Pyrolysis: A Theoretical and Experimental Study on the Conversion of Softwood Sawmill Residues to Biooil
Bibliographic record
Abstract
The pyrolysis of biomass is a complex process in terms of the kinetic and mass and heat transfer phenomena, which is necessary information for process optimization and large scale modeling. In this paper, an analysis of pyrolysis of sawmill residues is performed in a lab-scale pyrolysis unit to minimize the heat and mass transfer effects. Three factors, including pyrolysis temperature, feedstock particle size, and nitrogen flow rate, in the reactor are optimized using response surface methodology. Two factor-based models (a three factor quadratic model and a quadratic model without factor interactions coefficients) and two kinetic models (one based on primary reactions only, the second including secondary reactions) are compared. The factor-based models (with and without interactions) were developed using design of experiment software and Genetic Algorithm, respectively. The results showed that the quadratic model with interactions between factors predicts the experimental data more accurately compared to the other models. However, the quadratic models are experiment specific and can only be used as a design tool. The primary kinetic model predicts the experimental data trend below 550 °C well; however, as temperature rises (>550 °C), including secondary reactions gives better predictions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".