基于动态模拟的危险与可操作性分析在精馏塔中的应用

危险与可操作性分析是化工领域中广泛应用的安全分析的有效方法,近年来其定量化研究已经成为一种趋势.为了实现定量化的危险与可操作性分析,本文提出了基于动态模拟的危险与可操作性分析的新型方法,即在传统的危险与可操作性分析的基础上引入了动态模拟模型,利用动态模拟的定量化弥补传统危险与可操作性分析定量化不足的缺陷.通过对动态模拟模型中操作参数进行不同程度的拉偏可以方便地模拟危险与可操作性分析中引导词与工艺参数组成的偏差,模拟的结果变化就是偏差对系统所造成的影响,以此实现危险与可操作性分析的定量化推理.本文利用对一个萃取精馏塔的冷凝能力缺失和安全阀失效问题阐述了基于动态模拟的危险与可操作性分析方法的新型研究方法.最后,根据案例分析总结了这种新型分析方法的优点,并且研究证明其为一种行之有效且非常有潜力的分析方法.     

锌精馏塔的混合故障诊断方法

以锌精馏塔为研究对象,在利用小波分析进行数据预处理的基础上,将专家系统、模糊控制、神经网络等先进的理论和技术有机的结合起来,提出了模块化的混合故障诊断系统,实现对整个锌精馏塔生产系统的状态监测和故障诊断。     

软件集成环境下的精馏塔动态仿真

化工过程动态仿真的关键问题是物性数据的计算及非线性微分方程和代数方程的求解,商品化的过程稳态模拟软件能够精确计算纯组分及混合物的物性,而MATLAB/SIMULINK具有强大计算及仿真功能。该文通过将Matlab／Simulink与Aspen Plus模拟软件物性和热力学数据库集成,对精馏塔进行了动态仿真研究。文中阐述了软件集成的方法,建立了精馏塔的通用动态数学模型,在Simulink中以S-函数的方式进行了实现。通过软件集成进行过程的仿真研究,充分利用了现有软件的优势,减少了编程工作量,提高了开发效率。     

ChemCAD在精馏塔动态模拟中的应用

以丙酮回收塔为例,研究ChemCAD在精馏塔动态模拟中的应用,建立带有控制系统的动态精馏模型.分析精馏塔操作条件、进料状况等发生变化时,精馏塔在当前控制方案下的动态响应过程,得到并分析常规PID控制方案在精馏塔操作中存在的问题,为进一步剖析精馏过程特性,确定合适的优化及控制方案提供参考.     

苯酚丙酮装置烃化系统精馏塔的模拟与分析

介绍了苯酚丙酮装置烃化系统精馏塔模拟软件，给出了烃化系统精馏塔模拟与分析方法和策略，利用该软件对烃化系统精馏进行了模拟与分析，模拟结果与设计或实际生产数据基本一致；分析结果表明，ＣＴ－４０１和ＣＴ－４０２塔操作比较适宜，ＣＴ－５０２塔回流比较大应减小，若ＣＴ－５０２塔回流比从目前的１１４降为８０，该塔可节能３０％。     

基于小波神经网络的模拟电路故障诊断

模拟电路故障诊断具有诊断特性复杂，故障字典建立耗时长等特性，用传统的方法很难得到最佳的诊断效果。本文采用小波神经网络对故障电路建模，基于该网络学习收敛快，对网络输入不太敏感的特点，实现故障诊断。     

醋酸脱水系统的动态模拟及其控制

通过Aspen Plus软件模拟PTA生产的醋酸脱水共沸精馏系统.稳态模拟结果与现场数据吻合较好.在稳态模型的基础上,设计两种控制方案,利用Aspen Dynamics进行动态模拟,测试这两种控制方案的性能,找出较好的控制方案.模拟的结果对生产操作和控制方案的设计具有重要的指导意义.     

丙烯精馏塔优化操作分析

利用化工模拟软件,对丙烯精馏塔操作进行了模拟分析,为优化该塔操作提出了建议,以利于在保证丙烯产品质量的前提下,增加丙烯收率。     

基于遗传算法的模拟电路故障诊断激励优化

介绍了用改进的遗传算法优化激励信号的参数.仿真实验表明,经过优化后的激励信号能大大提高模拟电路的故障诊断效率,对线性和非线性模拟电路都适用.     

动态流程模拟及其在精馏塔的操作分析中的应用

采用HYSYS流程模拟软件,对丙烯丙烷精馏塔进行动态流程模拟,分析了丙烯丙烷精馏塔在操作条件变化和进料流量及进料成分干扰变化时,整个塔的动态响应变化过程,研究了常规PID控制方案以及通常的先进控制方案所存在的问题,为改进的集实时优化与先进控制于一体的控制方案设计提供了依据。     

Fault localization using a model checker

If a program does not fulfill its specification, a model checker can deliver a counterexample. However, although such a counterexample shows how the specification can be violated, it typically comprises large parts of the program and gives little information about which of the visited statements is responsible for the error. In this article, we show that model checkers can also be used to perform model‐based diagnosis and thus fault localization. The approach leads to significantly more precise diagnoses than the state‐of‐the‐art and typically rules out 90–99% of the code as possible fault locations. The approach is general and can be applied to any system that is amenable to model checking (with respect to language and complexity). To demonstrate the applicability and high precision of our approach, we present implementations for C programs using two different model checking tools and show experimental results from the TCAS case study and an integration with the DDVerify framework to debug Linux device drivers. Copyright © 2009 John Wiley & Sons, Ltd.     

