  Distillation process is a complex process of conduction, mass transfer and
heat conduction, which is mainly manifested as follows: The mechanism is
complex and changeable with uncertainty; the process is multivariate and strong
coupling; the system is nonlinear, hysteresis and time-varying. Neural networks
can perform effective learning based on corresponding samples, do not rely on
fixed mechanisms, have the ability to approximate arbitrary nonlinear mappings,
and can be used to establish system input and output models. The temperature
system of the rectification tower has a complicated structure and high accuracy
requirements. The neural network is used to control the temperature of the
system, which satisfies the requirements of the production process. This
article briefly describes the basic concepts and research progress of neural
network and distillation tower temperature control, and systematically
summarizes the application of neural network in distillation tower control,
aiming to provide reference for the development of related industries.
