Title :
Conceptual Design of Carbon Steels to Support Heavy Crude Refinement Using Neural Network Modeling and Evolutionary Optimization
Author :
Torres-Trevino, L. ; Reyes-Valdes, Arturo
Author_Institution :
Corp. Mexicana de Investig. en Mater. S. A de C. V., Coahuila
fDate :
Sept. 30 2008-Oct. 3 2008
Abstract :
The oil industries in the entire World and particularly in Mexico, have been taking an important relevance. There are two major challenges in this industry. The first one is the exploration and utilization of crude oil in deep sea, the second one is the scarce of light crude, the actual production report an increment of heavy crude, generating corrosion steel in the extraction and refinement processes. This paper presents an intelligent system to design conceptual steels considering its properties, taking into account some properties of oil crude and the temperature of the refinement process. The results provide information to choice the correct steel for every refinement phase.
Keywords :
carbon steel; corrosion; crude oil; petroleum industry; production engineering computing; Mexico; carbon steels; corrosion steel; crude oil; evolutionary optimization; extraction processes; heavy crude refinement; intelligent system; neural network modeling; oil industries; refinement processes; Corrosion; Data mining; Design optimization; Fuel processing industries; Intelligent systems; Neural networks; Petroleum industry; Production; Refining; Steel; heavy crude refinement process; soft computing applications;
Conference_Titel :
Electronics, Robotics and Automotive Mechanics Conference, 2008. CERMA '08
Conference_Location :
Morelos
Print_ISBN :
978-0-7695-3320-9
DOI :
10.1109/CERMA.2008.33
