• Energy Research
• 2021-2025close
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Bioenergy is considered a sustainable substitute to fossil-fuel resources and the development of a prudent combination of renewable and innovative conversion technologies are essential for the valorization and effective conversion of biowaste to value-added commodities. Here, a negative pressure-induced carbonization process was proposed for the valorization of lignin-enriched biowaste precursor to bio-oil and environmental materials (biochar) at various temperatures. The high heating values (HHV) of the as-prepared biochars from the lignin enriched precursor under negative pressure (low-medium vacuum) were within 25.9-31.5 MJ/kg, which matched satisfactorily to the commercial charcoal. Whereas, the bio-oils produced from the lignin enriched precursor under vacuum conditions was a blend of complex aromatic and straight-chain hydro-carbons, including aldehyde, ketone, phenol, and furans, exhibiting ability as potential heating-oil with HHV within 21.2-28.2 MJ/kg. Moreover, the biochars produced under vacuum environments at higher temperature showed greater stability (22.5-35.9%) than those produced under N2 atmosphere.

The transition towards renewable energy is leading to an important strain on the energy grids. The question of designing and deploying renewable energy technologies in symbiosis with existing grids and infrastructure is arising. While current energy system models mainly focus on the energy transformation system or only investigate the effect on one energy vector grid, we present a methodology to characterize different energy vector grids and storage, integrated into the multi-energy and multi-sector modeling framework EnergyScope. The characterization of energy grids is achieved through a traditional energy technology and grid modeling approach, integrating economic and technical parameters. The methodology has been applied to the case study of a country with a high existing transmission infrastructure density, e.g., Switzerland, switching from a fossil fuel-based system to a high share of renewable energy deployment. The results show that the economic optimum with high shares of renewable energy requires the electric distribution grid reinforcement with 2.439 GW (+61%) Low Voltage (LV) and 4.626 GW (+82%) Medium Voltage (MV), with no reinforcement required at transmission level [High Voltage (HV) and Extra High Voltage (EHV)]. The reinforcement is due to high shares of LV-Photovoltaic (PV) (15.4 GW) and MV-wind (20 GW) deployment. Without reinforcement, additional biomass is required for methane production, which is stored in 4.8–5.95 TWh methane storage tanks to compensate for seasonal intermittency using the existing gas infrastructure. In contrast, hydro storage capacity is used at a maximum of 8.9 TWh. Furthermore, the choice of less efficient technologies to avoid reinforcement results in a 8.5%–9.3% cost penalty compared to the cost of the reinforced system. This study considers a geographically averaged and aggregated model, assuming all production and consumption are made in one single spot, not considering the role of future decentralization of the energy system, leading to a possible overestimation of grid reinforcement needs.

To investigate the mechanism of flow-induced vibrations in the cooling system of a double crystal monochromator (DCM), this paper utilizes a multi-physics numerical simulation approach, employing ANSYS and FLUENT platforms to simulate the flow state of liquid nitrogen in the cooling system and explore the amplitude response of the DCM. Initially, simulations were conducted to examine the flow state of liquid nitrogen with varying frequency and amplitude pulsations. Subsequently, modal analysis was employed to investigate the amplitude response of the DCM in the pitch direction vibrations under pulsating excitation. Finally, this research investigated the influence of high heat load-induced liquid nitrogen boiling on a DCM. The results indicate that pipe resistance is the fundamental cause of vibration induced by pulsating excitation. Low-frequency excitation enhances the amplification factor of DCM vibration. In contrast, due to the rapid conversion of fluid kinetic energy to pressure potential energy, high-frequency excitation increases the pulsation amplitude in the pipe. Additionally, there is a linear relationship between the amplitude of liquid nitrogen velocity fluctuations and the response amplitude of a DCM. The slug flow formed after liquid nitrogen boiling generates low-frequency pulse signals, and intermittent fluid impacts cause significant vibrations in the DCM. These research findings provide a reference for the analysis and design of ultra-high-stability DCM cooling systems.

Abstract In the present study, Ru modified hierarchical zeolites xRu-MZSM were developed for cellulose fast pyrolysis. Comprehensive catalyst characterizations including XRD, XPS, N2 adsorption-desorption, Py-IR and so on, were performed to unveil the essential structural properties. The chemical modification and Ru decoration remarkably regulated the distribution of Bronsted/Lewis acid sites. The presence of RuOx species would supply abundant strong Lewis acid sites for participating catalytic cracking, dehydration, decarbonylation, decarboxylation, cyclization, aromatization, etc., in cooperation with Bronsted acid sites. In-situ generated active Ru0 centers during cellulose pyrolysis might facilitate the proceeding of deoxygenation, hydride transfer, and dehydrogenation. Among the as-prepared catalysts screened, 2Ru-MZSM was much more efficient in aromatics production from cellulose fast pyrolysis compared to parent ZSM, wherein the total aromatics yield achieved 16.8% at temperature of 650 °C and heating rate of 10 °C/ms with corresponding E value as low as 23.40 kJ/mol. Also, plausible reaction mechanism for xRu-MZSM involved cellulose fast pyrolysis was proposed in detail.

