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Hydrothermal liquefaction (HTL) is an effective way to treat solid wastes with high moisture content. The co-hydrothermal liquefaction (co-HTL) experiments of oily scum and poplar sawdust biochar at the different hydrothermal temperatures were performed in this work. The changes of the appearance and components of the liquid products were comprehensively studied. The results showed that the addition of biochar into oily scum significantly reduced the moisture content of the residue hydrochars obtained after co-HTL. As the hydrothermal temperature increased, the liquid products obtained from co-HTL turned clearer and lighter in color, and the recovery rate of the liquid products significantly increased. The co-HTL of bi-ochar and oily scum could effectively improve the liquid quality and enhance the recovery rate of hydrochars. The carbon numbers of the liquid products obtained from co-HTL were concentrated in C5-C11, which were main compositions of gas-oline. This work can provide basic data and theoretical reference for oily scum efficient treatment and engineering practice.

Solar-driven transformation of biomass and its derivatives has attracted tremendous attention in replacing fossil sources to generate chemicals. Developing high-performance photocatalysts for selective conversion of bio-platform molecules remains a great challenge. Herein, metal-doped photocatalyst was designed for the selective catalysis of biomass derivatives, 5-hydroxymethylfurfural (HMF) was efficiently and controllably converted to 2,5-dicarboxylic furan (DFF) or 5-formyl furantocarboxylic acid (FFCA). In the neutral solution, 64% HMF was converted within 12 h and mainly produced DFF with the selectivity of 74–82%. In 0.5 M Na2CO3 aqueous solution, 30% HMF was converted within 2 h and mainly produced FFCA with the selectivity of 69%. The characterization and photoelectrochemical measurement of photocatalyst showed that the modified material had higher carrier transmission efficiency and better visible light response. The mechanism analysis showed that the photogenerated h+ was the main active specie of the FeOOH/MP, and the introduction of FeOOH inhibited the formation of ˙OH in aqueous solution to realize the highly selective conversion process.

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

CaO captures CO2 from pyrolysis, forming CaCO3 that activates biochar via decomposition.

Abstract Future “net-zero” electricity systems in which all or most generation is renewable may require very high volumes of storage in order to manage the associated variability in the generation-demand balance. The physical and economic characteristics of storage technologies are such that a mixture of technologies is likely to be required. This poses nontrivial problems in storage dimensioning and in real-time management. We develop the mathematics of optimal scheduling for system adequacy, and show that, to a good approximation, the problem to be solved at each successive point in time reduces to a linear programme with a particularly simple solution. We argue that approximately optimal scheduling may be achieved without the need for a running forecast of the future generation-demand balance. We consider an extended application to GB storage needs, where savings of tens of billions of pounds may be achieved, relative to the use of a single technology, and explain why similar savings may be expected elsewhere.

Abstract This paper investigates the forced convection of alumina-water nanofluids within helical tubes, maintaining a constant wall temperature and assuming thermal equilibrium between the nanoparticles and the base fluid. The nanofluid model incorporates the effects of alumina (Al2O3) nanoparticle volume fraction, diameter, and temperature on thermophysical properties. The governing equations are solved using the Forward-Time Central-Space Finite Volume method in conjunction with the simple algorithm. Numerical results are validated against experimental data, demonstrating high accuracy. The study explores the effects of pitch size, curvature ratio, nanoparticle volume fraction, nanoparticle diameter, and Reynolds number on velocity contours, temperature profiles, secondary flow, thermophysical properties, friction coefficient, and heat transfer rate. Additionally, the figure of merit evaluates the impact of these parameters on the thermal performance of the system. The results indicate that an increase in Reynolds number and nanoparticle diameter negatively affects thermal performance, while higher nanoparticle volume fraction, curvature ratio, and pitch size enhance it. Furthermore, incorporating nanoparticles in straight tubes proves to be more advantageous compared to helical tubes. This study tested volumetric ratios of 1%, 2%, and 4%, which resulted in increases in heat transfer coefficients of 21%, 32%, and 43%, respectively, compared to pure water under similar conditions, such as Reynolds number and coil pitch.

