• Energy Research
• 2016-2025close
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Abstract Limiting the increase of global temperature requires electrification of the railway transport, which is relatively a distinct mode of transport in terms of economy, business and socialization of a country. However, battery complemented railway transport systems such as fuel cell hybrid railway propulsion systems are constrained by the high cost of the battery, and suboptimal performance of the fuel cell under varying loads. Optimized selection of battery can reduce its costs associated with sizing, life and utilization. Similarly, managing the load distribution between the battery and fuel cell can reduce the load variations concern for fuel cell. In this study, model-based selective characterization of the battery and fuel cell is done to identify the optimal features and constraints. Following characterization, the load allocation to battery and fuel cell is determined with the targets of collaborative utilization of optimal features and mitigation of constraints. Subsequently, considering this load allocation and results of characterization, conditions are proposed for reliable sizing and optimized selection of battery. The significance of the proposed conditions in reliable sizing is explored. Based on these conditions, certain commercially developed batteries are evaluated for optimized selection of battery. The results signify this study as a foundation for research on the reliable sizing and optimized selection of battery in fuel cell hybrid railway propulsion systems.

As smart cities become a global topic, interest in smart mobility, the core of smart cities, is also growing. The technology that comes closest to general users is “autonomous driving”. In particular, the successful market entry and establishment of some private companies proved that “autonomous driving” is not technology of the future but imminent reality. However, safety in autonomous vehicles that rely on sensors instead of the driver’s five senses has been the focus of attention from the beginning and continues to be so. In this study, we attempted to counter this interest. Based on the actual data of thirty traffic accidents, assuming the AEBS (Autonomous Emergency Braking System) was installed to assist the driver in safe driving, it was reinterpreted through simulation to see what changes occurred in the accident. In the computer program, PC-Crash, the results were first analyzed through simulation using Euro NCAP (New Car Assessment Program)’s AEBS test standards. Subsequently, the other variables in the AEBS were controlled and the accident was reinterpreted by changing only the angle of the radar detection sensor. As a result, it was confirmed that a total of 27 accidents out of thirty accidents could have been prevented with the AEBS. In addition, it proved that the crash avoidance rate of vehicles gradually increased as the radar angle increased.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.

The assessment of voltage stability margins is a promising direction for wide-area monitoring systems. Accurate monitoring architectures for long-term voltage instability are typically centralized and lack scalability, while completely decentralized approaches relying on local measurements tend towards inaccuracy. Here we present distributed linear algorithms for the online computation of voltage collapse sensitivity indices. The computations are collectively performed by processors embedded at each bus in the smart grid, using synchronized phasor measurements and communication of voltage phasors between neighboring buses. Our algorithms provably converge to the proper index values, as would be calculated using centralized information, but but do not require any central decision maker for coordination. Modifications of the algorithms to account for generator reactive power limits are discussed. We illustrate the effectiveness of our designs with a case study of the New England 39 bus system. 10 pages, submitted for publication

Domestic agro-byproduct potential in Korea is estimated to be approximately 4,018 × 103 tons of oil equivalent per year. Majority of agro-byproducts are unused and discarded. These unused agro-byproducts can potentially be converted into solid fuel for value addition. The energy potentials of solid fuels are generally characterized by proximate analysis and measuring calorific value; however, owing to the use of coal and coke, both the analyses could be expensive and time-consuming. Proximate analysis via thermogravimetric analysis is a relatively low cost and less time-consuming method than proximate analysis by ISO method (ISO 18134-1:2015, ISO 18123:2015, ISO 18122:2015). Thermogravimetric analysis-based proximate analysis of four agro-byproducts (pepper stem, perilla stem, corn stalk, and kenaf) was conducted. Additionally, the calorific values obtained from several equations based on the proximate analysis results were compared to that of the calorific value measured using calorimeter. The maximum difference between proximate analysis using thermogravimetric analysis and ISO method was 4.73%p, which was attributed to change in moisture content. It is recommended to measure the moisture content of biomass for 120–150 min (at 105 °C) and ash content for 80–120 min (at 815 °C). Three of the equations indicated a difference of less than 1.5 MJ kg−1 between the TGA-derived and the measured calorific value. For this study, It was determined that both proximate analysis and prediction calorific value can be conducted by thermogravimetric analysis.

Pollution is a significant problem in both industrialized and developing countries, as well as in some developed countries. Heavy metal pollution poses a crucial concern for communities due to its detrimental impact on human health and the long-term well-being of the planet. Heavy metals, such as cadmium, chromium, arsenic, lead, and mercury, do not degrade and can enter the food chain, leading to significant health risks. Therefore, it is essential to monitor and take necessary steps to eliminate heavy metal residues that may be present in food, beverages, and the environment. Although the exact role of nanomaterials in pollution cleanup is still unclear, nanotechnology holds promise as a future solution. In the first part of this comprehensive review, a brief overview of nanoremediation is provided. The subsequent sections delve into various nanomaterials and their potential applications in different pollution cleanup scenarios. Following that, critical analysis is conducted on different nano-remediation methods, including oxidation, adsorption, chemical precipitation, filtration, ion exchange, coagulation-flocculation, and photo-catalysis. Lastly, further suggestions are put forward regarding the future utilization of nanotechnology for more effective and enduring pollution cleanup, particularly in eliminating heavy metals.

Nowadays, a large part of energy is provided by steam turbines; thus, increasing the efficiency and improving the steam turbines performance are of special importance. The presence of the liquid phase in the low-pressure stage of the steam turbine can cause energy loss, efficiency drop, and erosion/corrosion problems; therefore, one of the essential issues is to identify wet steam flow and try to reduce condensation loss. In order to decrease the liquid fraction, the drainage groove technique can be applied. The drainage groove sucks the water droplets from the turbine blade surface and drains them into the condenser. In this study, the effect of the drainage groove location on the surface of steam turbine blades has been investigated on the condensation, droplet radius, inlet mass flow, erosion rate, liquid drainage ratio, condensation losses, and total drainage ratio. For modeling the condensing flow, the Eulerian–Eulerian approach has been applied. The results show that the location of the drainage groove affects the groove performance and flow pattern in the turbine blade. In the selected drainage, the liquid drainage ratio, condensation losses, and erosion rate are reduced by 7.6%, 12%, and 88%, respectively, compared with the no-drainage groove case. Also, the total drainage ratio is 7.2% in the selected drainage. The outcomes of the present work have been a major step forward in the techniques having a great influence on the lifetime, repair and maintenance, and the output power of steam power generation facilities.