored the different factors to optimize and introduce a technoeconomic evaluation and energy management of a stand-alone wind turbine (WT) system, diesel generator (DG), and battery storage (BS). The suggested WT/DG/BS system is implemented to feed seawater reverse osmosis (SWRO) unit in NEOM. The necessitated desalinated water per day is 100 m3. To determine the optimal size of WT/DG/BS corresponding to the minimum cost of energy (COE) and net present cost, two different ratings of the SWRO units (SWRO-100 and SWRO-150), three control dispatch strategies (load following, cycle charging, and combined dispatch), and five types of batteries are considered. HOMER software is performed to simulate and optimize the WT/DG/BS. The optimization results indicated that the best battery storage is the Trojan SAGM battery. In this case, the COE ranged between $0.337/kWh and $0.564/kWh. The lowest COE of $0.377/kWh is obtained when using a combined control strategy and SWRO-100 unit, whereas the worst COE of $0.564/kWh is obtained when using load following control strategy and SWRO-150 unit. The best option of the WT/DG/BS system to supply the SWRO unit is option number 26. This system includes one wind turbine of 90 kW, DG of 25 kW, 47 Trojan SAGM batteries, a 23.8 kW converter, a SWRO-100 unit, and a combined control strategy. The net present cost and the initial cost are $950,725 and $221,495, respectively. The annual operating cost and annual consumed fuel are $56,409 and 36,396 L, respectively. Compared with using only a 25 kW diesel generator, the COE reduced from $0.373/kWh (using only DG/BS) to $0.337/kWh (using the best option) by around 9.65%. Under this condition, the values for the internal rate of return, return on investment, and simple payback are 11%, 7.8%, and 8.3 years, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, most of the works are based on electric vehicle usage for sustainable transportation using traditional energy storage device, such as battery. Usage of batteries in electric vehicles is having several disadvantages, for example, life span, temperature, and charge estimation. In this paper, a novel control scheme for battery and supercapacitor- (SC-) based hybrid energy storage system (HESS) using hybrid proportional and integral- (PI-) sliding mode control (SMC) for electric vehicle (EV) applications is introduced and implemented. This HESS with hybrid controller proves the usage of batteries in EVs to its fullest potential. The conventional control strategy for HESS follows two-loop voltage and current PI controllers with low-pass filter (LPF) and involves tuning of multiple control parameters with variations of source and load disturbances. Performance of the system is affected by tuning PI controller constants. A slow response time with linear PI controllers is long which is not advisable for starting and sudden jerk conditions of EVs. Moreover, the PI controller performance is affected by the system parameter variations during load changes. And these parameters are dynamic in nature due to nonideal conditions. In this paper, a hybrid PI-sliding mode controller (SMC) scheme is designed to control the bidirectional DC-DC converters to overcome the drawbacks of aforementioned issues. The combined PI-SMC controller reduces the tuning effort and reduces the effect of shift in operating point in controller performance. Linear modeling is done using small signal analysis for each subsystems. Permanent magnet synchronous machine (PMSM) is used as electric vehicle. The entire system and its controllers are simulated using MATLAB-Simulink, and detailed comparison is carried between conventional PI and proposed hybrid PI-SMC scheme to regulate the DC link voltage. The results are tabulated and show that the hybrid PI-SMC scheme outperforms in transient and steady-state conditions than the traditional PI controller. A scaled hardware prototype of 48 W set-up is developed using dSPACE-1104, and the experimental results have been carried out to verify the proposed system's feasibility. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are increasingly used in power systems applications. One important application is the classification and localization of various types of transmission line faults. Using voltage and current measurements from phasor measurement units (PMUs), a number of useful features can be extracted, which can form the basis of a ML-based prediction of the fault type, line, and distance on the line. This paper proposes a technique to find the optimal number and placement of PMUs by performing thorough feature selection. The features are selected to maximize the accuracy of the ML classification and regression algorithms. The results show that for the IEEE 14 bus system, the use of only five PMUs is sufficient to obtain high levels of accuracy. For example, a testing accuracy of 99.0% and 97.1% can be achieved for the fault type and fault line location, respectively. As for the fault distance along the line, the testing MAE of 3.1% can be obtained along with an R 2 score of 94.4%. Adding more PMUs does not provide any additional value in terms of accuracy. [ABSTRACT FROM AUTHOR]

This paper is aimed at performing a comprehensive review of the approaches employed in nanosatellite platforms, specifically 1 U and 3 U, for electrical power systems (EPS). Moreover, it seeks to develop a robust maximum power point tracking (MPPT) controller using the sliding mode control (SMC) method. The proposed control strategy is intended to monitor the solar panel's maximum power point (MPP) and adapt to changes in temperatures and solar irradiance in low Earth orbit (LEO). The EPS model, designed with the proposed sizing method, is built in MATLAB-Simulink and integrates a solar panel, battery storage, and power converters controlled by diverse MPPT methods. Then, simulation results demonstrate the effectiveness of the proposed SMC approach compared to other conventional control methods for the designed EPS under varying LEO conditions. To achieve a robust validation of the most appropriate MPPT control method under steady-state conditions, this paper presents an experimental investigation into the proposed EPS hardware design. The proposed SMC method achieved an increase in power generation from 10% to 12% for buck and boost power converters, respectively, compared to traditional control methods. [ABSTRACT FROM AUTHOR]

This paper introduces a novel concept of a TRISO-fueled salt-cooled reactor (TFSCR). The core contains circulating pipes filled with molten salt carrying TRISO particles. The reactor achieves online refueling by slowly circulating the molten salt through the pipeline. The reactor utilizes the same molten salt as the coolant and graphite as the moderator. The reactor design has the characteristics of safety, economy, and nonproliferation. TRISO particles exhibit greater resistance to neutron irradiation, corrosion, oxidation, and high temperatures compared to conventional fuels. A molten salt-cooled reactor can also operate at higher temperatures, consequently enhancing power generation efficiency. Furthermore, lower operating pressures can mitigate the risk of significant damages and loss of coolant caused by accidents, thereby enhancing reactor safety. This paper presents the basic nuclear design of TFSCR under the assumptions concerning the average fuel power density, the volumetric core power density, and the core temperature. At the same time, the feasibility of online refueling and long-life operation was evaluated by fuel burnup calculation. [ABSTRACT FROM AUTHOR]

This paper presents a performance analysis and optimization of heat exchanger fin shapes in a thermoelectric power generation system for ship waste heat recovery. It proposes a new curved fin design aimed at enhancing heat transfer efficiency and maximizing thermoelectric power generation capacity. Numerical analysis and response surface methodology (RSM) focused on 225 parameters to identify the optimal fin shape. Computational analysis using CFX was conducted on the thermoelectric module system with curved fins. The methodology was verified by comparing and analyzing CFD and PIV experimental results for a curved fin. The paper comprehensively compares the fluid dynamics of curved fins and straight fins, highlighting how curved fins improve heat exchange by creating a tip leak vortex. The results demonstrate the superiority of the curved fin design in terms of heat transfer efficiency and net power generation over the conventional straight fin design. [ABSTRACT FROM AUTHOR]

This paper presents a multistrategy fusion particle swarm optimization model for dynamic economic dispatching of renewable energy in distribution networks. The objective is to minimize active network losses and system voltage deviation while considering the integration of distributed energy sources and static reactive power compensators. The algorithm incorporates specific strategies, including a particle position change strategy based on the midpipeline convergence approach, a strategy for generating exploding particles near the optimal particles, and a particle velocity update strategy relying on the global optimal particle position. The inertia weights and particle position update methods of the simplified particle swarm optimization algorithm are also utilized. Simulation experiments are conducted on an IEEE 33 bus radial distribution system, demonstrating the effective optimization of system losses while ensuring system voltage stability. This research contributes to the scientific understanding of renewable energy integration in distribution networks and its economic dispatching. [ABSTRACT FROM AUTHOR]

In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty-eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal. [ABSTRACT FROM AUTHOR]

The bioeconomy model provides an alternative view of global economic systems by putting sustainable practices combined with digital approaches at the forefront to tackle issues such as climate change. To address this new business trends, financial institutions began to set up the environmental, social, and governance (ESG) business units to evaluate their business strategies. This paper is aimed at examining the nonfinancial effect created by the digital transformation (DT) activities, highlighting the role of enterprise heterogeneity after the implementation of Environmental Protection Law (EPL) in China. We employ the panel data of A-share listed companies from 2010 to 2020, selecting DT and ESG indicators as the important representations of "Industry 5.0." Our empirical results demonstrate a positive impact of EPL on the ESG performance in sight of resource enterprises (REs), environmental enterprises (EEs), and polluting enterprises (PEs), but a negative impact of EPL on the DT indicators among those environmental related industries. Additional causal relationship regression reveals that enterprise DT has an intrinsic promoting effect on the ESG performance, emphasizing on the high risk of digitization process being the shock transmitters to enterprise nonfinancial indices. Notably, the connectedness of environmental policy illustrates dynamic patterns by parallel trend test and propensity score matching (PSM) DID regression. This paper is prone to benefit lawmakers, regulators, and firm executives responsible for analyzing and assessing enterprise digitization behavior by exploring the influence of macrolevel environmental policy. [ABSTRACT FROM AUTHOR]

Graphene‐based materials, which exhibit large surface areas and superior electrical properties, are promising materials as anodes in lithium‐ion batteries (LIBs). However, the formation of a solid electrolyte interphase (SEI) on the large surfaces of these electrodes causes the loss of active lithium, leading to a severe reduction in coulombic efficiency and cycle retention. In this study, we combined an electrochemical lithium deposition (ELD) strategy, wherein active lithium was inserted into an electrode to minimize lithium loss during cycling, with the use of a vertically aligned three‐dimensional (3D) graphene foam. This foam, which was created via freeze‐casting, facilitated uniform lithium distribution during ELD, enhancing active lithium utilization. Consequently, the lithium preplated vertically aligned graphene foam anode could improve charge transfer and stabilize the SEI. It exhibited a superior cycle retention of 86% at a current density of 0.5 C for 200 cycles in LIBs, which is superior to that of preplated film‐type anodes and lithium foils. Moreover, it enabled the easy infiltration of polymer electrolyte through aligned graphene sheets while maintaining its original cell performance with a liquid electrolyte. Furthermore, it exhibited a higher discharge capacity than that of lithium foil anodes with the same negative/positive ratio in high‐areal‐capacity lithium–sulfur batteries. Therefore, this paper indicates the potential of preplated vertically aligned graphene foam as a high safety, high‐areal‐capacity anode for various next‐generation rechargeable lithium batteries. [ABSTRACT FROM AUTHOR]

