Techno-economic analysis and strategic optimization of biobutanol production from lignocellulosic biomass in Mexico
Why this work is in the frame
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Bibliographic record
Abstract
Recent advancements in acetone-butanol-ethanol (ABE) fermentation, performed as a consolidated bioprocess, have resulted in high biobutanol concentrations of 23 g/L. This achievement has motivated the techno-economic analysis of industrial-scale biobutanol production in this study. To that end, biobutanol plants with capacities of 500 tonnes/day, 1500 tonnes/day, and 2400 tonnes/day are evaluated and deemed economically feasible with positive net present value (NPV). In addition, different mathematical programming models, with and without budget consideration, are developed to determine the optimal locations for establishing biobutanol plants in Mexico. The primary objective of these models is to maximize the NPV of the supply chain while meeting all the constraints including biobutanol demand. The mathematical programming model without budget limitation suggests establishing 16 biobutanol plants, 12 plants with a capacity of 2400 tonnes/day and 4 plants of 1500 tonnes/day, resulting in a positive total NPV of USD 3.57 billion. The model with a budget limitation of USD 0.69 billion suggests establishing three biorefineries with an NPV of USD 0.32 billion. Furthermore, to allow flexibility in deviating from the budget, a goal programming model is developed to minimize NPV and budget deviations. The goal programming model proposes establishing two biorefineries with a higher NPV (i.e., USD 0.72 billion) compared to the model with budget limitations because of the flexibility in deviating from the budget goal. The sensitivity analysis of the model without budget limitation indicates that the biobutanol selling price has the highest impact on the achieved NPV.
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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 | 0.000 | 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 | 0.000 | 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 it