Catalytic properties of dispersed iron oxides Fe2O3/MO2 (M = Zr,Ce, Ti and Si) for sulfuric acid decomposition reaction: Role of support

Supported iron oxides have been established as an important class of catalyst for high temperature sulfuric acid decomposition. With an objective to elucidate the role of support in modifying the overall catalytic properties of dispersed iron oxide catalysts, a series of supported iron oxide based catalysts, Fe₂O₃ (15 wt%)/MO₂ (M = Zr, Ce, Ti and Si), synthesized by adsorption-equilibrium method, is investigated for sulfuric acid decomposition reaction. The structure of dispersed iron oxide phases largely depended on the nature of the support oxide as revealed by the XRD and Mössbauer studies. α-Fe₂O₃ is found to be present as a major phase on ZrO₂ and CeO₂ support while ε-Fe₂O₃ was the major phase on silica supported iron oxide. On the other hand, presence of mixed oxide Fe₂TiO₅ was revealed over TiO₂ support. Strong dispersed metal oxide-support interactions inhibited the total reduction of the dispersed phase on SiO₂ and TiO₂ as compared to complete reduction of dispersed iron oxide on CeO₂ and ZrO₂ supports during temperature programmed reduction upto 1000 °C. The order of catalytic activity at a temperature of ～750 °C is observed as Fe₂O₃/SiO₂ > Fe₂TiO₅/TiO₂ > Fe₂O₃/ZrO₂ > Fe₂O₃/CeO₂, while at higher temperatures of ～900 °C the SO₂ yield is found to be comparable for all catalysts. A relationship between the rate of sulfate decomposition and catalytic activity is established through detailed TG-DTA investigations of sulfated catalyst and support. Considerable influence of the support oxide on the composition, structure, redox properties, morphology and catalytic activities of the active iron oxide dispersed phase has been observed. Thus, the support oxides operate as a critical component in the complex supported metal oxide catalysts and these findings might influence the design and development of future high temperature sulfuric acid decomposition catalysts.     

频带能量与样本熵在注意力脑电信号中的对比研究

脑电波是一种复杂的生物电信号,可反应出大脑内部的活动及注意力等精神状态。基于此,论文设计了注意力相关的脑电实验,并完成了受试者脑电数据的采集,对所采集的脑电数据分别从以下两种角度进行研究:从时频分析的角度,采用db4小波基对原始脑电信号进行7层小波包分解,提取了β波/θ波能量占比作为特征量;从非线性动力学的角度,提取脑电信号的样本熵作为特征,并分别对各受试者进行注意力的分级研究。通过对比分析,结果表明两者都能从一定程度上表征注意力水平的状况,但样本熵对于多级注意力的区分度更好。     

METCAL 在 Co3O4 制备工艺设计中的应用

采用 METCAL 软件创建计算模型，对沉淀 - 热分解法制备四氧化三钴材料的工艺进行了模拟，并简要介绍了过程模拟的步骤和思路。通过对工艺流程的模拟计算，得到了直观的物流分配走向、金属平衡和热平衡数据等，为四氧化三钴材料制备工艺工程设计提供了基础数据支撑。     

1,2,4-trichlorobenzene decomposition using non-thermal plasma technology

Non-thermal plasma(NTP)is regarded as a potential application for environmental pollution control due to its ability to remove pollutants.As a major precursor of dioxins,the influence of the parameters of 1,2,4-trichlorobenzene(TCB)decomposition using NTP technology was investigated through a series of experiments,including voltage,frequency,water content,initial concentration,flow rate,and oxygen content.The experimental results show that the energy injected into the NTP system has a positive correlation to voltage and frequency.Oxygen has the greatest influence on TCB decomposition.The optimal reaction condition was at 15 kV,1000 Hz,an initial concentration of 20 mg m^?3,a flow rate of 2 l min^?1,H2O at 4%,and O2 at 0%.Under this condition,the TCB removal efficiency could reach 92%.According to the generated product backstepping,the hydroxyl radical(·OH)plays an important role in TCB decomposition due to its strong oxidation,which participates in the dechlorination and oxidation reactions as free radicals,and the possible decomposition pathway of TCB by NTP is inferred from the identified byproducts.It is of great significance to investigate the influence of the parameters of TCB decomposition using NTP technology in order to provide references for industrial application.     

