Pyrolysis of spruce wood, wheat straw, switchgrass, miscanthus and swine manure using catalysts containing alkali and alkaline earth metals: thermogravimetric analysis and kinetic modeling
Bibliographic record
Abstract
This paper examines the pyrolysis of 5 biomass types (spruce wood, wheat straw, switchgrass, miscanthus and swine manure) when being catalyzed by additives (NaCl, KCl, CaCl 2 and MgCl 2 ) containing alkali and alkaline earth metals (AAEMs). Thermogravimetric analyses (TGA) were carried out with raw samples and with catalyzed ones prepared by wet impregnation. 4 different heating rates (5, 10, 15 and 30 K min −1 ) were used, and the associated experimental data were modeled by implementing 3 isoconversional approaches (Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (OFW) and Friedman). The inferred rate constant parameters were then used to compute the variations of the conversion degree of the fuels versus the temperature while considering different reaction mechanisms commonly employed in the literature, including order-based, diffusion, geometrical, nucleation and power law models. As highlights, the results obtained revealed that AAEM catalysts shift the decomposition process to lower temperatures. Besides, conversion profiles computed using each tested modeling approach were found to properly reproduce the TGA results as long as order-based models were selected. An analysis of the kinetic parameters estimated when implementing the three above-listed isoconversional methods showed that the rate constants tend to increase when adding catalysts, with CaCl 2 and MgCl 2 being found to exhibit a stronger capacity to increase pyrolysis rates as compared to NaCl and KCl. Finally, a sensitivity analysis focusing on the impact of the catalyst load on the pyrolysis kinetics revealed a so-called saturation phenomenon indicating that the AAEMs tend to promote the conversion of biomass more effectively when the concentration of metal cation in the impregnating solution is relatively low (not higher than ⁓0.15 mol L −1 in the present work).
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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) | 0.000 | 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".