Kinetic Analysis of Construction, Renovation, and Demolition (CRD) Wood Pyrolysis Using Model-Fitting and Model-Free Methods by Thermogravimetric Analysis (TGA)
Bibliographic record
Abstract
The pyrolysis of non-recyclable construction, renovation, and demolition (CRD) wood waste is a complex thermochemical process involving devolatilization, diffusion, phase transitions, and char formation. CRD wood, a low-ash biomass containing 24-32% lignin, includes both hardwood and softwood components, making it a viable heterogeneous feedstock for bioenergy production. Thermogravimetric analysis (TGA) of CRD wood residues was conducted at heating rates of 10, 20, 30, and 40°C/min up to 900°C, employing model-fitting (Coats-Redfern (CR)) and model-free (Ozawa-Flynn-Wall (OFW), Kissinger-Akahira-Sunose (KAS), Friedman (FM)) approaches to determine kinetic and thermodynamic parameters. The degradation process exhibited three stages, with peak weight loss occurring at 350-400°C. The Coats-Redfern method identified diffusion and phase interfacial models as highly correlated (R² > 0.99), with peak activation energy (Ea) at 30°C/min reaching 114.96 KJ/mol. Model-free methods yielded Ea values between 172-196 KJ/mol across conversion rates (α) of 0.2-0.8. Thermodynamic parameters showed enthalpy (ΔH) of 179-192 KJ/mol, Gibbs free energy (ΔG) of 215-275 KJ/mol, and entropy (ΔS) between -60 and -130 J/mol·K, indicating an endothermic, non-spontaneous process. These results support CRD wood’s potential for biochar production through controlled pyrolysis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".