Flooding Prognostic in Packed Columns Based on Electrical Capacitance Tomography and Convolution Neural Network

The flooding of packed columns is accompanied by a steep increase in liquid hold-up and pressure drop, resulting in lower mass transfer efficiency and potential damage to equipment. This study aims to investigate, for the first time, the feasibility of electrical capacitance tomography (ECT) and convolutional neural networks (CNNs) as an intensified alternative to conventional flooding prediction methods. ECT allows variations in the predominant characteristics of flooding events to be investigated in greater detail than in previous research. Combined with CNNs, the ECT sensor enables high accuracy on liquid hold-up calculation and strong robustness against noise-contaminated measurements. In this work, a detailed comparison is made between liquid hold-up results using CNNs and a more conventional ECT method based on the Maxwell equation. Both methods can accurately calculate the liquid hold-up at low gas flow rates. The liquid hold-up predicted according to the Maxwell equation did not match the measured values at high gas flow rates, showing discrepancies of up to 68%. In contrast, CNNs are much superior to the Maxwell equation method at high gas flow rates, giving only a 1% mean of difference from the reference liquid hold-up. ECT supported by CNNs shows great fidelity for noninvasive monitoring of local liquid hold-up, allowing for more accurate, localized prediction of loading point, and flooding point in packed columns.

Automated summary

This study investigates the feasibility of using electrical capacitance tomography (ECT) and convolutional neural networks (CNNs) as an intensified alternative to conventional flooding prediction methods. ECT combined with CNNs allows for more accurate calculation of liquid hold-up, especially at high gas flow rates.