Thermal Treatment of Biomass: A Bibliometric Analysis—The Torrefaction Case
Bibliographic record
Abstract
The aim of the paper was to summarize and discuss current research trends in biomass thermal treatment (torrefaction process). Quantitative analyses were carried out, in which the main countries, research units and scientists were indicated. The analysis showed a clear upward trend in number of publications after 2010. Most scientists on selected topics come from China, USA, Canada, South Korea, Republic of China, Poland (Web od Science—Core Collection (WoS-CC) and Scopus databases). Quantitative analysis also showed that the most relevant WoS-CC categories in the summary are: Energy Fuels, Engineering Chemical, Agricultural Engineering, Biotechnology Applied Microbiology and Thermodynamics and Scopus Subject area: Energy, Chemical Engineering, Environmental Science, Engineering and Chemistry. Thematic analysis included research topics, process parameters and raw materials used. Thematic groups were separated: torrefaction process (temp.: 150–400 °C), hydrothermal carbonization process (HTC) (temp: 120–500 °C), pyrolysis process (temp.: 200–650 °C) and gasification and co-combustion process (temp.: 350–1600 °C). In the years 2015–2019, current research topics were: new torrefaction technologies (e.g., HTC), improvement of the physico-mechanical, chemical and energetic properties of produced fuel as well as the use of torrefied biomass in the process of pyrolysis, gasification and co-combustion. The raw materials used in all types of biomass thermal treatment were: energy crops, wood from fast-growing and exotic trees, waste from the agri-food industry, sewage sludge and microalgae.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.097 | 0.201 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".