Optimization of Carbon Dioxide Dense Phase Injection Model Based on DBN Deep Learning Algorithm

Carbon dioxide dense phase injection images have providing new research ideas for differential detection. Aiming at the drawbacks of large data volume, low matching efficiency, and longtime consumption of high-resolution carbon dioxide dense phase injection models, a registration algorithm for carbon dioxide dense phase injection models based on quadratic matching is proposed. This algorithm first uses down sampling to reduce image dimensions. A difference detection algorithm based on weakly supervised deep confidence network is proposed to neural networks, as well as the high manual labeling workload, low efficiency, and insufficient labeled data of high-resolution carbon dioxide dense phase injection models. This article first explores the throttling of CO2 venting in pipelines through the analysis of CO2 phase equilibrium characteristics. The experiment shows that there is after the valve, the greater the temperature drop. At the same time, water content will affect the throttling temperature drop is about 1.5 degrees; when the gas-liquid ratio is 2500, the throttling temperature drop is 7.4 degrees. CO2 in the reactor to over 8MPa, achieving supercritical pressure. CO2 with the constant temperature water bath is 5~100 degrees, with a temperature control accuracy of ± 0.1 degrees. The temperature of the water inside the water bath jacket of the kettle is adjusted through circulation. The maximum pressure of the kettle is 25MPa and the volume is 6L.

@article{Zhou2024,
  title     = {Optimization of Carbon Dioxide Dense Phase Injection Model Based on DBN Deep Learning Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Juan Zhou and Dalong Wang and Tieya Jing and Zhiwen Liu and Yihe Liang and Yaowu Nie},
  doi       = {10.14569/IJACSA.2024.01511106},
  url       = {https://doi.org/10.14569/IJACSA.2024.01511106}
}