Development of autonomous monitoring and performance evaluation system of grid-tied photovoltaic station

This work aims to improve the existing monitoring systems MS for two grid-connected PV stations GCPVS of URERMS ADRAR, to eliminate its limitations. This improvement consists of developing an MS which is used for two PV stations with different configurations. This MS contains new LabVIEW-based monitoring software for visualizing real-time measured data and evaluating GCPVS performance. In addition, it illustrates the 2D and 3D real-time relationships of PV system parameters, which allow us to understand the dynamic behavior of PV system components. This developed monitoring software synchronizes also the various data acquisition units DAU of GCPVS, allowing simultaneous data access.To perform a reliable performance analysis and a comparative study of different GCPVS based on accurate measurements, the sensor's calibration is performed with its DAU. The MS autonomy is ensured by integrating developed PV-UPS. A graphical user interface is provided for the evaluation of PV-UPS performance.     

Thermal analysis and management of proton exchange membrane fuel cell stacks for automotive vehicle

The thermal management of a proton exchange membrane fuel cell (PEMFC) is crucial for fuel cell vehicles. This paper presents a new simulation model for the water-cooled PEMFC stacks for automotive vehicles and cooling systems. The cooling system model considers both the cooling of the stack and cooling of the compressed air through the intercooler. Theoretical analysis was carried out to calculate the heat dissipation requirements for the cooling system. The case study results show that more than 99.0% of heat dissipation requirement is for thermal management of the PEMFC stack; more than 98.5% of cooling water will be distributed to the stack cooling loop. It is also demonstrated that controlling cooling water flow rate and stack inlet cooling water temperature could effectively satisfy thermal management constraints. These thermal management constraints are differences in stack inlet and outlet cooling water temperature, stack temperature, fan power consumption, and pump power consumption.     

Techno-economic analysis of hydrogen energy for renewable energy power smoothing

With the increasing proportion of renewable energy (mainly wind power and photovoltaic) connected to the grid, the fluctuation of renewable energy power brings great challenges to the safe and reliable operation of power grid. As a clean, low-carbon secondary energy, hydrogen energy is applied in renewable energy (mainly wind power and photovoltaic) grid-connected power smoothing, which opens up a new way of coupling hydrogen storage energy with renewable energy. This paper focuses on the optimization of capacity of electrolyzers and fuel cells and the analysis of system economy in the process of power output smoothing of wind/photovoltaic coupled hydrogen energy grid-connected system. Based on the complementary characteristics of particle swarm optimization (PSO) and chemical reaction optimization algorithm (CROA), a particle swarm optimization-chemical reaction optimization algorithm (PSO-CROA) are proposed. Aiming at maximizing system profit, the capacity of electrolyzers and fuel cells are constrained by wind power fluctuation, and considering environmental benefits, government subsidies and time value of funds, the objective function and its constraints are established. According to the simulation analysis, by comparing the calculated results with PSO and CROA, it shows that PSO-CROA effectively evaluates the economy of the system, and optimizes the optimal capacity of the electrolyzers and fuel cells. The conclusion of this paper is of great significance for the application of hydrogen energy storage in the evaluation of power smoothness and economy of renewable energy grid connection and the calculation of economic allocation of hydrogen energy storage capacity.     

Effect of baking in different ovens on the quality and structural characteristics of saltine crackers

The aim of the study was to evaluate the physical and microstructural characteristics of crackers baked in four different industrial baking ovens (indirect radiation-cyclotherm, indirect convection, hybrid and industrial tunnel-ITO). Indirect convection and cyclotherm ovens provide the highest (5685.43 ± 51 W m⁻²) and the lowest (4860 ± 38.87 W m⁻²) amount of heat flux, respectively. Despite the amount of heat flux, indirect convection led to crackers with the highest moisture (7.86% vs. 4.82% in clyclotherm) and specific volume, but the lowest hardness. Cyclotherm resulted in crackers with lower specific volume, surface area, porosity, smooth and regular surface. Conversely, the hybrid and ITO ovens showed closer heat flux, leading to crackers with similar moisture content, texture parameters, specific volume, browning and inner porosity. Overall results show the potential of baking using different ovens for modifying the quality parameters of the crackers.     

