Numerical study of flow distribution uniformity for the optimization of gradient porosity configuration of porous copper fiber sintered felt for hydrogen production through methanol steam reforming micro-reactor

A macroscopic numerical method is proposed to study the flow distribution uniformity of a novel porous copper fiber sintered felt (PCFSF), which has gradient porosities and was developed as the methanol steam reforming micro-reactor catalyst support for hydrogen production for fuel cell applications. The macroscopic porous media developed by the ANSYS/FLUENT software is used to represent the PCFSF. Our results indicate that the gradient porosity can reshape the flow distribution of PCFSFs greatly, thus producing significant influence on their performance. It is further revealed that, for a PCFSF with a determined gradient porosity configuration but different reactant feed directions, the velocity uniformity can be used as a quantitative criterion to evaluate the performance of hydrogen production. Furthermore, new gradient PCFSFs are produced according to the flow distribution of original gradient PCFSFs. The preliminary experimental results of the new gradient PCFSFs of 0.8-0.9-0.7 and 0.7-0.9-0.8 exhibit better methanol conversion and H₂ flow rate. This indicates that the numerical method can be used for the optimization of PCFSFs' gradient porosity configuration, which consists of the shape and position of the interfaces between different porosity portions, the number of interfaces and the porosity distribution in different portions.     

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics.     

掘进巷道快速过断层施工技术应用

基于四候煤矿3108回风顺槽过F3断层期间,顶板出现严重破碎、下沉现象,提出了俯斜台阶法施工工艺,并对断层破碎带顶板采取MF-2型化学材料注浆加固以及"钢筋锚索网+U29梯形棚"联合支护措施。支护效果检验结果表明,联合支护有效控制了断层破碎区顶板下沉、破碎现象,保证了巷道顶板稳定性。     

The role of Pt loading on reduced graphene oxide support in the polyol synthesis of catalysts for oxygen reduction reaction

Common carbon-blacks have shown insufficient stability as cathodic catalyst supports for proton exchange membrane fuel cells (PEMFCs). In this regard, alternative supports have been proposed and, specifically graphene or reduced graphene oxide (rGO), have attracted special attention. Herein, a set of electrocatalysts using reduced graphene oxide (rGO) as support is synthetized by a modified polyol method. The influence of Pt loading on the support is studied and compared with conventional supports, considering Pt particle morphologies and oxygen reduction reaction (ORR) performance in rotating disk electrode (RDE). Despite Pt average particle size typically increases with the Pt loading, 30 wt% of Pt on rGO is the optimal Pt loading, yielding the highest ORR activity among the rGO-supported electrocatalysts. These results show that both Pt loading and type of support greatly impact on the morphology and electrochemical performance of Pt nanoparticles.     

含铜废水预处理设施脱氮升级改造工程

为了减轻太湖保护区范围内企业处理废水的负担,苏州白荡污水处理厂利用其含铜废水预处理设施的富余处理能力处理某公司产生的含氮废水。针对含氮废水高NO3^--N和低碳氮比等特点,在充分利用原处理设施的基础上,采用"A/O+固定床生物膜工艺+高效脱氮填料+乙酸钠碳源+内回流"组合工艺对含铜废水预处理设施进行脱氮升级改造。改造后实际运行结果表明,设施运行稳定,出水水质达到出水水质达到设计出水要求,TN和NH4^+-N的去除率分别达到85.4%和82.3%,Cu的去除率提高了17.6%。     

富水软岩巷道稳定性控制技术研究

富水条件下软岩巷道容易出现大变形及围岩强度大幅度降低等情况,对矿井的生产安全造成极大威胁。为探索富水软岩巷道稳定性控制技术,结合新上海一号煤矿113082工作面运输巷工程实例,采用理论分析方法研究了地下水对巷道围岩及支护结构的影响,提出围岩稳定性控制对策,并对控制方案进行数值模拟验证。针对富水软岩巷道,首先采取导、疏结合的控水措施,减小对原有围岩强度的影响,降低对支护结构的损害;其次采用全断面、多手段联合加固的支护措施,控制围岩变形。工程实际及数值模拟结果表明,对于富水软岩巷道采取以上技术措施,能够有效保证巷道的稳定性。     

浅谈高校图书馆资源共享

随着社会的发展，信息和文化资源大多来自于图书和网络，而高校图书馆有着丰富的藏书和网络资源，因此，高校图书馆网络资源的共建共享就是科学文化发展的必然结果。那么如何实现网络资源的共建共享，网络资源的共建共享又存在哪些问题，又该如何解决，本文在这里就简单的浅谈一下高校图书馆的资源共享。     

A systemic framework and analysis of urban water energy

Energy impacts of urban water systems are substantial, but not typically analysed systemically. We develop a new system boundary framework including a utility, the ‘bulk water supply authority’ (SB1); the ‘urban water system’ including water use (SB2); and the ‘regional water system’ (SB3). We use the framework to review existing models and show that most address only one boundary. We apply the framework to quantify thermal equivalents of water-related energy in SB1 and SB2, and identify that over 96% of water-related energy in South East Queensland (SEQ) is outside SB1 and within SB2. Consideration of energy influenced by water use is paramount to systemic energy efficiency and optimisation in the urban water system. Clear articulation of system boundaries will improve modelling and management of the energy impact of urban water. Systemic modelling will help decision makers answer increasingly integrated and cross-system and sector questions regarding water and energy interactions.     

A web-based screening model for climate risk to water supply systems in the northeastern United States

The aim of this study is to describe the development and application of a web-based decision support tool (ViRTUE) for performing climate risk evaluations of water supply systems. The tool is designed for small-scale water utilities in the northeastern United States that may lack the resources for detailed climate change risk investigations. Development of this tool demonstrates a relatively new approach to web application development using the Shiny framework for the R programming language to create an interactive environment for stakeholders and water managers to explore climate vulnerabilities. Using a decision-scaling framework, the tool allows the user to perform a climate stress test to evaluate the performance and vulnerability to water supply shortfalls of local reservoir systems over a wide range of potential climate change scenarios using a generic systems model. Probabilities of future climate conditions derived from climate projections then help inform utility operators of impending risk.     

Position computation models for high-speed train based on support vector machine approach

High-accuracy positioning is not only an essential issue for efficient running of high-speed train (HST), but also an important guarantee for the safe operation of high-speed train. Positioning error is zero when the train is passing through a balise. However, positioning error between adjacent balises is going up as the train is moving away from the previous balise. Although average speed method (ASM) is commonly used to compute the position of train in engineering, its positioning error is somewhat large by analyzing the field data. In this paper, we firstly establish a mathematical model for computing position of HST after analyzing wireless message from the train control system. Then, we propose three position computation models based on least square method (LSM), support vector machine (SVM) and least square support vector machine (LSSVM). Finally, the proposed models are trained and tested by the field data collected in Wuhan-Guangzhou high-speed railway. The results show that: (1) compared with ASM, the three models proposed are capable of reducing positioning error; (2) compared with ASM, the percentage error of LSM model is reduced by 50.2% in training and 53.9% in testing; (3) compared with LSM model, the percentage error of SVM model is further reduced by 38.8% in training and 14.3% in testing; (4) although LSSVM model performs almost the same with SVM model, LSSVM model has advantages over SVM model in terms of running time. We also put forward some online learning methods to update the parameters in the three models and better positioning accuracy is obtained. With the three position computation models we proposed, we can improve the positioning accuracy for HST and potentially reduce the number of balises to achieve the same positioning accuracy.