Prediction of crude oil light end yields using neural network modeling
Prediction of crude oil light end yields using neural network modeling
The objective of the project is to explore the feasibility of using neural network modeling technique to predict the yields of light hydrocarbon (specifically propane and butane) in crude oil, using easily measurable crude oil properties, such as specific gravity (S.G.) and yields of the various crude oil fractions. Another objective of this project is to compare the effectiveness of using neural network modeling technique to that achieved using multi-linear regression. Master of Science (Computer Control and Automation)
- Nanyang Technological University Singapore
330, :Engineering::Computer science and engineering::Computing methodologies [DRNTU], DRNTU::Engineering::Computer science and engineering::Computing methodologies
330, :Engineering::Computer science and engineering::Computing methodologies [DRNTU], DRNTU::Engineering::Computer science and engineering::Computing methodologies
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