As the backbone of modern power systems, the smart grid is one of the most critical applications of the Internet of Things. The intelligent electricity system faces various types of unauthorized malicious access, that is, cyber‐attacks. With the development and application of information and communication technologies in traditional power systems, the improvement of physical‐cyber systems in the smart grid also increases. Nowadays, the deployment of defensive measures has surged in response to the growing number of attacks aimed at the smart grid. Therefore, in this paper, we investigate cyber‐attack identification using artificial intelligence‐based models and identify them by estimating the state vector of the electricity network. We accomplish simulations by extracting data from the five‐bus IEEE network to test the effectiveness of the proposed algorithm. False data attack vectors are then injected into the healthy measurements. In this way, to check the detection power of the proposed algorithms, 2,000 measurement samples have been taken from the five‐bus network, half of which are considered healthy data and the other half as manipulated data. After labeling healthy and false data, machine‐learning algorithms such as decision trees and k‐nearest neighbor (KNN) have been used to investigate and identify this type of attack. Comparative analysis of the two proposed algorithms against commonly used methods demonstrates significantly improved accuracy. Specifically, according to the best depth for the decision‐tree algorithm and k for the KNN algorithm, it is drawn with k = 3 and the decision‐tree algorithm with a depth of 9. According to the algorithms proposed in this article, the decision‐tree algorithm with a depth of 9 in p‐value of 0.45 and 0.64 and the nearest neighbor algorithm with k = 3 in p is equal to 0.72, 0.98, and 1 represent better accuracy. Also, the results indicate that the two proposed algorithms have performed much more favorably than other classification methods. Additionally, the detection accuracy increases with higher p‐values for these two algorithms. This problem shows that the detectors can detect false data injection attacks that cause more severe disturbances in the system. [ABSTRACT FROM AUTHOR]

Cross‐regional energy dispatch project is a crucial strategy for countries to reduce energy transportation costs and achieve optimal energy allocation. This research constructs a quasi‐natural experiment based on the West‐to‐East Natural Gas Pipeline Project in China. It finds that cross‐regional energy dispatch significantly reduces firms' energy intensity, and that firms in economically developed regions and high‐polluting firms benefit more from it. The mechanism test shows that cross‐regional energy dispatching reduces enterprises' coal consumption, increases the proportion of cleaner energy consumption, and optimizes their energy consumption structure. Cross‐regional energy dispatch accelerates the exit of inefficient enterprises and the development of efficient enterprises. Cross‐regional energy dispatch enhances the green innovation activities of enterprises and accelerates the renewal of production equipment. Governments should encourage cross‐regional energy cooperation and dispatch to achieve efficient use and distribution of energy resources. [ABSTRACT FROM AUTHOR]

With the increase in temperature, urban areas face issues like urban heat islands. The reduction of green vegetation across urban areas has led to a tremendous rise in temperature. Many studies suggest significant cities are currently experiencing this issue to a greater extent. Many researchers are working on building paints and dyes to reduce the heat intake of the building. Organic thermochromic materials are one such alternative for building dye and stain. The main issue with using organic thermochromic materials is that they quickly degrade under sunlight, which makes them less useful for building paint. To overcome this issue, many organic thermochromic black dye materials (OTCBDM) are encapsulated with inorganic materials such as metal oxide, which block sunlight and reduce the effect of degradation. Encapsulation is achieved via the emulsification method of metal oxide. This paper deals with the single and double microencapsulation methods with different surfactant concentrations, metal oxides, and combinations thereof. Various techniques were used such as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) with energy‐dispersive X‐ray spectroscopy technique (EDS), and differential scanning calorimeter (DSC), UV‐Vis spectroscopy, thermogravimetric analysis (TGA), and CIE Lab space analysis to determine the materials' encapsulation and chromatic nature. The FTIR spectra suggested the absorption peaks for the Ti─OH bond (at 814, 903, and 913 cm−1) and Si─OH group (at 1,084 and 1,178 cm−1) for the commercial dye encapsulated with TiO2 and SiO2. The Ti─O─Si bonds at 1,054 and 1,145 cm−1 showed for the double encapsulation of SiO2 and TiO2 either (i) doped or in (ii) layered structures. From the EDS analysis, we can clearly say that the presence of SiO2 and TiO2 indicates the encapsulation of the commercial thermochromic black dye material. The SEM micrographs of the TiO2 encapsulation of OTCBDM seem more promising and uniform core–shell structure formation with CTAB as a surfactant than the SDBS. Moreover, the SEM microstructure results of double encapsulation using both TiO2 and SiO2 suggested thicker coating of particles when compared with single encapsulated counterparts. The cold phase of layered double encapsulation of OTCBDM + SiO2/TiO2 shows a darker color than the doped combination as obtained from the CIE Lab results. This feature suggested that an efficient encapsulation and chromatic transition are achieved in a layered combination of double encapsulation than in a doped combination. The DSC spectra indicated the increase in enthalpy at the heat cycle from 94.147 J/g for the SiO2 microencapsulated OTCBDM sample compared to all other samples shows thermal energy storage behavior for outdoor building applications. The UV–Vis absorbance spectra show two distinct peaks at 470 and at 610 nm related to the OTCBDM. The single‐layer encapsulated organic thermochromic black dye material shows less absorbance compared to the plain organic thermochromic black dye material. The double encapsulated sample has the highest absorbance compared to all other samples suggesting the material having better retention of peaks at 470 and at 610 nm. The thermographic analysis (TGA) shows when compared to the plain and single encapsulated OTCBDM samples, the double encapsulated samples demonstrated higher thermal endurance characteristics due to thicker layer coatings of the shell over the core structure. The novelty of this work relies on the double encapsulation of OTCBDMs using SiO2 and TiO2 to improve the photodegradation behavior of the organic core dye particles, and we have demonstrated that the cationic surfactant CTAB outperforms than the SDBS in microencapsulation processes. [ABSTRACT FROM AUTHOR]

