基于自编码器的汽油分子组成预测

Gasoline Molecular Composition Prediction Based on Autoencoder Algorithm

蔡广庆 胡益炯 李春澎 纪晔 王弘历

Guangqing CAI Yijiong HU Chunpeng LI Ye JI Hongli WANG

中国石油天然气股份有限公司规划总院，北京 100083

PetroChina Planning and Engineering Institute，Beijing 100083，China

汽油分子在线调和技术需要快速获取各种类型组分油的详细分子组成信息。开发了基于自编码器的汽油分子组成快速解析方法，该方法可由近红外光谱直接预测汽油详细单体烃的组成。构建的汽油分子组成自编码器模型可挖掘汽油组成的潜在特征，并利用潜在特征解码恢复原始分子组成。利用神经网络算法关联近红外光谱特征信息与汽油组成的潜在特征，并采用加氢汽油验证了模型的准确性。结果表明，平均绝对误差为0.033。开发的模型将自编码器算法应用在传统的石油化工过程中，对汽油分子在线调和与实时优化具有重要的指导意义。

Gasoline molecular blending technology on?line requires rapid access to detailed molecular composition information of various types of component oils. In this paper, an autoencoder?based method for the rapid resolution of gasoline molecular composition is developed, which can directly predict the detailed monomeric hydrocarbon composition of gasoline from near?infrared spectra. The constructed autoencoder model of gasoline molecular composition can explore the potential features and recover the original molecular composition by decoding the potential features. The artificial neural network algorithm is used to correlate the NIR spectral information with the potential features of gasoline composition. The accuracy of the model is verified by using hydrogenated gasoline with the average absolute error is 0.033. The model developed in this work applies the current popular autoencoder algorithm to the traditional petrochemical process, which is an important guideline for blending online and real?time optimization of gasoline molecules.

石油化工联合会基金（A类）重点支持项目(U1862204)

10.12422/j.issn.1672-6952.2024.02.001