Mathematical Modeling and Design of Experiments for a Heat-Integrated Biomass Downdraft Gasifier
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Authors
Haidar, Houda
Date
2025-02-19
Type
thesis
Language
eng
Keyword
Mathematical Modeling , Biomass Gasification , Model-Based Design of Experiments , Downdraft Gasifiers
Alternative Title
Abstract
A mathematical model for a heat-integrated biomass downdraft gasifier is developed. This one-dimensional steady-state model accounts for pyrolysis, combustion, and gasification reactions as well as transport phenomena within the gasifier and the heating system. This model predicts the gas and solid temperatures, flow rates and compositions. The model is validated using two experimental runs and gives good qualitative results. However, the model gives unsatisfactory predictions for the composition of some gas species. This is because the 40 model parameters are poorly known and require estimation. Due to complexity of the model and the limited available data, only a subset of the parameters can be estimated. The parameters are ranked from the most-estimable to the least-estimable. Then, a mean-squared-error criterion is used to determine that 27 parameters should be estimated using data with pine wood as the feedstock. The updated model results in better predictions. Simulations show that increasing the gas demand by 50% results in 15.2% decrease in H2/CO ratio, 52.6% increase in tar content, and 44% increase in electrical energy output.
The model is then extended to account for construction and demolition (C&D) waste as the feedstock. Several model equations are updated to account for the relatively high proportion of inerts in C&D waste. Eleven parameters are selected for tuning. The resulting model gives noticeably better predictions than when pine-wood parameters are used. This model is used to study the influence of different biomass feedstocks, feed moisture contents, energy demands, and solid removal rates on the producer gas. Simulations confirm that C&D waste is a promising feedstock for biomass gasification and electricity generation.
Sequential Bayesian model-based design of experiments (MBDoE) is then used to propose operating conditions for additional pine-wood experimental runs for this gasifier. This MBDoE approach is valuable because it accounts for model structure, prior information about plausible parameter values, and previous experimental data. Three types of MBDoE are considered: A-optimal, V-optimal, and a new type of focused V-optimal design. The focused V-optimal methodology is recommended because it results in greater reductions in uncertainties in predictions for tar concentrations and outlet temperature than the other methods.
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ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
Attribution 4.0 International