Measurement of CO2 leakage from pipelines under CCS conditions through acoustic emission detection and data driven modeling

CO2 leakage from carbon capture and storage (CCS) networks may lead to ecological hazards, bodily injury and economic losses. In addition, captured CO2 often contains impurities which affect the leakage behavior of CO2. This paper presents a method for continuous and quantitative measurements of CO2 leakage flowrate and the volume fraction of impurities by combining data-driven models with acoustic emission (AE) and temperature sensors. Three data-driven models based on artificial neural network (ANN), random forest (RF), and least squares support vector machine (LS-SVM) algorithms are established. The outputs from the three data-driven models are then integrated to give improved results. Experimental work was conducted on a purpose-built CO2 leakage test rig under a range of conditions. N2 was injected to the CO2 gas stream as an impurity medium. Results show that the integrated model yields a relative error within ±4.0% for leakage flowrate and ±3.4% for volume fraction of N2.