Study on the simulation control of neural network algorithm in thermally coupled distillation

TL;DR

This paper explores using neural networks to efficiently simulate and optimize thermally coupled distillation processes, overcoming traditional calculation complexities and improving control system performance.

Contribution

It introduces a neural network-based method for simulating and optimizing thermally coupled distillation, simplifying the process and enhancing solution speed and accuracy.

Findings

Neural networks effectively model thermally coupled distillation.

The method achieves faster optimization of process variables.

Improved control performance in complex distillation systems.

Abstract

Thermally coupled distillation is a new energy-saving method, but the traditional thermally coupled distillation simulation calculation process is complicated, and the optimization method based on the traditional simulation process is difficult to obtain a good feasible solution. The neural network algorithm has the advantages of fast learning and can approach nonlinear functions arbitrarily. For the problems in complex process control systems, neural network control does not require cumbersome control structures or precise mathematical models. When training the network, only the input and output samples it needs are given, so that the dynamics of the system can be controlled. Performance is approaching. This method can effectively solve the mathematical model of the thermally coupled distillation process, and quickly obtain the solution of the optimized variables and the objective function. This article summarizes the research progress of artificial neural network and the optimization control of thermally coupled distillation and the application of neural network in thermally coupled distillation.