ABSTRACT

This study employs machine learning algorithms and statistical methods (including cosine similarity, Pearson correlation, Mann-Whitney U, and Kolmogorov-Smirnov tests) to investigate suitable promoters for CO2 sequestration in marine hydrate reservoirs. Machine learning algorithms were applied to predict surface tension based on physicochemical properties, with optimal linear regression performance (MAE: 0.6, MSE: 0.01). Density exhibited a positive correlation (0.46) with surface tension. Citric acid, cyclodextrin, acetic acid, monosaccharides, acetone, amino acids, and biosurfactants demonstrated high correlation (≈0.999) as suitable, eco-friendly promoters. Ongoing research aims to incorporate additional kinetic and thermodynamic parameters to refine model predictions and elucidate hydrate-based CO2 sequestration mechanisms.

INTRODUCTION

Growing environmental concerns have made reducing greenhouse gas emissions a top priority. CO2 concentration in the atmosphere has been rising steadily since the industrial revolution, primarily due to the increased fossil fuel (coal, oil, and natural gas) consumption, contributing significantly to global warming and climate change. Given the serious consequences, there is an urgency to reduce anthropogenic CO2 emissions and other greenhouse gases which are primary drivers of global warming and climate change.

The Intergovernmental Panel on Climate Change (IPCC) has highlighted the critical importance of limiting global temperature rise to ∼1.5°C above pre-industrial levels to avoid the most catastrophic impacts of climate change. From Fig.1, the average annual atmospheric CO2 concentration has risen to ∼412.5 ppm in 2020, owing to CO2 emissions of up to ∼31.5 billion tons, with a growth rate of ∼5% during 2021. Increased greenhouse gas emissions have led to an increased global warming with the threshold of ∼1.5°C likely to be crossed before 2030. Even the aggressive CO2 reduction measures might take the next ∼80 years or more to limit the temperature ∼1.5°C. Energy sector is one of the major CO2 emission source. Despite efforts to transition to cleaner energy sources, the demand for coal and oil remains significant which makes it challenging to limit the global temperature rise to ∼1.5°C and achieve net zero emissions by 2050.

Various methods to limit CO2 emissions include transition to renewable energy sources (viz., wind, solar, and hydropower), increasing energy efficiency, and implementing carbon capture and storage (CCS) technologies. Additionally, re- and afforestation can help absorb atmospheric CO2. CCS involves capturing CO2 emissions at the source and storing them in subsurface geological formations including depleted oil and gas fields, saline aquifers, and coal seams (Englezos and Lee, 2005). Renewable energy sources like solar and wind offer a promising solution, but their widespread adoption faces challenges, including extensive infrastructure development and intermittent power supply, which requires advanced energy storage systems. While improvements in energy efficiency are helpful, fossil fuels are likely to remain dominant in the global energy mix for the foreseeable future, necessitating the development of carbon capture and storage (CCS) technologies.

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