Energy Science & Engineering最新文献

This study aimed to assess the feasibility of manufacturing fracturing proppants by microwave sintering and using low-grade bauxite as raw material. The effects of microwave hotspot SiC and sintering additive MnO₂ content on the performance of the mullite-based structural materials were studied, respectively. The optimum sintering condition was determined by single-factor experiments. The sintering process and mechanism were explored based on the analysis of physicochemical properties, phase transitions, and microstructure. The results showed that (1) mullite ceramic composites could be successfully prepared only with SiC added and with poor interparticle bonding microstructure. (2) With the addition of MnO₂ and CaO, the granular-shaped mullite crystals transformed into rod-like mullite crystals, forming a net structure. (3) As the input power increased, the overfast sintering rate would reduce proppants' mechanical properties, and it was also necessary to select a reasonable sintering time to avoid overburning. (4) When the mass ratio of MnO₂:CaO:SiC:bauxite was 2:1.5:12:84.5 and under the sintering condition of 1000 W, 2 h, the performance (breakage ratio of 8.5% under 28 MPa closed pressure, apparent density of 2.58 g/cm³, turbidity of 52 FTU, and acid solubility of 6.77%) could meet the requirements of the Chinese Petroleum and Gas Industry Standard (SY/T 5108–2014). This study provides a powerful way for reducing the fracturing cost, which not only improves the low-grade bauxite utilization scale within the ceramic industry, but also expands the application of microwave sintering technology in mullite structural materials for the petroleum and gas industry.

The Kizilsu River Basin in China is located in the desert Gobi area with high sediment content and high hardness. After 4798h operation, the maintenance of a high-head Francis turbine in this basin found that the guide vane was seriously worn, and even most of the top and lower skirts of the front of the guide vane were worn off. In this study, the k-ε turbulence model, ZGB cavitation model and sediment erosion prediction model were used to simulate the solid-liquid two-phase flow and cavitation of guide vane. The research results show that the guide vane of a high-head Francis turbine in a sandy river has a high sediment velocity and serious surface erosion, with the maximum erosion rate of 1.0 × 10⁻⁶ kg/(m²·s), while the area near the skirt of the guide vane is a low-pressure area lower than the saturated steam pressure, and cavitation is very easy to occur, with the maximum vapor volume fraction of 0.9. Serious cavitation erosion occurs near the skirt of the guide vane, and the combined effect of sand erosion and cavitation erosion on the surface of the skirt of the guide vane aggravates the damage of the skirt structure. The research results provide a technical basis for the antiabrasion design of the guide mechanism of the high-head Francis turbine and the operation and maintenance of the hydropower station in the sandy river.

The prediction of the efficiency of oil well pumping systems plays an important role in optimizing the energy efficiency parameters of these systems. Currently, the prediction of oil well pumping system efficiency relies primarily on mechanistic models, but these models are often overly complex in predicting efficiency. Some researchers have attempted to use deep learning to predict system efficiency, but due to insufficient consideration of influencing factors and the causal relationships between these factors and system efficiency, they often include irrelevant variables as influencing factors, leading to less accurate prediction models. In this paper, a hybrid model (MDS–SSA–GNN) is proposed for the prediction of pumping well system efficiency. The model consists of six parts: Pearson's product moment correlation coefficient (PPMCC), multidimensional scaling (MDS) transform, maximum–minimum normalization, sparrow optimization algorithm (SSA), graph neural network (GNN), and maximum–minimum inverse normalization. First, the size of the correlation coefficient between each influencing factor and the system efficiency is quantitatively calculated by using PPMCC. Second, the main influencing factors are downscaled by using MDS and normalized based on the principle of maximum–minimum normalization. Third, the GNN algorithm is used for the prediction of the pumping unit system efficiency, and the SSA algorithm is used for the optimization of the initial values of the network parameters. Finally, the prediction results are obtained by the maximum–minimum antinormalization. To validate the model's accuracy, this study randomly selected 100 actual oil wells for comparative analysis and analyzed the impact of structural parameters of the hybrid algorithm on the prediction accuracy of system efficiency. The analysis results demonstrate that the proposed model can effectively predict system efficiency and has a certain role in improving the accuracy of oil well pumping system efficiency predictions.

