Process design and techno‐economic analysis of ethyl levulinate production from carbon dioxide and 1,4‐butanediol as an alternative biofuel and fuel additive
Bibliographic record
Abstract
Carbon dioxide capture, utilization, and storage (CCUS) is one of the promising negative emission technologies (NET). Within various CCUS routes available, CO2 conversion into fuels is one of the attractive options. Currently, most of CO2 conversion into fuels requires hydrogen, which is expensive and consume large energy to produce. Hence, a different route of producing fuel from CO2 by utilizing 1,4-butanediol as the raw material is proposed and evaluated in this study. This alternative route comprises production of levulinic acid from the reaction between CO2 and 1,4-butanediol and production of ethyl levulinate, an alternative biofuel and biofuel additive, via an esterification reaction of levulinic acid with ethanol. The process is designed and simulated according to the available data and evaluated in terms of its technical features. Because of the unavailability of reaction data for synthesis of levulinic acid from 1,4-butanediol and CO2, several assumptions were taken, which may implicate the accuracy of the studied design. This technical evaluation is followed by cost estimations and sensitivity analysis. Because of the free CO2, the profitability of the plant depends strongly on the prices of the other chemicals and the price difference between 1,4-butanediol (raw material) and ethyl levulinate (product). Monte Carlo simulation indicates that the proposed plant will always be profitable if the ethyl levulinate is slightly more expensive than the 1,4-butanediol, highlighting that the process of producing ethyl levulinate from CO2 is economically profitable. Future research should be directed towards a catalytic system that can effectively convert CO2 into levulinic acid, by-products produced from the two reaction steps, and reduce the excess ethanol used in the second reaction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".