冷低压分离器油相系统铵盐垢下腐蚀风险的模拟预测

为探究某加氢装置高压换热器管束腐蚀泄漏原因，利用Aspen Plus工艺模拟软件计算了冷低压分离器油相（简称冷低分油）中水质量分数分别为1%,2%,3%时，冷低分油系统的露点温度、氯化铵结晶温度、氯化铵潮解点温度和相对湿度。结果表明：相较于经验的露点温度预测方法，通过引入潮解点、划分系统“湿环境”温度范围判断氯化铵垢下腐蚀风险区域的方法与实际腐蚀案例更为切合；在3种油相含水条件下，换热器管束存在氯化铵垢下腐蚀的“湿环境”温度范围分别为：50~103 ℃,50~161 ℃,50~176 ℃；随着油相中含水量的提高，“湿环境”腐蚀区域逐渐向高温部位迁移，预计铵盐导致的垢下腐蚀将会愈加严重。     

辊速差对连铸连轧7075铝板显微组织、织构及力学性能的影响

研究辊速差对连铸连轧7075铝板显微组织、织构及力学性能的影响。采用3种不同上辊/下辊转速比(ω/ω0,ω为上辊转速,ω0为下辊转速)1:1、1:1.2及1:1.4进行多次试验。结果显示,在最大辊速差条件下(ω/ω0=1:1.4),7075铝板在轧制方向的屈服强度和极限抗拉强度分别提高41.5%和21.9%。此外,当辊速比ω/ω0为1:1.4时,成品轧制板的平均晶粒尺寸减小36%,横剖面平均硬度增加约9.2%。织构研究结果显示,辊速差越大,成品各向同性及硬度越大。然而,采用不同辊速度的连铸连轧会导致变形板伸长率降低约6%。     

Cold plasma for the disinfection of industrial food-contact surfaces: An overview of current status and opportunities

Food safety is the primary goal for food and drink manufacturers. Cleaning and disinfection practices applied to the processing environment are vital to maintain this safety; yet, current approaches can incur costly downtime and the potential for microorganisms to grow and establish, if not effectively removed. For that reason, manufacturers are seeking nonthermal, online, and continuous disinfection processes to control the microbial levels within the processing environment. One such emerging technique, with great potential, is cold atmospheric pressure plasma (CAP). This review presents the latest advances and challenges associated with CAP-based technologies for the decontamination of surfaces and equipment found within the food-processing environment. It provides a detailed overview of the technology and a comprehensive analysis of the many CAP-based antimicrobial studies on food-contact surfaces and materials. As CAP is considered an emerging technique, many of the recent studies are still in the preliminary stages, with results obtained under widely different conditions. This lack of cohesive information and an inability to directly compare CAP systems has greatly impeded technological development. The review further explores the challenge of scaling CAP technology to meet industry needs, considering aspects such as regulatory constraints, environmental credentials, and cost of use. Finally, a discussion is presented on the future outlook for CAP technology in this area, identifying key challenges that must be addressed to promote industry uptake.     

基于滚动摩擦作用下玉米颗粒在模拟筒仓卸料中的力链分析（网络首发、推荐阅读）

针对颗粒滚动摩擦作用对筒仓中玉米颗粒的力链空间分布进行研究，通过EDEM离散元软件建立筒仓模型与仿真玉米颗粒模型进行卸粮仿真模拟，并与筒仓卸料实验作流态对比，验证模型与仿真结果的准确性。通过对模拟仓进行切片观察和数据处理，对比分析了不同摩擦情况下力链的细观参数随时间演化规律。模拟结果表明：颗粒间摩擦系数越大，卸粮完成的最终时间越长；颗粒间滚动摩擦系数越小，颗粒由整体流转变为管状流的时间越早。对于有漏斗的筒仓来说，减小颗粒间摩擦会改变整体流和管状流之间的极限，从而增加产生管状流的面积。标准滚动摩擦系数下玉米颗粒在卸料过程中会出现起拱-塌陷效应；减小滚动摩擦，玉米颗粒卸料较稳定，未出现起拱的应力突增、以及拱塌陷的应力衰减；增大颗粒间滚动摩擦不但会增加拱效应，且出现成拱高度距离漏斗口更高。     

