Nonlinear Dynamic Data Reconciliation via Process Simulation Software and Model Identification Tools

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• 作者：Semra Alici and Thomas F. Edgar
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2002
• 出版时间：August 7, 2002
• 年：2002
• 卷：41
• 期：16
• 页码：3984 - 3992
• 全文大小：141K
• 年卷期：v.41,no.16(August 7, 2002)
• ISSN：1520-5045

文摘

Existing strategies for the solution of the nonlinear dynamic data reconciliation problem usethe process model as a constraint which is expressed as a differential-algebraic equation system.Modeling a process using conservation laws may require a considerable number of equations toobtain an accurate representation of the system. It is possible to model a process using commercialdynamic simulation software. However, this also requires the solution of a large number ofequations interfaced to reliable optimization software in order to perform data reconciliation.This paper focuses on two new approaches for dynamic data reconciliation using modelidentification tools and commercial dynamic simulation software. The first one is based on ananalogy to the nonlinear dynamic data reconciliation method developed by Liebman et al.¹ Thesecond approach uses time series analysis to generate a simplified model of the plant. A simplifiedprocess model is generated by a model identification method to replace the simulation software.Several techniques including parametric and nonparametric methods can be applied to identifya local input-output model from simulation results. Data reconciliation constrained by thisreduced model yields a more computationally efficient algorithm.