| Download | - View final version: Modeling of a heat-integrated biomass downdraft gasifier: estimating key model parameters using experimental data (PDF, 3.0 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.enconman.2024.119372 |
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| Author | Search for: Haidar, Houda M.; Search for: Butler, James W.1ORCID identifier: https://orcid.org/0000-0001-6099-6864; Search for: Lotfi, Samira1ORCID identifier: https://orcid.org/0000-0001-9455-9857; Search for: Vo, Anh-Duong Dieu; Search for: Gogolek, Peter; Search for: McAuley, KimberleyORCID identifier: https://orcid.org/0000-0002-5201-0310 |
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| Affiliation | - National Research Council of Canada. Clean Energy Innovation
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| Funder | Search for: Natural Resources Canada; Search for: National Research Council Canada; Search for: Natural Sciences and Engineering Research Council of Canada |
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| Format | Text, Article |
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| Subject | biomass gasification; downdraft gasifier; parameter estimation; mathematical model; heat integration |
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| Abstract | Kinetic and transport parameters in a model of a heat-integrated biomass downdraft gasifier are poorly known and require estimation. The large number of parameters (40) arises from pyrolysis, combustion, and gasification reactions, as well as heat-transfer phenomena inside the gasifier and associated heat-integration system. Due to complexity of the model and the limited available data, only a subset of the parameters can be reliably estimated. A sensitivity-based approach is used to determine the appropriate number of parameters to estimate while preventing overfitting. It is hypothesized that estimating these important parameters will result in better model predictions. The 40 parameters are ranked from most-estimable to least-estimable based on sensitivity information and initial parameter uncertainties. A mean-squared-error criterion is then used to determine that 27 parameters should be estimated using data from 15 experimental runs, with the remaining 13 parameters fixed at their initial values. A diagnosis of the 13 low-ranked parameters reveals that 8 parameters are not estimated due to correlation with high-ranked parameters and that the remaining 5 parameters have little influence on model predictions. The model is validated using two runs not used for parameter tuning. The updated model is used to predict that a taller gasifier would not improve the quality of the producer gas. Simulations show that increasing the producer-gas demand by 50% results in a 15.2% decrease in H2/CO ratio, a 52.6% increase in tar content in the producer gas, and a 44% increase in electrical energy output. |
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| Publication date | 2024-12-13 |
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| Publisher | Elsevier |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | S019689042401313X |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | ce526461-e6ef-453d-9332-176d0beb210e |
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| Record created | 2025-06-17 |
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| Record modified | 2025-06-17 |
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