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.

该项目的目的是开发统计技术来监视化学植物运行过程中遇到的问题。 过去，工厂操作员和工程师进行了过程测量和数据获取，他们根据数据的知识和经验来检查数据的准确性和一致性。 现在，这些功能是通过计算机和技术自动执行的，以正确分析这些数据。 研究人员将开发算法，以调和测量差异，估计未测量的变量，检测和确定总错误，并有效地监视感兴趣的过程。 过程测量受两种类型的错误约束：随机错误通常被认为是正态分布的，平均为0；由非随机事件（例如设备故障（例如泄漏）以及由于仪器故障造成的测量错误）引起的总错误。 在尝试数据和解之前，必须消除总错误。