This paper presents a tracking method to improve the performance of switched reluctance generators in areas with low wind speeds. The main contribution of this work is the proposal of an advanced control system that employs dynamic tracking techniques to optimize the energy efficiency and response of the switched reluctance generators under various operating conditions. The methodology encompasses the construction of the generator, the development of a wind turbine model, and the coupling of the turbine with the switched reluctance generators. Simulations are conducted using real wind speed data to evaluate the use of switched reluctance generators in wind energy generation. The performance of the switched reluctance generators is improved by applying a tracking technique that operates on the converter's turn‐off angle, which controls the generator's switching. The tracking and optimization techniques implemented in this work maximize the performance of switched reluctance generators in wind energy generation, both in standalone and self‐excited operations. The results demonstrate that satisfactory wind energy generation can be achieved using the SRG, with wind speeds of 5.40 m/s producing approximately 1.5 kW without optimization and around 1.65 kW with optimization, reaching about 70% efficiency with a custom‐built generator. This makes the technology viable for regions characterized by low wind speeds. [ABSTRACT FROM AUTHOR]

Dielectric barrier discharge (DBD) nonthermal plasma is applied for the gasification of kitchen waste. The gas production performance of kitchen waste gasification based on DBD under different reaction ambientes is studied. Density functional theory (DFT) simulations are used to investigate the gas production mechanism via glucose and aspartate. Scanning electron microscopy (SEM) and Raman spectroscopy are used to investigate the physicochemical properties of the produced carbon materials and reveal the plasma reaction mechanism. The plasma treatment of millet and soybean has good gasification effect and can inhibit tar production. N2 and CO2 ambientes are more conducive to the gasification of millet and soybean. The H2 concentration in plasma‐treated millet gasification under plasma in N2 ambient can reach 4%. Glucose is finally decomposed through ring‐opening reaction, decarbonylation reaction, dehydration reaction, and carbon–carbon bond breaking. And the decomposition process of aspartic acid mainly includes decarbonylation and deammonia reaction. With the increase of gas ionization energy, the surface pore structure of the carbon material after plasma treatment is more developed, which indicates that the reaction between plasma and solid is on a layer by layer basis with the solid surface facing the inner surface. This paper aims to provide valuable theoretical support for the application of DBD in the treatment of kitchen waste. [ABSTRACT FROM AUTHOR]

In this paper, to improve the small signal stability of the power system, the optimal coordination of multi‐band power system stabilizers (MB‐PSSs) and static synchronous compensator (STATCOM) has been done. The study is conducted on a standard four machines and two areas power system. To create coordination, a modified version of the particle swarm algorithm as well as the integral time absolute error (ITAE) criterion are proposed. To evaluate the proposed stability method in different operating conditions and in the form of two scenarios, a three‐phase fault with a duration of 200 ms has been applied in the communication line between the two areas. After the simulation, the values of overshoot, undershoot, settling time, and ITAE criteria for the proposed stability method have been calculated and their values have been compared with the results of power system stabilizer (PSS) and MB‐PSS stabilization methods. The results indicate that the proposed combined method of MB‐PSS and STATCOM performed better than the other two methods in improving the dynamic stability of the power system and has the lowest value of the criteria. The values of ITAE for the PSS, MB‐PSS stabilization methods and the proposed MB‐PSS and STATCOM combined method in the first scenario are equal to 0.002354, 0.0.001496, and 0.001242 p.u. and in the second scenario it is equal to 0.0.007418, 0.004015, and 0.003435 p.u., respectively. [ABSTRACT FROM AUTHOR]

Graphite is widely used in molten salt reactors (MSRs) because of its excellent properties. However, the irradiation lifespan of graphite in MSR is much shorter than the life of a conventional nuclear power plant, which lowers the load factor and increases the economic burden. In this paper, to extend the graphite irradiation lifespan, one uniform radial fast neutron field was designed by changing the fuel distribution in different radial fuel channels, which is called continuous core zoning. A positive correlation correction algorithm was used to adjust the fuel volumetric fraction (VF) of each zone. The optimization was carried out among different reactor sizes and different thicknesses of the reflector to study flattening characteristics. After optimization, the difference in fast neutron flux between different zones of the flattened region was less than 1%. Compared to the unoptimized case, the peak value of the fast neutron flux can be most reduced by 48.6%. The study employed a single‐channel heat transfer model to investigate the temperature distribution within the both pre‐ and postoptimization core structure. The flow matching inlet condition and the average velocity inlet condition were considered. The results showed that to eliminate hot spots, a flow matching design is needed; otherwise, the temperature will rise greatly. Then the lifetime of graphite was calculated by combining fast neutron flux and temperature. Under average velocity inlet condition, graphite lifespan in CORE550‐20 increased from 11.4 years of the unoptimized core to 18.9 years of the optimized core, only representing a 66% enhancement due to higher temperatures. Under flow matching conditions, the lifespan of graphite in CORE450‐00 can be extended from 13.3 years of the unoptimized core to 26.8 years of the optimized core, indicating a 102% improvement. [ABSTRACT FROM AUTHOR]

