# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 1 Introduction  
The global reliance on natural gas as a cleaner energy source has surged in recent decades, driven by industrial expansion, population growth, and the transition from conventional fossil fuels [1]. However, the transportation of natural gas through pipelines is frequently disrupted by the formation of gas hydrates, ice-like crystalline structures that form under specific temperature, pressure, and water-content conditions [2, 3, 4]. These hydrates obstruct pipelines, reduce flow efficiency, and pose safety risks, resulting in operational downtime and economic losses estimated to be in the millions annually [5]. To prevent hydrate formation, thermodynamic inhibitors like methanol are widely employed in the oil and gas industry, leveraging their ability to shift hydrate equilibrium conditions and suppress crystallization [6, 7]. The accurate estimation of the minimal methanol required to inhibit hydrate formation is vital for operational efficiency, cost reduction, and sustainability as over-dosing escalates costs and environmental impacts while under-dosing fails to mitigate hydrate risks [8, 9]. Conventional approaches to estimate methanol requirements depend on empirical correlations such as the Hammerschmidt equation [4] or advanced process simulation tools like Aspen HYSYS [10]. Empirical methods like the Hammerschmidt equation suffer from oversimplified assumptions, leading to overestimations in high-pressure systems [11], and lack adaptability to dynamic conditions. Aspen HYSYS, although accurate with thermodynamic models like Peng-Robinson [10], is computationally intensive, requiring multiple simulation runs and lacking real-time adaptability due to static inputs [12]. These limitations are compounded in dynamic pipeline environments influenced by variables such as flow rate, temperature, pressure, gas specific gravity, and water content [13]. As a result, engineers frequently adopt conservative dosing strategies, escalating methanol use and the associated costs, or risk inadequate inhibition under fluctuating conditions [14]. To overcome these shortcomings, our study advanced beyond state-of-the-art methods by employing machine learning, which leverages large synthetic datasets to capture nonlinear relationships, reduces computational overheads, and enables real-time predictions, addressing the adaptability and efficiency gaps of traditional methods [15]. Machine learning (ML) has emerged as a powerful tool in process engineering, and is adept at capturing complex nonlinear relationships within large datasets, offering rapid, adaptable predictions [16; 17]. Unlike traditional methods, ML reduces reliance on iterative recalibration, making it suitable for real-time applications [18]. Previous studies have harnessed ML to predict hydrate formation temperature (HFT) and hydrate formation pressure (HFP), employing techniques like artificial neural networks (ANNs) [19], support vector machines (SVMs) [20], support vector regression (SVR) [21], and gradient boosting to model hydrate phase behavior with notable success [22]. [Recent advancements--including [PERSON] et al. (2024) using CatBoost to predict HFP with 99.22% accuracy (R\({}^{2}\) = 0.9922) with inhibitors, and [PERSON] et al. (2023) achieving over 90% accuracy in predicting hydrate formation with ML and inhibitor concentrations--highlight ML's growing role in hydrate inhibition [22; 23]]. [The North Kuwait Jurassic Gas Field, a case study by [PERSON] et al., demonstrated ML-driven hydrate prevention, recovering 5% of the annual production and avoiding USD 3 million in losses [24]]. However, these efforts have targeted hydrate formation conditions rather than the practical outcome of optimizing inhibitor quantities. To the authors' knowledge, no study has systematically applied ML to predict the minimum methanol required for hydrate inhibition despite the availability of simulated data and ML's established predictive capabilities [17; 18].