基于随机卷积核神经网络数据增强的软测量
Soft Sensor Based on Random Convolutional Kernel Neural Network Data Enhancement
钱慧 1刘瑞兰1
作者信息
- 1. 南京邮电大学 自动化学院、人工智能学院,江苏 南京 210023
- 折叠
摘要
精对苯二甲酸(PTA)生产过程中 PX氧化反应的副产品4-CBA难以在线测量,只能通过离线分析获得少量样本.针对该问题,提出一种基于随机卷积核神经网络数据增强的动态软测量模型RCKN-XGBoost.该模型首先采用随机卷积核神经网络(RCKN)进行数据增强,扩充样本数量并改善其多样性;然后将原始样本与扩充后的样本线性组合,构成新的数据集;最后采用XGBoost对网络进行训练与预测.在某化工厂PX氧化过程4-CBA含量数据集上对RCKN-XGBoost模型与XGBoost、CNN、CNN-XGBoost、Laplace-XGBoost模型进行比较,发现RCKN-XGBoost模型的MRE指标分别降低了1.07%、0.92%、0.80%和0.65%,RMSE分别降低了27.9、18.62、12.58和8.05,证明了该模型的有效性.
Abstract
The by-product 4-CBA of PX oxidation reaction in the production process of purified terephthalic acid(PTA)is difficult to mea-sure online,and only a small amount of samples can be obtained through offline analysis.A dynamic soft sensing model RCKN-XGBoost based on random convolutional kernel neural network data augmentation is proposed to address this issue.The model first uses random convolu-tional kernel neural network(RCKN)for data augmentation,expanding the sample size and improving its diversity;Then,the original sample is linearly combined with the expanded sample to form a new dataset;Finally,XGBoost was used to train and predict the network.On the 4-CBA content dataset of PX oxidation process in a certain chemical plant,the RCKN-XGBoost model was compared with XGBoost,CNN,CNN-XGBoost,and Laplace XGBoost models.It was found that the MRE index of the RCKN-XGBoost model decreased by 1.07%,0.92%,0.80%,and 0.65%,respectively,and the RMSE decreased by 27.9%,18.62%,12.58%,and 8.05%,proving the effectiveness of the model.
关键词
软测量/4-CBA/随机卷积核神经网络/数据增强/XGBoostKey words
soft sensor/4-CBA/random convolutional kernel neural network/data enhancement/XGBoost引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据