  Most chemical processes, such as distillation, absorption, extraction, and
catalytic reactions, are extremely complex processes that are affected by
multiple factors. The relationships between their input variables and output
variables are non-linear, and it is difficult to optimize or control them using
traditional methods. Artificial neural network (ANN) is a systematic structure
composed of multiple neuron models. Its main function is to simulate multiple
basic functions of the nervous system of living organisms. ANN can achieve
nonlinear control without relying on mathematical models, and is especially
suitable for more complex control objects. This article will introduce the
basic principles and development history of artificial neural networks, and
review its application research progress in chemical process control, fault
diagnosis, and process optimization.
