Multi-criteria analysis for screening of reversible metal hydrides in hydrogen gas storage and high pressure delivery applications

Metals and alloys forming reversible hydrides with hydrogen gas are potential building blocks for compact, solid state hydrogen storage systems. Based on the materials’ thermodynamic characteristics, their use as temperature-swing gas compression and delivery systems in the hydrogen economy is also possible. Given the wide variety of materials developed and tested at laboratory and pilot scales, a harmonized method of selecting the feasible material(s) for a particular real-life application is required. This study proposes a system selection framework based on a normalized, multi-criteria metric. Using calculated values of multi-criteria metric, multi-criteria screening and ranking of potential materials has been demonstrated for a particular use case. It is found that the alloy TiMn_1.52 having value of additive metric between 0.25 and 0.35 represents the best material for a single stage system. The alloy pair CaNi₅–Ti_1.5CrMn represents the best alternative for a two-stage system with additive metric values between 0.63 and 0.82. Energy and economic characteristics of the metal hydride gas compression and delivery systems are evaluated and compared with an equivalent mechanical compression system producing the same final effect (i.e., delivery of a given quantity of gas at a defined pressure).     

Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis

In this study, the separation of hydrogen from gas mixtures using a palladium membrane coupled with a vacuum environment on the permeate side was studied experimentally. The gas mixtures composed of H₂, N₂, and CO₂ were used as the feed. Hydrogen permeation fluxes were measured with membrane operating temperature in the range of 320–380 °C, pressures on the retentate side in the range of 2–5 atm, and vacuum pressures on the permeate side in the range of 15–51 kPa. The Taguchi method was used to design the operating conditions for the experiments based on an orthogonal array. Using the measured H₂ permeation fluxes from the Taguchi approach, the stepwise regression analysis was also employed for establishing the prediction models of H₂ permeation flux, followed by the analysis of variance (ANOVA) to identify the significance and suitability of operating conditions. Based on both the Taguchi approach and ANOVA, the H₂ permeation flux was mostly affected by the gas mixture composition, followed by the retentate side pressure, the vacuum degree, and the membrane temperature. The predicted optimal operating conditions were the gas mixture with 75% H₂ and 25% N₂, the membrane temperature of 320 °C, the retentate side pressure of 5 atm, and the vacuum degree of 51 kPa. Under these conditions, the H₂ permeation flux was 0.185 mol s^?1 m^?2. A second-order normalized regression model with a relative error of less than 7% was obtained based on the measured H₂ permeation flux.     

Formation of acrylamide in microwave-roasted sorghum and associated dietary risk

The effect of microwave roasting parameters (300, 450 and 600 W; 5, 10 and 15 min) on acrylamide content in sorghum grain was determined using High Pressure Liquid Chromatography (HPLC)-photo diode array (PDA) detector coupled with C-18 column. Samples roasted at 300 and 450 W did not possess acrylamide, whereas 600 W (15 min) favoured formation of 2740.19 µg/kg of acrylamide, levels far exceeding the defined European Union (EU) limits. The chronic daily intake (CDI) for acrylamide through consumption of such grain flour was 3.25–9.5-fold higher to Joint FAO/WHO Expert Committee on Food Additives (JECFA) defined high exposure limits. The margin of exposure (MOE) values ranged from 4.3 to 12.76 and from 11.07 to 32.27 for neoplastic and neurological effects, respectively, demonstrating high exposure and serious health concerns associated with dietary intake of this toxicant. This study assesses the risk for the Indian population and highlights the importance of optimising process parameters for food product to minimise such exposure risks.     

基于卷积神经网络的5G蜂窝网络无线定位方法

5G蜂窝网络发展迅猛，其覆盖面积将逐渐增大，因此使用5G蜂窝网络进行定位是有研究潜力的研究方向。本文提出一种新的深度学习技术来实现高效、高精度和低占用的定位，以代替传统指纹定位过程中繁重的指纹库生成以及距离计算。该方法建立了一个特殊的卷积神经网络，并根据5G天线信号的接收信号强度指示、相位和到达角等特征量，选择合适的输入数据格式构造样本组建训练集，对该卷积神经网络进行训练。训练得到的卷积神经网络可以替代指纹定位中的庞大指纹库，非常有利于直接在5G移动设备端实现定位。虽然卷积神经网络在训练过程中需要大量时间，但在训练完毕后直接进行分类定位的速度非常快，可以保障定位实现的实时性。本文所实现的卷积神经网络权重与偏置所占内存不到0.5 MB，且能够在实际应用环境中以95%的定位准确率以及0.1 m的平均定位精度实现高精度定位。     

Simulation of hydrogen and coal gas fueled flat-tubular solid oxide fuel cell (FT-SOFC)

