Time-delay neural network for the prediction of carbonation tower's temperature

The carbonation tower is a key reactor to manufacturing synthetic soda ash using the Solvay process. Because of the complexity of the reaction in the tower, it is difficult to control such a nonlinear large-time-delay system with normal measurement instrumentation. To solve this problem, a time-delay neural network (TDNN) is used in the soft measurement model in this paper. A special back-propagation algorithm is developed to train the neural network. Compared with the model based on multilayered perceptron, it is shown that TDNN can describe the system's dynamic character better and predict much more precisely. The influences of the input variables to the output of the model are analyzed with the online data. Analysis results show this model matches the reaction kinetics and the real operating conditions.