Energy Science & Engineering最新文献

In view of problems such as the narrow efficiency area, large hydraulic vibration area, pressure pulsation, and serious sediment wear of turbines at the Futang hydropower station, the technical transformation of turbine runners was carried out by modifying the blade shape and increasing the blade thickness, and a combination of numerical simulations based on shear stress transport k–ω turbulence model and tests was adopted to improve the operational stability of power station units. Calculation and testing demonstrate an enlargement of the high-efficiency zone. Specifically, the optimal efficiency of the runner increases by 0.37%, while the rated efficiency rises by 0.19%. Significant reductions are observed in pressure pulsation within the draft tube and vaneless area decrease of approximately 50%. There is a high-frequency pressure pulsation in the vaneless zone and the runner under low-load conditions, and the influence of dynamic and static interference gradually weakens with the increase of opening. The draft tube is prone to eccentric vortex bands under partial working conditions, which causes the unit to be affected by low-frequency pulsation. This optimization also leads to a notable decrease in runner blade wear, with the maximum sand and water velocity reduced from 45 to 40 m/s, resulting in a 30% reduction in sand wear. Moreover, there is a substantial enhancement in the runner's stiffness, with the thickness of the blade near the high stress area of the upper crown and lower ring increasing by over 50%, and the weight of each individual blade increasing by more than 50%. These research findings validate that modifying the runner blade effectively improves flow patterns, reduces eddy current generation, minimizes pressure pulsation, widens the high-efficiency zone, decreases wear, and enhances the operational stability of the unit. The technical transformation method and research results of this study have important guiding significance for similar technical transformation of other power stations

Jordan's energy sector faces significant challenges due to rising fuel prices, making the exploration of local energy resources crucial. The abundant oil shale deposits in Wadi Thamad present a promising opportunity. Since Wadi Thamad oil shale has never been studied before, this research focuses on the Wadi Thamad basin near Madaba, Jordan, aiming to comprehensively characterize its oil shale using advanced analytical techniques. Using X-ray diffraction, Fourier transform infrared spectroscopy, X-ray fluorescence, scanning electron microscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry, this study assesses the mineralogical, chemical, and thermal properties of Wadi Thamad oil shale. The findings reveal calcite and quartz as the primary minerals, with significant aliphatic, CO₂, hydroxyl, and carboxyl groups. Elemental analysis highlights essential oxides, such as CaO and SiO₂. Fischer assay results indicate an oil content of 5.3–10.1 wt%, a gross-calorific value of 4.56–7.69 MJ/kg, and a sulfur content of 1.77–2.10 wt%. The peak pyrolysis temperature is 432.4°C from TGA. This research's novelty lies in its comprehensive approach to characterizing the underexplored Wadi Thamad oil shale basin. The findings enhance the understanding of Wadi Thamad's geological composition and underscore its potential as a local energy resource, contributing valuable data to Jordan's energy portfolio and offering economic benefits.

The time-varying external environment is one of the main variables influencing heating energy consumptions, so that its influence should be rectified when energy savings of different heating modes are calculated. This paper proposes an energy consumption rectification method based on Bayesian linear regression and heating degree-days, to obtain heating energy consumptions without the influence of different outdoor temperatures. The proposed method consists of three main steps. First, a physical model of heating houses is used to prove a relationship between energy consumptions and heating degree-days. Second, Bayesian linear regression is exploited to estimate uncertainty ranges of heating energy consumptions. Finally, heating energy consumptions are rectified, and energy savings with their uncertainty ranges for different heating modes under the same outdoor temperature are obtained. The proposed method does not require the physical parameters of heating houses to facilitate practical implementation. Additionally, it provides uncertainty ranges of heating energy consumptions to measure the estimation accuracy. Numerical and experimental examples show that the proposed method provides more accurate estimates of heating energy consumptions than existing methods.

A direct consequence of the rapid expansion of civilization and modernization trends is the escalation in global warming and the consequential climatic upheavals. The world has actively advocated the adoption of electric vehicles (EVs) as a response to the environmental challenges posed by vehicular emissions. It is evident that conventional fuel-based charging infrastructures are economically impractical and lack organizational cohesion in light of the proliferation of EVs. An EV charging station powered by renewable energy presents a promising opportunity for enhancing flexibility and control. It is imperative that EV charging stations be equipped with solar power and standby batteries (SBBs). Consequently, this article presents and evaluates a system that utilizes a proportional-integral-derivative controller, a neural network-equipped grid and a charging station utilizing a Dragon Fly Optimization Algorithm to generate power and a maximum power point tracking controller. To achieve optimal power management within the charging station, MATLAB/Simulink is used to implement and rigorously test the proposed system. It orchestrates the interaction between the solar panel, backup battery, grid and EVs. Compared to existing systems in the literature, the comprehensive system exhibits commendable efficiency. Due to the pivotal role played by grid integration and the SBB, the system can ensure a reliable power supply to the charging station under any weather conditions.

