Optimization of the manure/water ratio for maximum biogas production: Case of the FONSTI-CRDI digester in the East of Côte d’Ivoire
Bibliographic record
Abstract
Effective waste management is crucial in addressing global environmental challenges. This study focuses on optimizing biogas production through anaerobic digestion of poultry manure, which is a significant agricultural waste in Côte d’Ivoire. The aim is to identify the optimal manure-to-water ratio to maximize biogas output using a FONSTI-CRDI digester with a capacity of 20 m³. The study was conducted at the Brin Foundation poultry farm in Côte d’Ivoire. Three manure-to-water ratios (1:3, 1:2, and 1:1) were tested over 15-day cycles. A range of laboratory and field equipment, including pH meters and biogas analyzers, was employed to monitor digestion conditions and measure biogas production. Physicochemical properties of the manure, such as pH, total solids, and the carbon-to-nitrogen ratio, were also analyzed to understand their impact on biogas yield. The 1:3 ratio yielded an average biogas production of 4.1 m³/day, with a methane content of 43%. The 1:2 ratio showed an improvement, achieving 5.3 m³/day with 51% methane. The 1:1 ratio produced the highest biogas output at 6.23 m³/day and 58% methane. However, operational challenges like digester blockages were observed at the 1:1 ratio. The digestate quality improved with lower water content, emitting less odor. The 1:2 manure-to-water ratio was determined to be the most effective for sustained biogas production, balancing high output and operational stability. This configuration supports renewable energy goals and sustainable agricultural practices, offering an efficient solution for managing poultry manure. Further research on co-substrates could enhance biogas yields and optimize the methanization process.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".