A new intelligent prediction model using machine learning linked to grey wolf optimizer algorithm for <scp>O<sub>2</sub></scp>/<scp>N<sub>2</sub></scp> adsorption
Bibliographic record
Abstract
Abstract To address the deficiency and predict the adsorption performance in different adsorbents, this study proposes a new optimizer linked to the machine learning (ML) model considering the performance of the adsorption process. The main goal is to predict adsorption under different process conditions with different adsorbents and provide a unified framework, leading to the prediction of adsorption phenomena instead of traditional isotherm models. This research focuses on predicting the adsorbed amount of O2 and N2 on several carbon‐based adsorbents using the ML approach linked to the grey wolf optimizer algorithm (GWO). Experimental isotherm data (dataset 1344) on adsorbent type, temperature, pressure, gas type, and adsorption capacity of the process adsorption were used as input and output datasets. The best algorithm was Broyden–Fletcher–Goldfarb–Shanno (BFGS), a two‐layer network from a multi‐layer perceptron (MLP) method applying 28 neurons. The new MLP‐GWO network would have the best mean square error (MSE) efficiencies of 0.00037, while the R2 (r‐squared) was 0.9934. The new ML‐generated model can accurately predict the adsorption process behaviour of different carbon‐based adsorbents under various process conditions. The results of this research have the potential to assist a wide range of gas separation industries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".