首页|Reports on Machine Learning Findings from National University of Singapore Provide New Insights (Evaluating Co2 Hydrate Kinetics In Multi-layered Sediments Using Experimental and Machine Learning Approach: Applicable To ...)
Reports on Machine Learning Findings from National University of Singapore Provide New Insights (Evaluating Co2 Hydrate Kinetics In Multi-layered Sediments Using Experimental and Machine Learning Approach: Applicable To ...)
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NETL
NSTL
Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news originating from Singapore, Singapore, by NewsRx correspondents, research stated, “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.” Funders for this research include Agency for Science Technology & Research (A*STAR), Guangdong Foreign Talent professorship, Shenzhen Science and Technology Committee, Tsinghua Shenzhen Interna- tional Graduate School.Our news journalists obtained a quote from the research from the National University of Singapore, “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 degrees 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) %].”
SingaporeSingaporeAsiaCyborgsEmerging TechnologiesMachine LearningNational University of Singapore
NETL
NSTL
Elsevier
