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On-line estimation of circulating fluidized bed boiler fuel composition |
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| Author: | Ikonen, Enso1; Neuvonen, Markus1; Selek, Istvan1; |
| Organizations: |
1Intelligent Machines and Systems (IMS) Research Unit, University of Oulu, PL 4300 FIN-90014 Oulun yliopisto, Finland 2Sumitomo SHI FW Energia Oy, Varkaus, Finland |
| Format: | article |
| Version: | accepted version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 1.6 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2022062850168 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2022
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| Publish Date: | 2022-06-28 |
| Description: |
AbstractEstimation of power plant fuel input fractions based on unscented Kalman filtering using a first principles simulation model of the furnace is considered. The approach is described, together with experimental results using data from a full scale circulating fluidized bed power plant. The results encourage the fusion of machine learning and physical models in monitoring of industrial processes. see all
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| ISBN: | 978-1-6654-5200-7 |
| ISBN Print: | 978-1-6654-5201-4 |
| Pages: | 136 - 141 |
| DOI: | 10.1109/Control55989.2022.9781460 |
| OADOI: | https://oadoi.org/10.1109/Control55989.2022.9781460 |
| Host publication: |
2022 UKACC 13th International Conference on Control (CONTROL), 20-22 April 2022, Plymouth, United Kingdom |
| Conference: |
International Conference on Control |
| Type of Publication: |
A4 Article in conference proceedings |
| Field of Science: |
213 Electronic, automation and communications engineering, electronics 215 Chemical engineering |
| 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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