This paper presents a measurement method that utilizes object recognition technology for continuous and quantitative real‐time monitoring of water levels in industrial boilers. Real‐time videos of water levels were monitored using a small camera, and the YOLO algorithm, a single‐stage detector, was employed to use the bounding boxes of detected objects within the video as variables, directly measuring the length ratio for each frame. The method demonstrated a high level of accuracy in water‐level measurement, with an average of 99.02%, and a stable performance, with a fluctuation of 0.13% in continuous measurements. Consequently, the proposed measurement method proves feasible for quantifying continuous water levels in industrial inspection systems even in low‐resource environments. These results demonstrate a new mechanism for monitoring technology, without requiring text detection, showing the potential for improving efficiency in complex boiler systems and the feasibility of reliable water‐level measurement and control. [ABSTRACT FROM AUTHOR]

In the urban water supply system, a significant proportion of energy consumption is attributed to the water supply pumping station (WSPS). The conventional manual scheduling method employed by water supply enterprises imposes a considerable economic burden. In this paper, we intend to minimize the energy cost of WSPS by dynamically adjusting the combination of pumps and their operational states while considering the pressure difference of the main pipe and switching times of pump group. Achieving this goal is challenging due to the lack of accurate mechanistic models of pumps, uncertainty in environmental parameters, and temporal coupling constraints in the database. Consequently, a WSPS pump scheduling algorithm based on physics‐informed long short‐term memory (PI‐LSTM) surrogate model and multiagent deep deterministic policy gradient (MADDPG) is proposed. The proposed algorithm operates without prior knowledge of an accurate mechanistic model of the pump units. Combining data‐driven with the physical laws of fluid mechanics improves the prediction accuracy of the model compared to traditional data‐based deep learning models, especially when the amount of data is small. Simulation results based on real‐world trajectories show that the proposed algorithm can reduce energy consumption by 13.38% compared with the original scheduling scheme. This study highlights the potential of integrating physics‐informed deep learning and reinforcement learning to optimize energy consumption in urban water supply systems. [ABSTRACT FROM AUTHOR]

Energy primarily comes from fossil fuels, which leads to environmental deterioration through increased carbon dioxide load and other greenhouse gases in the atmosphere. Renewable energy is a cheap alternative, and biomass, like municipal solid wastes (MSWs), can be suitably used for energy production. This paper reviews the impact of variations in MSW composition on its physical, chemical, and lignocellulosic properties. It further illustrates how these properties affect torrefaction products. It was observed that MSW can refer to either a combination of different waste types or independent wastes; hence, there is no standard composition of MSW. The variations in composition are responsible for fluctuating physical, chemical, and lignocellulosic properties. These properties, along with torrefaction process parameters, simultaneously affect the torrefied product, whereas lignocellulosic properties influence the biochar yield, and physical and chemical properties impact calorific value and ash content. Torrefying MSW containing low moisture content yields biochar with a high calorific value. Methods to improve the lignocellulosic properties of MSW have not been studied. Research is needed to assess the possibility of improving biochar yields in MSW by enhancing lignin percentages, possibly through blending MSW. A guide on the best blend combinations and ratios is required. Also, it is crucial to study optimal torrefaction process parameters. [ABSTRACT FROM AUTHOR]

Heat transfer analysis for the compression hydrogen storage tank (CHST) during the charge–discharge cycle is necessary to ensure quick and safe refueling for fuel cell vehicles. In this paper, a dual‐zone dual‐temperature (DZDT) model for a CHST during the charge–discharge cycle process is established. The constant/variable mass flow rates and heat transfer coefficients (HTCs) are combined to form three methods. Method 1 uses constant mass flow rate and constant HTC. Method 2 uses variable mass flow rate and variable HTC calculated through the energy conservation equation. Method 3 uses variable mass flow rate and variable HTC calculated through the empirical equation. Then, these methods are applied to the DZDT model for heat transfer analysis in three cases. Research shows that for the charging process, the simulated hydrogen temperatures by Method 2 agree well with experiment data for three CHSTs. Method 1 has a maximum error of about 20°C for 19 L CHST, 15°C for 29 L CHST, and 25°C for 40 L CHST. The error of Method 3 is between Methods 1 and 2. The simulated hydrogen pressures by Methods 2 and 3 agree well with the experimental data, while Method 1 has a maximum error of about 5 MPa for 19 L CHST, 10 MPa for 29 L CHST, and 3 MPa for 40 L CHST. For the discharge process, the simulated hydrogen temperatures by Methods 2 and 3 have a relatively slight difference with the experimental data, while Method 1 has relatively significant differences for three CHSTs. Only slight differences exist between the simulated hydrogen pressures by Methods 1, 2, and 3 with the experimental data for three CHSTs. In short, Method 2 can simulate the hydrogen temperature and pressure well during the charge–discharge process. Method 3 can simulate the approximate hydrogen temperature and precise hydrogen pressure during the charge–discharge process. Method 1 can only simulate the hydrogen pressure during the discharging process. The conclusions of this article can inform researchers which analysis methods are more reasonable to choose in future hydrogen‐filling studies. [ABSTRACT FROM AUTHOR]

