A STUDY OF FLUIDIZATION AND GASIFICATION OF BIOMASS PELLETS IN FLUIDIZED BED REACTORS
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Bibliographic record
Abstract
Fluidized bed reactors are generally used for biomass-to-energy conversion due to their excellent mass transfer/mixing and heat transfer between bed materials. As such, the efficiency of the process and the fluidized bed performance depend strongly on gasification operating conditions as well as hydrodynamic properties such as gas-solids distribution and mixing which are in turn influenced by bubble characteristics and random solids movement. The aim of this research was thus to study and optimize both hydrodynamic and gasification operating conditions to improve syngas yield. At the initial phase, fluidization parameters such as fluidization velocity, bubble size, bubble frequency, and bed expansion were experimentally investigated for binary mixtures at different loadings of three different biomass materials: soy hull pellets, oat hull pellets, and sawdust, in silica sand. The results from this study showed that the final fluidization velocity (Uff) of the binary mixture of non-pelletized biomass increased as the percentage composition of the biomass increased. For the pelletized biomass, however, a decrease in Uff was observed at higher compositions of biomass in the silica sand. Mixing and segregation of binary mixtures of these biomass and silica sand in a fluidized bed were also experimentally investigated. The results showed an increase in the extent of mixing as the fluidizing velocity increased for both pelletized materials while the mixing extent for sawdust decreased beyond a loading of 20 wt.%. Fluidization velocities below 2.5Uff were insufficient for proper mixing of the binary mixtures, especially at loadings above 15 wt.%, due to particle size, density, and shape. These results greatly contributed to understanding the hydrodynamics of binary and multicomponent mixtures involving biomass materials in fluidized beds in terms of loading and gas velocity requirements. Results from durability tests showed better pellet integrity and cohesiveness for soy hull pellets compared to oat hull pellets. The thermal decomposition characteristics and kinetics studies of the biomass were also investigated using thermogravimetric analysis (TGA). The thermal degradation properties of all biomass used showed three distinct stages each corresponding to moisture loss, volatile loss, and secondary reactions respectively. The apparent activation energies of the biomass were derived using the non-linear Vyazkovin model with activation energies ranging from 117.6 kJ/mol and 134.2 kJ/mol. After careful examination of results from hydrodynamics, durability, kinetic studies, and availability, soy hull pellet was chosen for the gasification experiments. Soy hull pellet gasification was carried out in a pilot-scale fluidized bed gasifier with air as the gasifying agent. The effect of four operating parameters; biomass loading at 10 – 30 wt.%, temperature (750 – 950 ℃), equivalence ratio, which is the ratio of the actual vs stoichiometric air-fuel ratio (0.2 – 0.4), and fluidization velocity (2.5 – 4Uff), on the syngas yield and composition were thus investigated. The results from this study showed that biomass loading, and fluidization gas velocity showed synergetic effects on gas yield due to efficient mixing. Higher loading of biomass was possible due to densification, thus increasing syngas yield. The final phase of the research involved catalytic steam gasification of soy hull pellets using alkali metal catalysts via a response surface methodology. This was done to explore the catalytic effect of inherent metallic compounds on gaseous composition, gas and tar yields. At optimal loading and gas velocity, the effects of temperature (750 - 950℃), steam-to-biomass ratio (1-3), and catalyst loading (1 – 5%) were investigated with results showing higher gas yields with catalyst loadings, higher H2 yields (2.5 times more) than air gasification and a proposed reaction mechanism for the catalytic process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it