Ikonen, E., Liukkonen, M., Hansen, A. H., Edelborg, M., Kjos, O., Selek, I., & Kettunen, A. (2023). Fouling monitoring in a circulating fluidized bed boiler using direct and indirect model-based analytics. In Fuel (Vol. 346, p. 128341). Elsevier BV. https://doi.org/10.1016/j.fuel.2023.128341
Fouling monitoring in a circulating fluidized bed boiler using direct and indirect model-based analytics
|Author:||Ikonen, Enso1; Liukkonen, Mika2; Hansen, Anders H.3;|
1University of Oulu, POB 4300 Linnanmaa, Oulu, 90014 Oulun yliopisto, Finland
2Sumitomo SHI-FW Energia Oy, Relanderinkatu 2, Varkaus, 78200, Finland
3SINTEF AS, Forskningsveien 1, Oslo, 0373, Norway
4Mälarenergi AB, Sjöhagsvägen 23, Västerås, 72132, Sweden
|Online Access:||PDF Full Text (PDF, 2.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023081495552
|Publish Date:|| 2023-08-14
Fouling is a phenomenon where material accumulates on the exterior of convective heat exchangers (HX) and other surfaces. In boilers fired by waste-derived or biomass fuels, these surfaces are cleaned frequently to maintain adequate heat transfer between the flue gas and fluid. However, excess cleaning of HX surfaces wastes money and resources, and the common practice of soot removal at fixed time intervals is not an optimal strategy. An adaptive timing method would be beneficial; however, real-time knowledge of HX condition is hard to obtain. In this paper, we present (1) a state estimation approach for fouling monitoring in a Circulating Fluidized Bed (CFB) boiler, fusing knowledge from a physical model with process measurement data, and (2) a novel condition monitoring scheme based on modal-vibrational sensing, with potential for a directly estimating the degree of fouling on heating surfaces. The results are demonstrated on a full-scale commercial CFB. Combining physical models, machine learning, and modal analysis in mutually supporting ways provides a solid basis for future sootblowing optimization efforts and improved fouling management.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
215 Chemical engineering
This work was conducted in the European Commission H2020 project COGNITWIN (grant number 870130).
|EU Grant Number:||
(870130) COGNITWIN - COGNITIVE PLANTS THROUGH PROACTIVE SELF-LEARNING HYBRID DIGITAL TWINS
The data that has been used is confidential.
© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).