Partial deoxygenation of biomass derived pyrolysis liquids
Bibliographic record
Abstract
Biomass pyrolysis liquids (also known as bio-oil), are derived from renewable lignocellulosic biomass residues by fast pyrolysis process. These second-generation oxygenated hydrocarbon resources have the potential to partially substitute for petroleum-derived feedstocks and thus enhance the economic and environmental sustainability of our natural resources. However, in contrast to petroleum fuels, biomass-derived pyrolysis liquids contain a large amount of oxygen, usually 40-50% wt% (wet basis). This undesirable high oxygen content in pyrolysis liquids is considered as the primary reason for its high polarity, high acidity, lower stability, lower energy density and very low miscibility with conventional crude refining feedstocks. There are two major pathways for upgrading the pyrolysis liquids. While hydrodeoxygenation route is one of the most explored options, it requires production and supply of large amounts of expensive hydrogen at high pressures, mandating large and centralized upgrading plants, and thus large capital investment. In this paper, we discuss an alternative method of pyrolysis liquid upgrading, using cheap and affordable hydrogen donor additives and catalysts to promote partial deoxygenation at near atmospheric pressure. This approach is preferably to be used as a pre-treatment and stabilizing method for pyrolysis liquids in the close vicinity of remote biomass pyrolysis plants. The pre-treated oil, then can be shipped for further hydrocracking process in a centralized co-processing facility. Preliminary results from the initial proof of concept experiments involving a 200 g/h gas-phase continuous fast catalytic cracking system with continuous coke removal to enhance deoxygenation performance are presented. These results indicate positive impact of catalyst bed on quality and yield of the upgraded bio-oil product in terms of pH, viscosity, degree of deoxygenation, oil yield and concentration of hydrogen in the off gases.
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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) | 0.001 | 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 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".