Abstract

The term randomly occurring deterministic disturbances refers to a class of process disturbances that occur randomly and infrequently in time and have a known effect on the behavior of the process. Since these disturbances occur infrequently in time, traditional filtering methods which assume identically distributed noise terms may not yield acceptable performance. The reason for this is that, when tuning the filter, a compromise must be made between sensitivity to noise and the ability to track these disturbances when they do occur. The stochastic model of these disturbances leads to a multi-filter approach for the state estimation. Since the number of filters grows exponentially with the data length, a suboptimal algorithm is required. The performance of this approach is evaluated for state/parameter estimation of a continuous polymerization reactor

Meeting Name

1995 American Control Conference

Department(s)

Materials Science and Engineering

Keywords and Phrases

Kalman Filters; Chemical Industry; Chemical Process Monitoring; Continuous Polymerization Reactor; Deterministic Disturbance Detection; Extended Kalman Filter; Fault Diagnosis; Filtering; Filtering Theory; Monitoring; Parameter Estimation; Process Control; State Estimation; Stochastic Model; Suboptimal Algorithm; Suboptimal Control

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1995 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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