Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models

Estimating the minimal methanol required to inhibit gas hydrate formation in pipelines is vital for operational efficiency, cost reduction, and sustainability. This task is complex due to dynamic variables like flow rate, temperature, pressure, gas specific gravity, and water content. Machine learning offers a robust solution by leveraging pipeline condition data to effectively forecast methanol needs. We evaluated four models—extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and k-nearest neighbors (KNN)—to predict the methanol quantities required for hydrate prevention in gas pipelines. Using a dataset of 74,000 samples, with 80% for training and 20% for testing, we enhanced model robustness with 50 Monte Carlo iterations and tenfold cross-validation. The results revealed that XGBoost was the top performer in precision and consistency, surpassing the others. The performance rankings, based on the adjusted R-squared (adj. R2), mean absolute error (MAE), and root mean square error (RMSE), were KNN < DT < RF < XGBoost. A feature analysis highlighted flow rate and temperature as dominant influencers of methanol needs. These findings support real-time methanol optimization in pipelines, enhancing safety and efficiency. A correlation analysis showed that flow rate positively boosted methanol demand while temperature reduced it, with pressure, gas specific gravity, and water content exerting milder positive effects.