Numerical Simulation of Erosion Characteristics and Residual Life Prediction of Defective Pipelines Based on Extreme Learning Machine

Qi Wang, Chao Sun, Yuelin Li and Yuechan Liu
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Qi Wang: School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
Chao Sun: School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
Yuelin Li: School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China
Yuechan Liu: School of Measurement and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China

Energies, 2022, vol. 15, issue 10, 1-18

Abstract: Aiming to solve the problem that the residual life of defective elbows is difficult to predict and the prediction accuracy of a traditional extreme learning machine (ELM) is unsatisfactory, a genetic algorithm optimization neural network extreme learning machine method (GA-ELM) that can effectively predict erosion rate and residual life is proposed. In this method, the input weight and hidden layer node threshold of the hidden layer node is mapped to GA, and the input weight and threshold of the ELM network error is selected by GA, which improves the generalization performance of the ELM. Firstly, the effects of solid particle velocity, particle size, and mass flow rate on the erosion of elbow are studied, and the erosion rates under the conditions of point erosion defect, groove defect, and double groove erosion defect are calculated. On this basis, the optimized GA-ELM network model is used to predict the residual life of the pipelines and then compared with the traditional ELM network model. The results show that the maximum erosion rate of defect free elbow is linearly correlated with solid particle velocity, particle size, and mass flow rate; The maximum erosion rate of defective bend is higher than that of nondefective bends, and the maximum erosion rate of defective bend is linearly related to mass flow rate, but nonlinear to solid particle flow rate and particle size; the GA-ELM model can effectively predict the erosion residual life of a defective elbow. The prediction accuracy and generalization ability of the GA-ELM model are better than those of the traditional ELM model.

Keywords: defective pipelines; erosion characteristics; extreme learning machine; residual life prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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