Moving-horizon State Estimation with Gross Error Detection for a Hydroformylation Mini-plant

被引：2

作者：

Hoffmann, Christian ^{[1
]}

Illner, Markus ^{[1
]}

Mueller, David ^{[2
]}

Esche, Erik ^{[1
]}

Wozny, Guenter ^{[1
]}

Biegler, Lorenz T. ^{[3
]}

Repke, Jens-Uwe ^{[4
]}

机构：

[1] Tech Univ Berlin, Proc Dynam & Operat Grp, Sekretariat KWT 9, D-10623 Berlin, Germany

[2] Evonik Technol & Infrastruct GmbH, Proc Technol & Engn, CAPE & Automat, Paul Baumann Str 1, D-45772 Marl, Germany

[3] Carnegie Mellon Univ, Dept Chem Engn, Doherty Hall,5000 Forbes Ave, Pittsburgh, PA 15213 USA

[4] Tech Univ Bergakad Freiberg, Inst Therm Vetfahrenstech Umwelt & Nat Stoffverfa, Sekretariat RAM 126, Leipziger Str 28, D-09596 Freiberg, Germany

来源：

26TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING (ESCAPE), PT B | 2016年 / 38B卷

关键词：

Real-time Optimization; Hydroformylation; State Estimation; Mini-plant; NONLINEAR-PROGRAMMING SENSITIVITY;

D O I：

10.1016/B978-0-444-63428-3.50252-6

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

This contribution discusses and compares the implementation of moving-horizon (MHE) and advanced step moving-horizon state estimation (asMHE) for the hydroformylation mini-plant at Technische Universitat Berlin as part of the Collaborative Research Center InPROMPT. Moreover, robust estimators are used to reduce the impact of gross error on the estimated states. The advantage to solve the state estimation problem offline with asMHE makes it a suitable method to complement real-time optimization in the future.

引用

页码：1485 / 1490

页数：6

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