By correlated the main coal quality indexes such as sulfur mass fraction, ash mass fraction, volatile mass fraction in coal, a coke quality prediction model was established using a GRU neural network model based on the Adma algorithm as the optimizer, continuously adjusting the model parameters, and the multi-label multi-classification method was developed on the basis of using the Sigmoid activation function to judge the accuracy of GRU neural network. The results show that when the number of hidden layer neurons in a three-layer GRU network is (64, 64, 64), the learning rate is 0.01, the batch size is 64, the training frequency is 50, and the dropout rate is 0.3, the model achieves optimal performance. Under these conditions, the predic-tion accuracy of the model reaches 97%. The coke quality prediction model is proposed, which is based on the GRU neural network and the multi-label multi-classification method, not only demonstrates high precision but can also produce accurate predictions of coke quality using small sample coal blending data. These results hold significant value for actual coal blending coking and can serve as an important reference.
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