Development of Computing Aids and Techniques for Process Analysis

过程分析计算辅助和技术的发展

• 批准号: 8519182
• 负责人: Richard Mah
• 金额: $ 16.18万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1986
• 资助国家: 美国
• 起止时间: 1986-02-01 至 1989-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8519182&HistoricalAwards=false
• 关键词: Development; Computing; Aids; Techniques; Process

The goal of this project is to develop statistical techniques to monitor problems encountered during the operation of chemical plants. In the past, process measurements and data acquisition were carried out by plant operators and engineers who checked the accuracy and consistency of the data based on their knowledge and experience. These functions are now performed automatically by computers and techniques to properly analyze this data are needed. The researchers will develop algorithms that reconcile measurement differences, estimate unmeasured variables, detect and identify gross errors, and efficiently monitor the process of interest. Process measurements are subject to two types of errors: random errors which are commonly assumed to be normally distributed with a mean of 0; and gross errors which are caused by non-random events such as equipment failure (e.g. leaks) as well as measurement errors due to malfunctioning instruments. Gross errors have to be eliminated before data reconciliation is attempted.

该项目的目标是发展统计技术，以监测化工厂运作期间遇到的问题。在过去，过程测量和数据采集是由工厂操作员和工程师进行的，他们根据自己的知识和经验检查数据的准确性和一致性。这些功能现在由计算机自动执行，需要适当分析这些数据的技术。研究人员将开发算法来调和测量差异，估计未测量的变量，检测和识别严重误差，并有效地监控感兴趣的过程。过程测量有两种类型的误差：随机误差，通常假定为正态分布，平均值为0；非随机事件引起的严重误差，如设备故障（如泄漏）以及仪器故障引起的测量误差。在试图进行数据核对之前，必须消除严重的错误。