乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析...乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析的复杂性,如何基于过程数据及时检测乙烯裂解炉工况变化成为亟需解决的问题。借鉴对比学习算法在图片分类中的优秀性能,提出一类基于对比学习的裂解炉运行工况识别方法。首先,将乙烯裂解炉工业数据经归一化后,使用不同长度的时间窗动态提取数据,将其转化为灰度图片。根据图片中的信息,将图片进行数据增强后输入编码器,得到图片的全局语义、类别、内容不变性等特征。将这些特征应用于计算对比学习的损失函数,通过最小化对比损失函数,实现对灰度图片的分类。通过本文方法,可以根据过程数据快速发现工况变化,其分类准确度较通用时间序列表示学习的自监督对比学习(self-supervised contrastive learning for universal time series representation learning,TimesURL)方法有明显提升,可有效实现乙烯裂解炉工况识别。展开更多
A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF ...A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.展开更多
文摘乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析的复杂性,如何基于过程数据及时检测乙烯裂解炉工况变化成为亟需解决的问题。借鉴对比学习算法在图片分类中的优秀性能,提出一类基于对比学习的裂解炉运行工况识别方法。首先,将乙烯裂解炉工业数据经归一化后,使用不同长度的时间窗动态提取数据,将其转化为灰度图片。根据图片中的信息,将图片进行数据增强后输入编码器,得到图片的全局语义、类别、内容不变性等特征。将这些特征应用于计算对比学习的损失函数,通过最小化对比损失函数,实现对灰度图片的分类。通过本文方法,可以根据过程数据快速发现工况变化,其分类准确度较通用时间序列表示学习的自监督对比学习(self-supervised contrastive learning for universal time series representation learning,TimesURL)方法有明显提升,可有效实现乙烯裂解炉工况识别。
基金Projects(60974031,60704011,61174128)supported by the National Natural Science Foundation of China
文摘A self-organizing radial basis function(RBF) neural network(SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit(FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness.
