Researchers Develop New Method to Predict CO2 Pipeline Leakage Hazard Distances

Researchers from the China University of Petroleum have developed a novel method for predicting potential hazard distances following CO2 pipeline leakages, a critical advancement in carbon capture, storage, and utilization (CCUS) technologies.

The study, published in the Journal of Pipeline Science and Engineering, focuses on addressing the significant safety challenges associated with supercritical CO2 transportation. By conducting the first full-size CO2 pipeline burst fracture test in China, the research team explored the complex dynamics of potential pipeline failures and their environmental consequences.

Lead researcher Prof. Yuxing Li emphasized the potential dangers of CO2 leaks, which can cause severe environmental and biological impacts. High-concentration CO2 releases can result in frostbite and potential asphyxiation for plants and animals in the surrounding areas, making accurate hazard prediction crucial.

The researchers conducted four comprehensive burst tests under varying initial conditions to understand CO2 concentration dispersal patterns. By developing a CO2 concentration diffusion model and verifying it against measured data, they created a sophisticated hazard distance calculation framework.

Recognizing the complexities of industrial-grade CO2 pipelines—which experience significant temperature and pressure variations—the team introduced a PSO-BP neural network capable of predicting hazard distances for leaks at any pipeline location. This innovative approach provides a more efficient and adaptable method for assessing potential risks.

The study's findings are particularly significant as the world increasingly relies on CCUS technologies to meet global carbon emission reduction targets. By improving safety prediction models, the research contributes to more reliable and secure CO2 transportation infrastructure.