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An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data
School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang 14300, Malaysia
* Author to whom correspondence should be addressed.
Received: 1 July 2013; in revised form: 2 August 2013 / Accepted: 8 August 2013 / Published: 26 August 2013
Abstract: 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.
Keywords: neural network; electrical capacitance tomography sensor; two-component flow; generic system; multi-layer perceptron
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Cite This Article
MDPI and ACS Style
Zainal-Mokhtar, K.; Mohamad-Saleh, J. An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data. Sensors 2013, 13, 11385-11406.
Zainal-Mokhtar K, Mohamad-Saleh J. An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data. Sensors. 2013; 13(9):11385-11406.
Zainal-Mokhtar, Khursiah; Mohamad-Saleh, Junita. 2013. "An Oil Fraction Neural Sensor Developed Using Electrical Capacitance Tomography Sensor Data." Sensors 13, no. 9: 11385-11406.