Abstract
Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.
Highlights
Due to the influence of the medium composition, temperature, terrain, and other factors, corrosive substances are produced in steel gas pipelines, which can lead to internal corrosion
Chou et al compared the prediction accuracy of the carbon steel corrosion rate in marine environments based on an artificial neural network (ANN), support vector machine (SVM), classification and regression tree (CART), linear regression (LR) and hybrid metaheuristic regression models, and the results showed that the hybrid metaheuristic regression model had superior prediction accuracy in this case [6]
Methods for dealing with this problem mainly include principal component analysis (PCA), which is a statistical method used for dimensional reduction and feature extraction. is method is suitable for dealing with situations where such factors are highly interrelated [21,22,23,24]. erefore, this paper proposes a method whose parameters are optimized by a PCA algorithm, which is called PCA-D-fuzzy neural network (FNN), to forecast the internal corrosion rate
Summary
Due to the influence of the medium composition, temperature, terrain, and other factors, corrosive substances are produced in steel gas pipelines, which can lead to internal corrosion. Corrosion will cause a thinning of the inner wall of the pipeline and reduce its structural strength, which will lead to natural gas leakage and seriously threaten the safety, integrity and economy of the whole gas transmission system [1,2,3] To prevent these phenomena, some in-line inspection instruments and internal detection instruments have been developed. The FNN only learns and optimizes the parameters in the fuzzy system and adaptive adjustments based on a preset neural network, which is time consuming and leads to low-accuracy structure identification [18,19,20]. To overcome the above mentioned problems, this paper proposes an internal corrosion rate forecasting model using the dynamic fuzzy neural (D-FNN). Erefore, this paper proposes a method whose parameters are optimized by a PCA algorithm, which is called PCA-D-FNN, to forecast the internal corrosion rate.
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