Research on Improving the Energy Efficiency of Plasma-Catalyzed Ammonia Synthesis Based on XGBoost and Bayesian Optimization
Bibliographic record
Abstract
Ammonia synthesis is vital for fertilizer production, but the traditional Haber-Bosch process is energyintensive and environmentally burdensome due to its high-temperature and high-pressure operations.Plasma-catalytic ammonia synthesis offers a sustainable alternative, generating large datasets under various experimental conditions.To optimize energy efficiency, we established a database with 305 data points and 7 experimental parameters, each linked to its corresponding energy efficiency.We employed an Extreme Gradient Boosting (XGBoost) regression tree model, achieving an average R² value of 0.9434 for predictions.Bayesian Optimization (BO), using Gaussian Process Regression as a surrogate model, systematically explored the experimental parameter space.It utilized XGBoost predictions to identify parameter combinations that maximized energy efficiency.After 50 iterations, the optimal parameters were identified: 6.4 g catalyst mass, 50 mm grounding electrode length, nickel metal catalyst, Al₂O₃ catalyst support, 5 W power, 160 ml•min⁻¹ flow rate, and a 1:2 feed ratio.Under these conditions, the energy efficiency of plasma-catalytic ammonia synthesis improved to 1.49 g•kW•h⁻¹, a 22.1% increase from the highest value of 1.22 g•kW•h⁻¹ in the dataset.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".