  Artificial neural network modeling does not need to consider the mechanism.
It can map the implicit relationship between input and output and predict the
performance of the system well. At the same time, it has the advantages of
self-learning ability and high fault tolerance. The gas-liquid two phases in
the rectification tower conduct interphase heat and mass transfer through
countercurrent contact. The functional relationship between the product
concentration at the top and bottom of the tower and the process parameters is
extremely complex. The functional relationship can be accurately controlled by
artificial neural network algorithms. The key components of the propylene
distillation tower are the propane concentration at the top of the tower and
the propylene concentration at the bottom of the tower. Accurate measurement of
them plays a key role in increasing propylene yield in ethylene production
enterprises. This article mainly introduces the neural network model and its
application in the propylene distillation tower.
