【摘要】 根据气田现场工况的调研情况,应用动电位极化法测量了典型气田环境中316L不锈钢的临界点蚀温度(CPT),并利用人工神经网络(ANN)技术对CPT进行了预测。结果表明,CPT随Cl-浓度升高而降低,p H值对CPT影响很小。建立的ANN模型对316L不锈钢的CPT具有良好的预测能力,可实现对气田各作业区复杂耦合环境下CPT的预测。ANN模型的预测结果表明,Cl-浓度和p H值对CPT的影响无交互作用,Cl-浓度是影响CPT的主要因素,因此Cl-将是气田防腐蚀工程的重点控制因素。更多还原

【Abstract】 316L stainless steel is widely used for enhancing the pitting resistance of pipelines in gas field. The corrosion environment is complex and diversified in different working districts of gas field. Therefore, it is necessary to develop a model for predicting the pitting resistance of pipelines serving in different corrosive environments. Critical pitting temperature(CPT) is considered as a criterion for evaluating the pitting resistance of stainless steel. Based on a survey on the operation situations of gas field, the relevant data of CPT for 316 L stainless steel is acquired by potentiodynamic polarization method in solutions with various Cl-concentrations and p H values, which are selected to correspond with the real environments in operation. Then, an artificial neutral network(ANN) model is developed to predict the CPT, and therewith to compare with the measured data.The results show that the CPT decreases with the increase of Cl-concentration, but on which p H value has little influence. The developed ANN model has good ability to predict the CPT of 316 L stainless steel, and can be used for the prediction of CPT in complex environments in gas field. It is also revealed that there is no interactive effect between Cl-concentration and p H value, and Cl-concentration was the main influencing factor on the CPT. Therefore, Cl-concentration will be peculiarly concerned with for the implementation of a corrosion control project in gas field.更多还原