Heat exchanger fouling estimation for combustion–thermal power plants including load level dynamics |
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Author: | Neuvonen, Markus1; Selek, István1; Ikonen, Enso1; |
Organizations: |
1Intelligent Machines and Systems, University of Oulu Oulu, Finland |
Format: | article |
Version: | accepted version |
Access: | open |
Online Access: | PDF Full Text (PDF, 0.5 MB) |
Persistent link: | http://urn.fi/urn:nbn:fi-fe202301317784 |
Language: | English |
Published: |
Institute of Electrical and Electronics Engineers,
2022
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Publish Date: | 2023-01-31 |
Description: |
AbstractThis paper presents a robust soft sensor for estimating heat exchanger fouling in combustion–thermal power plant context. The approach is data–driven and focuses on identifying the effect of plant load changes to fouling estimation. Proposed method is applied to real process measurements and results are presented. The method consists of two blocks; a static energy balance calculation block for “traditional” fouling indicator calculation and a dynamic subspace identification block for finding sootblowing– and load level dynamics components of the static fouling indicator signal. Results from applying the proposed method to real plant data show that load level dynamics can be decoupled from static fouling estimate. see all
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Series: |
IEEE International Conference on Systems, Man, and Cybernetics |
ISSN: | 2163-9590 |
ISSN-E: | 2577-1655 |
ISSN-L: | 2163-9590 |
ISBN: | 978-1-6654-5258-8 978-1-6654-5257-1 |
ISBN Print: | 978-1-6654-5259-5 |
Pages: | 2987 - 2992 |
DOI: | 10.1109/SMC53654.2022.9945541 |
OADOI: | https://oadoi.org/10.1109/SMC53654.2022.9945541 |
Host publication: |
2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), October 9-12, 2022, Prague, Czech Republic : proceedings |
Conference: |
IEEE International Conference on Systems, Man, and Cybernetics |
Type of Publication: |
A4 Article in conference proceedings |
Field of Science: |
222 Other engineering and technologies |
Subjects: | |
Funding: |
This work was conducted in the H2020 project COGNITWIN (grant number 870130). |
EU Grant Number: |
(870130) COGNITWIN - COGNITIVE PLANTS THROUGH PROACTIVE SELF-LEARNING HYBRID DIGITAL TWINS |
Copyright information: |
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