Integration of machine learning and first principles models

Model building and parameter estimation are traditional concepts widely used in chemical, biological, metallurgical, and manufacturing industries. Early modeling methodologies focused on mathematically capturing the process knowledge and domain expertise of the modeler. The models thus developed are termed first principles models (or white-box models). Over time, computational power became cheaper, and massive amounts of data became available for modeling. This led to the development of cutting edge machine learning models (black-box models) and artificial intelligence (AI) techniques. Hybrid models (gray-box models) are a combination of first principles and machine learning models. The development of hybrid models has captured the attention of researchers as this combines the best of both modeling paradigms. Recent attention to this field stems from the interest in explainable AI (XAI), a critical requirement as AI systems become more pervasive. This work aims at identifying and categorizing various hybrid models available in the literature that integrate machine-learning models with different forms of domain knowledge. Benefits such as enhanced predictive power, extrapolation capabilities, and other advantages of combining the two approaches are summarized. The goal of this article is to consolidate the published corpus in the area of hybrid modeling and develop a comprehensive framework to understand the various techniques presented. This framework can further be used as the foundation to explore rational associations between several models.     

电商平台产品共同购买网络的影响因素研究

共同购买网络的推荐系统应用越加广泛，仅基于网络内生结构变量研究其共同购买链接的经济意义已有局限，故加入网络口碑这一外生变量，进行更全面的分析。采用了社会网络方法中指数随机图模型进行建模，主要围绕产品销售量、产品入度、差评率和评论数4个方面因素，探究其对共同购买网络中共同购买链接形成的影响。结果显示，销售量、产品入度和评论数对共同购买链接形成的影响呈正比关系，而差评率则会显著地降低产品共同购买的几率。该指数随机图构建出的共同购买网络为在线电商平台管理网络口碑和推荐系统优化设计提供有益参考。     

腔磁力系统中的诱导透明和快慢光传播

为了在腔磁力系统中实现可控的磁子诱导透明、磁力诱导透明以及快慢光传播,建立了一个混合腔磁力系统.该系统由一个含有YIG球的微波腔和在z方向对球施加一个均匀的偏置磁场组成,并用强泵浦场驱动磁子和弱探测场驱动微波腔.研究表明,通过调节腔与磁子之间的相互作用强度和微波腔与磁子的耗散比,可以增加磁子诱导透明(MIT)、磁力诱导透明(MMIT)的效果和提高快慢光传播的速度.该研究结果可为磁力诱导放大、量子光学操纵和量子信息存储以及灵敏光开关的研究提供参考.     

我国鲜食玉米标准体系及营养标准研究

近年来我国鲜食玉米产业发展迅猛, 但同时也暴露出诸多产业乱象。梳理现有鲜食玉米标准体系是促进产业标准化、规范化的重要举措。本文通过对我国现行鲜食玉米标准进行梳理, 总结出目前我国鲜食玉米标准体系中抽样及一般性检测标准较完善、技术规程体系成熟、标准体系框架已建立的特点; 同时提出我国鲜食玉米标准体系存在技术规程标准内容重复、品质分级标准混乱、缺乏营养评价指标相关标准、感官评价标准混乱、缺乏甜糯玉米和笋玉米的相关标准、标准体系结构不合理等的问题; 并针对上述问题提出对应应统一技术规程标准, 建立营养评价体系, 统一品质分级标准, 统一感官评价指标, 完善甜糯玉米和笋玉米相关标准的解决办法。为鲜食玉米标准体系的建设及规范化提供参考和建议。     

Applications of analytical electron microscopy to guide the design of boron carbide

Compositional analysis of boron carbide on nanometer length scales to examine or interpret atomic mechanisms, for example, solid-state amorphization or grain-boundary segregation, is challenging. This work reviews advancements in high-resolution microanalysis to characterize multiple generations of boron carbide. First, ζ-factor microanalysis will be introduced as a powerful (scanning) transmission electron microscopy ((S)TEM) analytical framework to accurately characterize boron carbide. Three case studies involving the application of ζ-factor microanalysis will then be presented: (1) accurate stoichiometry determination of B-doped boron carbide using ζ-factor microanalysis and electron energy loss spectroscopy, (2) normalized quantification of silicon grain-boundary segregation in Si-doped boron carbide, and (3) calibration of a scanning electron microscope X-ray energy-dispersive spectroscopy (XEDS) system to measure compositional homogeneity differences of B/Si-doped arc-melted boron carbides in the as-melted and annealed conditions. Overall, the improvement and application of advanced analytical tools have helped better understand processing–microstructure–property relationships and successfully manufacture high-performance ceramics.     

A three-dimensional surface measurement system implemented with Gaussian process based adaptive sampling

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.