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Estimating fuel characteristics from simulated circulating fluidized bed furnace data |
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| Author: | Neuvonen, Markus1; Selek, Istvan1; 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, 0.6 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2022012710435 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2021
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| Publish Date: | 2022-01-27 |
| Description: |
AbstractThis paper proposes a soft sensor to estimate the elementary fuel characteristics in combustion-thermal power plants. The proposed approach is data-driven. The input-output data is generated by a digital twin. Application targets circulating fluidized bed boiler, where furnace (combustion) side is considered only. First, the nonlinear dynamics of the furnace is approximated with a linear time-invariant dynamic model. Then two separate methods, Kalman filter and internal governor, are applied for state estimation. Results show that the approach is viable and has low computational complexity, but the weakly observable modes are difficult to predict accurately. see all
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| Series: |
International Conference on Systems and Control |
| ISSN: | 2379-0059 |
| ISSN-E: | 2379-0067 |
| ISSN-L: | 2379-0059 |
| ISBN: | 978-1-6654-0782-3 |
| ISBN Print: | 978-1-6654-0783-0 |
| Pages: | 4107 - 112 |
| DOI: | 10.1109/ICSC50472.2021.9666596 |
| OADOI: | https://oadoi.org/10.1109/ICSC50472.2021.9666596 |
| Host publication: |
2021 9th International Conference on Systems and Control (ICSC) |
| Conference: |
International Conference on Systems and Control |
| Type of Publication: |
A4 Article in conference proceedings |
| Field of Science: |
213 Electronic, automation and communications engineering, electronics |
| 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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