Development of high pressure membraneless alkaline electrolyzer

A technology for cyclic generation of hydrogen and oxygen using electrodes made of variable valency material that does not need the use of separating ion-exchange membranes is presented. The technological solution enables to fabricate electrolyzers for uninterrupted producing high-pressure hydrogen with reduced energy intensity of the production. The total work for compressing 1 m³ of hydrogen and 0.5 m³ of oxygen has been estimated. Results of investigation of influence of discrete supply of DC current to the electrolysis cell, in order to improve the processes of gas evolution and to simplify the power systems of the electrolysis plant, have been considered. There is also considered an electrolysis installation equipped with a thermosorption compressor in which LaNi₅ is used as a hydride-forming compound. The comparative characteristics of the developed electrolyzer and the currently used hydrogen generators are given.     

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。     

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.     

Hydrogen supply scenarios for a climate neutral energy system in Germany

A climate neutral energy system in Germany will most likely require green hydrogen. Two important factors, that determine whether the hydrogen will be imported or produced locally from renewable energy are still uncertain though - the import price for green hydrogen and the upper limit for photovoltaic installations. To investigate the impact of these two factors, the authors calculate cost optimized climate neutral energy systems while varying the import price from 1.25 €/kg to 5 €/kg with unlimited import volume and the photovoltaic limit from 300 GW to unlimited. In all scenarios, hydrogen plays a significant role. At a medium import price of 3.75 €/kg and photovoltaic limits of 300–900 GW the hydrogen supply is around 1200 to 1300 TWh with import shares varying from 60 to 85%. In most scenarios the electrolysis profile is highly correlated with the photovoltaic power, which leads to full load hours of 1870 h–2770 h.     

Green Measurements for Software Product Based on Sustainability Dimensions

Software is a central component in the modern world and vastly affects the environment’s sustainability. The demand for energy and resource requirements is rising when producing hardware and software units. Literature study reveals that many studies focused on green hardware; however, limited efforts were made in the greenness of software products. Green software products are necessary to solve the issues and problems related to the long-term use of software, especially from a sustainability perspective. Without a proper mechanism for measuring the greenness of a particular software product executed in a specific environment, the mentioned benefits will not be attained. Currently, there are not enough works to address this problem, and the green status of software products is uncertain and unsure. This paper aims to identify the green measurements based on sustainable dimensions in a software product. The second objective is to reveal the relationships between the elements and measurements through empirical study. The study is conducted in two phases. The first phase is the theoretical phase, where the main components, measurements and practices that influence the sustainability of a software product are identified. The second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achieving green software products. This study has revealed seven green measurements of software product: Productivity, Usability, Cost Reduction, Employee Support, Energy Efficiency, Resource Efficiency and Tool Support. The relationships are statistically significant, with a significance level of less than 0.01 (p = 0.000). Thus, the hypothesised relationships were all accepted. The contributions of this study revolve around the research perspectives of the measurements to attain a green software product.     

Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.     

基于启发式BP算法的软件缺陷预测模型

针对软件缺陷预测时缺陷数据集中存在的类别分布不平衡问题，结合上采样算法SMOTE与Edited Nearest Neighbor (ENN) 数据清洗策略，提出了一种基于启发式BP神经网络算法的软件缺陷预测模型。模型中采用上采样算法SMOTE增加少数类样本以改善项目中的数据不平衡状况，并针对采样后数据噪声问题进行ENN数据清洗，结合基于启发式学习的模拟退火算法改进四层BP神经网络后建立分类预测模型，在AEEEM数据库上使用交叉验证对提出的方案进行性能评估，结果表明所提出的算法能够有效提高模型在预测类不平衡数据时的分类准确度。     

Theoretical framework for formulating MIP scheduling models with multiple and non-uniform discrete-time grids

We present a framework for the formulation of MIP scheduling models based on multiple and nonuniform discrete time grids. In a previous work we showed that it is possible to use different (possibly non-uniform) time grids for each task, unit, and material. Here, we generalize these ideas to account for general resources, and a range of processing characteristics such as limited intermediate storage and changeovers. Each resource has its own grid based on resource consumption and availability allowing resource constraints to be modeled more accurately without increasing the number of binary variables. We develop algorithms to define the unit-, task-, material-, and resource-specific grids directly from problem data. Importantly, we prove that the multi-grid formulation is able to find a schedule with the same optimal objective as the discrete-time single-grid model with an arbitrarily fine grid. The proposed framework leads to the formulation of models with reduced number of binary variables and constraints, which are able to find good solutions faster than existing models.