# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 4 Conclusions  
This study assessed the performance of four machine learning models (XGBoost, RF, DT, and KNN) in predicting the minimum methanol quantity needed for gas hydrate inhibition in pipelines, utilizing a synthetic dataset of 74,000 samples generated from  
Figure 12: Correlation matrix of input features and methanol injection rate.  
Aspen HYSYS simulations. The dataset was divided into an 80% training set and a 20% testing set, with model robustness enhanced using Monte Carlo simulations and tenfold cross-validation. Evaluation metrics revealed that XGBoost consistently outperformed the other models, demonstrating the highest accuracy and reliability in both the training and testing phases. RF ranked second, delivering strong predictive capability, although it was less consistent than XGBoost. This was followed by DT, with moderate performance, and KNN exhibited the weakest results. This established a clear performance hierarchy of XGBoost surpassing RF, DT, and KNN, with XGBoost excelling in fitting the training data and generalizing unseen pipeline scenarios.  
A feature importance analysis across the tree-based models (XGBoost, RF, and DT) identified flow rate and temperature as the most influential factors in determining methanol requirements, reflecting their critical roles in methanol distribution and hydrate stability, respectively. Pressure, gas specific gravity and water content were not as influential, with water content being the least important, likely because there was not as much variety in the dataset. These results were consistent with that of the correlation analysis, which indicated a strong positive relationship between flow rate and methanol needs, a strong negative relationship with temperature, and a weaker positive relationship with pressure, gas specific gravity, and water content. These analyses characterized flow rate and temperature as the two primary parameters informing the optimal approach to methanol injection in gas transportation pipelines.  
XGBoost's excellent performance positioned it as a potentially useful tool to tackle oil and gas problems, surpassing what a traditional empirical approach could have achieved and being less resource-demanding than a simulation process such as Aspen HYSYS. It could aid pipeline safety while lowering the operational costs and environmental impacts associated with over-dosing by offering accurate, adaptable forecasts of methanol requirements. However, as the study relied on synthetic data, its real-world applicability is limited, and validation using operational pipeline data is needed to confirm these findings. This study addressed a significant need in hydrate inhibition studies, suggesting machine learning's ability to act as an efficient, data-first approach to inhibitor optimization. Therefore, the combination of ML and high-fidelity simulation data lays the groundwork for a new and versatile platform to improve methanol management, with relevance beyond the simple mitigation of hydrate formation to larger process engineering problems. As the industry shifts toward data-centric approaches, XGBoost represents a key enabler in driving the evolution of more efficient and sustainable gas transportation systems, pending such validations.