In recent times, the significance of renewable energy generation has increased and photovoltaic-thermoelectric (PV-TE) technologies have emerged as a promising solution. However, the incorporation of these technologies still faces difficulties in energy storage and optimization. This review paper addresses these challenges by providing a comprehensive overview of the latest advancements in PV-TE technologies. The paper emphasizes the integration of phase change materials (PCMs) for thermal energy storage, also buttressing the use of encapsulated PCM for thermal storage and efficiency, and the use of hybrid PCM to enhance overall performance. Furthermore, reviews on the use of machine learning techniques for efficient optimization and the integration of thermoelectric modules into tandem perovskite silicon solar cells have been comprehensively analyzed. The advancements in photovoltaic-thermoelectric systems, as reviewed in this article, signify significant progress in attaining sustainable and effective energy production and storage. This review comprehensively addresses the 4Es, underlining their importance. It not only consolidates recent developments but also charts a path for future research in the field of PV-TE technologies, offering precise insights to guide upcoming studies and innovations. [ABSTRACT FROM AUTHOR]

Vibration control has been of great interest to scientists and engineers for many years. Although linear vibration absorbers have been shown to be effective in mitigating vibrations at specific frequencies, their vibration reduction effect is usually limited to a narrow frequency bandwidth. Nonlinear energy sinks have attracted attention due to their better vibration reduction effect over a wider frequency bandwidth. In practical applications, the nonlinear energy sink devices can effectively absorb, dissipate, and convert energy from broadband excitation, so as to achieve vibration reduction and energy harvesting. However, research on energy harvesting based on the nonlinear energy sink is less mature than in linear systems. Multiple parameters of device design (e.g., damping size) affect the actual performance of the nonlinear energy sinks, but there is no exact method to simplify the design of the multiparameter nonlinear energy sinks. Since it is more difficult to implement electromagnetic and electrostatic energy harvesters, more research has focused on piezoelectric energy harvesters. This paper summarizes the research on the nonlinear energy sink and energy harvesting technology, including the introduction of the nonlinear energy sink, energy harvesting based on the nonlinear energy sink, and its application in various fields of energy harvesting. The paper also summarizes some important methods for solving the dynamical equations, as well as their advantages and disadvantages. The conclusions provide an outlook on the subsequent research of the nonlinear energy sink technology, such as the introduction of piezoelectric materials with high energy density, the benefits of balanced vibration suppression and harvesting of vibration energy, and the self-tuning of parameters in complex environments. It provides a powerful reference for the popularization and application of energy harvesting technology based on nonlinear energy sinks. [ABSTRACT FROM AUTHOR]

Hydrogen is an attractive future fuel with the potential to play a crucial role in reducing carbon dioxide emissions, but the major obstacles to implement hydrogen are associated with its storage, transportation, and safety. As a solution, ammonia has been recognized as a promising carbon‐free hydrogen carrier; however, due to the poor combustion performance of ammonia fuel, this review first outlines the significance of on‐site ammonia decomposition to generate COx‐free H2‐rich fuel. Furthermore, to demonstrate the potential of this approach in different fields, the main focus of this review is on the pivotal role of integrating waste heat recovery and thermal ammonia decomposition across various industries including power plants, transportation, and industrial furnaces, enabling researchers to gain insights and benefit from the work of each other. As a means to enhance fuel saving and reduce greenhouse gas emissions, two significant methods including thermochemical recuperation and autothermal reforming are investigated considering the impact of several factors like exhaust gas species, temperature, and the performance parameters of the main fuel consumers. Finally, the paper provides vital recommendations for research directions in waste heat recovery‐based ammonia decomposition systems to promote sustainable and eco‐friendly solutions applicable to various industries. These include but are not limited to conducting life cycle analyses considering green ammonia, optimization of the independency level for the decomposition system, and integrating it with various components such as dual‐fuel engines, hydrogen purification, solar energy, and fuel cells. [ABSTRACT FROM AUTHOR]

This study investigates the application of Cs2BiAgI6 and La2NiMnO6 double perovskite compounds as absorbers in perovskite solar cells (PSCs). Computational simulations were employed to assess the performance of these compounds in isolation, employing various hole transport layers (HTLs). The simulations were geared towards optimizing the HTL thickness for each absorber, ensuring proficient charge transport and current alignment. Through these simulations, valuable insights into the electronic properties and light absorption characteristics of the individual absorbers were obtained. Subsequently, a tandem simulation was executed, aligning the current outputs of both devices through precise adjustment of thickness. This tandem configuration was designed to maximize the overall efficiency of the PSC system. The simulation outcomes explicitly demonstrated that the integration of Cs2BiAgI6 and La2NiMnO6 in a tandem configuration led to superior performance in comparison to the standalone absorbers. The optimized HTL thicknesses facilitated enhanced charge transport and guaranteed current alignment between the two devices. This paper offers significant contributions to the conceptualization and fine‐tuning of tandem PSCs utilizing Cs2BiAgI6 and La2NiMnO6 as absorber materials, presenting potential avenues for augmenting the overall efficiency of PSC systems. Subsequently, further optimization of the tandem configuration led to an impressive power conversion efficiency (PCE) of 30.256%, open circuit voltage (Voc) of 1.682 V, fill factor (FF) of 79.74%, and short‐circuit current density (Jsc) of 24.11 mA/cm2, respectively. [ABSTRACT FROM AUTHOR]

