An oil fraction neural sensor developed using electrical capacitance tomography sensor data.
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.
School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Penang 14300, Malaysia. jms@eng.usm.my.
Khursiah
K
Zainal-Mokhtar
Junita
J
Mohamad-Saleh
D045424:Complex Mixtures / Q000032:analysis*; D058266:Dielectric Spectroscopy / Q000295:instrumentation* / Q000379:methods; D025802:Electric Capacitance; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005648:Fuel Oils / Q000032:analysis*; D005740:Gases / Q000032:analysis*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D014054:Tomography / Q000295:instrumentation; D014159:Transducers
D045424:Complex Mixtures; D005648:Fuel Oils; D005740:Gases