• Energy Research
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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.

Abstract In the present work, the results of testing panel and box solar cookers are used to investigate the suitability of using the linear regression for estimation of the performance parameters of a solar cooker. The panel cooker and the box cooker were experimentally tested with glycerine and peanut oil, respectively. An exponential fitting to the plot of measured load temperature versus time is used to derive the linear regression between the instantaneous efficiency and the specific difference of temperature. The linear regression curve is compared with the corresponding experimental curve. Minor deviations are observed in the case of the panel cooker, but only in the middle part of the test. In the case of the box cooker, the deviations are very significant during the whole test. The present work presents the simplified formulation associated with the physical problem. It points out the importance of performing further research to develop a more accurate procedure. The determination of parameters based on the linear regression cannot be seen as a universal procedure applicable to all types of cookers. The opto-thermal ratio and the maximum achievable load temperature are overestimated.

Abstract The present study was carried out to investigate biodiesel production via transesterification of sunflower oil employing heterogeneous catalyst derived from indigenous ginger (Zingiber Officinale) leaves. It also aims to compare the techno-economy performance of the ginger-based catalysts in 3 different forms viz. calcinated (CGL), activated by KOH (KGL) and NaOH (NGL). The plant-based catalysts were characterised by Scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) and Fourier transform infrared spectroscopy (FTIR). The parametric effects on the biodiesel production such as reaction time, methanol to oil ratio and catalyst loading were investigated. The experimental result shows that 1.6 wt % catalyst, 6:1 M ratio of alcohol to oil, 1 h 30 min of reaction time with a speed of 200 rpm gave the best results. It was found that the KGL obtained highest biodiesel yield of 93.83% under optimum conditions. Subsequently, the specific energy and energy productivity of KGL catalyst was found to be 1.2728, 26.1544 MJ/kg and 0.0382 kg/MJ, respectively, per 1 L of biodiesel. Meanwhile, the renewable energy to non-renewable energy ratio for CGL, KGL and NGL is found to be 3.17, 4.01 and 3.67, respectively. A higher sustainable renewable energy-yield ratio and overall economical profit cost ratio are preferable for the biodiesel production process.

The conversion of biogas to biomethane represents an attractive solution to replace fossil gas with a renewable gas. However, removal of such a large percentage of CO2 from a fuel gas comes at a significant energy cost using the conventional CO2 capture technologies and hence has led to an opportunity to develop an alternative technique for large-scale carbon capture. Results of the current study suggest that employing an anion exchange membrane (AEM)-based alkaline water electrolyser for CO2 removal from gas mixtures offers an energy-efficient strategy for the capture and removal of CO2 from biogas. After capturing CO2 in an aqueous absorption column, the resulting bicarbonate solution was fed through the cathode of an AEM-based electrolyser. Although the CO2 absorption rate increased from about 300 to 900 mol m−3 h−1 when the pH was elevated from 9 to 13, the system's energy requirement was lowest at pH = 9. The economic assessment shows that the electrochemical work requirement for CO2 removal from biogas using the AEM-based alkaline electrolyser ranges between 0.25 and 0.92 kWh/kg CO2 at optimum conditions (pH = 9). This could potentially reduce the energy input for CO2 removal by about 50% compared to commercially available biogas upgrading technologies.