Prediction of operating conditions of batch distillation process based on LSTM and BP neural networks

The batch distillation process is an important separation and purification process, and its operating condition prediction plays an important role in ensuring the smooth operation of the batch distillation process, optimizing the production quality and yield of batch distillation. This article conducted in-depth research on the model establishment, operating condition prediction algorithm, and simulation software design of the fine chemical D1 batch distillation process. Firstly, a data-driven model for the D1 batch distillation process was established using operation data of historical production, combined with the characteristics of long short term memory (LSTM) and back propagation (BP) neural networks, to predict the rising vapor temperature, distillate temperature, distillation endpoint time, and final product purity. Then, the above work was combined through Matlab's graphical user interface (GUI) to develop a simulation GUI for the D1 batch distillation process, which achieved the prediction of operating parameters and control simulation from data processing to final results. The simulation test results showed that the prediction of batch distillation conditions was fast and accurate, and had important reference value for guiding actual process operations. © 2024 Chemical Industry Press Co., Ltd.. All rights reserved.