Free‐piston engine generator is a new type of hybrid power device and is regarded as the next‐generation energy conversion device which can replace the traditional internal combustion engine. This paper focused on the combustion stability and combines experimental results to study the key factors affecting the stability of the free‐piston engine, such as ignition time, intake pressure, equivalence ratio, and operating frequency. The simulation results showed that as the ignition advance angle increased, the indicated mean effective pressure increased significantly and the coefficient of variation of the indicated mean effective pressure was effectively reduced from 15.55% to 1.02% as the advance in ignition time from −15 to −30°ECA. When the intake pressure was increased to 1.2 bar, the average value of indicated mean effective pressure reached about 5.15 bar. When the equivalence ratio was in the range of 1.0–1.4, the coefficient of variation of the indicated mean effective pressure can be kept below 10%. The indicated mean effective pressure decreased monotonically from 4.79 to 3.62 bar, and the coefficient of variation increased by five times as the engine speed increased as the engine speed increased from 1,000 to 2,500 RPM. [ABSTRACT FROM AUTHOR]

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions. [ABSTRACT FROM AUTHOR]

In this paper, a new simultaneous framework of distribution network operation is proposed based on the scheduling of photovoltaic/wind/battery multi-microgrids with network reconfiguration considering self-healing. The objective function is considered to minimize the energy losses, and multi-microgrids cost and improve the reliability indices including minimization of energy not-supplied (ENS) and minimizing system average interruption duration (SAIDI) and system average interruption frequency (SAIFI) indices. The optimization variables are defined as the situation of the distribution network switches to find the network's optimal configuration with the installation location and size of renewable resources and battery energy storage during 24 hours. An improved beluga whale optimization (IBWO) based on a nonlinearly diminishing inertia weight (NDIW) approach is used to find the optimal variable set of the problem. The recommended methodology is implemented on 33-bus and real 59-bus distribution networks. The results demonstrated that by obtaining an optimal state of the network switches in the event of a fault, as well as the optimal scheduling of the two microgrids, the energy losses have decreased and the reliability indices have improved. The outcomes of the proposed methodology based on the hybrid multi-microgrid allocation and network reconfiguration are stated that the losses, ENS, SAIDI, and SAIFI are reduced by 66.39%, 54.00%, 50.24%, and 33.61%, respectively, for 33-bus network and are declined by 65.29%, 57.44%, 48.63%, and 62.33%, respectively, for the real 59-bus Ahvaz network in comparison with the base network. The obtained results illustrated that in the condition of the network line outage, by simultaneously implementing the reconfiguration with the change of network switches and the optimal allocation and scheduling of HMGs in the network, self-healing is provided to prevent a significant weakening of the network performance so that the resilience is improved in addition to minimizing the energy losses and the cost of HMG energy injection compared to the base network. The findings revealed that the objectives including losses, ENS, SAIFI, and SAIDI are increased by 78.29%, 33.00%, 83.33%, and 35.11%, respectively, due to outage of line 22 of the 33-bus network and these objectives are increased by 52.32%, 10.30%, 32.29%, and 79.66%, respectively, due to outage of line 12 of the 59-bus network compared with not considering the line outage. Moreover, the superior capability of the recommended NDIW-based IBWO has been confirmed in comparison with the well-known particle swarm optimizer (PSO) and ant lion optimizer (ALO) in solving the problem to achieve better objective value. [ABSTRACT FROM AUTHOR]

The increasing global dependence on fossil fuels for energy has prompted researchers to explore alternative power generation sources that offer higher efficiency, cost-effectiveness, and low environmental impact. Developed countries have been making continuous efforts to reduce their reliance on depleting fossil fuel sources to curb carbon emissions. As a result, hydrogen has gained significant attention among researchers as a potential future fuel that meets all the necessary criteria. Many countries aiming for a hydrogen economy have invested in research on fuel cells. Among various fuel cells, the solid oxide fuel cell (SOFC) has emerged as a commercially viable power source at a small scale. This paper provides an extensive review of the components, materials, design, operation, and integration strategies of SOFCs with existing thermal-based power plants. The performance and prospects of various SOFCs in standalone and hybrid modes are summarized, along with the limitations. Additionally, the research and commercialization efforts that can enhance the potential applications of SOFCs as a sustainable and reliable system in the long term are outlined. [ABSTRACT FROM AUTHOR]

In the past few years, there has been a notable interest in the application of machine learning methods to enhance energy efficiency in the smart building industry. The paper discusses the use of machine learning in smart buildings to improve energy efficiency by analyzing data on energy usage, occupancy patterns, and environmental conditions. The study focuses on implementing and evaluating energy consumption prediction models using algorithms like long short-term memory (LSTM), random forest, and gradient boosting regressor. Real-life case studies on educational buildings are conducted to assess the practical applicability of these models. The data is rigorously analyzed and preprocessed, and performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to compare the effectiveness of the algorithms. The results highlight the importance of tailoring predictive models to the specific characteristics of each building's energy consumption. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) are seeing rapid market growth due to the changing worldwide landscape of electricity distribution and consumption. An ESS must possess the capability to oversee the functioning of the system's modules under abnormal circumstances, while also having the ability to supervise, manage, and optimize the performance of one or more battery modules. At present, the condition of batteries is assessed based on two factors: the level of charge (SOC) and the overall condition (SOH). By using these two characteristics, it becomes feasible to compute the anticipated battery lifespan and evaluate a battery's efficiency. The assessment of SOH is a crucial determinant in guaranteeing the effectiveness, dependability, and security of batteries in electric vehicles (EVs). Nevertheless, the safety issues resulting from the imprecise estimation and forecasting of battery health status have garnered significant attention in academic circles. This study presents a comprehensive evaluation of several SOH monitoring techniques. In order to achieve this objective, various scientific and technical literatures are examined and the corresponding methodologies are categorized into distinct groupings. The groupings are categorized based on the manner in which the procedure is executed: methods and techniques used in experiments and models. This paper provides a comprehensive overview of the benefits and drawbacks of several SOH assessment and prediction techniques, along with the associated obstacles in SOH estimation. [ABSTRACT FROM AUTHOR]

