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Abstract In hot and humid climate such as in Japan and south east Asia, dehumidification in summer is really important for air conditioning. Temperature and humidity independent control (THIC) of air conditioning system can handle sensible heat and latent heat separately, and provide good indoor environment and achieve energy conservation. Desiccant air handling unit is one of the major solution for THIC of air conditioning system. It needs hot water to regenerate sorbent which absorbs moist in the air. Combined heat and power can supply hot water at almost constant temperature for desiccant air handling system and also contribute to the business continuity plan of commercial buildings. However, there are still uncertainties about the factors which affect energy performance of desiccant air handling unit and the optimum design and operations in hot and humid climate. The objectives of this paper are to prove factors which affect energy performance of desiccant air handling unit by measurement analysis and show optimum condition of the desiccant air handling unit under various room conditions by simulation. Measurement analysis shows that energy performance of desiccant air handling unit depends not only on the inlet air condition to dehumidification wheel but also on designed supply air humidity. Furthermore, simulation results show the optimum inlet air condition entering dehumidification wheel under various supply air absolute humidity which is determined by design room conditions. These results provide useful information of desiccant air handling unit during design and operation phase of buildings in hot and humid climate.

A novel exciterless- and brushless-type commutatorless motor driven by a voltage source inverter is presented, and the analysis of the characteristics and the test results are reported. In the commutatorless motor, the stator armature winding is connected electrically with the secondary winding of a transformer. An AC exciting current flows in the armature winding simultaneously with the load current. The ratio between the number of poles made by the load current and the number of poles by the AC exciting current is 2 or 1/2. Thus, the motor can be made exciterless and brushless. Since the rotating direction of the rotating field made by the AC exciting current is opposite to that of the rotor in this commutatorless motor, speed control in a low-speed range is possible, because a large current can flow in the field winding even if the rotor rotates in a low-speed range. As a result, the motor can be controlled in a wide range, from high speed to low speed. It has been found from the analysis and the test results that a shunt wound characteristic between the speed and the torque and other characteristics can be obtained. >

Abstract Recently, many studies have exploited the potential of deep learning to forecast energy demand, but they cannot explain the results. They only analyze the simple correlations between the input and output to discover the most important input features, or they depend on the manual investigation of the latent space embedded with power demand patterns. In this paper, to overcome these shortcomings, we propose a deep autoencoder that can explain the prediction results by manipulating the latent space. It consists of 1) a power encoder that embeds power information, 2) an auxiliary encoder that embeds auxiliary information for an interpretable latent space in two dimensions, 3) a predictor that predicts power demand by using concatenated values of the latent variables extracted from the two encoders, and 4) an explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables. Several experiments on a dataset of household electric energy demand show that the proposed model not only performs better than conventional models, with a mean squared error of 0.376 in predicting electricity demand for 60 min, but also provides the capacity to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted.

Biomass burning (BB) is one of the largest sources of carbonaceous aerosols with adverse impacts on air quality, visibility, health and climate. BB emits a few specific aromatic acids (p-hydroxybenzoic, vanillic, syringic and dehydroabietic acids) which have been widely used as key indicators for source identification of BB-derived carbonaceous aerosols in various environmental matrices. In addition, measurement of p-hydroxybenzoic and vanillic acids in snow and ice cores have revealed the historical records of the fire emissions. Despite their uniqueness and importance as tracers, our current understanding of analytical methods, concentrations, diagnostic ratios and degradation processes are rather limited and scattered in literature. In this review paper, firstly we have summarized the most established methods and protocols for the measurement of these aromatic acids in aerosols and ice cores. Secondly, we have highlighted the geographical variability in the abundances of these acids, their diagnostic ratios and degradation processes in the environments. The review of the existing data indicates that the concentrations of aromatic acids in aerosols vary greatly with locations worldwide, typically more abundant in urban atmosphere where biomass fuels are commonly used for residential heating and/or cooking purposes. In contrast, their concentrations are lowest in the polar regions which are avoid of localized emissions and largely influenced by long-range transport. The diagnostic ratios among aromatic acids can be used as good indicators for the relative amounts and types of biomass (e.g. hardwood, softwood and herbaceous plants) as well as photochemical oxidation processes. Although studies suggest that the degradation processes of the aromatic acids may be controlled by light, pH and hygroscopicity, a more careful investigation, including closed chamber studies, is highly appreciated.

