Title :
High-Solidifying Crude Oil Temperature Soft-Sensing Based on RBF Neural Network
Author :
Yilin, Zhou ; Huanran, Guo
Author_Institution :
Inst. of Autom. & Electron. Eng., Qingdao Univ. of Sci. & Technol., Qingdao, China
Abstract :
In the underwater crude oil storage system which adopts oil-water separation method, in order to keep the oil fluid, make the oil storage and transport running smoothly, the oil temperature control becomes a crucial consideration. Measuring temperature directly exists some difficulty. RBF neural network soft-sensing model is established to obtain the temperature field distributing of crude oil in storage tank.
Keywords :
crude oil; fuel storage; offshore installations; oil technology; production engineering computing; radial basis function networks; separation; solidification; tanks (containers); temperature sensors; RBF neural network; high-solidifying crude oil temperature soft-sensing; oil fluid; oil storage; oil temperature control; oil-water separation method; storage tank; underwater crude oil storage system; Force measurement; Neural networks; Ocean temperature; Petroleum; Pipelines; Sea measurements; Storage automation; Temperature control; Temperature measurement; Thermal force; high-solidifying crude oil; neural network; soft-sensing modle;
Conference_Titel :
Intelligent Computation Technology and Automation, 2009. ICICTA '09. Second International Conference on
Print_ISBN :
978-0-7695-3804-4
DOI :
10.1109/ICICTA.2009.87
