Insights into the anaerobic digestion of fecal sludge and food waste in Tanzania
Bibliographic record
Abstract
With the increasing demand for renewable energy and environmental protection, biogas technology has attracted considerable attention around the world. Fecal sludge (FS) is rich in organic matter, and it contains high concentrations of excreted pathogens that cause gastro-intestinal infection. In Tanzania, fecal sludge management from on-site sanitation systems poses a threat on environmental safety. This study aimed to assess the feasibility of the use of anaerobic digestion (AD) for the treatment of FS and the production of biogas as renewable energy to achieve multiple benefits in Tanzania. For the experiments, FS and food waste (FW) were used as feedstock, and rice straw-derived biochar (RSB) was added as an additive to improve biogas production. The mesophilic anaerobic digestion resulted in a methane yield of 287.5 ml/g VS for FS + FW co-digestion and 396 ml/g VS for FS + FW + RSB co-digestion. At ambient temperature (20–26°C), the system produced a methane yield of 234 ml/g VS for FS + FW co-digestion and 275 ml/g VS for FS + FW + RSB co-digestion. Three different scenarios (digester with volumes of 4, 100, and 400 m3, respectively) and strategies for FS treatment by AD in Tanzania were proposed and analyzed. These treatments can produce methane volumes of 1.95, 49.5, and 199.5 m3 with pay-back periods of 3, 5, and 15 years and net present values of + 28, +1,337, and +52,351 USD, respectively. The calculations also showed that the heat value from the produced biogas and energy needed to heat the digester at 26–37°C resulted in energy balance values of + 0.012, + 0.53, and + 2.22 GJ/day for the 4, 100, and 400 m3 digester volumes, respectively.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".