• Energy Research
• 2016-2025close
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Abstract To obtain high yield and high concentration glucose from corncob residues (CRs), tetrahydrofuran (THF) + H2O co-solvent pretreatment was employed. The pretreatment parameters were firstly optimized by Box-Behnken Design of response surface method, and the maximum glucose yield (498.2 mg/g CRs) was obtained via enzymatic hydrolysis after pretreated under optimized conditions (53.7% THF concentration, 202.3 °C, 1.05 h). Then, the pretreated CRs under optimized conditions (optimized-CRs) were enzymatically hydrolyzed at high solid loading (15%–20%) and low enzyme input (5–8 FPU/g cellulose). The high concentration glucose production (128.6 mg/mL) was obtained from optimized-CRs after enzymatic hydrolysis at 20% solid loading with 10 FPU/g cellulose for 72 h, and then showed potential for reduction of enzyme input. Finally, the interactions between the 9 substrate-related factors and cellulose conversion were analyzed through correlation analysis, and found that Klason lignin remained in the cellulose-rich fractions and pseudo lignin condensed on the surface of the recovered substrate were the predominant inhibitors for enzymatic hydrolysis.

The wind power forecasting error restricts the benefit of the wind farm in the electricity market. Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated bidding/operation model for the wind farm to improve its benefits in the electricity market. The maximum entropy based deep reinforcement learning (RL) algorithm, Soft Actor-Critic (SAC) is used to construct the model. The maximum entropy framework enables the designed agent to explore various optimal possibilities, which means the learned coordinated bidding/operation strategy is more stable considering the forecasting error. Particularly, penalty terms are introduced into the benefit function to relax the constraints and improve the convergency. The case study illustrates that the learned policy can effectively improve the wind farm benefit while ensuring robustness.

This study numerically investigates the energy storage characteristic of the latent heat energy storage (LHES) component which can be used in building envelope. A simplified numerical unit is extracted from the component to analyze the effects of the style and ratio (0.5, 0.75, 1, 1.5) of fin and the inclination angle (45° and 90°) of the unit on melting at a constant tube temperature. The fin styles include cross shape (z style fin) and inclined cross shape (x style fin). Results show that the longer the fin, the faster the melting rate is. The temperature also changes more uniformly during melting. For the unit, z-1.5-90 (z style fin-fin ratio at 1.5-the inclination angle at 90°) presents the fastest melting rate and highest average energy storage power due to the division effect of fins. Therefore, the z-1.5-90 is used in the component and analyzed in the range of tube temperature at 55–80 °C. It is found that the average energy storage power of the component can reach up to 1567.37W, 4.05 times z-1.5–90 unit. An exponential relationship between the completely melting time and the tube temperature has also been found through curve fitting.

The two essential terms in environmental sciences – dematerialization and decoupling – have always been controversial. This study aims to trace the evolution path on the methodologies, the evidence...

Publisher Copyright: IEEE With the ever-escalating scale of urban distribution networks (UDNs), the traditional model-based reconfiguration methods are becoming inadequate for smart system control. On the contrary, the data-driven deep reinforcement learning method can facilitate the swift decision-making but the large action space would adversely affect the learning performance of its agents. Consequently, this paper presents a novel multi-agent deep reinforcement learning method for the reconfiguration of UDNs by introducing the concept of 'switch contribution'. First, a quantification method is proposed based on the mathematical UDN reconfiguration model. The contributions of controllable switches are effective quantified. By excluding the controllable switches with low contributions during network reconfiguration, the dimensionality of action space can be significantly reduced. Then, an improved QMIX algorithm is introduced to improve the policy of multiple agents by assigning the weights. Besides, a novel two-stage learning structure based on a reward-sharing mechanism is presented to further decompose tasks and enhance the learning efficiency of multiple agents. In the first stage, agents control the switches with higher contributions while switches with lower contributions will be controlled in the second stage. During the two-stage process, the proposed reward-sharing mechanism could guarantee a reliable UND reconfiguration and the convergence of our learning method. Finally, numerical results based on a practical 297-node system are performed to validate our method's effectiveness. Peer reviewed

AbstractIncreased human activity and climate change are driving numerous tree species to endangered status, and in the worst cases extinction. Here we examine the genomic signatures of the critically endangered ironwood treeOstrya rehderianaand its widespread congenerO. chinensis. Both species have similar demographic histories prior to the Last Glacial Maximum (LGM); however, the effective population size ofO. rehderianacontinued to decrease through the last 10,000 years, whereasO. chinensisrecovered to Pre-LGM numbers.O. rehderianaaccumulated more deleterious mutations, but purged more severely deleterious recessive variations than inO. chinensis. This purging and the gradually reduced inbreeding depression together may have mitigated extinction and contributed to the possible future survival of the outcrossingO. rehderiana. Our findings provide critical insights into the evolutionary history of population collapse and the potential for future recovery of the endangered trees.

Hydrofoil sections are broadly used in the field of fluid machinery. The leading edge of a hydrofoil structure is closely related to drag and cavitation performance. In this study, we adopt the concept of a bionic design. The fin structure of a fish is simplified and applied to the traditional National Advisory Committee for Aeronautics 0015 hydrofoil. We adopt the Grey–Taguchi method to calculate the best layout. This helps obtain the best drag and cavitation performance. Large eddy simulations are used to verify the scheme. When the ratio of fin depth to length h/L is 0.3, the fin layout length Xn is 7 L, the inclination of fins θ is 7°, and the bionic hydrofoil exhibits the best hydrodynamic performance. Application of the best bionic hydrofoil to the fluid machinery (pump) demonstrates that the hydraulic performance of the pump has been considerably improved.

Focusing on the high-valued utilization of the widespread silicon-rich waste biomass, a sustainable route by simultaneous utilization of carbon and silicon from silicon-rich rice husk was proposed in this work. Specifically, porous carbon-zeolite composite with hierarchical porous structure of micro/meso pores (carbon) and ultra-microporous pores (Na-X zeolite) was in situ prepared by a facile one-pot method. The obtained porous carbon-zeolite composite (PC2-Z) had a higher yield of 67.66% compared to the porous carbon without silicon (PC2) of 43.33%. Moreover, due to the high ultra-micropore volume of the PC2-Z sample (up to 0.181 cm3/g), it exhibited high dynamic CO2 adsorption capacity of 1.81 mmol/g and CO2/N2 selectivity of 9.80 (1 bar), which were higher than 1.67 mmol/g and 7.01 (1 bar) for PC2, respectively. PC2-Z also showed good regeneration efficiency above 99% after ten cycles. Furthermore, the economic and energy consumption assessment of this utilization route was conducted. Overall, a facile one-pot route was developed to prepare highly efficient composite absorbents from silicon-rich biomass, which can be widely used in different environmental applications.