Technical and sustainability assessment of power production system based on cotton stalk and rice husk gasification in an isolated area in Burkina
Bibliographic record
Abstract
Biomass gasification systems may be relevant for decentralized power generation from these residues in the isolated areas. This work deals with the analysis of both the technical and economic feasibility, and then the environmental and social impacts of a 95 kWe gasification plant project in order to generate electricity for Badara village in Burkina. Two biomasses feedstock: cotton stalk and rice husk are used. Mass and energy balances of the processes were performed to assess technical performance. Levelized cost of electricity (LCOE) analysis is used to estimate the production cost of the two biomasses options. The environmental impacts were assessed by using a Life Cycle Assessment (LCA) approach. The results show that the valorisation of the rice husk is the solution that offers the low production cost (0.34 €/kWh) compared to cotton stalk (0.38 €/kWh) despite its low electricity efficiency (11.9% against 12.6%). Nevertheless, the use of the cotton stalks is most interesting in terms of direct job creation (9.58 full-time equivalent jobs /year against 8 full-time equivalent jobs /year). Concerning the environmental issue, the two biomasses present the similar impact levels. This assessment represents an important step that could assist the policy maker to make a relevant decision for this local conditions and available resources.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".