Kolmogorov–Arnold Network for Predicting CO2 Corrosion and Performance Comparison with Traditional Data-Driven Approaches
Bibliographic record
Abstract
Accurate prediction of CO2 corrosion under dense-phase and supercritical conditions remains a critical challenge for oil and gas pipeline integrity management. While machine learning (ML) has been applied in this field, prevailing models like the Multilayer Perceptron (MLP) often struggle to capture the complex, non-linear interactions between multiple environmental parameters, limiting their predictive accuracy and robustness. To bridge this gap, this study innovatively introduces the Kolmogorov–Arnold Network (KAN) algorithm for CO2 corrosion rate prediction. Utilizing a unique dataset of field-collected parameters (including dissolved O2, H2S, SO2 concentrations, and water cut), we developed a KAN model and conducted systematic hyperparameter optimization. Our investigation revealed the optimal network configuration (3 layers, grid = 3) and, counterintuitively, that the steps parameter does not correlate positively with performance. Most significantly, comparative experiments demonstrated that the KAN model substantially outperforms traditional MLP, achieving superior prediction accuracy alongside faster computational speed and lower loss values. These findings not only provide a robust tool for precise corrosion prevention in oilfield operations but also highlight the potential of KAN as a novel, efficient, and highly accurate framework for tackling complex problems in materials degradation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".