Fast Pyrolysis of Lignocellulosic Biomass for the Production of Energy and Chemicals: A Critical Review
Bibliographic record
Abstract
As a consequence of the shortage of traditional resources and escalating environmental constraints, the feedstocks for the production of energy and chemicals are swiftly changing. Biomass has received remarkable attention from both academia and industries as it is the most promising feedstock for these applications. Understanding the conversion mechanisms of such renewable low-value material to value-added products would lead to providing insights to enhance the product yield and/or quality and, in turn, help the new products compete with the traditional ones. In this regard, this paper provides an updated review on the processing of biomass for the production of valuable-products that could replace a part of the fossil fuel-based energy and chemicals. The common technologies that are performed in the conversion processes are demonstrated, and the thermochemical technique is emphasized. Several chemical reactors and their processes for fast pyrolysis applications are presented. The organic chemistry of pyrolysis of a biomass plant and its three constituents (i.e., cellulose, hemicellulose, and lignin) is debated. The effect of the heating mechanism, process parameters, loading of catalyst and other aspects are discussed. Eventually, the economics aspect of fast pyrolysis of biomass is evaluated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".