关键词: 机器学习, 乙烯裂解炉, 算法, 模型, 预测, 神经网络

Abstract:

Convolutional neural network is one of the representative algorithms of deep learning, and has made remarkable achievements in many fields such as computer vision and natural language processing in recent years, among which visual geometry group (VGG) is a widely used convolutional neural network model, and shows great potential in image recognition and classification tasks. However, the application of this algorithm in the data regression prediction scenario is not good, and there are limitations of large number of parameters and large memory consumption. To this end, a new deep VGG architecture (D-VGG) is proposed, which improves the different convolution kernel configurations of block1, block2, block3, block4 and block5 in VGG, adopts a more efficient conv5-32 layout, and adds batch normalization layers in each layer of the block. A Dropout layer is added before the fully connected layer to effectively alleviate overfitting problems and accelerate the convergence of the training process. The improved D-VGG network architecture has demonstrated excellent simulation prediction performance, solving the problem of poor regression prediction performance of convolutional neural networks. The prediction performance of the network is evaluated using the data set of an ethylene cracking furnace in a factory, and compared with other machine learning models CNN, CNN-LSTM, BP neural network, SVR, etc. The experimental results show that the prediction performance of D-VGG model is superior to other models, the test set R² reaches the highest of 0.9748, RMSE decreases by 37.5% compared with VGG16 model, and its MAE, MBE and RMSE error evaluation indexes are the smallest.

Key words: machine learning, ethylene cracker, algorithm, model, prediction, neural networks