Corrosion is the main mode of failure of oil and gas injection pipelines.Improving the prediction accuracy of inter-nal corrosion of oil and gas injection pipelines is an important means to prevent premature failure of pipelines and reduce the risk of economic loss and casualties to enterprises.Based on the original grey theory,this paper introduces the OGM(1,N)model to pre-dict the internal corrosion data,which improves the prediction accuracy significantly compared to the traditional grey prediction model.In order to further reduce the error,the adaptive particle swarm algorithm is used to optimise the selection of background val-ues in the model and to improve the OGM(1,N)model by combining with metabolic arrays.The improved OGM(1,N)model is vali-dated by experimental data and the prediction accuracy is again improved by 24.9%compared to the pre-optimisation model.
维普
万方数据