Background: Compared with the traditional power system, the large-scale access of distributed energy resources in the new power system has a great impact on the structure and operation mode of the power grid, and it is also more susceptible to device-level and network-level FDI attacks. Objective: In order to improve the accuracy and precision of detecting false data injection attacks in distributed energy resources integration into distribution networks and to further explore time series modeling methods for measurement data, it is helpful for the FDIAs detection method to be widely adopted and applied in new power systems. Methods: To address false data injection attacks on distributed energy resource integration into distribution grids within new power systems, a data-driven time series anomaly detection method is employed. Firstly, time-aware shapelets are extracted from time series data, and then the shapelet evolution graph is constructed to capture the correlation between the shapelets. Finally, time series representation vectors are learned using segment embeddings derived from the shapelet evolution graph through the DeepWalk algorithm. These representation vectors are then input into a BO-XGBoost anomaly detector, facilitating the detection of FDIAs. Results: After multiple rounds of parameter tuning, the parameters of Shapelet quantity (K=40) and segment length (L=4) achieved an accuracy of 92.8% in FDIA detection. Comparative experimental results with different algorithms indicate that, compared to other unsupervised learning methods, this approach exhibits an accuracy improvement of 20-40%. In the case of BOXGBOOST, it achieves a 5% increase in accuracy compared to the unmodified XGBOOST. Conclusion: The experimental results indicate that this method can effectively detect false data injection attacks on the integration of distributed energy resources into distribution grids within new power systems. This model significantly enhances detection accuracy and precision while also imparting physical significance to the dynamic evolution of time series models.

In this study, a coupled numerical computation approach integrating aerothermal and thermomechanical effects was employed to investigate the cooling efficiency and thermal stress characteristics of gas turbine stator blades. A comprehensive analysis was conducted considering varying turbulence intensities in the coolant flow (spanning from 0.05 to 0.15) and different coolant media configurations, including pure air, dual-medium mixture of air and steam, and pure steam. The distributional traits of cooling efficiency and thermal stress on the stator blade surface under these conditions were meticulously examined. Furthermore, quantitative assessments were performed to determine the extent to which coolant turbulence intensity and coolant type affect the average cooling efficiency and maximum equivalent thermal stress of turbine stator blades, thereby revealing the influence laws. The results reveal that the minimum cooling efficiency on the stator blade surface predominantly occurs at the position of channel 4 on the pressure surface, while the highest cooling efficiency is generally found near the leading edge of the suction surface. Regions of elevated thermal stress were consistently concentrated around the stator blade tip and root areas. When the coolant turbulence intensity increased from 0.05 to 0.15, the average cooling efficiency on the stator blade surface improved by 2.06%, accompanied by a reduction of 1.12% in the maximum thermal stress. In comparison to pure air cooling, dual-medium (air and steam) cooling and pure steam cooling lead to respective enhancements in the average cooling efficiency of approximately 3.3% and 13.2%, with corresponding decreases in the maximum thermal stress of 2.18% and 10.2%.

Abstract Coal-fired power plants are commonly used as adjustable power sources to complement the fluctuating output of wind and solar energy. The investigation is required to determine the flexible peak-shaving capabilities of coal-fired boilers. A modified scheme for a lignite-fired power plant to further improve the primary air temperature using the outlet steam from the low-temperature reheater is studied while increasing the inlet flue gas temperature of the air preheater is considered the conventional scheme. Thermodynamic models of the power plant are constructed using ebsilon software. The operational characteristics of both schemes are compared under 30% turbine heat acceptance (THA)–100%THA conditions and the economic performance of the modified scheme is also evaluated. Results indicate that the modified scheme exhibits superior thermodynamic and economic performances compared to the conventional scheme. The disparity in power generation efficiency between the conventional and modified schemes reaches a maximum of 0.23 percentage points under 75%THA conditions. The net present value of the modified scheme amounts to 4.51 million dollars over the power plant lifespan of 30 years. The modified scheme allows the conventional denitrification catalyst to maintain an optimal temperature range even under 30%THA conditions, resulting in a power generation efficiency only 4.8 percentage points lower than that under 100%THA conditions, thus demonstrating remarkable operational flexibility. This study presents an efficient, cost-effective, and adaptable approach for lignite-fired power plants.