School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang 14300, Malaysia.
Sensors (Basel). 2013 Aug 26;13(9):11385-406. doi: 10.3390/s130911385.
This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical Capacitance Tomography (ECT) data. An artificial Neural Network (ANN) has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP) ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes.
本文提出了一种基于电容层析成像（ECT）数据的通用智能油分传感器的新研究。采用人工神经网络（ANN）作为智能系统，从管道中包含油和气体的两相流的横截面中感应和估计油分。以前的工作仅侧重于基于固定的 ECT 传感器参数来估计管道中的油分。使用固定的 ECT 设计传感器，可以训练油分神经网络传感器来处理基于特定传感器参数的 ECT 数据，因此神经网络传感器不是通用的。这项工作专注于开发基于训练多层感知器（MLP）ANN 的通用神经油分传感器，该 ANN 使用各种 ECT 传感器参数。平均而言，所提出的油分神经网络传感器在各种 ECT 传感器尺寸下能够给出 3.05%的平均绝对误差。
