FJW涡轮分级机的分级机理研究

为了研究涡轮分级机的分级机理对于合理设计分级腔的结构、优化系统操作参数的影响,介绍了一种高效精密分级设备——150FJW型卧式涡轮分级机的结构和工作参数,分析了其分级机理,根据实验探讨了影响其性能的关键因素。研究表明:①颗粒的浓度对分级精度有较大的影响;②分级粒径的大小与涡轮转速、抽风机风量、风压及涡轮结构参数有关;③分析分级精度时要考虑分级机内部紊流的影响。     

涡流空气分级机的分级特性

通过对重质碳酸钙和石英在CF型涡流分级机不同转速下进行分级实验,并利用计算流体力学软件对分级特性进行数值模拟。结果表明,随着转速增加,粒度d50和d90都减小,分级效率先增大后减小;在相同的参数下,不同物料的分级效果是不同的;随着转速的增加,涡流分级机流场的涡流现象增强,当转速增加到一定的程度时,出现了反流现象,使已经分离出的细粒又返回到分级区,从而影响分级机的分级效率。     

立式涡轮分级机分级性能研究

为了研究立式涡轮分级机外筒体倾斜角度对分级效率的影响,经过分析分级机的气流流场、分级性能及存在的问题,采用自制CP-20型气流粉碎机在不同外筒体倾斜角度的情况下对河砂进行粉碎和分级实验,以获得最佳外筒体倾斜角度。结果表明:对于河砂而言,外筒体倾斜角度为0~15°时,随着角度的增大,分级效率先增大后减小;最佳外筒体倾斜角度为9°。     

涡轮式气流分级机分级精度影响因素分析

气流分级机分级粒径的不稳定是造成精度下降的根本原因。本文对不均匀气流速度场导致分级粒径沿涡轮轴向的空间波动，湍流脉动导致分级粒径的时间波动，范德华引力产生的假大颗粒导致鱼钩效应而影响细粉的分级以及与一次风、二次风均不发生作用的物料直接进入粗粉，相当于分级粒径为０，而使分级精度明显下降等因素进行了分析，推导出的各影响因素作用的数学方程可用于定性分析。     

涡轮式气流分级机分级精度影响因素分析

气流分级机机分级粒径的不稳定是造成精度下降的根本原因，本文对不均匀气流速度场导致分级粒径沿涡轮轴和的空间波动，湍流脉动导致分级粒径的时间波动，范德华引力产生的假大颗粒导致鱼钩效应而影响细粉的分级以及一次风，二次风均不发生作用的物料直接进入粗粉，相当于分级粒径为０，而使分级精度明显下降等因素进行了分析，推导出的各影响因素作用的数学方程可用于定性分析。     

分级轮转速对粒径分布的影响

通过计算流体力学软件Fluent对FTW-350型涡轮空气分级机内部的分级流场进行数值模拟,根据模拟结果对不同分级轮转速时分级机内部流场的动态压力、湍动能和流动速度的分布特性进行对比分析;通过实验研究涡轮空气分级机中分级轮转速对粒径分布的影响,在每种分级轮转速时进行3种不同的加料速度实验,对激光粒度仪测得的分级产物的累积粒径、平均粒径、累积体积分布和粒径频率分布进行对比分析。结果表明:转速增加有利于分级流场的稳定,并且在转速较小时,增大分级轮转速有利于改善分级产物的粒径分布;分级轮转速变化对粒径分布的影响随着加料速度的增大不断减小。     

挡板对卧轮式气流选粉机性能的影响

为了研究卧轮式气流分级机结构对分级性能的影响,在分级室不同位置安装挡板,考察不同进气量与叶轮转速时分级机对流化催化裂化催化剂的分选情况,分析挡板对粗粉收率、粗粉中细粉含量以及分级精度的影响。结果表明,与无挡板及增设下游挡板相比,分级室增设上游挡板可使粗粉收率增大5%~6%,分级精度明显提高,同时进气量消耗减少约50%,有效改善了分级机的分级性能。     

微细分级机转子结构参数对分级点的影响分析

通过理论分析和推导,建立粒径与分级机转子结构参数之间的关系式,分析影响粒径的结构因素和分级机转子结构参数对分级点所产生的影响,即:在分级机转速和通过的风量一定的情况下,转子结构参数影响产品的最大分级粒径;锥角、叶片倾角影响分级效率;转子叶片数的增多并不能使临界分级粒径变小。     

径向气流对旋风分级器的影响

为了改善分级器性能,试验引入径向气流对旋风分级器中的颗粒进行再次分离;试验物料选用催化裂化(FCC)和S-Zorb催化剂,对分级机进行了5组不同径向气速的试验。结果表明:该分级机对2种物料的最小切割粒径分别为6.5、3.3μm;试验中分级粒径为2μm时,FCC和S-Zorb催化剂的牛顿分级效率分别为91.9%和54.1%;径向风的引入破坏了康达效应的形成条件,有利于提高分级效果;进出口压降随着径向气流量的增大而降低;分级机对FCC催化剂的去"细"能力较好,对S-Zorb催化剂的去"粗"能力较好。     

离心逆流式超细粉干式分级机的研制

介绍了离心逆流式超细粉分级机的基本工作原理及特性，采用Ａｌ＿２＿３物料进行了超细粉分级试验。结果表明这种分级机适于超细粉分级并具有较高的分级精度，可与国外同类分级机相比，具有较大的应用前景。     

Classifying Risks on Information Technology Projects

ABSTRACT

Through an examination of the literature and a review of industrial case studies, a qualitative model is developed of the risks associated with implementing information technology (IT) projects. When this approach was applied to case studies drawn from academia and from companies in the manufacturing and service industries, potential high risk factors were identified and classified. Ongoing research seeks to develop an intelligent risk analyzer that incorporates this qualitative model and industry best practices to give IT project managers significant decision support in the area of risk management by enabling them to match system development strategies with project complexity.     

应用支持向量机进行网上信息自动分类

从SVMs的原理出发，介绍了用SVMs对网上信息进行自动分类的方法，分析了它能够防止出现“过学习”现象的原因以及对网上信息分类的适应性，并讨论了它的不足和可改进之处。     

On the Classifying of Photographic Developing Agents

Abstract

Desensitization of silver-digested emulsions¹ by optical sensitizers begins at much lower concentrations of dye than is the case with sulphur-sensitized emulsions. The degree of optical sensitization is not greatly affected by the method of digestion. Certain concentrations of dye fog a silver-digested emulsion; this effect can be simulated with an inorganic oxidizing agent, ferricyanide. It is concluded that dyes can bring about a rearrangement or complete oxidation of the “silver” produced in the grain by the chemical sensitization process of silver-digestion.     

Classifying multiclass relationships between ASes using graph convolutional network

Precisely understanding the business relationships between autonomous systems (ASes) is essential for studying the Internet structure. To date, many inference algorithms, which mainly focus on peer-to-peer (P2P) and provider-to-customer (P2C) binary classification, have been proposed to classify the AS relationships and have achieved excellent results. However, business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent years. Existing algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C relationships. In this study, we focus on multiclassification of AS relationship for the first time. We first summarize the differences between AS relationships under the structural and attribute features, and the reasons why multiclass relationships are difficult to be inferred. We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to solve this multiclassification problem under complex scenes. The proposed framework considers the global network structure and local link features concurrently. Experiments on real Internet topological data validate the effectiveness of our method, that is, AS-GCN. The proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task, with an overall metrics above 95%.