摘要
车用柴油的闪点是产品安全性指标,使用时出现过低海拔地区的合格产品运输到高海拔地区闪点不合格的问题。为了提高车用柴油的使用安全,应在运输前就了解高海拔地区闪点的真实情况。提出了基于人工神经网络的柴油闪点预测模型。以所测柴油闪点的大气压、柴油C_(11)及以下各组分质量分数和柴油C_(12)及以上组分总质量分数为输入,以柴油闪点为输出建立人工神经网络模型。结果表明:所建人工神经网络模型对柴油闪点预测准确率为99.52%。
The flash point of automotive diesel is a product safety indicator,and there is a problem of unqualified flash point when transporting qualified products from low altitude areas to high altitude areas during use.In order to improve the safety of using automotive diesel,it is necessary to understand the true situation of flash points in high-altitude areas before transportation.In this article,a diesel flash point prediction model based on artificial neural networks was proposed.Taking the atmospheric pressure of determining diesel flash point,the mass fraction of diesel C_(11) and below,and the total mass fraction of diesel C_(12) and above as inputs,and the diesel flash point as output,an artificial neural network model was established.The result showed that,the accuracy of the artificial neural network model for predicting diesel flash point was 99.52%.
作者
王硕
纪博睿
赵晓弘
杨猛
胡西娅
祁艳
安亚彬
金美洛布
德庆央宗
格桑珠嘎
李龙娟
朗杰
拉珍
任克京
WANG Shuo;JI Borui;ZHAO Xiaohong;Yang Meng;HU Xiya;QI Yan;AN Yabin;JINMEILUOBU;DEQINGYANGZONG;GESANGZHUGA;LI Longjuan;LANG Jie;LA Zhen;REN Kejing(Liaoning Inspection,Examination&Certification Centre,Shenyang Liaoning 110032,China;Key Laboratory of Testing and Quality Control for Petroleum Products,State Administration for Market Regulation,Shenyang Liaoning 110144,China;School of Traditional Chinese Materia Medica,Shenyang Pharmaceutical University,Shenyang Liaoning 110016,China;Naqu Inspection,Examination&Certification Centre,Naqu Xizang 852003,China)
出处
《当代化工》
2025年第2期474-477,共4页
Contemporary Chemical Industry
基金
辽宁省自然科学联合基金项目(项目编号:2022-NLTS-12-03)
辽宁省市场局科学计划项目(项目编号:2022JS003)
国家市场监管总局科技计划项目(项目编号:2023YJ14)
辽宁省市场局科学计划项目(项目编号:2024JS008)。
关键词
神经网络
车用柴油
闪点
大气压
算法
计算机模拟
预测
Neural network
Diesel oil
Flash point
Barometric pressure
Algorithm
Computer simulation
Prediction
