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Process control utilizing data-based models: Applications of statistical techniques and neural networks.

机译：利用基于数据的模型进行过程控制：统计技术和神经网络的应用。

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摘要

There is an increasing demand in chemical industry to produce high quality products at low cost. Such an objective can be achieved through optimal operation of chemical plants. Optimal operation primarily depends upon reliable control schemes and good understanding of chemical processes. It has been demonstrated by many industrial applications that statistical techniques and neural networks are useful tools in using readily available plant data to model chemical processes. This dissertation investigates some new approaches to the applications of statistical techniques and neural networks.;Based on multivariate statistical methods, namely multi-way principal component analysis (MPCA) and multi-block principal component analysis, a dynamic monitoring approach has been developed for continuous processes. By arranging the dynamic data into a three-dimensional array and projecting the array into a low dimensional space defined by principal components, dynamic processes can be easily monitored by tracking their progress in the low dimensional space. A promising feature of the monitoring approach is its ability to predict faults. The application results show that the dynamic monitoring approach has many advantages over existing approaches. A multi-block monitoring approach for large continuous processes is also discussed. The application results show that the multi-block monitoring approach has a timing advantage over single block approaches and it is helpful in locating the process faults.;The statistical concept of representing processes by latent variables has been applied in process control. The goal is to decrease the variations in product quality without on line quality measurements. The controlled variables are defined by the variations embedded in the process data using a PCA technique. The control objective is defined as maintaining the latent variables within a certain acceptable region defined from historical data based on the assumption of an implicit correlation between measurements and quality variables. A steady state controller is designed using static PCA models. For dynamic processes, MPCA is used to model the performance of continuous processes. This controller is usually developed from and implemented on top of an existing conventional PID control system. Limited experimental testing is required to develop the controller. Model predictive control (MPC) is used to formulate the control algorithm. Examples show excellent results for both the steady state and dynamic cases.
机译：化学工业中以低成本生产高质量产品的需求不断增长。这样的目标可以通过优化化工厂的运行来实现。最佳操作主要取决于可靠的控制方案和对化学过程的充分理解。在许多工业应用中已经证明，统计技术和神经网络是使用随时可用的工厂数据对化学过程进行建模的有用工具。本文研究了统计技术和神经网络应用的一些新方法。基于多变量统计方法，即多向主成分分析（MPCA）和多块主成分分析，开发了一种连续的动态监测方法流程。通过将动态数据排列成三维数组并将其投影到由主成分定义的低维空间中，可以通过跟踪动态过程在低维空间中的进度来轻松地监视动态过程。监视方法的一个有前途的功能是其预测故障的能力。应用结果表明，动态监测方法比现有方法具有很多优势。还讨论了用于大型连续过程的多块监视方法。应用结果表明，多块监控方法比单块监控方法具有时序优势，有利于定位过程故障。；以潜在变量表示过程的统计概念已应用于过程控制中。目的是在不进行在线质量测量的情况下减少产品质量的变化。受控变量是使用PCA技术通过过程数据中嵌入的变量定义的。控制目标被定义为基于测量值和质量变量之间隐式相关的假设，将潜变量保持在从历史数据定义的某个可接受区域内。稳态控制器是使用静态PCA模型设计的。对于动态过程，MPCA用于对连续过程的性能进行建模。该控制器通常是在现有的常规PID控制系统的基础上开发并实现的。开发控制器需要进行有限的实验测试。模型预测控制（MPC）用于制定控制算法。实例显示了在稳态和动态情况下的出色结果。

著录项

  • 作者

    Chen, Gang.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Engineering Chemical.;Engineering System Science.;Engineering Industrial.
  • 学位 Ph.D.
  • 年度 1996
  • 页码 200 p.
  • 总页数 200
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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