锅炉补给水化学一级除盐设备的设计选型及程控分析

以阳城国际发电有限责任公司6×350MW机组为例,主要介绍了阳城电厂锅炉补给水化学一级除盐系统设备的选型及程控分析,论述了阳城国际发电有限责任公司阳城电厂锅炉补给水化学一级除盐系统设备运行情况及存在问题的处理,重点对锅炉补给水化学一级除盐系统的程控进行了分析探讨。

Application of Kernel Independent Component Analysis for Multivariate Statistical Process Monitoring

In this research, a new fault detection method based on kernel independent component analysis （kernel ICA） is developed. Kernel ICA is an improvement of independent component analysis （ICA）, and is different from kernel principal component analysis （KPCA） proposed for nonlinear process monitoring. The basic idea of our approach is to use the kernel ICA to extract independent components efficiently and to combine the selected essential independent components with process monitoring techniques. 12 （the sum of the squared independent scores） and squared prediction error （SPE） charts are adopted as statistical quantities. The proposed monitoring method is applied to Tennessee Eastman process, and the simulation results clearly show the advantages of kernel ICA monitoring in comparison to ICA monitoring.

Integration of Fault Analysis and Interlock Controller Synthesis for Batch Processes

Integration amongst various decision-making processes, such as planning, design, and operation is necessary to dynamic and flexible batch production. To achieve a batch production integration, utilization of common models used for various decision-making processes is an effective approach. From this point of view, a batch system common model as described by a Petri net is proposed. In this article, a fault diagnosis technique for batch processes is presented using information about fault propagation and the possibilities of integration of fault analysis and controller synthesis are discussed on the basis of the Petri net based common models.

On-line Batch Process Monitoring with Improved Multi-way Independent Component Analysis

In the past decades, on-line monitoring of batch processes using multi-way independent component analysis （MICA） has received considerable attention in both academia and industry. This paper focuses on two troublesome issues concerning selecting dominant independent components without a standard criterion and deter- mining the control limits of monitoring statistics in the presence of non-Gaussian distribution. To optimize the number of key independent components~ we introctuce-anoveiconcept of-system-cleviation, which is ab^e＇io＇evalu[ ate the reconstructed observations with different independent components. The monitored statistics arc transformed to Gaussian distribution data by means of Box-Cox transformation, which helps readily determine the control limits. The proposed method is applied to on-line monitoring of a fed-hatch penicillin fermentation simulator, and the ex- _perimental results indicate the advantages of the improved MICA monitoring compared to the conventional methods.

Dynamic failure in coal seams Implications of coal composition for bump susceptibility

As a contributing factor in the dynamic failure(bumping) of coal pillars,a bump-prone coal seam has been described as one that is ‘‘uncleated or poorly cleated,strong...that sustains high stresses."Despite extensive research regarding engineering controls to help reduce the risk for coal bumps,there is a paucity of research related to the properties of coal itself and how those properties might contribute to the mechanics of failures. Geographic distribution of reportable dynamic failure events reveals a highly localized clustering of incidents despite widespread mining activities. This suggests that unique,contributing geologic characteristics exist within these regions that are less prevalent elsewhere. To investigate a new approach for identifying coal characteristics that might lead to bumping,a principal component analysis(PCA) was performed on 306 coal records from the Pennsylvania State Coal Sample database to determine which characteristics were most closely linked with a positive history of reportable bumping. Selected material properties from the data records for coal samples were chosen as variables for the PCA and included petrographic,elemental,and molecular properties. Results of the PCA suggest a clear correlation between low organic sulfur content and the occurrence of dynamic failure,and a secondary correlation between volatile matter and dynamic failure phenomena. The ratio of volatile matter to sulfur in the samples shows strong correlation with bump-prone regions,with a minimum threshold value of approximately 20,while correlations determined for other petrographic and elemental variables were more ambiguous. Results suggest that the composition of the coal itself is directly linked to how likely a coal is to have experienced a reportable dynamic failure event. These compositional controls are distinct from other previously established engineering and geologic criteria and represent a missing piece to the bump prediction puzzle.

