Machine Learning‐Driven Multi‐Objective Optimization of Microchannel Reactors for CO₂ Conversion
Bibliographic record
Abstract
Abstract Recently, the power‐to‐gas (PtG) concept, specifically thermocatalytic CO₂ conversion via the Sabatier process, emerges as a promising route for mitigating greenhouse gas emissions. The process transforms CO₂ and H₂ into methane and water under low‐temperature methanation conditions. This study suggests a new way to improve the performance of a microchannel reactor by combining computational fluid dynamics (CFD), response surface methodology (RSM), machine learning (ML), and multi‐objective optimization. Key design variables include inlet velocity, temperature, and channel length ratios. The RSM approach is for generating datasets for simulation; while, data augmentation assists ML model training. Six ML models—linear, ensemble, tree, Gaussian, support vector machine (SVM), and neural networks are evaluated for regression accuracy against RSM‐based correlation. The Gaussian process model is found superior and integrated with a multi‐objective optimization algorithm. A decision‐making score (DMS) levels and normalizes performance indicators. It finds the best reactor designs with CO₂ conversion rates of ≈78.6% and CH₄ selectivity close to 99.9%. These results demonstrate an advanced approach for significantly reducing computational demand (24 h to 1.471 ms) against CFD simulations; while, maintaining accuracy, thereby enabling cost‐effective, efficient solutions for reactor design optimization across various engineering applications in real‐world PtG 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 | 0.000 |
| 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 | 0.000 |
| 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".