Prediction of outlet SO2 concentration based on a novel CNN-LSTM model

Abstract

Reliable and accurate prediction of SO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> concentration would be conducive to the effective manipulation and maintenance for wet flue gas desulfurization (WFGD) unit and is of great significance for saving resources and protecting the environment. The desulfurization system has a large time delay and strong nonlinearity. Aiming at the problems above, this paper proposed a novel hybrid model based on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) for predicting the SO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> concentration at outlet. Firstly, the CNN method is used to extract features from the inputs, and decrease the dimension of data. Secondly, LSTM has unique advantages for processing time series which is applied to solve the problem of time delay in desulfurization system. Dataset is sampled from a practical wet limestone-gypsum desulfurization system to evaluate the performance of the proposed model, and experiments are carried out in which the proposed model is compared with the LSTM model. The results indicate that the proposed CNN-LSTM model has lower errors and outperforms on the predicted results than LSTM model.