文献类型：期刊文献

英文题名：Research on data reconciliation based on generalized T distribution with historical data

作者：Wu, Shengxi[1];Ye, Qiang[1];Chen, Cheng[1];Gun, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2016

卷号：175

期号：PartA

起止页码：808

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20155001671493);WOS:【SCI-EXPANDED(收录号:WOS:000367756600077)】；

语种：英文

外文关键词：Data reconciliation; Maximum likelihood estimation; Generalized T distribution; Historical data; PSO algorithm

摘要：In the most of previous data reconciliation(DR) studies, process data were conventionally characterized by normal Gaussian distribution, so the optimality/validity of DR estimator is implicitly based on a main assumption that errors follow normal Gaussian distribution. When this assumption is not satisfied, conventional data reconciliation approaches will become unavailable. However, normal distribution usually does not exist in real chemical engineering practice, as it is hard to ensure the normality even for high-quality measurements. So it is necessary to propose a new DR method which can accommodate more variety of measurement error distribution. In this paper, generalized T distribution is applied to accommodate measurement error distribution, meanwhile, historical data is introduced to estimate the objective function parameters by using Particle Swarm Optimization (PSO) algorithm. A novel robust data reconciliation method is proposed based on GT distribution and historical data, at the same time, its robustness characteristics are investigated. The new method is demonstrated on a steam-metering system for a methanol synthesis unit. Based on the comparison with other DR methods, the novel robust DR method can effectively improve the reliability of reconciled data even when errors do not follow normal distribution. (C) 2015 Published by Elsevier B.V.