Stochastic reactor-based fuel bed model for grate furnacesShow others and affiliations
2020 (English)In: Energy & Fuels, ISSN 0887-0624, E-ISSN 1520-5029, Vol. 34, no 12, p. 16599-16612Article in journal (Refereed) Published
Abstract [en]
Biomass devolatilization and incineration in grate-fired plants are characterized by heterogeneous fuel mixtures, often incompletely mixed, dynamical processes in the fuel bed and on the particle scale, as well as heterogeneous and homogeneous chemistry. This makes modeling using detailed kinetics favorable but computationally expensive. Therefore, a computationally efficient model based on zero-dimensional stochastic reactors and reduced chemistry schemes, consisting of 83 gas-phase species and 18 species for surface reactions, is developed. Each reactor is enabled to account for the three phases: the solid phase, pore gas surrounding the solid, and the bulk gas. The stochastic reactors are connected to build a reactor network that represents the fuel bed in grate-fired furnaces. The use of stochastic reactors allows us to account for incompletely mixed fuel feeds, distributions of local temperature and local equivalence ratio within each reactor and the fuel bed. This allows us to predict the released gases and emission precursors more accurately than if a homogeneous reactor network approach was employed. The model approach is demonstrated by predicting pyrolysis conditions and two fuel beds of grate-fired plants from the literature. The developed approach can predict global operating parameters, such as the fuel bed length, species release to the freeboard, and species distributions within the fuel bed to a high degree of accuracy when compared to experiments. © 2020 American Chemical Society
Place, publisher, year, edition, pages
American Chemical Society , 2020. Vol. 34, no 12, p. 16599-16612
Keywords [en]
Forecasting, Fuels, Gas dynamics, Incineration, Stochastic models, Surface reactions, Computationally efficient, Heterogeneous fuels, High degree of accuracy, Homogeneous chemistry, Local equivalence ratio, Operating parameters, Species distributions, Stochastic reactors, Stochastic systems
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-52249DOI: 10.1021/acs.energyfuels.0c02868Scopus ID: 2-s2.0-85096523805OAI: oai:DiVA.org:ri-52249DiVA, id: diva2:1525865
Note
Funding details: Norges Forskningsråd; Funding details: Vattenfall; Funding text 1: The authors acknowledge the financial support by the Knowledge-Building Project GrateCFD (267957), which is funded by Statkraft Varme AS, EGE Oslo, Vattenfall AB, Hitachi Zosen Inova AG, Returkraft AS, and LOGE AB, together with the Research Council of Norway through the ENERGIX program. UNINET Sigma2 and NTNU HPC Group provided high-performance computational resources.
2021-02-042021-02-042023-06-08Bibliographically approved