Bending analysis and control of rolled plate during snake hot rolling

In order to study the bending behavior of aluminum alloy 7050 thick plate during snake hot rolling, several coupled thermo-mechanical finite element(FE) models were established. Effects of different initial thicknesses, pass reductions, speed ratios and offset distances on the bending value of the plate were analyzed. ‘Quasi smooth plate' and optimum offset distance were defined and quasi smooth plate could be acquired by adjusting offset distance, and then bending control equation was fitted. The results show that bending value of the plate as well as the extent of the increase grows with the increase of pass reduction and decrease of initial thickness; the bending value firstly increases and then keeps steady with the ascending speed ratio; the bending value can be reduced by enlarging the offset distance. The optimum offset distance varies for different rolling parameters and it is augmented with the increase of pass reduction and speed ratio and the decrease of initial thickness. A proper offset distance for different rolling parameters can be calculated by the bending control equation and this equation can be a guidance to acquire a quasi smooth plate. The FEM results agree well with experimental results.

Improved performance of process monitoring based on selection of key principal components

Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.

Stability control of underground construction

This paper presents the reason for instability of underground construction. In order to know failure mechanism during the whole construction process, a research framework of multi-scale based on experiments and numerical analysis is established. Some promising aspects in the topics of stability control are also given in the paper.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.

Analytical Modeling and Experimental Validation of Electromagnetic Field Radiated by In-house PLC Lines

Broadband PLC （power line communication） technology is a main factor of the development of digital convergence in the indoor network. It uses the already existing power cable infrastructure for communication purposes. The EM （electromagnetic） field radiating from the cable could, however, disturb other communication systems, and thus should be evaluated. The MoM （method of moment） and the FEM （finite element method） have been studied to estimate the EVI field emitted from the power cable. However, the M oM is difficult to treat the dielectric material of the cable and the FEM is time consuming. This paperpresents a new approach to estimate the radiated EM fields caused by PLC sy stems from the CM current along the cable, based on the transmission line theory. The proposed model has the advantage of using the measured primary parameters of the cable. An experimental analysis of the EM radiation distribution is also presented. A comparison showed that the model results agree quite well with the measurements performed in this study.

SMT工厂成功导入新产品的要素与方法

当前的电子产品制造及SMT加工企业，主要是采用委托代工（OEM）、委托设计与制造（ODM）或两者兼用的生产经营方式。电子产品的日益纷繁复杂、快速地更新换代和技术指标不断攀高，无论是对于OEM还是ODM产品，SMT新品导入（NPI）都不可等闲视之。

Study on seismic response control of a single-tower self-anchored suspension bridge with elastic-plastic steel damper

Elastic-plastic steel damper(EPSD) is a new device controlling seismic responses.The mechanical principle of EPSD was presented and a comparison was conducted between the theoretical formulas and finite element(FE) simulation of damper units.The verified force-displacement hysteretic curve of the damper system was obtained with reference to tests.The Nanjing Jiangxinzhou Bridge(NJB) was subsequently taken as the case to investigate the seismic response control effect of EPSDs on single-tower self-anchored suspension bridges.A 3-dimensional FE model of the bridge was established in ANSYS and the dynamic and static analyses of the bridge were conducted,the control effect of EPSDs under different seismic waves was further investigated through nonlinear time-history analysis based on the validated model.Results showed that both the simplified theoretical and FE simulation methods can preferable reflect the mechanical performance of EPSD,and that seismic responses of NJB with EPSDs are better than those with elastic connection device or fluid viscous damper.However,the control effect of EPSDs is influenced by seismic wave characteristics.

An on-line remote supervisory system for microparticles based on image analysis

A new on-line remote particle analysis system based on image processing has been developed to measure microparticles. The system is composed of particle collector sensor （PCS）, particle image sensor （PIS）, image remote transmit module and image processing system. Then some details of image processing are discussed. The main advantage of this system is more convenient in particle sample collection and particle image acquisition. The particle size can be obtained using the system with a deviation abot less than 1 μm, and the particle number can be obtained without deviation. The developed system is also convenient and versatile for other analyses of microparticle for academic and indttstrial application.

Optimal multi-objective trajectory design based on close-looped control for autonomous rendezvous

This paper considers the problem of optimal multi-objective trajectory design for autonomous rendezvous. Total velocity cost and relative state robustness of close-looped control are selected as the objective functions. Based on relative dynamics equations, the state equations and measurement equations for angles-only relative navigation between spacecraffs are set forth. According to the method of linear covariance analysis, the close-looped control covariance of the true relative state from the reference relative state is analyzed, and the objective functions of relative state robustness are formulated. Considering the total velocity cost and the relative state robustness, the multi-objective optimization algorithm of NSGA-II is employed to solve this multi-impulsive rendezvous problem. Lastly, the validity of the objective functions and the covariance results are demonstrated through 1 00 times Monte Carlo simulation.