Critical impacts of energy targeting on the sustainability of advanced biobutanol separation
Bibliographic record
Abstract
Biobutanol stands out as an advanced renewable biofuel, yet its production through fermentation yields a low butanol concentration, necessitating expensive and energy-intensive separation methods, particularly by distillation. Alternative approaches, including adsorptive separation, have emerged, with the 2-column zeolite-based process showing promise. This study employed Aspen Plus for simulating adsorptive separation, utilized Pinch technology for heat integration, and analyzed various alternatives using the life cycle assessment (LCA) approach. Compared to the base case, which relied on our previously acquired experimental data and further purification through atmospheric distillation, the adoption of indirect heating/cooling adsorption reduced heating energy demand by 59.5%. Additionally, cooling energy usage was increased notably by 68.9%, and chilling prerequisites were eliminated. The implementation of Pinch technology further reduced heating and cooling energy requirements by approximately 36%. Multi-pressure distillation was also explored, revealing its potential to reduce heating energy consumption by 46.6%, accompanied by a modest 6.2% increase in cooling energy demand. A gate-to-gate LCA framework was used to evaluate the environmental impacts. The results showed that the combination of indirect heating/cooling adsorption, multi-pressure distillation, and energy-efficient practices resulted in over a 98% reduction in damages related to human health, ecosystem well-being, and resource depletion compared to the base case. Prioritization of key performance indicators revealed that human health had the most significant influence, with prominent midpoint effects attributed to human toxicity and global warming. This study underscores the pivotal role of energy targeting in curtailing energy consumption and enhancing the sustainability of adsorptive biobutanol separation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".