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E. Ikonen, M. Neuvonen, I. Selek, M. Salo and M. Liukkonen, "On-line estimation of circulating fluidized bed boiler fuel composition," 2022 UKACC 13th International Conference on Control (CONTROL), 2022, pp. 136-141, doi: 10.1109/Control55989.2022.9781460

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
Publish Date: 2022-06-28
Description:

Abstract

Estimation 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.

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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: 2022 UKACC 13th International Conference on Control (CONTROL)
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
215 Chemical engineering
Subjects:
biomass
energy production
fouling
machine learning
monitoring
power plants
Article
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
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