Publication: Artificial neural network for gas-oil flow pattern recognition using capacitance tomography data
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Tan, Kim Leng | |
| dc.date.accessioned | 2024-07-22T03:04:31Z | |
| dc.date.available | 2024-07-22T03:04:31Z | |
| dc.date.issued | 2009-04-01 | |
| dc.description.abstract | The technique to recognize the oil and gas flow pattern in a pipe is needed in the oil and gas industry to monitoring the condition of the oil and gas pipe system. Any mistake or malfunction may lead to serious loss and endanger the workers and environments. Generally there are lots of flow pattern such as Empty, Full, Stratified, Bubble, Core and Annular. The Electrical Capacitance Tomography (ECT) technique is used to take the cross sectional data of the pipe. The Artificial Neural Networks (ANNs) is used to recognize the flow patterns. This project uses the Multilayer Perceptron (MLP) as the ANNs model. The MLP is trained, validated, and tested with the ECT data. The ECT data is divided into three groups, training, validation, and testing. The Matlab software is used to build the MLP architecture. The learning algorithms used for this project is the Levenberg-Marquardt algorithms and the Quasi-Newton algorithms. Result show that trained MLP is able to give a percentage of accuracy of 99.102% in oil and gas flow pattern recognition. This shows that the MLP is suitable to be used in the oil and gas flow pattern recognition. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/19793 | |
| dc.language.iso | en | |
| dc.title | Artificial neural network for gas-oil flow pattern recognition using capacitance tomography data | |
| dc.type | Resource Types::text::report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |