Random forest regression for catalyst performance prediction and validation of tri‐reforming of methane ( <scp>TRM</scp> )
Bibliographic record
Abstract
Abstract Carbon dioxide‐reduced hydrogen can be synthesized through various methods such as dry‐reforming (DRM), steam reforming (SMR), and partial oxidation (POX). Tri‐reforming of methane (TRM) is a promising technology which combines all the above‐mentioned processes for the simultaneous production of hydrogen and syngas with high energy efficiency. However, catalyst design for TRM is challenging due to complex reaction kinetics and the need for catalyst. This way, the use of data‐driven models, such as random forest models (RFs), can be used to assist in the search of new material compositions, leading to a faster scale‐up process. In this study, a comprehensive database was built, encompassing approximately 6000 data points and 6 input parameters collected from literature published between 2013 and 2022. Three different random forest models were developed and demonstrated to accurately predict the methane conversion using temperature, metal wt.%, and feed ratios as input features, being later incorporated into a singular model based of the reaction kinetics of TRM. All models achieved high accuracy, with R 2 values exceeding 93%. The reaction temperature input parameter, ranging from 623 to 1173 K, was identified as having the greatest relative importance (60.7%), significantly influencing the changes in CH 4 conversion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".