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.     

Attitude-behaviour dissonance regarding the importance of food preservation for customers

The aim of the study was to identify the importance of individual food product attributes (the use of preservatives, processing method, shelf-life period and nutritional value) as well as their relation to the purchase behaviour. To achieve this, consumer preferences were decomposed in conditions of full access to information, and data was compared with actual consumer behaviour related to making purchasing decisions in front of the store shelf. Based on data from 338 respondents, conjoint analysis and repeated ANOVA measurements were carried out, allowing to eliminate individual behavioural patterns. The results showed a dissonance between the consumers attitudes towards the attributes and their shopping behaviour. The processing method was the most important declared attribute for consumers, meanwhile this information was the least searched for during purchase. At the same time, shelf-life period marked as the least important was the main information searched for by consumers when shopping. The results also show a clear dislike towards the use of microwaves as a preservation method, while traditional thermal preservation was marked as the most positive for consumers. In addition, consumers were classified into 3 heterogeneous groups, identifying differences in the preferences of food attributes. The obtained results have practical value relating not only to the preferences of individual attributes, but also to their various variants. The results can be a guide for companies to properly label food products. The indicated dissonance illustrates the educational gap and related insufficient motivation to get to know the product before buying it. This is extremely important in connection with technological development and new food processing methods.     

Benchmark value determination of energy efficiency indexes for coal-fired power units based on data mining methods

The operational optimisation of coal-fired power units is important for saving energy and reducing losses in the electric power industry. One of the key issues is how to determine the benchmark values of the energy efficiency indexes of the units. Therefore, a new framework for determining these benchmark values is proposed, based on data mining methods. First, the energy efficiency key performance indicators (KPIs) associated with the net coal consumption rate (NCCR) were selected based on the domain knowledge. Second, the decision-making samples with minimal NCCR were acquired with the fuzzy C-means (FCM) clustering algorithm, and the corresponding clustering centres were employed as the benchmark values. Finally, based on the support vector regression (SVR) algorithm, the target values of the NCCR were obtained with the KPIs as input, and the energy saving potential was evaluated by comparing the target values with the historical values of the NCCR. An actual on-duty 1000 MW unit was taken as study unit, and the results show that the energy saving potential is remarkable when the operators adjust the KPIs based on the calculated benchmark values.     

汽车悬架球头拉杆冷成形技术

为提高生产效率、改变原来通过车削加工汽车悬架球头拉杆的方式,提出了一种球头拉杆的多工位冷镦成形工艺。基于DEFORM-3D软件建立了有限元模型,对球头拉杆的塑性成形过程进行了仿真,研究了预成形凸模入模角α、预成形凹模圆角R1、终成形凹模圆角R2以及终成形凹模入模角β等工艺参数对球头拉杆成形的影响,并进行了正交试验,以模具在球头拉杆成形过程中所受载荷及工件损伤值为优化目标,获得了合理的工艺参数组合。结果表明:在保证工件成形质量及模具寿命的前提下,当预成形凸模入模角α为30°、预成形凹模圆角R1为12 mm、终成形凹模圆角R2为3 mm、终成形凹模入模角β为60°时,模具载荷及工件损伤有显著的降低。采用优化后的参数进行试模生产,工件成形质量较好,验证了模拟结果的可信性,能够有效地指导工艺设计人员的设计工作。     

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。     

Latent communities of digital publications: The role of editors,followers, and advertisers

This research explores how digital publications may be turned into something more than just a meeting point for people who are seeking information and may become a latent community. The paper proposes that the network of editors, followers, and advertisers of digital publications determine the user sense of community towards the publication. While the pattern of connections among editors and followers reinforces the sense of community, advertisers act as intruders who weaken the feasibility of building a community. Data were collected from followers of digital publications. The dynamics of the sense of community is described as a process in which informational value and identification are essential to nurturing readers’ soft or hard commitment. Findings indicate that the strong ties between editors and followers as well as the diversity positively impacts on informational value and identification, whereas advertising intrusiveness diminishes the informational value.     

浅谈高压中速磨粉系统在磷酸二铵生产中的应用

通过对磷酸二铵生产造粒特性分析,高压中速磨粉机原理分析,结合实践生产中的工艺需求,利用高压中速磨粉系统将颗粒状不合格品磨成细粉后添加进系统重新喷浆造粒,提高返料添加量,减少不合格品的产生,腾空仓库有限货位,以获取更大的经济价值。     

Preparation of composites based on recycled polypropylene and automotive shredder residue

In past research, mechanical recycling of automotive shredder residue (ASR) has led to serious deterioration of material performance, and real‐scale application in this way still remains a challenge. Here, we report a sustainable approach called solid‐state shear milling (SSSM) for the production of high‐performance polypropylene (PP)/ASR composites with robust mechanical performance on a commercial scale. After the SSSM process, the obtained 50/50 wt% PP/ASR composite exhibited a 41.3% increase in tensile strength, 32.9% increase in flexural strength and 55.0% increase in impact toughness when compared with corresponding composites made by traditional direct melt blending. In particular, the toughness of the material can be improved by further addition of PP grafted with maleic anhydride with toughness comparable to that of recycled PP, and a 325% increase in toughness can be obtained with addition of styrene–butadiene–styrene block copolymer grafted with maleic anhydride. This PP/ASR composite shows good processability and high thermal stability, and meets the requirements of many applications for nonstructural products. The approach presented in this paper highlights a novel technique for ASR recycling. © 2018 Society of Chemical Industry