SGER: Experimental Design for Estimating Process Parameters

SGER：估计过程参数的实验设计

• 批准号: 0706792
• 负责人: Juergen Hahn
• 金额: $ 6.37万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-01 至 2008-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706792&HistoricalAwards=false
• 关键词: SGER; Experimental; Design; Estimating; Process

PI: Juergen Hahn Institution: Texas A & M UniversityProposal Number: 0706792Title: Autothermal Reforming of Greenhouse Gases-SGERModels derived from first-principles can be found in applications ranging from model predictive control, dynamic data reconciliation, and fault diagnosis to plant-wide real-time optimization. While these techniques were originally based upon linear models, more applications relying on nonlinear models have emerged over the last couple of decades. In many cases it is the accuracy of the model rather than the actual algorithm that determines the quality of a controller, fault detection scheme, or optimization. Therefore, typically a model is adapted to data collected from plant operations. However, first-principles-based models tend to consist of a dozen to thousands of equations and usually contain even more parameters than equations. It is virtually impossible to re-estimate the values of such a large number of parameters due to the requirements that this would place on the available data as well as the fact that many of these parameters cannot be individually estimated from process data. One approach is to select a small subset of parameters which are re-estimated from process data. However, the choice of which parameters to include in this set of important parameters is usually made using a combination of trial-and-error and experience with the process. Additionally, much work has been conducted in the area of experimental design. Unfortunately, experimental design techniques have been developed and applied in isolation of parameter selection and estimation. No method has found wide acceptance for selecting the set of parameters to be estimated and no work has been performed on determining the effect that the available data has on estimating the parameters. One last aspect that has not been investigated so far is the interplay between experimental design and choice of an "optimal" parameter set to be estimated. It is the purpose of this Small Grant for Exploratory Research (SGER) to develop an integrated technique for experimental design and parameter selection for nonlinear systems. This approach will optimize the model accuracy that can be achieved by re-estimating model parameters. The PI also plans to develop a coordinated activity between the areas of experimental design and parameter selection/estimation.Broad Impact:This work could have a significant impact on any application where models are used online and updated with experimental and/or plant data. These include, but are not limited to model-based control, data reconciliation, fault diagnosis, and real-time optimization. Improved process monitoring and control has a direct economical and ecological impact as it allows improved plant operations by minimizing waste production, by lowering the raw materials usage, by quick detection and correction of upset conditions, and by generally resulting in safer plant operation.

PI：Juergen Hahn 机构：德克萨斯农工大学提案编号：0706792 标题：温室气体自热重整-SGER 从第一原理导出的模型可以在从模型预测控制、动态数据协调、故障诊断到全厂实时优化的应用中找到。虽然这些技术最初基于线性模型，但在过去几十年中出现了更多依赖非线性模型的应用。 在许多情况下，决定控制器、故障检测方案或优化质量的是模型的准确性，而不是实际算法。 因此，模型通常适用于从工厂运营中收集的数据。 然而，基于第一原理的模型往往由十几个到数千个方程组成，并且通常包含比方程更多的参数。 由于这对可用数据的要求以及许多参数无法根据过程数据单独估计，因此实际上不可能重新估计如此大量的参数值。 一种方法是选择一小部分参数，这些参数是根据过程数据重新估计的。 然而，选择哪些参数包含在这组重要参数中通常是结合试错和过程经验来进行的。 此外，在实验设计领域也进行了大量工作。 不幸的是，实验设计技术的开发和应用与参数选择和估计无关。 没有一种方法被广泛接受来选择要估计的参数集，并且没有进行任何工作来确定可用数据对估计参数的影响。 迄今为止尚未研究的最后一个方面是实验设计和要估计的“最佳”参数集的选择之间的相互作用。探索性研究小额资助 (SGER) 的目的是开发一种用于非线性系统实验设计和参数选择的集成技术。这种方法将优化通过重新估计模型参数可以实现的模型精度。 PI 还计划在实验设计和参数选择/估计领域之间开展协调活动。 广泛影响：这项工作可能会对在线使用模型并使用实验和/或工厂数据更新模型的任何应用产生重大影响。 这些包括但不限于基于模型的控制、数据协调、故障诊断和实时优化。 改进的过程监测和控制具有直接的经济和生态影响，因为它可以通过最大限度地减少废物产生、降低原材料使用量、快速检测和纠正异常情况以及总体上实现更安全的工厂运营来改善工厂运营。