深过冷Fe76B12Si12超细晶材料的凝固组织与软磁性能

首次采用深过冷加水淬的方法,成功地制备了直径为16 mm的样品,高为15 mm,组织中颗粒平均尺寸约为120nm左右的Fe76B12Si12合金超细晶材料.其磁损耗P1T400和P1T1000分别为普通硅钢片的40%和65%.系统研究了组织类型及Fe2B(Si)相尺度对该合金软磁性能的影响,发现该合金的最佳软磁性能位于Fe2B(Si)相完全粒化的非规则共晶组织区,并且当Fe2B(Si)相尺寸细化至0.36～0.50μm时,其软磁性能随着相尺寸的减小而大幅度改善.对以上结果产生的原因进行了讨论.     

Viscosity of bulk free radical polymerizing systems under near-isothermal and non-isothermal conditions

A viscometer-reactor assembly is used to generate data on the viscosity, η(t), of an example polymerizing system exhibiting the Trommsdorff effect, namely, the bulk free radical polymerization of methyl methacrylate (MMA), at different temperature conditions [near-isothermal and non-isothermal (near-step increase and near-step decrease in temperature)] and at two different initiator, 2,2′-azoisobutyronitrile (AIBN), concentrations. Two types of cup and bob assemblies, viz., the Haake^® SV-2 and the Haake^® HV-DIN, have been used to measure η(t) of the reaction mass, until reasonably high values of viscosity, well into the gel effect region. Only three sets of experimental data on x_m(t), M_w(t) and η(t) under near-isothermal conditions, are used to develop general correlations for the viscosity. These tuned correlations predict the values of the viscosity for a whole variety of other experimental conditions, including non-isothermal cases, reflecting that the physics of the system is well represented by them. Hence, these correlations can be used for other systems after tuning their parameters. The feasibility of on-line soft sensing is demonstrated for a few cases.     

基于Wiener结构的软测量模型及辨识算法

Wiener模型结构能有效地表征系统的动态和静态特性, 因此这里首先基于这一结构建立软测量模型, 利用动态与静态子模型分别建立辅助变量与主导变量间的动态与静态关系, 并说明该软测量模型的可行性, 给出模型具体表达式. 其次, 针对所提模型, 提出分步辨识方式获得最优模型参数, 说明其可行性. 再次, 为了减少计算和实现模型在线更新, 这里提出参数辨识递推算法, 并给出软测量模型参数的收敛性结论. 通过实例仿真, 可以看出本文提出模型的可行性, 以及分步辨识方式与递推算法的有效性.     

Development of a variable selection method for soft sensor using artificial neural network and nonnegative garrote

This paper developed a new variable selection method for soft sensor applications using the nonnegative garrote (NNG) and artificial neural network (ANN). The proposed method employs the ANN to generate a well-trained network, and then uses the NNG to conduct the accurate shrinkage of input weights of the ANN. This paper took Bayesian information criterion as the model evaluation criterion, and the optimal garrote parameter s was determined by v-fold cross-validation. The performance of the proposed algorithm was compared to existing state-of-art variable selection methods. Two artificial dataset examples and a real industrial application for air separation process were applied to demonstrate the performance of the methods. The experimental results showed that the proposed method presented better model accuracy with fewer variables selected, compared to other state-of-art methods.     

Modeling for soft sensor systems and parameters updating online

Soft sensor technology is an important means to estimate important process variables in real-time. Modeling for soft sensor system is the core of this technology. Most nonlinear dynamic modeling methods integrate the processes of building the dynamic and static relationships between secondary and primary variables, which limits the estimation accuracy for primary variables. To avoid the problem, a kind of soft sensor model consisting of a dynamic model in cascade with a static one is proposed. The model identification and update online are conducted in substep way. In order to improve the model update efficiency, two improved Gauss–Newton recursive algorithms, which avoid nonsingular covariance matrix, are proposed for time-invariant and time-variant soft sensor systems. The uniform convergence for dynamic model parameter and the existence of estimation deviations for static model parameters are proved for time-invariant soft sensor system. The parameters of time-variant soft sensor system would be boundedly convergent. Case study confirms that, on the basis of the proposed model and recursive algorithms, the dynamic and static characteristics of soft sensor system can be described efficiently, and the primary variables are ensured to be estimated accurately.     

锅炉主燃料跳闸保护的设计与分析

锅炉是火力发电的主要设备之一,锅炉MFT（Main Fuel Trip）是锅炉主要设备发生故障时重要的保护手段,也是电厂热工保护的重要内容”,。结合包铝热电厂330MW亚临界机组锅炉设备的特点及实际调试过程,本文分别从锅炉MFT系统的总体结构、控制软逻辑的设计、后备硬跳闸回路的设计等方面对锅炉MFT系统进行详细的介绍,进一步对其安全性和可靠性进行分析,为同类型的锅炉主燃料跳闸系统提供参考。     

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future.     

Development of soft sensor by incorporating the delayed infrequent and irregular measurements

Originated from challenging industrial applications, this paper addresses a soft sensor development problem for linear systems with two types of measurements. One is fast, regular and delay-free measurements. The other is infrequent, irregular measurements with time-varying delays. The approach to be taken is based on the Kalman filter and data fusion technique, avoiding running the full augmented state Kalman filter, and leading to a considerably lower implementation cost. Although it is suboptimal, the loss in performance is minor compared to the optimal filter. Two simulation examples demonstrate the advantages of the proposed approach. An industrial soft sensor application example is also used to demonstrate its practicality.     

基于云模型和模糊聚合的应急方案评估

应急方案评估是灾害应急响应中的重要环节.现有方法大都依赖于指挥员个人的知识和经验,而且不能综合不同领域专家的建议.因此,在分析和总结相关工作的基础上,引入云模型和模糊软集合对应急方案进行评估.对于给定的应急方案,首先基于云模型对不同领域专家的评价信息进行定性到定量的转换,然后利用模糊软集合对所有专家的定量评价信息进行聚合,得到综合评判结果.对比实例表明该方法的有效性,并可为应急决策提供理论指导和技术支持.     

安县晓坝镇震后新建场址主要岩土工程问题及对策

安县晓坝镇处于＂5.12＂汶川大地震极重灾区,受灾严重,不具备重建条件,需选址重建。在野外详细调查的基础上,通过现场勘查和室内试验,深入研究了晓坝镇灾后新建场址的主要岩土工程问题。研究表明,新建场址主要土层为粉质粘土和淤泥质土,属高压缩性软弱土;地下水埋深浅,对基础施工和地基土承载力影响大;建筑场地类别Ⅱ类,属抗震不利地段。提出了深层水泥土搅拌法和换填地基两种软基处理方案,以及加强地基基础抗震设计等措施。