SGER: Experimental Design for Estimating Process Parameters

项目编号：0706792题目：温室气体的自热重整- sger模型源自第一性原理，可以在从模型预测控制，动态数据协调，故障诊断到工厂范围的实时优化等应用中找到。虽然这些技术最初是基于线性模型的，但在过去的几十年里，越来越多的应用依赖于非线性模型。在许多情况下，是模型的准确性而不是实际的算法决定了控制器、故障检测方案或优化的质量。因此，模型通常适合于从工厂操作中收集的数据。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。由于对可用数据的要求以及许多参数无法从过程数据单独估计的事实，几乎不可能重新估计如此大量的参数值。一种方法是从过程数据中选择一小部分参数进行重新估计。然而，在这组重要参数中包含哪些参数的选择通常是通过试错和过程经验的结合来完成的。此外，在实验设计领域也进行了大量的工作。不幸的是，实验设计技术的发展和应用是孤立于参数选择和估计的。没有一种方法被广泛接受来选择要估计的一组参数，也没有进行工作来确定可用数据对估计参数的影响。到目前为止尚未研究的最后一个方面是实验设计和选择待估计的“最佳”参数集之间的相互作用。这项探索性研究小额拨款的目的是开发非线性系统实验设计和参数选择的综合技术。该方法可以通过重新估计模型参数来优化模型的精度。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：这项工作可能对在线使用模型并根据实验和/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通常导致更安全的工厂操作来改善工厂操作。

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.