New powertrain technologies, such as those used in Hybrid Electric Vehicles, have a price premium which is often offset by lower running costs. Total Cost of Ownership (TCO) combines these purchase and operating expenses to identify the most economical vehicles overall. This is a valuable assessment for both private and fleet purchasers. To date, no studies have compared Total Cost of Ownership across more than two vehicle markets or analysed historic costs. To address this gap, this research provides a more extensive assessment of TCO in three industrialized countries — the U.S.A. (California specifically), the UK, and Republic of Korea — for the time period from 2015 ∼ 2019. As an early analysis of the regional characteristics of TCO, this study aims to examine how TCO varies across different geographic regions and develop an analysis methodology for future analysis of environmental vehicles. To analyze the TCO under identical conditions for each region, the TCO result is calculated using data from conventional vehicles by utilizing sufficient statistical data. The TCO model includes registration and road taxes as well as insurance, fuel, financial interest, depreciation, and maintenance costs. To represent the local features, the research considers four vehicles from different manufacturers. Using the proposed TCO model, the regional characteristic is analyzed and a sensitivity analysis is performed with the parameters constituting the TCO model. This research has implications for both fleet purchasers and private owners considering new vehicles. The findings are also relevant for carmakers aiming to develop vehicle sales strategies optimized for specific regions.

Abstract We succeeded in growing CZ monocrystalline silicon crystals with a longer lifetime than previously achieved. The MCZ technique was not used; instead, we employed melt-phobic quartz crucibles in a conventional CZ furnace. The improved lifetime is the result of reduced carbon incorporation into the growing crystals due to the suppression of SiO evaporation from the melt in the melt-phobic crucible. The melt-phobic effect of our crucibles has the potential to control the convection of molten silicon.

Abstract In this paper, we examine the time-varying causal relationship between green bonds and other assets including US conventional bonds, WilderHill clean energy (equity) index, and CO2 emission allowances price during the period spanning from 30 July 2014 to 10 February 2020. We apply the novel time-varying Granger causality test (Shi et al. 2018) based on the recursive evolving algorithm introduced by Phillips et al. (2015a, 2015b) for controlling financial bubbles to detect real–time causality, detecting possible changes in the causal direction and dating financial turbulences, The study based on this algorithm reveals a significant causality running from the US 10-year Treasury bond index to green bonds starting from the end of the year 2016 until the end of the sample period. Besides, we find that the link CO2 emission allowances price causing green bonds is significant from the beginning of the sample period to the end of the year 2015. Furthermore, by using the recursive-evolving causality algorithm of the Shi et al. (2018) test, we find that the causality running from the clean energy index to green bonds is very limited to the year 2019. On the other hand, there is no significant causality running from green bonds to all considered assets, indicating no predictive power for this asset in its proper domain, which is not yet examined in the literature.

Carbon capture and storage (CCS) technology has been studied actively in recent years to address global warming. This paper aimed to make a consistent comparison of different capture technologies applied to the natural gas-fired combined cycle (NGCC). Multiple power plant systems based on a standard NGCC using three different carbon capture technologies (post-combustion, pre-combustion, and oxycombustion) were proposed, and their net performance was compared. The optimal pressure ratio of the oxy-combustion technology system was obtained. The variations in the net cycle performance of the three systems were compared using the specific CO2 capture. The net power of the post-combustion capture scheme is lower than that of all other systems, but it has the highest efficiency. However, its biggest disadvantage is a much lower CO2 capture rate than the oxy-combustion capture which exhibits nearly 100 % capture rate. The conclusion is that the oxy-combustion capture would provide both the highest net efficiency and power output if a high capture rate of over 92 % was required.

Enriching the interface: metal–organic framework-derived copper oxide nanowires with abundant crystalline interfaces contribute to the efficient electrochemical CO2 reduction towards fast hydrocarbon generation.