• Energy Research
• 2021-2025close
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Abstract Sunlight Concentrated and transmission for daylighting via optical fibers is a booming technology of direct utilization of solar energy. It uses optical fibers to introduce sunlight deep into building interior. In this work, a sunlight concentrating and transmitting system via plastic optical fibers was developed and tested. This study addressed the cooling problem of the plastic optical fibers under highly irradiation. Polymethylmethacrylate (PMMA) plastic fibers have a strong absorption effect on infrared light, which means that plastic fibers heat up so much when they transmit focused sunlight that they burn. In this study, a comprehensive cooling approach was developed to solve the overheating problem of plastic optical fibers, which consists of an infrared filter, cooling water and a homogenizer. The transmission efficiency of the system was measured to be about 15%. Experiments show that the comprehensive cooling measures developed can ensure that PMMA plastic optical fibers are in a safe temperature range. This shows that it is feasible to use low-cost plastic optical fibers instead of expensive silica fiber to transmit high flux for daylighting.

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.

Abstract For the first time the electrical conductivity of bamboo biographite-based material reported a ground-breaking milestone of 4.4 × 104 (S/m). This reported conductivity by far exceeded all previous reported conductivity measurements obtained from renewable carbon. Controlled high-temperature thermal carbonization of biomass, notably Asian bamboo, at extended residence times elicited surprising growth of nano-layered biographitic structures with a layer-to-layer distance of less than 0.3440 nm. Moreover, thermodynamically dispersed bamboo and pine biographitic nano-layered carbon-based lightweight composites in a polyamide matrix were found to be intrinsically conductive both thermally and electrically. Electromagnetic interference (EMI) shielding device made from bamboo renewable carbon/cellulose nanofiber (CNF) composites possesses EMI shielding effectiveness (SE) of ∼23 dB. These results constitute a new advancement in the materials science of nano-layered graphites from renewables and their applications as EMI filtering devices and as electrode materials in air cathodes, electronics, supercapacitors in energy storage devices, and thermal management of batteries and sensors.

Abstract Reliability analysis of IESs (Integrated Energy System) is complicated because of the complexity of system topology and dynamics and different kinds of uncertainties. Reliability is often calculated based on statistic methods, which always focus on historical performances and neglect the importance of their dynamics and structure. To overcome this problem, in this paper, a systematic framework for dynamically analysing the real-time reliability of IESs is proposed by integrating different machine learning methods and statistics. Firstly, the bootstrap-based Extreme Learning Machine is developed to forecast the conditional probability distributions of the productions of renewable energies and the energy consumptions. Then, the dynamic behaviour of IESs is simulated based on a stacked auto-encoder model, instead of using traditional mechanism-based simulation models, for improving computational efficiency. Besides, the variables representing the transient properties of natural gas pipeline networks, such as delivery pressures and flow rates, are taken as the indicators for quantifying the energy supply security in natural gas pipeline networks. The time-dependent relationships among these indicators and their statistic correlations are modelled for improving the effectiveness of the analysis results. Finally, the reliability assessment is performed by estimating the probability distribution of each functional state of the target IES. A case study of a realistic bi-directional IES is carried out to demonstrate the effectiveness of the proposed method. The results show that the method is able to effectively evaluate the reliability of IESs, which can provide useful information for system operation and management.