Advances, Applications, and Perspectives of Machine Learning Approaches in Predicting Gas Hydrate Phase Equilibrium

Abstract

Given the urgent environmental issues posed by rising carbon emissions and a global temperature increase, the modern world must develop effective solutions. The deployment of technology associated with hydrates represents a viable strategy for the mitigation of environmental degradation. This is achieved by employing methane hydrates as an alternative, more environmentally friendly energy resource and utilizing carbon dioxide hydrates for carbon sequestration and storage. In the study of hydrates, accurately determining hydrate phase equilibrium conditions is crucial for understanding and controlling the gas hydrate formation and stability. With the rise of machine learning, artificial intelligence algorithms have become increasingly relevant to hydrate research, particularly in the development of predictive models for hydrate phase equilibrium. These algorithms offer both high feasibility and a necessity in addressing complex hydrate-related problems. This paper focuses on the application of machine learning, specifically the Gradient Boosted Regression Tree (GBRT) algorithm, to predict hydrate phase equilibrium conditions. The rationale for selecting GBRT, along with the model construction process, training, and validation methods, is discussed in detail. This integration of hydrate research and machine learning techniques promises to advance our predictive capabilities and optimize the extraction and utilization of hydrates as a sustainable energy resource.