Mechanism of methanol decomposition on the Pd/WC(0001) surface unveiled by first-principles calculations

In this study, the decomposition of methanol into the CO and H species on the Pd/tungsten carbide (WC)(0001) surface is systematically investigated using periodic density functional theory (DFT) calculations. The possible reaction pathways and intermediates are determined. The results reveal that saturated molecules, i.e., methanol and formaldehyde, adsorb weakly on the Pd/ WC(0001) surface. Both CO and H prefer three-fold sites, with adsorption energies of −1.51 and −2.67 eV, respectively. On the other hand, CH₃O stably binds at three-fold and bridge sites, with an adsorption energy of −2.58 eV. However, most of the other intermediates tend to adsorb to the surface with the carbon and oxygen atoms in their sp³ and hydroxyl-like configurations, respectively. Hence, the C atom of CH₂OH preferentially attaches to the top sites, CHOH and CH₂O adsorb at the bridge sites, while COH and CHO occupy the three-fold sites. The DFT calculations indicate that the rupture of the initial C–H bond promotes the decomposition of CH₃OH and CH₂OH, whereas in the case of CHOH, O–H bond scission is favored over the C–H bond rupture. Thus, the most probable methanol decomposition pathway on the Pd/WC(0001) surface is CH₃OH → CH₂OH → trans-CHOH → CHO → CO. The present study demonstrates that the synergistic effect of WC (as carrier) and Pd (as catalyst) alters the CH₃OH decomposition pathway and reduces the noble metal utilization.     

鲸鱼优化支持向量机的短期风电功率预测

为提高风电预测的精度,提出一种鲸鱼优化支持向量机SVM(support vector machine)的组合预测模型。该模型针对风电序列的非平稳波动特性,首先应用集合经验模态分解技术EEMD(ensemble empirical mode de?composition)将原始风电序列分解为一系列不同特征尺度的子序列;并引入鲸鱼优化算法WOA(whales optimiza?tion algorithm)解决SVM中学习参数选择难的问题,进而对各子序列建立WOA_SVM预测模型;最后,叠加各子序列的预测值以得到最终预测值。仿真表明,所提EEMD_WOA_SVM模型具有较高的风电预测精度,显著优于其他基本模型。     

多共振分量融合CNN的行星齿轮箱故障诊断

针对行星齿轮箱中各部件所激起的振动成分混叠、早期故障特征经常被较强的各级齿轮谐波成分以及环境噪声所湮没的问题，提出一种多共振分量融合卷积神经网络（multi-resonance component fusion based convolutional neural network，简称MRCF-CNN）的行星齿轮箱故障诊断方法。首先，对振动信号进行共振稀疏分解，得到包含齿轮谐波成分的高共振分量和可能包含轴承故障冲击成分的低共振分量；其次，构建多共振分量融合卷积神经网络，将得到的高、低共振分量和原始振动信号进行自适应的特征级融合，通过有监督的方式训练模型并进行行星齿轮箱故障诊断。对行星齿轮箱实验数据的分析结果表明，该方法能够有效分类行星齿轮箱中滚动轴承和齿轮的故障，成功对行星齿轮箱故障进行诊断，同时能够进一步增强卷积神经网络对振动信号所蕴含的故障信息的辨识能力。     

基于广义逆矩阵理论的杆系结构形态创构方法#br#

广义逆矩阵理论被广泛应用于不稳定结构的形态分析。不稳定结构在荷载作用下，其形状会发生变化直至其势能达到最低，此时的结构处于无弯矩的平衡状态。根据该原理并结合广义逆矩阵理论提出一种适用于杆系结构的形态创构方法。该方法将杆系模型中杆单元进行分组，在每一组中杆单元总长度不变的条件下，建立控制结构形状变化的移形方程。利用广义逆矩阵理论和势能梯度确定使模型势能下降最快的方向，并逐步调整节点位置直至势能达到最低。临时单元和单元组的引入使得该方法可以应用于多种形式结构的形态创构，合理地设置单元组和临时单元可以实现单元长度与单元内力的重新分配进而实现诸多功能。算例分析说明该方法的特性并验证其有效性。     

Concept design of a novel reformer producing hydrogen for internal combustion engines using fuel decomposition method: Performance evaluation of coated monolith suitable for on-board applications

Hydrogen addition effectively reduces the fuel consumption of spark ignition engines. We propose a new on-board reformer that produces hydrogen at high concentrations and enables multi-mode operations. For the proposed reformer, we employ a catalytic fuel decomposition reaction via a commercial NiO–CaAl₂O₄ catalyst. We explore the physical and chemical aspects of the reforming process using a fixed bed micro-reactor operating at temperatures of 550–700 °C. During reduction, methane is decomposed to form hydrogen and carbon. Carbon formation is critical to hydrogen production, and free space for carbon growth is essential at low temperatures (≤600 °C). We define a new accumulated conversion ratio that quantitatively measures highly transient catalytic decomposition. The free space of the coated monolith clearly aided low-temperature decomposition with negligible pressure drop. The coated substrate is therefore suitable for on-board applications considering that our reformer concept also utilizes the catalytic fuel decomposition reaction.     

