Techno-economic analysis and strategic optimization of biobutanol production from lignocellulosic biomass in Mexico
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".