To intuitively and systematically grasp the development status and trend of safety evaluation research in China's coal mining operations, we consulted relevant literature in the fields of “coal mine” and “safety evaluation” collected by China Knowledge Network, and deployed CiteSpace V software to summarize and analyze research pertaining to safety evaluation in China's coal mines from three aspects: research institutions, authors, and hot keywords. With respect to research institutions, the results show that although many researchers have conducted in-depth evaluations of coal mine safety, most of these studies were independent, and two-way information exchange and cooperation between research institutions remains scarce. With respect to authors, most cooperation between authors has been limited to team members, and relatively stable research teams have been formed. However, most of these research teams are independent. With respect to hot keywords, the trends of coal mine safety evaluation studies exhibit continuous change, with an overall increase in richness. According to the frequency burst times of important keywords, “entropy weight method” and “mining with pressure” are expected to remain important keywords in future evaluations of coal mine safety in China.

This research explores the dynamics of total electricity generation (TEG) in South Africa through an analysis of data from the International Energy Agency database from 1990 to 2020. A comprehensive examination of various energy sources, including coal, oil, biofuels, nuclear, hydro, solar photovoltaic (PV), solar thermal, and wind, is conducted to ascertain their respective contributions to TEG. Employing the R software environment, the study employs a methodical analytical framework encompassing meticulous data preparation, statistical analysis, and model formulation. The data preparation phase involves intricate processes such as structuring, cleansing, and visualization aimed at eliminating stochastic variables and outliers. Missing data are addressed through the application of the Piecewise Cubic Hermite Interpolating Polynomial method. Subsequent statistical analyses are informed by tests for normality and homogeneity of variance, revealing deviations from normality and disparate variances across energy source groups. Consequently, non-parametric methodologies such as the Kruskal–Wallis test are adopted. Findings underscore the significant role of nuclear energy in TEG despite facing challenges. Model development entails the construction of multiple linear regression models with varying predictor sizes, with Model m06 emerging as the optimal choice, incorporating key predictors such as coal, nuclear, and solar PV. Rigorous diagnostic assessments confirm the robustness of Model m06 and its suitability for TEG prediction. Comparative analysis against actual data validates its superior performance, characterized by minimal errors and high predictive accuracy. The efficacy of Model m06 in capturing TEG dynamics underscores its utility for informing energy planning initiatives. Recommendations derived from the study advocate for prioritizing renewable energy integration, infrastructure investment, research endeavors, monitoring mechanisms, and public awareness campaigns to advance sustainable energy development goals in South Africa.

The growing demand for alternative energy sources to alleviate environmental impacts highlights the need to move from fossil fuels to renewable energy. This study demonstrated the technical feasibility of using a solar photovoltaic (PV) system for the production of green hydrogen. This research examined electrical and power data from a PV plant in Irecê, Bahia, using open data sources to provide insights into the production of green hydrogen from renewable sources. The system mainly depends on the use of a renewable source, PV solar energy, integrated with batteries, electrolyzers, and hydrogen tanks. Electrolyzer, battery, and hydrogen tank sizing analysis for optimal hydrogen production was effectively conducted using HOMER Energy software. The predicted system topology prioritizes a local DC network, optimizing efficiency for electrolyzers that have inherently low efficiency. The electrolyzer simulation involves initial Python-based sizing and comprehensive sizing with HOMER Energy software, ensuring accuracy within a 10% discrepancy limit. This highlights the importance of analytical calculations and optimization software for sizing more complex systems.

The biomethane potential of insect frass and leachates from waste (larvae feedstock) stockpile was evaluated. Frass was derived from a black soldier fly larvae (BSFL) operation that uses a fruit and vegetable waste diet. The two waste streams were characterized and the results of their compositional, proximate, and ultimate analysis were used in estimating methane yields from these materials. The estimated biomethane potential (BMP) of the frass ranged 149–275 L/kgVS depending on the calculation method used. This result compares well with that of empirical studies for cow manure whose BMP ranges 110–275 L/kgVS. Cow manure is the most popular biogas feedstock globally. Leachates reported an estimated BMP of 150 L/kgCOD. We conclude that it may be technically feasible to produce biogas from BSFL frass and from fruit and vegetable waste leachates. However, standard BMP experiments and semicontinuous pilot studies are still necessary before commercialization of these findings. These extra tests are executed to validate these findings and ascertain the life cycle attribute and long-term process stability and hence determine the economics of using these potential substrates for biogas production as a strategy to implementing circularity in the fruit and vegetable industry.

Widely concerns over the carbon emission problems have been aroused. Prior studies have documented the correlation between the carbon emission market and industries separately. This study compared the tail risks and risk spillover effect of the carbon emission and 11 industries markets in China and the European Union (EU) by using the multivariate multi-quartile conditional autoregressive at-risk model. Moreover, to evaluate the risk spillover of each market under extreme risk conditions in time domain and frequency domain, DY spillover index and BK spillover index were constructed via generalized forecast error variance decomposition and generalized causation spectrum, respectively. Findings are as follows: (1) The tail risks and fluctuation of the trend of Chinese industry markets reflects more higher and larger than those in the EU; (2) The EU suffers from smaller external shocks, while China has the opposite result and can recover relatively faster; (3) In China, energy, industrial, information technology, financial, real estate, consumer goods, carbon emissions, and discretionary consumption industries are risk spillover industries, while healthcare, materials, telecommunication services, and utilities industries are risk receiving industries. In contrast, the risk spillover industries in the EU remain consistent with those of the Chinese markets except for the materials, discretionary consumption, consumer goods, information technology and real estate industries. (4) On the short-term, medium-term and long-term scales, the risk spillover of China's carbon emission trading market and various industries is basically consistent with that of the EU. These findings contribute to reducing greenhouse gas emissions and achieving the goal of carbon peak and carbon neutrality.