Review on the catalytic effects of alkali and alkaline earth metals (AAEMs) including sodium, potassium, calcium and magnesium on the pyrolysis of lignocellulosic biomass and on the co-pyrolysis of coal with biomass
Bibliographic record
Abstract
Alongside the development of pyrolysis processes aimed at converting solid biomass into upgraded biofuels and biochemicals, interest has been growing in the analysis of the catalytic effects induced by inherent or externally added alkali and alkaline earth metals (AAEMs). These AAEMs greatly affect the thermal conversion of biomass, although their effects are only partly understood. Furthermore, while coal currently plays a major role in global energy demand, its massive use in a carbon-constrained world has prompted the need to identify alternative and carbon-neutral energy sources. In this context, the co-pyrolysis of biomass with coal has been shown to be a promising way to support the transition from fossil to renewable energy carriers. Because AAEMs can significantly impact such a co-processing approach, there is therefore the need for a firm understanding of their catalytic role. Consequently, and to examine and summarize the main research advances that have been made in this field, the present review first covers a description of the main properties of lignocellulosic biomass and coal, along with their decomposition processes. It then focuses on AAEM catalysts and on their impact on pyrolysis reaction pathways and kinetics. In terms of highlights, the review illustrates that the presence of inherent or impregnated AAEMs shifts the decomposition of biomass to lower temperatures while increasing the char and gas yields at the expense of bio-oil. Moreover, these effects depend significantly on the nature of the catalyst considered and on the way it is mixed with biomass. As examples, potassium tends to favor the production of low molecular weight compounds and gaseous species, magnesium promotes dehydration reactions, whereas calcium and magnesium oxides allow to upgrade volatiles by deoxygenation and deacidification. A discussion of pyrolysis reaction mechanisms is also proposed by reviewing the different pathways involved in the decomposition of the main components of biomass and coal, noting that the emphasis is particularly on the changes induced by AAEM catalysts. The synergistic effects between coal and biomass which are likely to enhance the co-pyrolysis process are then discussed. Eventually, a comprehensive reaction pathway is proposed to better explain the important role played by AAEM catalysts during primary and secondary pyrolysis reactions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".