During the development of oil reservoirs, a rapid increase in water cut following reservoir flooding leads to inefficient or ineffective circulation of injected water, rendering a significant portion of the remaining oil in the reservoir inaccessible. The displacement method using polymer particle dispersion systems effectively solves the issue of rapid water breakthrough in oil reservoirs. Owing to the particle phase separation phenomenon, polymer particles can selectively penetrate into the larger pores where water circulation is inefficient, enhance their flow resistance, and thereby achieve equilibrium displacement along with an increased swept volume. This paper investigates the heterogeneous distribution of polymer particles within a porous medium, incorporates the red blood cell dendrite concentration distribution theory from biological fluid mechanics, and develops a mathematical model to delineate the viscosity characteristics of polymer particle dispersion systems, taking into account the phase separation phenomenon. Building on this foundation, it formulates a capillary bundle model for the polymer particle dispersion system specifically designed for oil displacement and proceeds to determine its relative permeability curve. Simulation outcomes reveal that at a water saturation level of 0.063, the concentration of polymer particles in fractured large pore capillaries is markedly elevated, yet capillaries with a pore size under 26 µm remain devoid of polymer particles. With the increase of water saturation, the concentration of polymer particles in large pore capillaries reduces, whereas it progressively augments in medium pore capillaries. Upon reaching a peak water saturation of 0.751, capillaries smaller than 18 µm are entirely free of polymer particles. These findings suggest that the heterogeneous distribution of polymer particles markedly inhibits the percolation capabilities of the dispersed system following a water phase breakthrough, facilitating the entry of more dispersion into oil-laden capillaries and thus enhancing the flow capacity of the oil phase. [ABSTRACT FROM AUTHOR]

In pressurized water reactor accident scenarios, the injection of water from the emergency core cooling system (ECCS) (ECC injection) might induce a pressurized thermal shock (PTS), affecting the reactor pressure vessel (RPV) integrity. Therefore, PTS is a vital research issue in reactor safety, and its analysis is essential for evaluating the integrity of RPVs, which determines the reactor life. The PTS analysis comprises a coupled analysis between thermal–hydraulic and structural analyses. The thermal–hydraulic approach is particularly crucial, and reliable computational fluid dynamic (CFD) simulations should play a vital role in the future because predicting the temperature gradient of the RPV wall requires data on the transient temperature distribution of the downcomer (DC). Since one-dimensional codes cannot predict the complex three-dimensional flow features during ECC injection, PTS is one reactor safety issue where CFD simulation can benefit from complement evaluations with thermal–hydraulic system analysis codes. This study reviewed from the viewpoint of the turbulence models most affecting PTS analysis based on papers published since 2010 on single- and two-phase flow CFD simulation for the experiment on PTS performed in the Rossendorf coolant mixing model (ROCOM), transient two-phase flow (TOPFLOW), upper plenum test facility (UPTF), and large-scale test facility (LSTF). The results revealed that in single-phase flow CFD simulation, where knowledge and experience are sufficient, various turbulence models have been considered, and many analyses using large eddy simulation (LES) have been reported. For two-phase flow analysis of air–water conditions, interface capturing/tracking methods were used in addition to two-fluid models. The standard k – ε and shear stress transport (SST) k – ω models were still in the validated phase, and various turbulence models have yet to be fully validated. In the two-phase flow analysis of steam–water conditions, many studies have used two-fluid models and Reynolds-averaged Navier-Stoke (RANS), and NEPTUNE_CFD, in particular, has been reported to show excellent prediction performance based on years of accumulated validation. [ABSTRACT FROM AUTHOR]

Recent experience with the strongest earthquakes greater than magnitude 5.4 in Korea leads to public interest in the safety and resilience of critical infrastructures. There are many nuclear power plants near the epicenters of the earthquakes. Nuclear power plants are essential infrastructures that provide stable and enough energy for human life, and simultaneously, control systems for the safety and security of nuclear power plants are critical due to the risk of nuclear accidents on public health and the environment. The nuclear area uses probabilistic risk assessment to estimate the risk of structures, systems, and components in nuclear power plants, and the evaluation of the fragility curve is a key process for probabilistic risk assessment. The challenges of a seismic fragility analysis lie in estimating the influence of various uncertainties in material, geometry, and earthquake and improving the existing fragility analysis methods, which require time-consuming nonlinear time history analysis. Thus, this paper conducts a multivariate seismic fragility analysis using surrogate models for reinforced concrete squat shear walls and proposes a simplified closed form equation for a chosen surrogate seismic demand model. The surrogate models are trained and validated by several approaches: response surface method, support vector machine, Gaussian process regression, and neural network. In addition, a correlation analysis is used to evaluate the relative importance of the variables to the seismic demand to simplify the surrogate model further. Finally, simplified surrogate models based on the importance of the variables are proposed as closed form of polynomials, and the performance of these models on the fragility analysis is evaluated. [ABSTRACT FROM AUTHOR]