The strength and failure of rock masses are ambiguous due to their complex structure. Investigating the strength and failure of jointed rock mass remains a persistent concern in mining engineering. This study aims to investigate the effects of joint dip angles of complex discrete joints on the mechanical properties and fracture evolution of rock-like material. Rock-like specimens with complex joints were prepared using 3D printing technology and then tested under uniaxial compression loading. The experimental results reveal the following findings: (1) The anisotropy of rock-like material with complex joints is lower compared to the rock-like material with simple joints. (2) The ratio of long-term strength to uniaxial compressive strength of rock-like material remains consistent despite changes in the dip angle of the joints. (3) Differing from intact rocks, AE events of the rock-like material with complex joints are obvious in the initial loading stage and elastic deformation stage. (4) When the dip angle of the joint sets is 0 and 90°, fractures progressively propagate, and the failure mode of the rock-like material demonstrates tensile failure along the pre-existing joints. Conversely, when the dip angle of the joint sets is 45° and 135°, fractures are simultaneously initiated at various locations within the rock-like specimen, resulting in a failure mode of rotational failure by the newly generated block.

Utilizing the direct current-side electrical data resources of the photovoltaic power generation system on the FusionSolar platform, this study investigates the impact of hot spot faults on the output characteristics of photovoltaic strings and proposes a hot spot fault diagnosis method based on time series waveform characteristics. By analyzing the mechanisms of hot spot generation and evolution, as well as the characteristic differences in I–V curves and time series compared to other types of faults, the waveform variation patterns of hot spots in current and voltage time series are obtained. A function form suitable for hot spot fault waveform characteristics in time series graphs is constructed, and fault diagnosis feature vectors are extracted. Combining field operation and maintenance experience, a fuzzy reasoning fault diagnosis system is established to determine the causes and estimate the severity of hot spot faults. Experimental results indicate that hot spot faults have unique and corresponding variations in the current/voltage time series waveforms of the string output. The constructed function form can clearly represent the waveform variation patterns, and the established fuzzy reasoning system can achieve effective and reliable diagnosis of hot spot faults.

This study investigates the viability of hybrid photovoltaic (PV), wind, and fuel cell (FC) systems for on-grid and off-grid operations for the Ashrayan-3 housing project in Bangladesh, with an increased focus on sustainable energy solutions. Motivated by the issue of the delivery of proper and sustainable energy services to remote locations, we conducted an extensive analysis of load demand and found that an average daily demand of 46,176.65 kWh exists, with a peak load of 4852.8 kW. In this research, the HOMER software has been used to make a simulation of five different hybrid system configurations with differing mixes of renewable technologies. From the analyses, the systems based 100% on renewable resources suffer more initial capital costs, with a total net present cost increase of up to 20%, in comparison to conventional systems. On the other hand, the systems give much lower operational costs and cost of energies (COEs) of a minimum of $0.0253/kWh, reported from the on-grid PV-based system. On the other hand, the off-grid PV–FC–wind turbine system showed a COE of $0.286/kWh, along with a decrease in CO₂ emissions by about 15,000 kg/year, showing a 30% decrease, compared with on-grid systems. The results form a basis for the conclusion that such hybrid renewable energy systems are both economically and environmentally feasible. They can reduce COEs by up to 70% in off-grid systems. This proves that the quality of life and energy security in developing regions will be highly increased, supporting the goals of sustainable development.

