Production prediction modeling of industrial processes based on Bi-LSTM

Abstract

The analysis and prediction of industrial production plants are of great significance for reducing energy consumption, improving economic efficiency. Therefore, a production prediction method based on bidirectional long short-term memory (Bi-LSTM) is proposed to accurately analyze and evaluate the energy efficiency status of ethylene production plants in industrial processes. Bi-LSTM is a Indirection ally connected network with two layers of long short-term memory (LSTM), it gives full consideration to the relationship between the current data and the data before and after it. Bi-LSTM solves the gradient disappearance or gradient explosion problem in recurrent neural network (RNN), and overcomes the drawback that LSTM only consider the relationship between the current data and its previous data. The comparison results show that the prediction effect of the Bi-LSTM model is superior to that of the back propagation (BP) neural network model, and the average relative error is reduced by 70%, which proves that the Bi-LSTM can effectively raise the accuracy and stability of the ethylene production prediction.