Automatic Modeling using Chemical Plant Data

使用化工厂数据自动建模

• 批准号: 9632992
• 负责人: Lyle Ungar
• 金额: $ 30.83万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9632992&HistoricalAwards=false
• 关键词: Automatic; Modeling; using; Chemical; Plant

Abstract - Ungar & Seider - 9632992 Process design engineers spend much of their time developing and refining process models. Such models are typically built using graphical editors to assemble model fragments (for individual units or processes from code and equation libraries) into simulators. In addition, extensive historical plant data is also available. Process models often fail to accurately predict this plant data, due to inaccurate parameters in the model, incorrect model equations and structure, or faults, disturbances, or inaccurate measurements. The PIOs plan to address two related questions: (a) how to use available data effectively to improve process models, and (b) how to use data and models to detect and diagnose abnormalities in plant operation. The goal is to build intelligent simulators which will aid in understanding and improving models based on measured data. This project is to develop methods for automating the process of preparing simulations, using chemical process simulators like ASPEN PLUS and SPEEDUP, and interpreting their results. Its long-range goal is to develop techniques for the automatic generation, modification, and interpretation of dynamic simulations and to apply them to data reconciliation, fault diagnosis, plant and controller design, and operator training. In this work, the PIOs plan to concentrate on an initial stage of the project: the development of a designerOs aid that can prepare a simulation model, compare the results of a simulation with plant data and guide the process of improving the model. This involves the development of methodologies for (1) the detection of changes and outliers, (2) the representation of information describing the model fragments, (3) the initialization of dynamic models, (4) the selection of model fragments, and (5) the generation of explanations. Historical data, from operating plants, as well as reference models, will be obtained from the CIM/21 system.

摘要- Ungar Seider - 9632992 流程设计工程师花费大量时间开发和改进流程模型。 这种模型通常使用图形编辑器构建，以将模型片段（用于来自代码和方程库的单个单元或过程）组装到模拟器中。 此外，还提供了大量的历史工厂数据。 由于模型中的不准确参数、不正确的模型方程和结构、或故障、干扰或不准确测量，过程模型通常不能准确地预测该工厂数据。 PIO计划解决两个相关问题：（a）如何有效地使用可用数据来改进过程模型，以及（B）如何使用数据和模型来检测和诊断工厂运行中的异常。 我们的目标是建立智能模拟器，这将有助于理解和改进基于测量数据的模型。 该项目旨在开发使用白杨PLUS和SPEEDUP等化学过程模拟器自动准备模拟过程并解释其结果的方法。 其长期目标是开发动态模拟的自动生成、修改和解释技术，并将其应用于数据协调、故障诊断、工厂和控制器设计以及操作员培训。 在这项工作中，PIO计划集中在项目的初始阶段：开发一个designerOs援助，可以准备一个模拟模型，比较模拟结果与工厂数据，并指导改进模型的过程。 这涉及开发用于以下方面的方法：（1）检测变化和异常值，（2）表示描述模型片段的信息，（3）初始化动态模型，（4）选择模型片段，以及（5）生成解释。 将从CIM/21系统中获得运行工厂的历史数据以及参考模型。