The research of recognition of peep door open state of ethylene cracking furnace based on deep learning.

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Abstract

In the chemical industry, the ethylene cracking furnace is the core ethylene production equipment, and its safe and stable operation must be ensured. The fire gate is the only observation window to understand the high temperature operating conditions inside the cracking furnace. In the automatic monitoring process of ethylene production, the accurate identification of the opening and closing status of the fire door is particularly important. Through the research on the ethylene cracking production process, based on deep learning, the open and closed state of the fire gate is recognized and studied. First of all, a series of preprocessing and augmentation are performed on the originally collected image data of the fire gate. Then, a recognition model is constructed based on convolutional neural network, and the preprocessed data is used to train the model. Optimization algorithms such as Adam are used to update the model parameters to improve the generalization ability of the model. Finally, the proposed recognition model is verified based on the test set and is compared with the transfer learning model. The experimental results show that the proposed model can accurately recognize the open state of the fire door and is more stable than the migration learning model.