Energy Analysis of Small-Scale Ethanol Production from Cassava: A Case Study of the Cassakero Project in Nigeria
Bibliographic record
Abstract
The Federal Government of Nigeria recently announced the replacement of kerosene household cooking fuel with ethanol produced from cassava feedstock. The project was called “cassakero”. The cassakero project aims to install 10,000 units of small-scale bio-ethanol refineries, operated by small-scale agro-processors across the country. The aim of this article is to present the results of an energy analysis of the ethanol cooking fuel produced from cassava feedstock by small-scale processors under Nigerian conditions Results show that for small-scale cassava ethanol production with the use of agrochemicals is: 11.61 MJ/l for total energy input, a Net Energy Ratio of 1.20, 2.29 MJ/l for Net Energy Gain, and 11.01 MJ/l for Net Renewable Energy Value. Without the use of agrochemicals ethanol production is 10.38 MJ/l for total energy input, a Net Energy Ratio of 1. 34, 3.52 MJ/l for Net Energy Gain, and 12.25 MJ/l for Net Renewable Energy Value. This is the first time that energy analysis has been carried out for small-scale cassava ethanol production under Nigerian conditions.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".