Artificial Neural Network and Its Application Research Progress in Chemical Process
Li Sun *
School of Chemical Engineering, East China University of Science and Technology, 200237, Shanghai, China.
Fei Liang
School of Chemical Engineering, East China University of Science and Technology, 200237, Shanghai, China.
Wutai Cui
School of Chemical Engineering, East China University of Science and Technology, 200237, Shanghai, China.
*Author to whom correspondence should be addressed.
Abstract
Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions, are extremely complex processes affected by multiple factors. As a result, the relationships between their input and output variables are non-linear, and it is not easy to optimize or control them using traditional methods. Artificial neural network is a systematic structure composed of multiple neuron models. By simulating many basic functions of the nervous system of living organisms, nonlinear control can be realized without relying on mathematical models, and it is especially suitable for more complex control objects. This article will introduce artificial neural networks' basic principles and development history, and review its application research progress in chemical process control, fault diagnosis, and process optimization.
Keywords: Artificial Neural Network, chemical processes, human brain, model predictive control