化学水处理程控故障分析与改进

化学水处理在热电厂运行中非常重要，不仅为全厂安全发电供水，还为周边地区供热。如发生程控故障，直接对机组构成安全隐患并造成了热源损失。经分析其异常现象，通过改进措施如改变阀门口径、定位固定螺丝、更换位置反馈装置、改进网络链接，可以相当程度的降低化学水处理程控故障次数，确保机组的安全性和稳定性。

大型炼油厂PSA氢提纯单元运行经验与故障处理

通过分析某大型炼油厂制氢装置PSA(变压吸附)单元实际生产情况,发现PSA单元运行中出现的问题主要集中在吸附剂粉碎和程控阀故障两方面。经分析认为:吸附剂粉碎主要原因为均压过程压差和气体流速太大、吸附塔顶吸附剂压网和压板的影响、原料气带水;程控阀故障则主要包括阀检报警、电磁阀故障、阀芯密封面损坏、填料压盖泄漏以及阀板轴套开裂和阀销折断等。根据长期实践经验,总结列出判断故障的依据,分析造成各类故障的原因,制定出细化设备安装检查、加强日常维护保养、优化操作参数设置、升级相关配件材质等有效措施,可以消除PSA单元的常见故障,确保其长周期稳定运行。

制氢Ⅱ套PSA程控阀故障及改进措施

以某石化公司制氢Ⅱ套开工过程中程控阀故障造成CO超标、提纯氢气纯度不足为例,认真分析原因,并提出了一系列的改进措施。从目前后续的开工应用效果看,彻底解决了程控阀故障。

变压吸附装置运行故障处理

对变压吸附装置运行期间出现的故障进行了分析,变压吸附装置的核心设备是程控阀。程控阀故障集中表现为程控阀内漏、程控阀执行机构渗油、阀杆反馈杆滑动导致回讯故障进而切塔、程控阀齿轮齿条磨损导致阀门关闭不到位。从工艺流程方面对变压吸附流程进行了改进,提出整改措施,经多次改进,逐步解决了变压吸附程控阀故障率高的问题,保障了变压吸附装置长周期运行。

化学水处理程序控制故障分析及改进

在热电厂安全发电、供热的运行中,化学水处理PLC程控装置能否正常运行至关重要。倘若PLC程控发生故障,直接会对机组安全运行构成威胁并造成热源损失。通过深度排查隐患,针对化学水处理PLC程控装置在运行中发生的异常现象,采取改变管道阀门口径尺寸、更换位置反馈装置、加设程控复位界面、完善网络链接等技术措施,提高了水处理自动程序控制的水平,降低了PLC程控故障次数,取得了改造效果。

重整再生器内网损坏原因分析及改进措施

该文总结了某石化企业1~#重整装置再生器历次检修内网损坏及检修情况,分析再生器内网损坏的原因主要是再生系统烧焦能力与反应系统规模不匹配、程控阀频繁故障、系统粉尘量大、热偶检测不具有代表性等引起的床层局部超温,并针对损坏原因提出了增设柔性热偶、降低程控阀故障率、“不停车”换阀等改进措施,应用效果表明各项措施能够明显降低床层局部超温风险,防止再生内网损坏,能够显著提升1~#重整再生系统运行稳定性。

Nonlinear online process monitoring and fault diagnosis of condenser based on kernel PCA plus FDA

A novel online process monitoring and fault diagnosis method of condenser based on kernel principle component analysis （KPCA） and Fisher discriminant analysis （FDA） is presented. The basic idea of this method is： First map data from the original space into high-dimensional feature space via nonlinear kernel function and then extract optimal feature vector and discriminant vector in feature space and calculate the Euclidean distance between feature vectors to perform process monitoring. Similar degree between the present discriminant vector and optimal discriminant vector of fault in historical dataset is used for diagnosis. The proposed method can effectively capture the nonlinear relationship among process variables. Simulating results of the turbo generator＇s fault data set prove that the proposed method is effective.

Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.

液氮洗装置的优化与改进

介绍液氮洗装置运行2a来存在的冷箱冷量过剩、燃料气热值低无法回收、程控阀故障率高等一系列问题,对这些问题进行深入的探讨和分析,提出切实可行的解决思路。通过对改造后的生产实践总结,证明了对工艺优化和装置改造的科学性、合理性。

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.

S Zorb装置运行存在问题分析及对策

中国石化荆门分公司S Zorb装置自2016年9月首次开工以来已运行21个月,出现了反应器ME101差压上涨过快、原料换热器E101结垢导致换热效果下降、闭锁料斗程控阀(控制阀)磨损故障等一系列影响长周期运行的问题。通过对装置运行存在的问题进行技术分析,采取了调整ME101反吹系统运行参数、监控反吹阀运行;加强原料管理、及时切换E101进行清洗;对程控阀定期进行试漏、更换程控阀等措施,有效保障了装置运行周期。

Nonlinear Statistical Process Monitoring and Fault Detection Using Kernel ICA

A novel nonlinear process monitoring and fault detection method based on kernel independent component analysis(ICA) is proposed.The kernel ICA method is a two-phase algorithm:whitened kernel principal component(KPCA) plus ICA.KPCA spheres data and makes the data structure become as linearly separable as possible by virtue of an implicit nonlinear mapping determined by kernel.ICA seeks the projection directions in the KPCA whitened space,making the distribution of the projected data as non-gaussian as possible.The application to the fluid catalytic cracking unit(FCCU) simulated process indicates that the proposed process monitoring method based on kernel ICA can effectively capture the nonlinear relationship in process variables.Its performance significantly outperforms monitoring method based on ICA or KPCA.

Implementation of remote monitoring system for prediction of tool wear and failure using ART2

Remote monitoring of tools for prediction of tool wear in cutting processes was considered, and a method of implementation of a remote-monitoring system previously developed was proposed. Sensor signals were received and tool wear was predicted in the local system using an ART2 algorithm, while the monitoring result was transferred to the remote system via intemet. The monitoring system was installed at an on-site machine tool for monitoring three kinds of tools cutting titanium alloys, and the tool wear was evaluated on the basis of vigilances, similarities between vibration signals received and the normal patterns previously trained. A number of experiments were carried out to evaluate the performance of the proposed system, and the results show that the wears of finishing-cut tools are successfully detected when the moving average vigilance becomes lower than the critical vigilance, thus the appropriate tool replacement time is notified before the breakage.

Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy

Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process.

Fault detection of large-scale process control system with higher-order statistical and interpretative structural model

Nonlinear characteristic fault detection and diagnosis method based on higher-order statistical(HOS) is an effective data-driven method, but the calculation costs much for a large-scale process control system. An HOS-ISM fault diagnosis framework combining interpretative structural model(ISM) and HOS is proposed:(1) the adjacency matrix is determined by partial correlation coefficient;(2) the modified adjacency matrix is defined by directed graph with prior knowledge of process piping and instrument diagram;(3) interpretative structural for large-scale process control system is built by this ISM method; and(4) non-Gaussianity index, nonlinearity index, and total nonlinearity index are calculated dynamically based on interpretative structural to effectively eliminate uncertainty of the nonlinear characteristic diagnostic method with reasonable sampling period and data window. The proposed HOS-ISM fault diagnosis framework is verified by the Tennessee Eastman process and presents improvement for highly non-linear characteristic for selected fault cases.

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.