Dynamic reconciliation of process measurements.
All process measurements obtained from measurement devices are corrupted with noise. In any modern chemical plant, thousands of measurements are performed every few seconds. Before such data can be used for plant optimization and control, it is typically filtered / reconciled to allow better estimat...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2009
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/16675 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-16675 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-166752023-03-03T15:37:05Z Dynamic reconciliation of process measurements. Lee, Alvin Zhi Hui. School of Chemical and Biomedical Engineering Chen Tao DRNTU::Engineering::Chemical engineering::Processes and operations All process measurements obtained from measurement devices are corrupted with noise. In any modern chemical plant, thousands of measurements are performed every few seconds. Before such data can be used for plant optimization and control, it is typically filtered / reconciled to allow better estimates of key process states (e.g. temperatures, pressures, concentrations). This thesis focuses on the classic data reconciliation technique for linear systems, the Kalman filter, published by Rudolph Emil Kalman in the 1960s. A brief overview of the technique will be discussed and how it can be modified to yield the extended Kalman filter, suited for data reconciliation of nonlinear systems. Within the scope of this thesis, an extended Kalman filter MATLAB toolbox was written. State estimation using the MATLAB toolbox will be demonstrated on a continuous stirred tank reactor having three irreversible parallel reactions. Bachelor of Engineering (Chemical and Biomolecular Engineering) 2009-05-28T01:58:11Z 2009-05-28T01:58:11Z 2009 2009 Final Year Project (FYP) http://hdl.handle.net/10356/16675 en Nanyang Technological University 60 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Chemical engineering::Processes and operations |
spellingShingle |
DRNTU::Engineering::Chemical engineering::Processes and operations Lee, Alvin Zhi Hui. Dynamic reconciliation of process measurements. |
description |
All process measurements obtained from measurement devices are corrupted with noise. In any modern chemical plant, thousands of measurements are performed every few seconds. Before such data can be used for plant optimization and control, it is typically filtered / reconciled to allow better estimates of key process states (e.g. temperatures, pressures, concentrations).
This thesis focuses on the classic data reconciliation technique for linear systems, the Kalman filter, published by Rudolph Emil Kalman in the 1960s. A brief overview of the technique will be discussed and how it can be modified to yield the extended Kalman filter, suited for data reconciliation of nonlinear systems.
Within the scope of this thesis, an extended Kalman filter MATLAB toolbox was written.
State estimation using the MATLAB toolbox will be demonstrated on a continuous stirred tank reactor having three irreversible parallel reactions. |
author2 |
School of Chemical and Biomedical Engineering |
author_facet |
School of Chemical and Biomedical Engineering Lee, Alvin Zhi Hui. |
format |
Final Year Project |
author |
Lee, Alvin Zhi Hui. |
author_sort |
Lee, Alvin Zhi Hui. |
title |
Dynamic reconciliation of process measurements. |
title_short |
Dynamic reconciliation of process measurements. |
title_full |
Dynamic reconciliation of process measurements. |
title_fullStr |
Dynamic reconciliation of process measurements. |
title_full_unstemmed |
Dynamic reconciliation of process measurements. |
title_sort |
dynamic reconciliation of process measurements. |
publishDate |
2009 |
url |
http://hdl.handle.net/10356/16675 |
_version_ |
1759855978434527232 |