Title :
Measurement for Water Content in Oil-Water Two Phase Flow Based on Novel Hybrid Intelligent Prediction Model
Author :
Dongzhi, Zhang ; Bokai, Xia ; Tao, Fu
Author_Institution :
China Univ. of Pet., Dongying
Abstract :
Some parameters affecting the measurement of water content in oil/water two-phase flow are detected using multi-sensor technology, and a novel hybrid intelligent prediction model is proposed to improve measuring precision of water content. Some advanced information processing technologies, such as neural networks optimized by hybrid genetic algorithm, combined method for decisions of multiple sub-modules, are introduced in this intelligent prediction model, which guarantee a good prediction effect with global and fast convergence, strong generalization capability and high precision. The research result in this paper indicates that prediction precision is improved to a great extent in the all-round measuring range for water content, while the development cost is at a low valuation. It is a new and effective method for measuring water content in oil/water two-phase flow.
Keywords :
flow measurement; genetic algorithms; mechanical engineering computing; neural nets; sensor fusion; two-phase flow; hybrid genetic algorithm; hybrid intelligent prediction model; information processing technologies; multisensor technology; neural networks; oil-water two phase flow; water content measurement; Costs; Fluid flow measurement; Genetic algorithms; Information processing; Intelligent networks; Neural networks; Optimization methods; Petroleum; Phase measurement; Predictive models; Intelligent Prediction; Multi-sensor; Neural Network; Two-phase Flow;
Conference_Titel :
Control Conference, 2007. CCC 2007. Chinese
Conference_Location :
Hunan
Print_ISBN :
978-7-81124-055-9
Electronic_ISBN :
978-7-900719-22-5
DOI :
10.1109/CHICC.2006.4347072
