Predicting hydrogen production from formic acid dehydrogenation using smart connectionist models
Bibliographic record
Abstract
Hydrogen is a promising clean energy source that can be a promising alternative to fossil fuels without toxic emissions. It can be generated from formic acid (FA) through an FA dehydrogenation reaction using an active catalyst. Activated carbon-supported palladium (Pd/C) catalyst has superior activity properties for FA dehydrogenation and can be reused after deactivation. This study focuses on predicting the FA conversion to H 2 (%) in the presence of Pd/C using machine learning techniques and experimental data (1544 data points). Six different machine learning algorithms are employed, including random forest (RF), extremely randomized trees (ET), decision tree (DT), K nearest neighbors (KNN), support vector machine (SVM), and linear regression (LR). Temperature, time, FA concentration, catalyst size, catalyst weight, sodium formate (SF) concentration, and solution volume are considered as the input data, while the FA conversion to H 2 (%) is the target value. Based on the train and test outcomes, the ET is the most accurate model for the prediction of FA conversion to H 2 (%), and its accuracy is assessed by root mean squared error (RMSE), R-squared (R 2 ), and mean absolute error (MAE), which are 3.16, 0.97, and 0.75, respectively. In addition, the results reveal that solution volume is the most significant feature in the model development process that affects the amount of FA conversion to H 2 (%). These techniques can be used to assess the efficiency of other catalysts in terms of type, size, weight percentage, and their effects on the amount of FA conversion to H 2 (%). Moreover, the results of this study can be used to optimize the energy, cost, and environmental aspects of the FA dehydrogenation process.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".