Apparent Kinetics of Co‐Gasification of Biomass and Vacuum Gas Oil (VGO)
Bibliographic record
Abstract
Abstract This communication reports the beneficial effects of co‐gasification of biomass and residual oil to produce syngas. In this regard, various blends of glucose (a biomass surrogate) to vacuum gas oil (VGO) have been employed to investigate the synergic effects on the gasification process. The non‐isothermal co‐gasification experiments were conducted in a thermogravimetric analyzer at different heating rates and gasifying agents. The analysis showed that the co‐gasification rate increased with the increase of glucose content in the feedstock. The presence of the oxygen in the biomass molecules helped the overall gasification process. The maximum gasification rate of 42.70 wt/min (DTG max ) was observed with 25 wt% glucose containing sample. The use of gasifying agents appeared to have some influence, especially during high temperature gasification of the glucose‐VGO blends. At a same gasification temperature, the co‐gasification rate of glucose‐VGO blends were found to be 125.7 wt/min and 98.59 wt%/min for N 2 and CO 2 , respectively. The kinetics of the co‐gasification of glucose‐VGO blends was conducted based on modified random pore model using TGA experimental data and implemented in MATLAB. The estimated activation energy and rate constants were found to be consistent to the observed co‐gasification rates. The apparent activation energies of co‐gasification of VGO/biomass blends with different weight percentages shows values ranging 60.56–48.25 kJ/mol. The kinetics analysis suggested that the addition of biomass helped to increase the reaction rate by lowering the activation energy required for accomplishing the reactions compared with petroleum carbonaceous feedstocks. The reaction rate constants isotherms are plotted to show that the k‐values are exhibiting similar trends at moderate heating rates between 20 and 60 °C/min. This remark arises due to the nature of the reactions involved which are considered to be inherently similar in this range of heating rate.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".