The large-scale exploitation and wanton use of fossil energy have led to the increasing global warming and environmental pollution. The development and utilization of clean energy urgently need to be put on the agenda. At the same time, the development of Internet technology and big data technology is constantly promoting the development and popularization of clean energy. However, Internet information security is the number one factor threatening the development and supply of clean energy in today’s society. Therefore, based on the relevant theories of statistical learning, an evaluation model of information security reliability of clean energy internet based on statistical learning is constructed. At the same time, the reliability of the evaluation model is tested and analyzed. Finally, the role of the evaluation model in the carbon sequestration of natural gas hydrate, the reduction of greenhouse effect and the development of clean energy is analyzed. It is expected to lay a foundation for the efficient development and environmental protection of clean energy (natural gas) through this research. It is found that the predicted results of data transmission by coaxial cable are completely consistent with the actual results, and neither will generate hydrate within 2.5 m from the entrance. Moreover, the reliability of data transmission using coaxial cable is higher than that of wireless transmission. The study also found that the increase of carbon dioxide injection rate will accelerate the decomposition and gas production of hydrate, and it is more obvious in the small range of carbon dioxide injection rate. Considering the development efficiency and burial efficiency, the carbon dioxide injection rate is designed as 20·104 m3/day is the best. At the same time, the greenhouse effect of carbon dioxide will become more and more significant with the increase of its concentration, and based on the prediction of the built model, it is found that the replacement rate of natural gas in hydrate by carbon dioxide can reach 92.35%.
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Translated from Khimiya i Tekhnologiya Topliv i Masel, No. 6, pp. 96–102, November – December, 2023
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Zhang, H., Liu, X., Liu, D. et al. Reliability Evaluation of Clean Energy Internet Information Security Based on Statistical Learning Methods. Chem Technol Fuels Oils 59, 1211–1220 (2024). https://doi.org/10.1007/s10553-024-01637-6
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DOI: https://doi.org/10.1007/s10553-024-01637-6