Turning Construction, Renovation, and Demolition (CRD) Wood Waste into Biochar: A Scalable and Sustainable Solution for Energy and Environmental Applications
Bibliographic record
Abstract
This study investigates the pyrolysis of construction, renovation, and demolition (CRD) wood waste to produce biochar, with a focus on its robustness, scalability, and characterization for energy and environmental applications. Pyrolysis conditions, including the temperature, biomass residence time (BRT), and feedstock mass, were varied to evaluate their effects on biochar properties. High-temperature biochars (B800) showed the highest fixed carbon (FC) (87%) and thermostable fraction (TSF) (96%) and the lowest volatile carbon (VC) (9%), with a high carbon content (92%), a large BET surface area (300 m2/g), and a high micropore volume (0.146 cm3/g). However, the hydrogen (0.9%) and oxygen (2.2%) content, Van-Krevelen parameters (H/C: 0.1; O/C: 0.02), and biochar yield (21%) decreased with increasing temperature. Moderate-temperature biochars (B600) have balanced physicochemical properties and yields, making them suitable for adsorption applications. Methyl orange dye removal exceeded 90% under the optimal conditions, with B600 fitting well with the Freundlich isotherm model (R2 = 0.97; 1/n = 0.5) and pseudo-second-order kinetic model (R2 = 1). The study highlights biochar’s suitability for varied applications, emphasizing the need for scalability in CRD wood 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 | 0.000 | 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".