电沉积Ni-W-P合金电极在微生物电解池产氢技术中的研究

通过电沉积方法制备了Ni-W-P合金,系统研究了硫酸镍浓度、钨酸钠浓度、次亚磷酸钠浓度、热处理温度等条件对Ni-W-P合金电极析氢活性的影响,并利用扫描电镜、X射线荧光光谱仪等技术对样品的表面形貌和组成等物理性质进行表征。实验结果表明:Ni-W-P合金电极是微生物电解池产氢技术的优良阴极材料,在外加电源0.9V条件下,其最大氢气产率为1.09m3/(m3.d),相应的电流密度和COD去除率分别为131A/m3与91.2%。     

Multi-electrode continuous flow microbial electrolysis cell for biogas production from acetate

Most microbial electrolysis cells (MECs) contain only a single set of electrodes. In order to examine the scalability of a multiple-electrode design, we constructed a 2.5 L MEC containing 8 separate electrode pairs made of graphite fiber brush anodes pre-acclimated for current generation using acetate, and 304 stainless steel mesh cathodes (64 m²/m³). Under continuous flow conditions and a one day hydraulic retention time, the maximum current was 181 mA (1.18 A/m², cathode surface area; 74 A/m³) within three days of operation. The maximum hydrogen production (day 3) was 0.53 L/L-d, reaching an energy efficiency relative to electrical energy input of η_E = 144%. Current production remained relatively steady (days 3–18), but the gas composition dramatically shifted over time. By day 16, there was little H₂ gas recovered and methane production increased from 0.049 L/L-d (day 3) to 0.118 L/L-d. When considering the energy value of both hydrogen and methane, efficiency relative to electrical input remained above 100% until near the end of the experiment (day 17) when only methane gas was being produced. Our results show that MECs can be scaled up primarily based on cathode surface area, but that hydrogen can be completely consumed in a continuous flow system unless methanogens can be completely eliminated from the system.     

A novel Variable-Reluctance Resolver proposal and its performance analysis under healthy and eccentric cases

A Variable-Reluctance resolver (VR-Resolver) with a novel structure is proposed in this paper as an absolute position sensor, where two possible structures are analyzed. The signals and excitation windings are located on the stator. Hence the slip rings are eliminated in the rotor. The rotor is considered by slot/teeth configuration so the rotor is shaped in a simple variable reluctance structure with a low sensitivity to the manufacturing tolerance. The excitation winding is wound in toroidal configuration that leads to having a smaller fill factor as well as turns can be considered for the windings. Maximum position error (MPE) is evaluated for the proposed resolvers in both healthy and eccentric cases. Therefore, the resolvers performance and their sensitivity to the eccentricity fault are evaluated. A flexible Magnetic Equivalent Circuit (MEC) with adjustable accuracy is used for modeling thanks to its capability and shorter processing time compared to the Finite-Element Method (FEM), where different cases are analyzed by a unique MEC model. Finally, comparison between the MEC and FEM-based results as well as experimental tests are presented to show effeteness of the proposed resolver as an accurate absolute position sensor for industries applications.     

超密集网络中基于MEC的动态任务卸载方案

超密集网络(Ultra-dense Network,UDN)中集成移动边缘计算(Mobile Edge Computing,MEC),是5G中为用户提供计算资源的可靠方式,在多种因素影响下进行MEC任务卸载决策一直都是一个研究热点。目前已存在大量任务卸载相关的方案,但是这些方案中很少将重心放在用户在不同条件下的能耗需求差异上,无法有效提升用户体验质量(Quality of Experience,QoE)。在动态MEC系统中提出了一个考虑用户能耗需求的多用户任务卸载问题,通过最大化满意度的方式提升用户QoE,并将现有的深度强化学习算法进行了改进,使其更加适合求解所提优化问题。仿真结果表明,所提算法较现有算法在算法收敛性以及稳定性上具有一定提升。     

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning-based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.     

边缘云的技术发展与应用思考

在云网融合的背景下,传统中心云业务在用户边缘位置无法满足超低时延、高带宽、高安全等业务需求现象.为解决此问题,首先对5G网络向边缘云分流方式和分流策略智能化进行研究;其次,对边缘云网融合的算力需求定制化和异构化进行深挖;最后,通过验证和归纳总结,制定出一整套综合解决方案解决或缓解边缘计算应用网络延迟和算力异构化问题的方...     

5G边缘计算视讯体验增强解决方案

5G大带宽、低时延的特性正在促进高清视频业务的发展,快速高清视频分发、360°全景、AR/VR/全息视频等多种形式的视频应用极大地丰富了视频应用场景.这些视频业务,如高清视频、网购、直播、虚拟与增强现实等均需要更高的带宽、更低的网络时延为用户提供更好的视讯体验.通过深入分析5G网络中视讯资源下沉到网络边缘所需要解决的应...     

基于5G MEC的水泥智慧工厂解决方案研究与验证

随着新型工业化进程不断推进,5G如何赋能工业数字化转型、助力企业实现产业升级受到越来越多的关注。根据水泥生产流程,分析了行业的需求,提出了基于5G多接入边缘计算（multi-access edge computing,MEC）的水泥智慧工厂解决方案,并给出了典型的5G应用场景。结合实际的商用案例,介绍了详细的部署组网和安全防护方案,验证了方案在助力企业降本增效方面的显著效果,为水泥建材行业乃至流程工业的5G MEC场景化应用提供了参考。     

从WAP的应用看移动因特网的发展

ＷＡＰ技术的开发与应用揭开了移动因特网发展的崭新一页,据预测,到２００４年亚太地区移动用户数将达到３．７亿,其中移动数据用户将占１．６亿;移动电子商务市场也将于２００３年超过固定电子商务市场。移动互联网未来的热点包括企业网的无线应用、与位置相关的服务以及移动电子商务。     

Domestic wastewater treatment in parallel with methane production in a microbial electrolysis cell

Microbial electrolysis cells (MECs) have great potential as a technology for wastewater treatment in parallel to energy production. In this study we explore the feasibility of using a low-cost, membraneless MEC for domestic wastewater treatment and methane production in both batch and continuous modes. Low-strength wastewater can be successfully treated by means of an MEC, obtaining significant amounts of methane. The results also suggest that hydrogenotrophic methanogenesis reduce the incidence of homoacetogenic activity, thus improving the overall MEC performance. However, gas production rates are low and important aspects such as methane solubility in water still remain a challenge. Overall, MECs can offer competitive advantages not only for low-strength wastewater treatment but also as an aid to anaerobic methane production by improving the chemical oxygen demand (COD) removal and methane production rates.