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

Abstract

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 and Convolutional Neural Networks as an intensified alternative to conventional flooding prediction methods. Electrical Capacitance Tomography allows variations in the predominant characteristics of flooding events to be investigated in greater detail than in previous research. Combined with Convolutional Neural Networks, the Electrical Capacitance Tomography 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 Convolutional Neural Networks and a more conventional Electrical Capacitance Tomography method based on Maxwell equation. Both methods can accurately calculate the liquid hold-up at low gas flow rates. The liquid hold-up predicted according to Maxwell equation did not match the measured values at high gas flow rates, showing discrepancies of up to 68%. In contrast, Convolutional Neural Networks is much superior to the Maxwell equation method at high gas flow rates, giving only 1% mean of difference than the reference liquid hold-up. Electrical Capacitance Tomography supported by Convolutional Neural Networks shows great fidelity for non-invasive monitoring of local liquid hold-up, allowing for more accurate, localized prediction of loading point and flooding point in packed columns.