Investigation of parameters influencing gas production and gasification kinetics of Ziguinchor biomass
Bibliographic record
Abstract
This study presents the gasification of three types of biomass residues (wood, stem and shells) under CO2 and water steam, using the different analyses X-ray fluorescence (XRF). Generally, the experiments are carried out using XRF installations and a fixed bed reactor system. The tests are carried out on wood, stems, and shells, because of their energy contents (Lower heating value LHV), and their high availability in the Ziguinchor region (Senegal). The solid residues obtained after pyrolysis were used to carry out the gasification tests. Thus, several gasification tests were carried out and the results were interpreted using the Arrhenius equation. Two kinetic models (Volume Reaction Model, and Shrinking Core Model) were used to explain the influence of experimental parameters (nature of biomass, reagent type, and temperature) on synthesis gas production. From the experimental results, it is found that the nature of the sample, the reagent, and the variation in temperature have significant effect on the char kinetics conversion. In addition to the differences in the chemical composition of the raw sample, ash and char density, an explanation on the parameters effects, which vary the conversion kinetics during the gasification tests is given. The purpose of this work is to understand the kinetic variations of raw materials in the fixed bed reactor during gasification. Key words: Biomass residues, gasification, kinetic conversion, ash chemical composition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".