Sparse decomposition method based on time–frequency spectrum segmentation for fault signals in rotating machinery

The impulse signal in large rotating machinery with damage fault is sparse, weak, coupled, and even nonperiodic in intermittent operation. To extract this complex signal is a key topic in machinery fault diagnosis. Sparse decomposition (SD) has excellent adaptability in describing arbitrary complex signals based on over-complete dictionary. However, the pursuit speed of best atom is a serious drawback. To alleviate this, a method of sparse decomposition based on time–frequency spectrum segmentation (SD-TFSS) is introduced. Generalized S transform (GST) provides the capability to show the distribution of vibration signals, but the resolution is susceptible to noise, multiresolution generalized S-transform (MGST) is developed to generate multiresolution time–frequency spectrums. Then, spectrums fusion with an appropriate threshold is adopted to acquire multiresolution binary spectrums and produce an optimal binary spectrum. From this optimal binary spectrum, all the connectivity areas are extracted and marked by spectrum segmentation. Thus, an optimal library can be constructed by selecting the optimal atoms of every connectivity area, and the signal can be expressed with this library. We conduct simulations and experiments demonstrating that the proposed method performs well with lower pursuit complexity, higher decomposition efficiency, and better approximation precision.     

A parallel noninvasive multiscale strategy for a mixed domain decomposition method with frictional contact

A multiscale extension for a parallel noninvasive mixed domain decomposition method is presented. After briefly exposing our noninvasive implementation of the Latin method, we present how the scalability of the algorithm is obtained by the partial verification of the constitutive law of the interfaces. We propose a new interpretation of the classical macrostrategy that imposes the overall balance of interface's forces. We also propose a new study of the macrostrategy that consists in enforcing the coarse continuity of the interfaces' displacement.     

蝗虫优化相关向量机模型在径流预测中的应用

针对径流序列不稳定导致预测精度不高的问题,提出一种基于变分模态分解(VMD)和蝗虫优化算法(GOA)优化相关向量机(RVM)的组合径流预测模型。首先对原始非平稳的径流序列采用VMD得到若干个相对稳定的分量序列,再分别建立RVM预测模型,并采用GOA优化RVM中核函数的参数,最后累加所有分量的预测值得到径流序列的预测值。实例结果发现,较传统的BP神经网络、支持向量机及基于经验模态分解的支持向量机等模型,该模型预测精度更高,预测结果能为水电站的经济运行、水资源的有效利用等提供决策依据。     

Fe-Cr-Ni系不锈钢在热老化和退火过程中铁素体调幅分解的相场法研究

用基于Cahn-Hilliard方程的相场法研究了Fe-Cr-Ni系不锈钢中的铁素体在热老化和后续退火过程中调幅分解的演化过程，结果表明：在热老化过程中调幅分解生成相连的网络状α'相，调幅分解引起的Cr成分波动的波长和振幅都随着热老化时间的延长而增大；在随后的退火过程中α'相逐渐溶解而Cr成分波动的振幅迅速减小，但是波长继续增大。还讨论了热老化时的调幅分解对铁素体纳米压痕硬度的影响以及退火温度对调幅分解回复(α'相溶解)所需时间的影响，结果表明：铁素体的纳米压痕硬度主要与调幅分解的振幅有关，且随着振幅的增大而提高。同时，提高退火温度能显著缩短调幅分解回复所需的时间，退火回复时间与退火温度之间有Arrhenius形式的关系。     

基于小波分析的谐波在线监测系统

针对传统谐波在线监测系统存的结果不准确，无法描述谐波动态变化特点，设计了基于小波分析的谐波在线监测系统。首先设计了谐波在线监测系统的硬件模块，然后谐波在线监测系统的软件模块，依据小波变换分析谐波信号特征，得到的谐波变化率，从而谐波在线监测系统的监测结果，最后采用具体实验进行了谐波在线监测系统性能的仿真实验，由实验结果可知，该系统的谐波监测精度高，可以有效检测各种谐波分量，保证电力系统稳定运行。