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

¹ Institute of Electric Power, Inner Mongolia University of Technology, Hohhot, Inner Mongolia, 010051, China
² Inner Mongolia Lingyi High-tech Group, Hohhot, Inner Mongolia, 010010, China

^* Corresponding author’s e-mail: LJ-LY@163.com

Abstract

During the normal production process of the brewery, 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 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.