摘要
安全阀是保障石化设备安全的最后一道屏障,对安全阀失效风险的预测越准确,越有利于石化设备的安全长周期运行和安全阀检修计划的合理安排。为高效准确预测安全阀的失效风险,提出一种基于XGBoost算法的安全阀失效风险评估方法。该方法是一种基于数据驱动的预测方法,采用XGBoost算法建模,优选影响安全阀失效风险的关键特征参量来实现安全阀失效风险的预测。实验数据表明,该方法对安全阀失效风险预测结果良好,在安全阀测试集上准确率为94.0%,优于传统机器学习方法的精度。此外,该方法还可为安全阀校验周期的确定提供参考依据。
The safety valve is the last barrier to ensure the safety of petrochemical equipment.The more accurate the prediction of the risk of safety valve failure,the more conducive it is to the safe long-term operation of petrochemical equipment and the reasonable arrangement of safety valve maintenance plans.To efficiently and accurately predict the failure risk of safety valves,a risk assessment method based on XGBoost algorithm for safety valve failure is proposed.The method is a data-driven prediction method based on the XGBoost algorithm modelling,and the key feature parameters affecting the failure risk of safety valves are preferred to predict safety valve failure risk.The experimental data indicate that this method has good performance in predicting safety valve failure risk with an accuracy of 94.0%on the safety valve test set,which is better than the accuracy of traditional machine learning methods.In addition,this method can also provide reference basis for the determination of test and calibration cycle of safety valves.
作者
陈中官
袁文彬
程伟
Chen Zhongguan;Yuan Wenbin;Cheng Wei(SINOPEC Zhenhai Refining&Chemical Company,Ningbo,Zhejiang,315207;Hefei General Machinery Research Institute Co.,Ltd.,Hefei,Anhui,230031)
出处
《石油化工设备技术》
CAS
2024年第6期1-4,30,I0001,共6页
Petrochemical Equipment Technology
基金
安徽省科学技术厅(批准号:202203a07020001)资助的课题
中国石油化工股份有限公司(批准号:30650601-22-FW1703-0023)资助的课题。
