Assessing Technology Options to Reduce the Carbon Intensity of Bioethanol Production
Bibliographic record
Abstract
Significant reductions in carbon dioxide (CO2) emissions are required to mitigate the effects of climate change. For hard-to-abate sectors like transportation, the use of biofuels is playing and will play a growing role in decarbonization. While much research has focused on the environmental benefits of second-generation biofuels, technological advances could apply to the production of the most widely used biofuel in transport today: first-generation bioethanol. This could reduce its carbon intensity in the short term, thereby increasing its competitiveness as a transportation fuel and opening new possibilities for its usage as a feedstock for chemicals before alternative technologies, such as electric vehicles and lignocellulosic-based biofuel, can be deployed at scale. This thesis evaluates two technology options for their potential to reduce the carbon intensity of corn ethanol using Aspen Plus® simulation coupled with techno-economic and life cycle assessment. The first option is the capture of fermentation emissions and oxycombustion capture of fossil fuel-based emissions for steam utility provision in the ethanol production step of the life cycle. In lieu of this, the electrification of process heat provision through the integration of heat pumps is investigated. Capturing the emissions has the potential of removing up to 99.6% of total emission, which is 187 ktCO2 annually in a 40 MGY dry grind ethanol mill, at an additional purchased equipment cost of $15.5M, with the composition of the compressed CO2 meeting a pipeline transport requirement. In the alternative electrification scenario, there is a 63% reduction from 7.55MJ/L in the base plant heat demand, with a 259% increase in electricity demand at an additional purchased equipment cost of $8.7M. LCA and TEA results showed a net reduction of the life cycle carbon intensity by 84% to 8.9 gCO2eq/MJ when both biogenic and fossil CO2 are captured at the cost of 95 USD/tCO2. There is almost no change to life cycle emissions in the electrification case due to the relatively high carbon intensity of the current US electricity grid. However, the life cycle carbon intensity can be reduced by 113% to -7.1 gCO2eq/MJ when zero-emission renewable electricity is introduced with all technology interventions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".