Environmental impact assessment of water hyacinth pyrolysis to produce bio‐oil
Bibliographic record
Abstract
Abstract The future of sustainable chemicals lies in the adoption of renewable sources such as biomass conversion. One unique biomass that can be utilized is water hyacinth. Apart from being able to overcome river pollution, its valorization can produce bio‐oil. In this study, the environmental impacts of water hyacinth pyrolysis were assessed. It employed the life cycle assessment (LCA) approach with a cradle‐to‐gate system boundary and a functional unit of 50 kg of bio‐oil. The pyrolysis of 250 kg of pretreated water hyacinth at 400°C demonstrated a yield of 19.45% ± 1.02% for bio‐oil. Following the results, the pyrolysis process predominantly contributes to 66.2%–99.7% of environmental impacts due to the high liquid petroleum gas (LPG) requirement. The sensitivity results exhibit a meaningful change of impacts under alteration of ±10% LPG consumption. The findings underscore the environmental benefits of converting water hyacinth into bio‐oil to address its ecological challenges while simultaneously enhancing sustainability.
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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 | 0.001 |
| 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".