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Oil palm empty fruit bunches (EFB) were pretreated by aqueous ammonia soaking for ethanol production. Pretreated EFB, which were pretreated at the optimal conditions of 60 °C, 12 h, and 21% (w/w) aqueous ammonia, showed 19.5% and 41.4% glucose yields during an enzymatic digestibility test for 96 h when using 15 and 60 FPU of cellulase, respectively. Using the pretreated EFB, simultaneous saccharification and fermentation for 168 h with 5% (w/v) glucan loading and 60 FPU of cellulase and 30 CBU of β-glucosidase per gram glucan resulted in ethanol production of 18.6 g/L titer, 65.6% of theoretical maximum yield, and 0.11 g/L/h of productivity.

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.

The current status and challenges associated with the production and utilization of cellulosic ethanol in Korea are reviewed in this paper. Cellulosic ethanol has emerged as a promising option for mitigating Korea's CO(2) emissions and enhancing its energy security. Korea's limited biomass resources is the most critical barrier to achieving its implementation targets for cellulosic ethanol. Efforts to identify new suitable biomass resources for cellulosic ethanol production are ongoing and intensive. Aquatic biomasses including macroalgae and plantation wastes collected in the Southeast Asia region have been found to have great potential as feedstocks for the production of cellulosic ethanol. R&D explorations into the development of technologies that can convert biomass materials to ethanol more efficiently also are underway. It is expected that cellulosic ethanol will be in supply from 2020 and that, by 2030, its use will have effectively reduced Korea's total gasoline consumption by 10%.

A detailed analysis of the effects of ground grid configuration on grounding performance has been carried out for uniform and horizontally stratified soils with multiple layers. The results of the analysis reveal that the most efficient and cost-effective design is highly dependent on soil structure type and characteristics. In the absence of ground rods, grounding grids with uniform mesh size are quite efficient in soils having a thin (relative to grid size) high resistivity (relative to the lower layers) top soil, while grids with small mesh size at the periphery of the grid provide optimum performance in uniform soils and soils with low resistivity top soils. Ground rods were found to be effective only when a significant portion of their length is in contact with a low resistivity soil, as expected.

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.

This paper proposed lithium-ion based high-voltage battery simulator. Matlab based battery parameters and modeling were implemented, and the characteristics of the modeled batteries were output by using three-phase grid-connected AC / DC PWM converters. The proposed methods were verified by PSIM.

In this paper, absolute and differential eddy current signals from various shapes and sizes of defects in the steam generator tube are numerically predicted and their slope characteristics are investigated. The signal variations due to frequency increase are also observed. Then, the analysis of mixed defect signals affected by a ferromagnetic support plate is attempted. Axisymmetric finite element modeling is used for signal prediction and the slope angle of signal is used importantly in analyzing the signal. Results show that differential signals are useful for locating the position of a defect inside the support plate hole, while absolute signals are easy to presume and interpret even though the effect of support plate is mixed in the signal. Understanding of respective signal characteristics and relationship between them will be helpful for more reliable defect characterization.

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