Effect of incipient removal of hydrogen through palladium membrane on the conversion of methane steam reforming: Experimental and modeling

This paper presents a mathematical model based on the reaction rate expressions to describe the displacement of methane conversion in the steam reforming. The effect of several parameters including weight hourly space velocity (WHSV), load-to-surface ratio, reaction pressure, hydrogen partial pressure in permeate side and reaction temperature were investigated. Simulation and experimental results showed that a conversion higher than 80% could be achieved in a palladium membrane reactor at reaction temperature of 500 °C relative to 850 °C in a conventional fixed bed reactor (FBR). Besides, the yield of CO (<2%) in membrane reactor was much lower than that (>50%) in the FBR, which indicated the significant depression of CO production in use of membrane reactor.     

金属网架天线罩中金属梁杆的感应电流矩量法分析

用矩量法对金属网架天线罩中金属梁杆的感应电流率进行了理论分析,并利用此方法对导电圆柱体的感应电流率进行了仿真分析,数值仿真结果与经典方法吻合较好。针对常规矩量法在处理规则矩形柱体感应电流率中存在的问题提出了改进模型,结果可较理想地满足工程设计的需要。     

图的b-边染色数及b-边连续性研究

图染色及色数问题是图论中的一个重要内容,也是图论中的一个十分活跃的领域,同时有着深刻而丰富的理论结果和广泛的实际应用,其理论和方法在离散数学中占有重要地位.本文在图的b-染色数和b-连续概念的基础上提出图的b-边染色数及b-边连续的概念,给出了路图、圈图以及满n叉树图的b-边染色数,并且证明了这些图都是b-边连续的.     

La对CH_4-CO_2重整反应催化剂性能的影响

采用等体积浸渍法制备了Ni/γ-Al2O3和Ni/La2O3-γ-Al2O3系列催化剂,通过固定床反应、热重分析等方法,考察了催化剂的性能。结果表明：Ni质量分数为10%的Ni/γ-Al2O3催化剂具有较高的活性;稀土元素La的加入,提高了催化剂的抗积炭性能;在相同的反应条件下,10%Ni/3%La2O3-γ-Al2O3催化剂的积炭量比10%Ni/γ-Al2O3催化剂积炭量降低了40%,稳定性大大提高。以Ni/La2O3-γ-Al2O3催化剂中Ni质量分数10%,并且La质量分数3%为最佳,实验条件下制得的合成气CO/H2接近1/1。     

Mass transfer modelling for GS heavy water plants: Part 1: Point efficiency on GS sieve trays

A model that takes into account the gas-phase and liquid-phase resistance to mass transfer has been developed, where the overall mass transfer coefficient (K_OGa) is expressed as a function of the equivalent Sauter-mean bubble diameter. This parameter was back calculated from mass transfer measurements made at a pilot plant on single pass sieve trays of 0.311 m diameter. Hydraulic parameters were measured for these trays as well. Mean bubble diameters were then correlated as a function of active area F-factor and dispersion height for various tray geometries, and these correlations are used to predict point efficiencies on production plant trays up to 8.5 m in diameter.     

构建我国可信网络空间的思路和建议

随着移动互联网、物联网成长和普及,网络空间不仅成为人与人沟通的工具,也成为物体与物体沟通、人与物体沟通的工具。网络空间已经突破了计算机网络的限制,成为全球性的社交、协作平台,成为人类不可或缺的＂第二生存空间＂。与此同时,网络安全事件层出不穷,严重制约着网络空间的健康发展。一个可信、可靠的网络空间,对于我国经济社会发展具有重要意义。开展构建我国可信网络空间战略研究,有助于针对性地解决我国可信网络空间面临着诸多的问题和挑战,抢占网络空间竞争的制高点。     

Long-term oxidation behaviour of Ni3Al alloys with and without chromium additions

The oxidation behaviour of Ni₃Al alloys with and without chromium additions was studied after long-term exposure in an air furnace over a wide temperature range from 560–1300C. The chromium-containing alloy exhibited a better oxidation resistance below 1150C. Above this temperature the penetration depth of oxides into and along the grain boundaries in the chromium-containing alloy became larger than that in the alloy without chromium. Also the penetration depth increased drastically at 1150C for both alloys. Through energy dispersive X-ray spectroscopy, X-ray diffraction and X-ray photoelectron spectroscopy analyses, the compositions and structures of the oxide scales for both alloys were also identified.     

快速卷积算法的综述研究

卷积神经网络是深度学习算法应用最广泛的方向之一,目前卷积神经网络的应用不仅仅是停留在科技领域,已经扩展到医学、军事等领域,并且已在相关领域发挥着巨大的作用。卷积是卷积神经网络中最为核心的一部分,卷积运算占整个网络70%以上的时间,所以针对卷积运算的加速研究就显得十分重要。首先介绍近年来的卷积算法,并对其复杂度进行分析,总结了这些算法各自的优点和不足,最后对其理论研究和应用领域可能存在的突破进行了探讨和展望。     

基于双重权重偏差建模的无监督域适应

无监督域适应(Unsupervised Domain Adaptation,UDA)是一类新兴的机器学习范式,其通过对源域知识在无标记目标域上的迁移利用,来促进目标域模型的训练。为建模源域与目标域之间的域分布差异,最大均值差异(Maximum Mean Discrepancy,MMD)建模被广泛应用,其对UDA的性能提升起到了有效的促进作用。然而,这些方法通常忽视了领域之间对应类规模与类分布等结构信息,因为目标域与源域的数据类规模与数据分布通常并非一致。为此,文中提出了一种基于跨域类和数据样本双重加权的无监督域适应模型(Sample weighted and Class weighted based Unsupervised Domain Adaptation Network,SCUDAN)。具体而言,一方面,通过源域类层面的适应性加权来调整源域类权重,以实现源域与目标域之间的类分布对齐;另一方面,通过目标域样本层面的适应性加权来调整目标域样本权重,以实现目标域与源域类中心的对齐。此外,文中还提出了一种CEM(Classification Expectation Maximization)优化算法,以实现对SCUDAN的优化求解。最后,通过对比实验和分析,验证了所提模型和算法的有效性。     

基于图卷积神经网络的药物靶标作用关系预测方法

药物-靶标作用关系预测在药物研发以及药物重定位中扮演着重要角色,但现有的机器学习方法在正负样本高度不平衡的数据上仍存在预测能力不足的问题.为此,提出一种基于图卷积神经网络的药物靶标作用关系预测方法.该方法首先构造一个结合多种药物(靶标)相关信息的异质信息网络,然后采用图卷积神经网络在此异质信息网络上学习得到能精确表达每个节点拓扑特征及邻居特征信息的低维向量表征,最后利用这些向量信息通过向量空间投影预测节点间概率的评分.在DrugBank_FDA和Yammanishi_08数据集上进行的药物-靶标作用关系预测的对比实验中,所提方法的AUPR(Area Under the Precision-Recall Curve)值都优于其他4种方法,并且在较大型数据集上也有较好的表现.实验结果表明,所提方法提高了样本高度不平衡时的药物-靶标作用关系预测性能;同时在生物药物数据库上的实验也验证了所提方法所发现的未知药物-靶标作用关系的有效性.