离线式带钢双面翻转检查站方案设计及实践

针对某酸轧机组检查站穿带困难、检查效率低、产生废品多、故障率高的问题,开发了一种离线式带钢双面检查站。该检查站新增了带钢翻转装置,将在入口分切剪处完成定尺剪切的带钢试样由两侧压板夹紧,中间可升降皮带机落下,翻转装置将带钢试样张紧,并翻转90°或者180°,实现带钢的双面检查。皮带机两侧布置有可升降、可横移的活动操作平台,检测人员站在平台上对带钢进行打磨检查,可同时对带钢上下表面进行检测。由于缩短了带钢的传输距离,有效减少了其他擦划伤的影响,提高了带钢表面质量检测准确性。     

Effect of spheroidized microstructure on the impact toughness and electrochemical performance of a high-carbon steel

The influence of cementite spheroidization on the impact toughness and electrochemical properties of a high-carbon steel has been thoroughly investigated in this study. Heavy warm rolling, followed by 2 h of annealing, has resulted in near-complete spheroidization, leading to a microstructure consisting of nano-cementite globules dispersed in the ultrafine-grained ferritic matrix. The Charpy impact test exhibited superior impact toughness with increased spheroidization. It is validated by the presence of abundant dimples in the fractographs of spheroidized specimens, in contrast to the as-received one that experienced a brittle failure due to its lamellar pearlitic structure. Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) carried out in a 3.5% NaCl solution revealed that the corrosion resistance of the alloy gets improved with the increase in the degree of spheroidization. This is attributed to the lower susceptibility of the spheroidized specimen to microgalvanic corrosion owing to the minimum area of contact between nano-spheroidized cementite and ferrite, as elucidated with the help of EIS results aided by equivalent electrical circuit model.     

Exploring the Cold-Adaptation Mechanism of Serine Hydroxymethyltransferase by Comparative Molecular Dynamics Simulations

Cold-adapted enzymes feature a lower thermostability and higher catalytic activity compared to their warm-active homologues, which are considered as a consequence of increased flexibility of their molecular structures. The complexity of the (thermo)stability-flexibility-activity relationship makes it difficult to define the strategies and formulate a general theory for enzyme cold adaptation. Here, the psychrophilic serine hydroxymethyltransferase (pSHMT) from Psychromonas ingrahamii and its mesophilic counterpart, mSHMT from Escherichia coli, were subjected to μs-scale multiple-replica molecular dynamics (MD) simulations to explore the cold-adaptation mechanism of the dimeric SHMT. The comparative analyses of MD trajectories reveal that pSHMT exhibits larger structural fluctuations and inter-monomer positional movements, a higher global flexibility, and considerably enhanced local flexibility involving the surface loops and active sites. The largest-amplitude motion mode of pSHMT describes the trends of inter-monomer dissociation and enlargement of the active-site cavity, whereas that of mSHMT characterizes the opposite trends. Based on the comparison of the calculated structural parameters and constructed free energy landscapes (FELs) between the two enzymes, we discuss in-depth the physicochemical principles underlying the stability-flexibility-activity relationships and conclude that (i) pSHMT adopts the global-flexibility mechanism to adapt to the cold environment and, (ii) optimizing the protein-solvent interactions and loosening the inter-monomer association are the main strategies for pSHMT to enhance its flexibility.     

Effect of different control strategies on rapid cold start-up of a 30-cell proton exchange membrane fuel cell stack

Many places experience extreme temperatures below −30 °C, which is a great challenge for the fuel cell vehicle (FCV). The aim of this study is to optimize the strategy to achieve rapid cold start-up of the 30-cell stack at different temperature conditions. The test shows that the stack rapidly starts within 30 s at an ambient temperature of −20 °C. Turning on the coolant at −25 °C show stability of the cell voltage at both ends due to the end-plate heating, however, voltage of intermediate cells fluctuates sharply, and successful start-up is completed after 60 s. The cold start strategy changes to load-voltage cooperative control mode when the ambient temperature reduced to −30 °C, the voltage of multiple cells in the middle of the stack fluctuate more drastic, and start-up takes 113 s. The performance and consistency of the stack did not decay after 20 cold start-up experiments, which indicates that our control strategies effectively avoided irreversible damage to the stack caused by freeze-thaw process.     

A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.