Researchers from the PipeChina Group have pioneered a comprehensive method for predicting potential hazard distances following carbon dioxide (CO2) pipeline leakages, addressing a significant gap in current industrial safety protocols. The study, published in the Journal of Pipeline Science and Engineering, represents a critical advancement in understanding and mitigating risks associated with CO2 transportation infrastructure.
The research focuses on supercritical and dense-phase CO2 transportation, a key component of carbon capture, storage, and utilization (CCUS) technologies crucial for global carbon emission reduction strategies. Lead researcher Prof. Yuxing Li emphasized the potential dangers of CO2 leaks, which can cause severe environmental and biological consequences, including plant and animal asphyxiation and frostbite.
The team conducted four full-size burst tests under varying initial conditions to evaluate pipeline fracture characteristics and CO2 concentration dispersal patterns. By developing a sophisticated CO2 concentration diffusion model, the researchers created a novel approach to calculating potential hazard distances that accounts for complex variables such as temperature, pressure differences, and pipeline configuration.
One of the study's most significant innovations is a PSO-BP neural network capable of predicting hazard distances for leaks at any pipeline location. This method provides a more computationally efficient alternative to traditional modeling approaches while maintaining high accuracy in risk assessment.
The research addresses a critical challenge in industrial CO2 transportation safety: the difficulty of predicting leakage consequences across diverse pipeline environments. By considering factors like relative distance to cut-off valves and varied leakage points, the study offers a more nuanced and adaptable risk assessment framework.
This breakthrough has substantial implications for the expanding CCUS industry, providing engineers and safety professionals with a more robust tool for evaluating potential risks in CO2 transportation infrastructure. As global efforts to reduce carbon emissions intensify, such advanced safety modeling becomes increasingly important in developing reliable and secure carbon management technologies.
