Dynamic soft measuring method of 4-CBA content based on convolutional neural network

Abstract

The invention discloses a dynamic soft measuring method of 4-CBA content based on a convolutional neural network. The dynamic soft measuring method of the 4-CBA content based on the convolutional neural network is used for calculating the content of 4-CBA generated in the PTA oxidation process. The dynamic soft measuring method of the 4-CBA content based on the convolutional neural network comprises the following steps: firstly, constructing a mapping relation between an input and an output of a dynamic soft measurement model on the basis of the convolutional neural network, using a time sequence data block of a relevant measurable variable in the PTA oxidation process as the input of the dynamic soft measurement model and using the 4-CBA as the output of the dynamic soft measurement model; secondly, inputting the time sequence data block into the convolutional neural network in which convolutional layers and pooling layers are alternately distributed, wherein the layer numbers of theconvolutional layers and the pooling layers are both 2, the first layer of pooling adopts characteristics after convolution is extracted in a one-dimensional max-pooling manner, and the second layer of pooling adopts max-pooling equivalent to the sizes of characteristic graphs output by the convolutional layers to perform sampling; calculating the output of the last layer of pooling by using a linear function to obtain an output result; and comparing the result with 4-CBA analysis data and updating parameters. The dynamic soft measuring method of the 4-CBA content based on the convolutional neural network, disclosed by the invention, has the advantages that the dynamic soft measurement model is simple and easy to realization, and the measurement accuracy of the model is improved.

Description

technical field [0001] The invention belongs to the field of chemical engineering, and in particular relates to a dynamic soft sensing method of 4-CBA content based on a convolutional neural network. Background technique [0002] Convolutional neural network is a classic feedforward neural network, which is mainly proposed by the concept of receptive field in biology. It is an important model in deep learning. Each layer of the network consists of a convolutional layer and subsequent downsampling. The convolution layer uses the convolution kernel to perform convolution operations on the output of the upper network. Compared with the fully connected method, this structure is more in line with the working method of biological neurons, and reduces network parameters, thereby suppressing overfitting and speeding up training; downsampling performs statistical calculations on convolution results, enabling The features of have certain translation invariance and rotation invariance...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to provide a dynamic soft-sensing method of 4-CBA content based on convolutional neural network, the method uses the time series data blocks of the relevant measurable variables in the PTA oxidation process as model input, and the The dynamic characteristics of variables are introduced into the model to obtain an efficient dynamic soft sensor model for 4-CBA content, which effectively solves the problems of low measurement accuracy and poor robustness of the existing static soft sensor model. The specific technical solutions are as follows:

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