钢铁行业烟气多污染物协同控制技术应用实践

本研究分析了钢铁企业焦化和烧结两个重点工序中烟气污染物排放现状和钢铁行业当前的环保政策。对焦化及烧结烟气污染物的排放特征进行了分析，并通过对比燃煤电厂烟气特点，提出可以综合电厂烟气治理模式和自身特点改进的技术路线。结合某大型国有钢铁企业的脱硫脱硝装备对其环保现状进行了分析。针对焦化和烧结工序的典型污染物硫、硝、尘现有的源头减排、过程控制及末端处理技术，分析其优缺点。进而提出了3条可实现烟气污染物超低排放的技术路线，即半干法脱硫耦合选择性催化还原脱硝、半干法/湿法耦合臭氧氧化脱硝、活性焦脱硫脱硝一体化技术，重点介绍了这些技术在某大型钢铁企业的应用实践及应用效果。并基于全过程耦合技术，分别在焦化和烧结工序中提出了多污染物协同去除技术及应用，即焦炉低氮燃烧技术耦合末端活性焦多污染物协同控制技术、烧结烟气循环技术耦合末端活性焦多污染物协同控制技术。最后结合几种技术路线的应用实践，对未来钢铁产业的烧结及焦化工序超低排放技术的选择提出合理化建议。     

石油磺酸盐降压增注试验研究

针对低渗区块注水困难的状况,在室内开展了石油磺酸盐的溶解性、表面张力变化及岩心试验,检验石油磺酸盐的降压增注效果,并对添加石油磺酸盐是否对注水井造成损害进行了结垢趋势和腐蚀试验。结果表明低浓度石油磺酸盐可降低污水的表面张力,具有降压增注的功效,并且可以起到一定的缓蚀作用。     

蒸发冷却电机新冷却介质FF31L的实验研究

根据介质的热物理特性和公害性等性质初选了一种新冷却介质ＦＦ３１Ｌ。将初选的新介质ＦＦ３１Ｌ在自循环蒸发冷却实验模型上进行了实验，测取了不同负载下线棒沿长度上的温度分布，观察了自循环冷却情况。建立了介质传质特性研究的实验模型，进行了新介质传质特性的实验研究，得到了蒸汽品质和汽液二相流阻放大倍数的关系曲线以及蒸汽品质和汽液二相中汽占体积比之间的关系曲线。     

填料-旋流板脱硫塔在净化加压变脱改造中的设计与应用

阐述了以塑料阶梯环填料与带溢流锥双程叶片旋流塔板组合的新型复合脱硫塔的设计开发,其结构特点为旋流板塔板采用了双程叶片结构,溢流装置采用新型带螺旋叶片结构,塔上部设置高2.53m填料层,改进并优化了喷淋装置及气体进、出口结构等。通过实际应用,证明该型脱硫塔在净化加压变换气脱硫工序中的应用是成功的。     

领域知识图谱快速构建和应用框架

快速构建和应用领域知识图谱(domain knowledge graphs, DKG)已成为企业的迫切需求。然而,领域知识图谱的快速构建和应用仍然存在问题:1）构建前,复杂领域场景涉及知识维度广,业务专家短时间内难以构建完备的领域schema;2）构建中,业务和技术专家深度耦合,图谱构建缓慢,难以适应业务快速发展需求;3）构建后,图谱应用严重依赖技术人员开发,业务专家无法自主基于领域知识图谱探索问题解决方案。为解决上述问题,本文提出了领域知识图谱的快速构建和应用框架,其中包括:多人协作构建领域schema解决领域知识的复杂性问题,将业务和技术专家解耦合,提高领域知识图谱的构建效率,最后通过建立基于行业schema的auto-KBQA((knowledge base question answering)解决领域知识图谱在知识问答应用的快速落地。通过实际项目的应用落地,验证了该框架可有效加快领域知识图谱的落地应用,期望该框架给领域知识图谱的快速构建和应用带来一定的启发和帮助。     

Some Results for Pythagorean Fuzzy Sets

Pythagorean fuzzy sets (PFSs), originally proposed by Yager (Yager, Abbasov. Int J Intell Syst 2013;28:436–452), are a new tool to deal with vagueness considering the membership grades are pairs satisfying the condition . As a generalized set, PFSs have close relationship with intuitionistic fuzzy sets (IFSs). PFSs can be reduced to IFSs satisfying the condition . However, the related operations of PFSs do not take different conditions into consideration. To better understand PFSs, we propose two operations: division and subtraction, and discuss their properties in detail. Then, based on Pythagorean fuzzy aggregation operators, their properties such as boundedness, idempotency, and monotonicity are investigated. Later, we develop a Pythagorean fuzzy superiority and inferiority ranking method to solve uncertainty multiple attribute group decision making problem. Finally, an illustrative example for evaluating the Internet stocks performance is given to verify the developed approach and to demonstrate its practicality and effectiveness.     

大数据和云计算技术探析

随着大数据时代的来临,海量数据的处理成为了新的难题.结合大数据和云计算技术的背景,阐述了大数据和云计算的产生、发展和一些关键技术以及将来面临的诸多问题及新的挑战.     

Conceptual equivalence for contrast mining in classification learning

Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning.     

An Agent-Based Hybrid System for Microarray Data Analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.