The routine practice accompanying the operation of nuclear facilities involves the discharge of radioactive effluents from nuclear power plants (NPPs). Regulation of this discharge to the environment hinges on three criteria: radioactivity concentration, public dose, and radioactivity. Among these, radioactive carbon-14 holds particular significance as it possesses an extensive half-life of 5730 years, making it a primary source of radiation dose to communities residing around NPPs. In Korea, the monitoring of carbon-14 discharges from pressurized water reactors (PWRs) in gaseous effluents has been ongoing since 2012, whereas before 2012, monitoring exclusively focused on carbon-14 discharges from pressurized heavy water reactors (PHWRs). Analysis of carbon-14 discharges from Korean PHWRs indicates that their emission constituted less than 1% of total radioactive effluents over the past two decades. In the context of Korean PWRs, carbon-14 discharge monitoring was absent from 2002 to 2011, resulting in an absence of data regarding such discharges during that period. After introducing carbon-14 discharge monitoring in gaseous effluents from Korean PWRs, emissions from 2012 to 2021 contributed 3% of the total gaseous effluents. These findings indicate that despite being the primary contributor to public dose, carbon-14 discharges from NPPs constitute a minor portion of the radioactive effluent discharge.

In Korea, Kori Unit 1 was permanently closed in 2017 and scheduled for decommissioning. Kori Unit 1 is currently scheduled to undergo system decontamination and to be decommissioned by December 2038. Measures to properly control and remove radiological hazards must be established to go through decommissioning a nuclear power plant (NPP). Radiological characterization must precede NPP decommissioning. Radiological characterization generates a lot of data during this process. The consistency of radiological characterization data is important for systematically decommissioning NPPs. However, the data generated from radiological characterization may not be systematically managed because a database of radiological characterizations for Kori Unit 1 has not been properly established. Therefore, this study has proposed a database to be filled during radiological characterization in terms of occupational radiation exposure of personnel and decommissioning waste safety using key performance indicators presented by the International Atomic Energy Agency. From the perspective of occupational exposure, databases have been proposed for operational history investigations, decommissioning source term calculations, and investigation of the level of radiological contamination. From the perspective of the safety of decommissioning waste, databases were proposed for operational history investigation, decommissioning source term calculation, investigation of the level of radiological contamination, and investigation of radiological characteristics of decommissioning waste.

Monitoring the health of lithium batteries is a crucial undertaking in ensuring the safe and dependable functioning of electric vehicles. Data-driven methods have been proved to be an effective method for identifying the complex degradation process of batteries. To augment the precision of predicting the remaining useful life (RUL), this paper introduces a pioneering architecture for a denoising autoencoder (DAE). This architecture integrates a stacked convolutional neural network with subsequent layers of bidirectional gated recurrent units within an encoder–decoder framework. The utilization of the DAE network is employed as a means to effectively capture and represent the intricate and nonlinear knowledge associated with degradation data acquired from measured sources. Simultaneously, the reconstruction loss is incorporated into the total loss to improve the accuracy and generalization of the prediction model. The efficacy of the proposed approach is substantiated through the utilization of data sets sourced from the NASA Ames Prognostics Data Repository. The comparative findings suggest that the proposed approach demonstrates an exceptional ability to achieve precise and robust estimation in predicting the RUL, surpassing other advanced methodologies.

Dye-sensitized solar cells (DSSCs) are among the most attractive third-generation photovoltaic technologies due to their low toxicity, versatility, roll-to-roll compatibility, ultralightness, and attractive power conversion efficiencies (PCEs). However, their transition from the laboratory scale to the industrial scale has been slow due to their inability to compete with silicon-based cells in terms of efficiencies and stabilities. Research activities on DSSCs have been ongoing for several decades to improve the efficiency and cost-effectiveness of photovoltaics but these attempts are still inadequate. Their chemical and physical properties must be refined to increase efficiency and commercialization. This review provides a concise overview of the recent advances taking place in the DSSCs research field, including molecular engineering technologies, the quest for superior carrier transport materials (CTMs), efficient sensitizers, and better electrodes. Also, this review compiles knowledge of the historical development of DSSCs, the current advancements such as control of surface morphologies, doping strategies, modeling and simulation, characterization, and recent cutting-edge research happenings in photovoltaic research. Finally, nanostructured materials that have been used as photoelectrodes and the practical applications of DSSCs in internet of things (IoT) and portable electronics are examined to identify challenges and future advancements. The main aim of this work is to be a pathfinder for scientific researchers in this field exploring various energy harvesting materials and optimization strategies of different components of DSSCs.