Fault diagnosis in analog circuits using element modulation

Analog fault diagnostic methods are reviewed. A branch fault-diagnosis technique that requires a single excitation source at one test frequency is introduced. The technique lets users construct linearly independent branch-diagnosis equations by modulating selected network elements. An example of the technique applied to a two-stage amplifier is given     

基于粗糙集的故障诊断特征提取

故障的特征提取对于进行准确可靠的诊断非常重要。而实际的故障诊断数据样本的分类边界常常是不确定的,并且故障与征兆之间的关系往往也是不确定的。粗糙集理论是处理模糊和不确定性问题的新的数学工具。论文将粗糙集理论引入到故障诊断特征提取,提出了一种基于粗糙集的故障诊断特征提取方法。并通过两个故障诊断实例对该方法进行了验证。结果表明:在有效地保持故障诊断分类结果的情况下,该方法可以提取出最能反映故障的特征,从而为粗糙集在故障诊断中的深入应用打下了基础。     

拉格朗日插值算法在图解法精馏塔设计中的应用

图解法计算精馏塔理论塔板数是精馏塔设计中常用的一种方法,有简单、直观的特点,但精度相对较差,为提高图解法计算理论塔板数的精度,本文研究二元理想体系的图解法精馏塔设计,采用拉格朗日插值算法拟合汽液平衡曲线,通过在VC++环境中建立相关的类,再计算液相中的组分得到与之平衡的气相巾的组分,拟合出汽液平衡曲线,同时分析相应点的误差,并通过软件设计实现q线、精馏段操作线、提馏段操作线的数值计算和曲线的绘制,从而完成了理论塔板数计算,为精馏塔设计提供了较好的数值计算方法,提高了图解法计算理论塔板数的精度.     

Fault diagnosis of a cstr using fuzzy neural networks

On-line process fault diagnosis using fuzzy neural networks is described in this paper. The fuzzy neural network is obtained by adding a fuzzification layer to a conventional feed forward neural network. The fuzzification layer converts increments in on-line measurements and controller outputs into three fuzzy sets: “increase”, “steady”, and “decrease”. Abnormalities in a process are represented by qualitative increments in on-line measurements and controller outputs. These are classified into various categories by the network. By representing abnormalities in qualitative form, training data can be condensed. The fuzzy approach ensures smooth transitions from one fuzzy sets to another and, hence, robustness to measurement noise is enhanced. The technique has been successfully applied to a CSTR system.     

Fault diagnosis of ball bearings using machine learning methods

Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.     

Fault diagnosis in reversible circuits under missing-gate fault model

This article presents a novel technique for fault detection as well as fault location in a reversible combinational circuit under the missing gate fault model. It is shown that in an (n × n) reversible circuit implemented with k-CNOT gates, addition of only one extra control line along with duplication each k-CNOT gate, yields an easily testable design, which admits a universal test set (UTS) of size (n + 1) that detects all single missing-gate faults (SMGFs), repeated-gate faults (RGFs), and partial missing-gate faults (PMGFs) in the circuit. Furthermore, storage of only one vector (seed) of the UTS is required; the rest can be generated by n successive cyclic bit-shifts from the seed. For fault location under the SMGF model, a technique for identifying the faulty gate is also presented that needs application of a single test vector, provided the circuit is augmented with some additional observable outputs.     

多智能体的分布式智能故障诊断

基于分布式人工智能的思想 ,将多Agent技术引入复杂故障诊断领域 ,分析了基于MAS的分布式智能故障诊断方法和过程 ;讨论了基于模式聚类的故障求解机制及对诊断问题任务辨识、分解 ;研究了多Agent宏观上的约束和关联 ;设计了应用Agent工作状态的表达机制 ;确定了应用Agent间的工作状态影响关系及多Agent间的交互、协作和通讯 ;构建了多Agent模糊关联模型 ;给出了多Agent诊断系统局部诊断决策与全局诊断决策的集成描述结构 ;建立了一种分布式Agent诊断系统结构及其原型系统 .在某电力企     

基于粗糙集人工神经网络的锅炉故障诊断模型

针对锅炉这种大型特种设备,提出了一种基于粗糙集和人工神经网络集成的智能故障诊断方法.该方法先利用Rs理论建立故障决策表,对原始数据进行约简,并按照一定的原则选取多个约简;然后建立神经网络故障诊断子系统,使用粗糙集处理后的数据计算出故障发生程度,研究结果表明:该方法能够正确而且高效地诊断出锅炉中各种部件的故障发生的严重程度.     

Fault diagnosis for track circuit using AOK-TFRs and AGA

This paper presents a collaborative fault diagnosis system for compensation capacitor in track circuit using adaptive optimal kernel time–frequency representations (AOK-TFRs) and adaptive genetic algorithm (AGA) based on train operational data recorded in cab signal. First, AOK-TFRs technique is applied for single compensation capacitor fault diagnosis. To deal with multiple faults, the AGA based method is introduced, however, with a relatively low efficiency of diagnosis. According to the characteristics of both methods, a collaborative fault diagnosis system is established. Experiments show that the system greatly improves the diagnosis efficiency and can be used successfully.