A Moving Horizon State Estimation algorithm applied to the Tennessee Eastman Benchmark Process

A moving horizon state estimation algorithm (MHE) is applied to the nonlinear and unstable Tennessee Eastman process, a well-known benchmark problem in the chemical process engineering community. The estimator fuses past measurements within a given time horizon and calculates the actual states in a...

Full description

Saved in:
Bibliographic Details
Published in2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems pp. 377 - 382
Main Authors Kraus, T., Kuhl, P., Wirsching, L., Bock, H.G., Diehl, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2006
Subjects
Chemical processes
Fuses
Intelligent systems
Nonlinear systems
Optimization methods
Parameter estimation
Scientific computing
State estimation
Time measurement
Upper bound
Online AccessGet full text

Cover

More Information
Summary:A moving horizon state estimation algorithm (MHE) is applied to the nonlinear and unstable Tennessee Eastman process, a well-known benchmark problem in the chemical process engineering community. The estimator fuses past measurements within a given time horizon and calculates the actual states in a maximum-likelihood fashion. The calculations are based on a first-principles process model. The arising least-squares optimization problem is solved numerically by the direct multiple shooting technique. Due to an intelligent real-time iteration approach, estimation CPU times lie between 0.5 and 4.5 seconds on a standard PC. Furthermore, an upper bound of the CPU time needed for one estimation task can be determined a priori. This makes the proposed MHE a true real-time algorithm for state estimation
ISBN:1424405661
9781424405664
DOI:10.1109/MFI.2006.265620