Review of Neural Network Algorithm and Its Application in Reactive Distillation
Huihui Wang *
School of Chemical Engineering, East China University of Science and Technology,Shanghai, 200237, China.
Ruyang Mo
School of Chemical Engineering, East China University of Science and Technology,Shanghai, 200237, China.
*Author to whom correspondence should be addressed.
Abstract
Artificial Neural Networks (ANN) can accurately identify and learn the potential relationship between input and output, and have self-learning capabilities and high fault tolerance, which can be used to predict or optimize the performance of complex systems. Reactive distillation integrates reaction and rectification into one device, so that the two processes occur at the same time and at the same place, but at the same time it also produces highly nonlinear robust behavior, making its process control and optimization unable to use conventional methods. Instead, neural network algorithms must be used. This paper briefly describes the research progress of neural network algorithms and reactive distillation technology, and summarizes the application of neural network algorithms in reactive distillation, aiming to provide reference for the development and innovation of industry technology.
Keywords: Reactive distillation, neural network algorithms, BP neural network, RBF neural network