An improved prediction algorithm of seamless tubing corrosion based on an extension neural network. (24th October 2018)

• Record Type: Journal Article
• Authors: Wang, Ti‐chun; Tong, Chang‐sheng; Yao, Sheng‐hu
• Other Names: Sundhararajan guestEditor.; Bhuiyan Md Zakirul Alam guestEditor.; Li Xiong guestEditor.; Rawal Bharat S. guestEditor.
• Is Part Of: Concurrency and computation. Volume 31:Number 12(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-10-24
• Subjects: corrosion prediction algorithm -- data processing -- extension neural network -- seamless steel tubes -- simulation analysis; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4801
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In view of the fact that the corrosiveness of seamless steel tubes under actual conditions was difficult to predict, the main influencing factors of seamless oil tube corrosion under actual conditions were analyzed, and a model for predicting the corrosion of seamless oil tubes was constructed. The algorithm of the extension neural network was introduced. The Python language was used to implement the extensible neural network algorithm, and the influencing factors of steel tube corrosion were trained. The extension neural network was simulated and verified, and finally, the system is implemented by the mobile APP.