In the development of a Risk Monitor probabilistic safety assessment (PSA) model from the basic PSA model of a nuclear power plant, the modeling of common-cause failure (CCF) is very important. At present, some approximate modeling methods are widely used, but there lacks criterion of modeling accuracy and error analysis. In this paper, aiming at ensuring the accuracy of risk assessment and minimizing the Risk Monitor PSA models size, we present three basic issues of CCF model resulted from the changes of a nuclear power plant configuration, put forward corresponding modeling methods, and derive accuracy criteria of CCF modeling based on minimum cut sets and risk indicators according to the requirements of risk monitoring. Finally, a nuclear power plant Risk Monitor PSA model is taken as an example to demonstrate the effectiveness of the proposed modeling method and accuracy criteria, and the application scope of the idea of this paper is also discussed.

This paper proposes a series of parallel optimizations on a high-resolution ocean model, the LASG/IAP Climate System Ocean Model (LICOM), which was independently developed by the Institute of Atmospheric Physics of the Chinese Academy of Sciences. The version of LICOM that we used was LICOM 2.1. In order to improve the parallel performance of LICOM, a series of parallel optimization methods were applied. We optimized the parallelization scheme to tackle the problem of load imbalance. Some communication optimizations were implemented, including data packing, the application of the least communication algorithm, and the replacement of communications with calculations. Furthermore, for the calculation procedures, we implemented some mature optimizations and expanded functions in a loop. Additionally, a hybrid of MPI and OpenMP, as well as an asynchronous parallel IO, was used. In this work, the optimized version of LICOM 2.1 was able to achieve a speedup of more than two times compared with the original code. The parallelization scheme optimization and the communication optimization produced considerable improvement in performance in the large-scale parallelization. Meanwhile, the newly optimized LICOM could scale up to 245,760 processor cores. However, for the original version, there was no speedup when scaled up to over 10,000 processor cores. Additionally, the problem of jumpy wall time during the time integration process was also tackled with this optimization. Finally, we conducted a practical simulation from 1993 to 2007 by using the optimized version of LICOM 2.1. The results showed that the mesoscale vortex was well simulated by the model.

Wind power, as one of the primary clean energies, is an important way to achieve the goals of carbon peak and carbon neutrality. Therefore, high-resolution measurement and accurate forecasting of wind speed are very important in the organization and dispatching of the wind farm. In this study, several methodologies, including the mesoscale WRF (Weather Research and Forecasting(WRF) model, mathematical statistics algorithms, and machine learning algorithms, were adopted to systematically explore the predictability and optimization of wind speed of a Gobi grassland wind farm located in western Inner Mongolia. Results show that the rear-row turbines were significantly affected by upwind turbine wakes. The output power of upwind-group turbines was 591 KW with an average wind speed of 7.66 m/s, followed by 532 KW and 7.02 m/s in the middle group and 519 KW and 6.92 m/s in the downwind group. The higher the wind speed was, the more significantly the wake effect was presented. Intercomparison between observations and WRF simulations showed an average deviation of 3.73 m/s. Two postprocessing methods of bilinear interpolation and nearest replacement could effectively reduce the errors by 34.85% and 36.19%, respectively, with average deviations of 2.43 m/s and 2.38 m/s. A cycle correction algorithm named Average Variance–Trend (AVT) can further optimize the errors to 2.14 m/s and 2.13 m/s. In another aspect, the categorical boosting (CatBoost) artificial intelligence algorithm also showed a great performance in improving the accuracy of WRF outputs, and the four-day average deviation of 26–29 September decreased from 3.21 m/s to around 2.50 m/s. However, because of the influence of large-scale circulations, there still exist large errors in the results of various correction algorithms. It is therefore suggested through the investigation that data assimilation of the northwest and Mongolian plateau, boundary layer parameterization scheme optimization, and embedding of high-resolution topographic data could have great potential for obtaining more accurate forecasting products.

The greenhouse’s energy consumption is a major limiting factor for output and development. To address this, it is necessary to adopt green and low-carbon heating technologies to replace traditional fuels. This will not only help conserve energy but will also reduce emissions, thereby improving the thermal environmental conditions for agriculture. This paper aims to research and develop a vertical heat exchange tube array device specifically designed for greenhouses. The focus is on enhancing the passive heat absorption and heat storage efficiency of the device and its influence on the thermal environment of the greenhouse. In order to improve the heat absorption and storage efficiency of the heat exchanger device and its impact on the greenhouse thermal environment, experimental comparative analysis was conducted using air, water, and phase-change materials as working fluids inside the pipes. Through a combination of experiments and simulations, it was verified that the heat exchanger device is capable of actively regulating the greenhouse thermal environment. The results show that heat exchangers of all three types of working fluids can effectively improve the stability of soil temperature and play a “shifting the peak and filling the valley” role in regulating the indoor air temperature while positively regulating the relative humidity of the air. Notably, when the working fluid is a phase-change material, it has the most significant impact on the thermal environment of the greenhouse.