Optimization of Biochar Production from Lemongrass Solid Waste at Different Pyrolysis Temperatures
Bibliographic record
Abstract
The waste from refining lemongrass oil has not been utilized properly.The potential waste produced can reach 2-3 tons per week.This waste optimization is carried out to reduce the impact of environmental pollution by converting waste into biochar using a pyrolysis process.This research aims to examine the chemical characteristics of biochar from lemongrass solid waste using a pyrolysis process at different temperatures.This research used a completely randomized design (CRD) with three replications.The pyrolysis process was carried out at temperatures 200, 250, 300, and 350℃ for 60 minutes.Pyrolysis temperature significantly affects yield, proximate composition, pH, electrical conductivity (EC), cation exchange capacity (CEC), organic C, and total N.At a temperature of 200℃, the pyrolysis process produced functional groups and carbon minerals, as well as the highest yield of 26.57% (char) compared to other temperatures, and nutrient composition of 8.89% C, 1.37% N, 9.04% P, 31.09%K, 8.98% S, 33.41% Ca, 4.30% Cl, 1.08% Mn, 2.10% Fe, and 7.35% Si.Lemongrass waste has the potential to be used as biochar for soil ameliorants.Biochar has been shown to have the potential for sustainable agricultural practices and contribute to waste management and environmental sustainability.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".