Catalytic hydrothermal liquefaction of lignocellulosic biomass for biocrude production and process optimization
Bibliographic record
Abstract
Abstract Hydrothermal liquefaction is a promising technology to process wet lignocellulosic biomass, without the need for the energy intensive drying process. The objective of this study is to explore the recovery of biocrude from lignocellulosic biomass (orange peel, dairy manure, and food waste) with and without the addition of catalyst and the optimization of process parameters in terms of both biocrude quantity and quality. Potassium alkali catalysts were more effective in the chemical degradation of biomass to biocrude and the orange peel sample showed a higher energy recovery of about 54.4% under both catalytic and non‐catalytic conditions compared to dairy manure (36%) and food waste (28.5%). The ideal parameters for achieving the highest biocrude yield (32% by weight) were determined to be a temperature of 250°C, a total solids concentration of 25%, the addition of 3% K 2 CO 3 , and a reaction time of 30 min. The extracted biocrude exhibited fuel characteristics similar to those of diesel and biodiesel, with a higher heating value between 32 and 38 MJ/kg and a flash point ranging from 90 to 108°C. The distillation of biocrude showed a higher diesel distribution of 45% with the boiling range between (270–345°C), which falls within the typical boiling range of conventional diesel fuels. This indicates that a significant fraction of the biocrude can be directly utilized or further refined as a substitute for petroleum‐based diesel, enhancing its viability as a renewable transportation fuel.
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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".