Valorization of orange solid waste through pyrolysis: production of biochar and its potential as an enhancer of the anaerobic digestion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it