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- W4237167866 abstract "Abstract Corrosion in the petroleum industry is one the major concerns of the managers and engineers. Both upstream and downstream parts face this problem and the companies spend billions of dollars in this area. To remain competitive with the world market and lea the field, this cost must be kept to a minimum. Therefore there is a high demand for a reliable corrosion prediction model, which could assist our decision making. Deterioration modeling is an essential element for both design and operation of oil and gas systems and will increase the life productivity of company assets. On the other hand in many engineering problems the available information is vague and sometimes measured data or expert knowledge is too imprecise to justify the use of numbers. Fuzzy logic is a good solution here and helps us to compute with words. However the problem is that you cannot train the fuzzy systems, so neural networks may be useful here to add learning capability to fuzzy systems. In this paper the problem of corrosion modeling will be discussed using hybrid neurofuzzy approach. Teaching the fuzzy system with networks, and using pressure, temperature, oil and gas production rate, CO2 and H2S mole percentages of the stream as the model inputs and the corrosion rate as the output, 3-D surfaces for the corrosion rate were obtained and the prediction results of the model which is based on the pattern recognition between input and output parameters is tested. These graphs can be used as a reliable tool in corrosion prediction by managers and engineers." @default.
- W4237167866 created "2022-05-12" @default.
- W4237167866 creator A5049294255 @default.
- W4237167866 date "2007-11-11" @default.
- W4237167866 modified "2023-09-25" @default.
- W4237167866 title "Neural Networks Can Enhance Fuzzy Corrosion Modeling" @default.
- W4237167866 doi "https://doi.org/10.2118/113027-stu" @default.
- W4237167866 hasPublicationYear "2007" @default.
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