Data set for " CNN-aided Flooding Prognostic in Packed Column using Electrical Capacitance Tomography."

A dataset is presented for Flooding Prognostic in Packed Column using CNN-aided Electrical Capacitance Tomography (ECT). Numeric data containing measurement of ECT capacitance, pressure drop, liquid hold-up profiles. The ECT system used in this work includes sensors, data acquisition system and a computer with imaging software. The frame rate of the ECT system is up to 714 frames per second. The pressure inside the bed is recorded using pressure meters and data loggers, the pressure meters are placed on the capture column at the top and bottom of the column. The liquid hold-up inside the bed is derived from level meter measurements. We include data of ECT capacitance, pressure and liquid hold-up during the whole progress. The proposed CNN aided ECT accurately predicts the local liquid hold-up and enables the earliest detection of loading point at the bottom of the packed column and warning of flooding point at the top of the packed column, and therefore is highly suitable for flooding prognostic. This dataset could be used to accurate measurement of loading point and flooding point, and its application to flooding prognostic is significant to maximum the packed column mass transfer efficiency. Data supporting Chen et al (prepare to submission).

本数据集面向填料塔液泛预警任务，采用卷积神经网络（Convolutional Neural Network, CNN）辅助电容层析成像（Electrical Capacitance Tomography, ECT）技术构建。数据集包含ECT电容、压降及持液量分布的数值测量数据。本研究采用的ECT系统由传感器、数据采集装置及搭载成像软件的计算机组成。该ECT系统的最高帧率可达714帧每秒。填料床内的压力通过压力变送器与数据记录仪采集，压力变送器分别安装于填料塔的顶部与底部位置。填料床内的持液量由液位计测量数据推导得到。本数据集涵盖全流程中的ECT电容、压力及持液量数据。本文提出的CNN辅助ECT技术可精准预测局部持液量，能够最早检测填料塔底部的载点，并对填料塔顶部的液泛点发出预警，因此极适用于液泛预警任务。本数据集可用于精准检测载点与液泛点，将其应用于液泛预警对最大化填料塔传质效率具有重要意义。本数据集为Chen等人（待投稿）的研究提供数据支撑。