Evaluating CO2 hydrate kinetics in multi-layered sediments using experimental and machine learning approach: Applicable to CO2 sequestration

Vikas Dhamu, Xiao Mengqi, M Fahed Qureshi, Zhenyuan Yin, Amiya K. Jana and Praveen Linga

Energy, 2024, vol. 290, issue C

Abstract: The transition to a low-carbon economy requires the implementation of effective carbon capture and sequestration (CCS) strategies. One of the potential CCS strategies is to capture industrial CO2 emissions and inject them into the oceanic sediments to be stored as CO2 hydrates. However, the success of this technique depends on a few key factors such as the type of sediments where CO2 is injected, the kinetics of CO2 hydrate formation and dissociation, the accuracy of the models for prediction of formation kinetics, and the CO2 hydrates morphology. So, in this first-gen work, a highly complex set of experiments was carried out to examine the CO2 hydrate formation and dissociation processes by injecting CO2 via injection tube into different size wet sediments, i.e., coarse (diameter: 0.5–1.5 mm), granules (diameter:1.5–3.0 mm) and dual-layered sand (coarse + granules), embedded inside high-pressure reactor as the artificial seabed. The experiments were carried out at 3.5 MPa at T = 1.5–2.0 °C with 500 ppm of the eco-friendly hydrate promotor (l-tryptophan). The images of morphological changes during hydrate formation/dissociation, the Scanning Electron Microscope analysis of the sediments, and the estimated water-to-hydrate conversations have been reported in this work. A novel mathematical four-parameter-based CO2 hydrate kinetics model was also developed. A set of 32,843 experimental data points was used to train a supervised machine learning algorithm using two parameters with the other two taken from published literature. The water-to-hydrate conversion was estimated and follows the order of dual-layered sand [88.26 (±4.62) %] > coarse [77.77 (±5.72) %] > granules [65.36 (±2.3) %]. The proposed ML-based model predicted the water-to-hydrate conversion with an Average Absolute Relative Deviation [%AARD] of 4.23–13.29 %. This work serves as a step forward in developing a sustainable hydrate-based oceanic carbon storage technology.

Keywords: Hydrate-based CO2 storage; CO2 hydrates; Multiple sediment layers; Scanning Electron Microscope; Machine learning; Kinetics (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223033418
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544223033418

DOI: 10.1016/j.energy.2023.129947

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().