Spray Combustion and Emissions of a Hydrothermal Liquefaction Biofuel for Gas Turbine Applications
Bibliographic record
Abstract
Abstract Biomass liquefaction oil (BLO), obtained from biomass resources through thermochemical processes like fast pyrolysis (FP) or hydrothermal liquefaction (HTL), is widely regarded as one of the most economically feasible energy solutions in our future sustainable energy mix. However, the utilization of BLO as a drop-in fuel in the current in-line gas turbines has encountered several challenges due mainly to the difficulties in atomization and ignition, originating from the fuel’s chemical composition and physicochemical properties. The present study compares the combustion performance and gas- and solid-phase emissions of a HTL oil (also called biocrude), as well as its diesel blends, with those of a conventional FP oil (also called bio-oil). Considering diesel as a baseline fuel, the properties and combustion performance of the HTL oil compare favorably with those of the FP oil. An anchored spray flame using the 100% HTL oil is achieved in the experiments with much smaller degrees of fuel coking and nozzle clogging problems and a lower amount of particulate matter (PM) emissions. However, its CO and NO emissions are measured higher by more than two times. The insights drawn from the present study indicates the potentials of high quality HTL oils as sustainable fuels to replace fossil fuels in gas turbines and other energy conversion systems.
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".