Dynamic prediction of steam consumption in beer production process based on Attention Mechanism CNN-BiLSTM

Dynamic prediction of steam consumption in beer production process based on Attention Mechanism CNN-BiLSTM

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During the normal production process of the brewery, 3 piece horse wall art the steam consumption can be accurately predicted, and the boiler steam output can be planned to achieve a balance between steam production and use.In order to improve the accuracy of steam consumption prediction, this paper proposes a dynamic prediction method of steam consumption based on attention mechanism-convolution-bidirectional long short-term memory neural network (CNN-BiLSTM).In this paper, the real-time monitoring data of the brewery energy management system is selected as the experimental data borstlist självhäftande for analysis, and the CNN-BiLSTM network model based on attention mechanism is compared with the results of CNN net-work, LSTM network, BiLSTM network and CNN-BiLSTM network prediction.The experimental results show that the mean absolute error, root mean square error and R-Square of the model are 0.

0329, 0.0449 and 97.5%, which are better than the other four models, and can predict the steam consumption of brewery more accurately.