Solid oxide fuel cells (SOFCs) are considered an important technology in terms of high efficiency and clean energy generation. Flat-tubular solid oxide fuel cell (FT-SOFC) which is a combination of tubular and planar cell geometries stands out with its performance values and low costs. In this study, the performance of an FT-SOFC is analyzed numerically by using finite element method-based design as a result of changing parameters by using different fuels which are pure hydrogen and coal gas with various proportions of CO. In addition, cell performance values for different temperatures were analyzed and interpreted. Analyzes have been performed by using COMSOL Multiphysics software. The rates of CO composition used are 10%, 20%, and 40%, respectively. In addition, the air was used as the oxidizer in all cases. The cell voltage and average cell power of the FT-SOFC were examined under the 800 °C operating condition. The maximum power value and current density value were obtained as 710 W/m² and 1420 A/m² for the flat-tubular cell, respectively. As a result of the study, it was observed that the maximum cell power densities increased with increasing temperature. Analysis results showed that FT-SOFCs have suitable properties for different fuel usage and different operating temperatures. High-performance values and design features in different operating conditions are expected to make FT-SOFC the focus of many studies in the future.     

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。     

基于机器视觉的轮胎表面缺陷检测系统的研究与应用

在传统的轮胎表面缺陷依靠人工检测,存在劳动强度高、受人的主观影响大以及效率低下的问题。针对这一现象,研究了一种基于机器视觉的轮胎表面缺陷3D检测系统。该系统依靠机器视觉系统获取检测轮胎的表面图像,然后创建3D模型、判定缺陷类型,最终实现实时自动预警,为轮胎生产商提供一种自动化检测方案。系统集成了先进的技术、软件和工具,配套的信息管控系统可以对轮胎型号和生产数据进行采集、存储、分析,以便在生产过程中实现更高效、更可靠的质量控制,具有较高的实际应用推广价值。     

An infinite chain, {[Ni(tn)2]3[Fe(CN)4(μ-CN)2]2}n,a new catalyst for electrochemical-driven water splitting and photochemical-driven hydrogen evolution from water under blue light

A new catalyst for both water reduction and oxidation, based on an infinite chain, {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n, is formed by the reaction of NiCl₂, 1,3-propanediamine (tn) and K₃ [Fe(CN)₆]. {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n can electro-catalyze hydrogen evolution from a neutral aqueous buffer (pH 7.0) with a turnover frequency (TOF) of 1561 mol of hydrogen per mole of catalyst per hour (H₂/mol catalyst/h) at an overpotential (OP) of 837 mV {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n also can electro-catalyze O₂ production from water with a TOF of ～45 mol O₂ (mol cat)^?1s^?1 at an OP of 591 mV. Under blue light (λ = 469 nm), together with CdS nanorods (CdS NRs) as a photosensitizer, and ascorbic acid (H₂A) as a sacrificial electron donor, {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n can photo-catalyze hydrogen generation from an aqueous buffer (pH 4.0) with a turnover number (TON) of 11,450 mol H₂ per mole of catalyst (mol of H₂ (mol of cat)^?1) during 10 h irradiation. The average of apparent quantum yield (AQY) is as high as 40.96% during 10 h irradiation. Studies indicate that {[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n exists in two forms: a cyano-bridged chain ({[Ni(tn)₂]₃ [Fe(CN)₄ (μ-CN)₂]₂}_n) in solid, and a salt ([Ni(tn)₂]₃ [Fe(CN)₆]₂) in aqueous media; Catalytic reaction occurs on the nickel center of [Ni(tn)₂]²⁺, and the introduction of [Fe(CN)₆]^3- can improve the catalytic efficiency of [Ni(tn)₂]²⁺ for H₂ or O₂ generation. We hope these findings can afford a new method for the design of catalysts for both water reduction and oxidation.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Current trends in structural development and modification strategies for metal-organic frameworks (MOFs) towards photocatalytic H2 production: A review

Photocatalytic H₂ generation using semiconductor photocatalysts is considered as a cost-effective and eco-friendly technology for solar to energy conversion; however, the present photocatalysts have been recognized to depict low efficiency. Currently, porous coordination polymers known as metal-organic frameworks (MOFs) constituting flexible and modifiable porous structure and having excess active sites are considered to be appropriate for photocatalytic H₂ production. This review highlights current progress in structural development of MOF materials along with modification strategies for enhanced photoactivity. Initially, the review discusses the photocatalytic H₂ production mechanism with the concepts of thermodynamics and mass transfer with particular focus on MOFs. Elaboration of the structural categories of MOFs into Type I, Type II, Type III and classification of MOFs for H₂ generation into transition metal based, post-transition metal based, noble-metal based and hetero-metal based has been systematically discussed. The review also critically deliberate various modification approaches of band engineering, improvement of charge separation, efficient irradiation utilization and overall efficiency of MOFs including metal modification, heterojunction formation, Z-scheme formation, by introducing electron mediator, and dye based composites. Also, the MOF synthesized derivatives for photocatalytic H₂ generation are elaborated. Finally, future perspectives of MOFs for H₂ generation and approaches for efficiency improvement have been suggested.