MgFe and Mg–Co–Fe mixed oxides derived from hydrotalcites: Highly efficient catalysts for COx free hydrogen production from NH3

Mg–Fe Layered Double Hydroxide (LDH) with M²⁺: M³⁺ 3:1 stoichiometric ratio was synthesized and employed as catalyst precursor for CO_x-free hydrogen production from ammonia. The resulting catalyst showed good catalytic activity. A series of Mg/Co–Fe layered double hydroxides were synthesized by replacing Mg²⁺ with Co²⁺ without disturbing M²⁺:M³⁺ ratio. The influence of nature and extent of Co(II) substitution on structure, morphology and surface properties were studied. A systematic study was carried out using these materials as catalyst precursors for ammonia decomposition. BET, XRD, TPR, XPS, CO₂-TPD and TEM techniques were used to characterize the synthesized catalysts. These Fe-based catalysts are highly active, highly stable and not promoting any stable surface nitridation during the ammonia decomposition reaction. Among all catalysts, the Mg₃Co₃Fe₂ catalyst showed the highest activity i.e. 100% conversion at 6,000 h⁻¹ and 60% at 50,000 h⁻¹ space velocities at 550 °C. The registered superior catalytic activity was result of the formed specific catalyst's properties like high surface area, high surface Co and Fe atomic concentration and suitable basicity. These Fe-based materials are, cost-effective, easily synthesize and highly stable, thus attractive for large-scale operation.     

Cobalt doping of α-Fe/Al2O3 catalysts for the production of hydrogen and high-quality carbon nanotubes by thermal decomposition of methane

Fe–Co/Al₂O₃ catalysts were developed and tested in the catalytic decomposition of methane (CDM) for the synthesis of multi-wall carbon nanotubes (MWCNT) and the CO₂-free hydrogen production. While Fe (54.5–66.7 mol.%) is the main active phase for the carbon formation on the catalyst, Co acts as dopant aiming to improve its overall catalytic behaviour. Catalysts with Co contents of up to 18.2 M% showed the presence of α-Fe and Fe–Co crystallites with different size and lattice parameter. Fe_1-xCo_x alloy with bcc crystal system was identified only for Co contents of 14.0% and above, and presented a lattice constant lower than α-Fe, which would modify the carbon diffusion of the metal particle during the MWCNT growth. Co inhibited the Fe₃C formation during CDM resulting in higher carbon formations and longer activity times. This phase, shown in undoped catalysts, favored the presence of bamboo-type carbon nanotubes.     

基于改进ReaxFF 力场的全氟己酮热分解机理研究

采用基于密度泛函的紧束缚模型（DFTB）和改进的反作用力场（ReaxFF）对全氟己酮的热分解机理进行反应分子动力学仿真研究，分析了热分解的初始反应路径、微观反应过程以及体系内气体的生成机理，探讨了温度对全氟己酮热分解的影响，建立了以仿真数据为基础的一级动力学方程并对热分解过程进行一级动力学评估。结果表明，全氟己酮热分解的起始反应为：全氟己酮→全氟异戊烷+一氧化碳，全氟己酮热分解的主要产物为一氧化碳、四氟甲烷、六氟丙烯、全氟异丁烯、十氟丁烷、全氟异戊烷及大量活性基团。经一级动力学评估得到指前因子和表观活化能分别为5.021 7×1014 s-1和1 771.96 kJ/mol。     

基于VMD瞬时能量与GA- RBF的滚动轴承故障诊断

针对滚动轴承早期故障的有效识别,提出了一种基于VMD瞬时能量与GA优化的RBF神经网络的滚动轴承故障诊断方法,可以有效对滚动故障做出诊断。首先,VMD将滚动轴承振动信号进行分解成合适数目的本证模态函数;其次,计算本证模态函数分量的瞬时能量并组成特征向量;最后,将特征向量输入到GA优化的RBF神经网络实现轴承故障识别。通过滚动轴承故障诊断实验对该方法进行验证。结果表明,该方法识别滚动轴承故障的准确率为96.43%,较默认参数的RBF神经网络和EEMD瞬时能量与GA-RBF神经网络有明显的提高,证明了所提方法的可行性。     

回转窑人工智能专家系统的应用实践

根据水泥熟料煅烧过程操作控制原理研发的"回转窑人工智能专家系统"采用了人工智能模拟技术、实时数据清洗技术、控制数据关联匹配技术。该系统由分解炉出口温度自动控制系统、篦速自动控制系统和氨水用量自动控制系统三个模块组成。该系统投运后,分解炉出口温度控制有效率由之前人工控制的平均60%~70%提高至平均80%~95%;篦下压力和二次风温稳定性提高;氨水消耗量下降显著;窑系统投料量增加10 t/h左右,熟料标准煤耗降低2.85 kg/t,熟料强度均有提高。