Valorization of orange solid waste through pyrolysis: production of biochar and its potential as an enhancer of the anaerobic digestion
Bibliographic record
Abstract
Objective: To valorize orange solid waste through pyrolysis to obtain biochar and to analyze its potential application as an enhancer of anaerobic digestion. Design/methodology/approach: Orange solid waste was conditioned and subjected to pyrolysis at 550 °C in an Auger-type reactor. The produced biochar was characterized by measuring pH, ash content, total solids, volatile solids, cation exchange capacity, electrical conductivity, and carbon content. Additionally, an anaerobic hybrid reactor was conditioned and monitored by measuring pH, total and soluble COD, TSS, VSS, and biogas production to subsequently evaluate the effect of the biochar on the reactor performance. Results: Biochar exhibited alkaline properties pH (8.6), a carbon content of 60%, and an increase in cation exchange capacity (42.6 meq·100 g-¹), indicating the development of a porous and conductive structure favorable for microbial adhesion and the mitigation of inhibitory compounds. Meanwhile, the anaerobic hybrid reactor was stabilized, maintaining a pH between 7.1 and 7.4, achieving 90% removal of total and soluble COD, as well as 4.6 L biogas/d, favoring a balanced biological environment. Limitations on study/implications: The effect of biochar addition in the anaerobic hybrid reactor will be evaluated to determine its influence on anaerobic digestion performance. However, further studies are required to confirm its long-term stability and scalability. Findings/conclusions: Biochar derived from orange solid waste represents an environmentally sustainable alternative to optimize the anaerobic digestion process and valorize agro-industrial waste within the framework of a circular economy.
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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".