Abstract
The paper introduces a numerical internal corrosion rate prediction method into the internal corrosion direct assessment (ICDA) process for wet gas gathering pipelines based on the back propagation (BP), the genetic algorithm (GA) and BP, and the particle swarm optimization and BP artificial neural networks (ANNs). The basic data were collected in accordance with the terms established by the National Association of Corrosion Engineers in the Wet Gas Internal Corrosion Direct Assessment (WG-ICDA) SP0110, and the corrosion influencing factors, which are the input variables of the ANN model, are identified and refined by the grey relational analysis method. A total of 116 groups of basic data and inspection data from seven gathering pipelines in Sichuan (China) are used to develop the numerical prediction model. Ninety-five of the 116 groups of data are selected to train the neural network. The remaining 21 groups of data are chosen to test the three ANNs. The test results show that the GA and BP ANN yield the smallest number of absolute errors and should be selected as the preferred model for the prediction of corrosion rates. The accuracy of the model was validated by another 54 groups of excavation data obtained from pipeline No. 8, whose internal environment parameters are similar to those found in the training and testing pipelines. The results show that the numerical method yields significantly better absolute errors than either the de Waard 95 model or the Top-of-Line corrosion model in WG-ICDA when applying the approach to specific pipelines, and it can be used to investigate a specific pipeline for which the data have been collected and the ANN has been developed in WG-ICDA SP0110.
Highlights
Wet natural gas is defined as natural gas saturated with water or other natural gas liquids
It should be noted that the back propagation (BP) technique uses the BP neural network as the numerical prediction method; in GA and BP, the BP neural network is used as the basic numerical prediction method and the genetic algorithm is used as the optimization method; in PSO and BP, the BP neural network is used as the basic numerical prediction method and the particle swarm optimization technique is used as the optimization method
The GA and BP artificial neural networks (ANNs) model is effective in the prediction of corrosion rates for the specific wet gas gathering pipelines in the internal corrosion direct assessment (ICDA) process
Summary
Wet natural gas is defined as natural gas saturated with water or other natural gas liquids. For the Top-of-Line corrosion model, 95 of 116 excavation points (81.90% of the total) gave absolute errors greater than 0.05 mm/a, and 17 of 116 excavation points (14.65% of the total) had absolute errors greater than 0.1 mm/a It is both necessary and of significant importance that an applicable model for the wet gas gathering pipelines in this specific area of the Sichuan Province be developed to be able to obtain effective ICDAs for those pipelines. The applicable model is developed based on 116 groups of data and is proven to be useful for wet gas gathering pipelines which are similar to the training pipelines used in the internal environments This method can be used for specific pipelines for which the data have been collected and the corresponding ANN has been developed in accordance with WG-ICDA SP0110
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