Kinetic Monte Carlo simulation of hydrogen production from photocatalytic water splitting in the presence of methanol by 1 wt% Au/TiO2

In this research, using the kinetic Monte Carlo simulation (KMC), the hydrogen production from a water-methanol mixture using Au/TiO₂ photocatalyst is investigated. A mechanism is proposed, and the rate constants of the reaction steps are specified. The reaction rate constants of different steps and the concentration of the active sites on the photocatalyst surface were determined. An excellent match between simulated and experimental data confirms the results. The electron-hole pair production, methanol adsorption on the photocatalyst surface, and electron-hole recombination steps are considered the most critical steps. To study the effects of independent variables (initial concentration of methanol, photocatalyst dosage, pH, and time of reaction) on the produced hydrogen, a combination of KMC simulation and design of experiment was employed. The concentration of photocatalysis has the highest and pH has the lowest effect on the hydrogen production. The optimal conditions for photocatalytic hydrogen production are presented.     

基于ERNIE-BiGRU模型的摘要语步自动识别研究

学术文献的摘要是对文献主要内容的浓缩,摘要不同部分的语步具有不同的信息,语步的自动识别和抽取对于学术摘要的后续研究有着重要的应用价值,而目前语步识别的研究相对较少,并且相关算法的效果还需要提高。针对上述问题,该文提出了一种基于ERNIE-BiGRU模型的语步识别算法。该算法首先结合中文句法分析理论提出基于句法依存关系的多语步结构拆分法,对学术文献摘要多语步结构进行自动拆分,获得多个单语步结构;然后构建用于训练的单语步结构语料库,并利用知识增强语义表示预训练模型,训练出句子级词向量;最后将训练出的单语步结构词向量信息输入双向门限循环单元(BiGRU)进行摘要语步自动化识别,取得了良好的效果。实验结果表明,该算法具有较好的鲁棒性和较高的识别精度,在结构化和非结构化摘要上的识别准确率分别达到了96.57%和93.75%。     

复杂环境下氧化铝储槽定向爆破拆除

在较为复杂的环境下,爆破拆除钢筋混凝土氧化铝储槽。该储槽自重大、呈圆形,内有4根立柱支撑下料漏斗。为使储槽顺利定向倒塌,通过爆破方案选择、参数确定,采取梯形切口和预处理以及安全防护和减振措施,使储槽爆破拆除获圆满成功。     

建筑结构控制爆破拆除的力学分析与应用

应用运动学、动力学理论建立建(构)筑物控爆坍落过程的力学模型,进而研究在非定常约束条件下坍落结构的力学特征。首次提出了采用延时起爆技术时,非自由质点系及其广义坐标应是建立在每一段位上的瞬间虚位移。在此基础上,分析求解运动过程中时控单元的质点自由度,为精确选定爆破部位及时序提供了设计理论和计算依据。同时,结合振动理论,将控爆时差、结构载荷分布调制为振动系统,形成坍落结构梁板单元的强迫振动,充分实现在运动中进一步解体、破碎其自身结构,并形成具有明显缓冲效果的下落过程。应用实践结果证明力学分析是正确的,为工程结构爆破拆除计算机辅助设计及动态模拟技术的有效应用奠定了基础。     

基准平面垂直断面法在爆破漏斗试验中的应用

系统阐述了基准平面垂直断面法在爆破漏斗试验中测量爆破漏斗体积的基本原理,并将隧道激光断面仪应用于金厂河矿1 750 m水平15^#采场底部切割巷道爆破漏斗试验爆破漏斗体积测量中。通过与传统体重法等计算法所得漏斗体积分析比较,结果表明基于隧道激光断面仪与3D Mine软件分析的基准平面垂直断面法实用性强、操作方便、结果直观可靠,达到试验预期目的。     

Dynamic simulation of hydrogen-based off-grid zero energy buildings with hydrogen storage considering Fanger model thermal comfort

In this study, some locations with different climates, off-grid zero energy buildings with hydrogen energy storage systems are designed, and transient analysis is conducted. These considered buildings supply their electricity consumption without using the electrical grid and PV panels or wind turbines. Also, they supply thermal comfort to occupants by using a vapor compression chiller and humidifier. Domestic hot water of occupants is supplied using solar collectors. For analyzing building's performance and objectives achievement, TRNSYS software is used. Also, for evaluating occupant thermal comfort, the Fanger model is used. The considered building is a one-story building with a 150 m² area. Four occupants are considered. Both of them are seated at rest, and another is seated with light working such as typing. Using the Fanger model equation and MATLAB software, the thermal comfort of occupants is determined. For domestic hot water consumption, verified profiles that vary during 24 h of the day are considered. Achieved results show that for humid and cold cities, PV panels with an area of 73 and 76 m² can be supplied the required electricity of considered building with four occupants and battery state of charge is higher than 50% and 10%, respectively. Moreover, with a suitable air conditioner system, the predicted percentage of dissatisfied (PPD) can be lower than 12% and 8% for humid and cold cities. Therefore, the building can be converted to a zero-energy building using its rooftop area.     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

Flow field simulation and pressure drop modeling by a porous medium in PEM fuel cells

For proton-exchange membrane fuel cells, the distribution of reactant flow in the stack is critical to the fuel cell's efficiency. The uneven distribution of reactant flow in the stack may cause poor current density, low performance, and material degradation. To understand and accurately predict the flow field in the proton-exchange membrane fuel cell system, the present study aims to develop a simple correlation to analyze the pressure drop in fuel cell stacks. The flow channel in each cell of a stack is treated as a porous medium, and a power-law model is used to approximate the porous medium momentum source term. For the stacks with fewer cell numbers, namely, 1, 5, and 10 cells, the parameters in the power law are established based on the experimental data. Then, a correlation is developed to simulate the flow and predict the pressure drop in the stack with higher cell numbers (ie, 20 and 40 cells). The simulations show that the pressure drop in each cell of a stack is almost invariable, and the average pressure drop decreases with increasing the number of cells. The flow uniformity in the stacks with different cell numbers is evaluated using the dimensionless pressure drop and the pressure drop ratios. It suggests that the lower the cell number, the more uniform the pressure drop. The developed model is conducive to efficiently designing the flow channel for a fuel cell stack with large cell numbers.     

Improving accuracy of automatic optical inspection with machine learning

Electronic devices require the printed circuit board(PCB)to support the whole structure,but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices(SMDs)resistors.The automated optical inspection(AOI)machine,widely used in industrial production,can take the image of PCBs and examine the welding issue.However,the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs.This paper proposes a machine learning based method to improve the accuracy of AOI.In particular,we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image.We present a practical scheme including two machine learning procedures to mitigate AOI errors.We conduct experiments with the real dataset from a production line for three months,the experimental results show that our method can reduce the rate of misjudgment from 0.3%–0.5%to 0.02%–0.03%,which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.