Integrated kinetics-computational fluid dynamic-optimization for catalytic hydrogenation of CO2 to formic acid
Bibliographic record
Abstract
As enormous research findings indicate, carbon dioxide (CO2) can be converted to important products such as formic acid using catalytic hydrogenation of CO2 technologies. In this work a three-dimensional computational fluid dynamic (CFD) reactor model for the catalytic hydrogenation of CO2 to formic acid in the presence of triethylamine and water was developed, and the nature of the flow and reaction occurring inside the reactor was demonstrated. A kinetic model which estimates kinetic rate expressions was also developed and validated using experimental data. The kinetic parameters from the kinetic model were used as reaction source terms for the CFD reactor model development. Sensitivity analyses were performed on the design variables by integrating the kinetic parameters from the developed kinetic model. The Bayesian optimization algorithm was used to optimize the catalytic CO2 hydrogenation reactor. The optimal design was acquired, and the CO2 conversion increased by 32.6% compared to the initial base case. An optimized reactor design was proposed for the catalytic hydrogenation of CO2 to formic acid within a catalytic trickle-bed reactor based on the integration of reaction kinetic modeling and CFD analysis. The integrated kinetic-CFD-optimization framework proposed in this work was effectively applied to the catalytic CO2 hydrogenation reactor and the results reported on this work could give important design and operational insight to the further development of catalytic CO2 hydrogenation reactors for CO2 to formic acid conversion in carbon capture and utilization applications.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".