Development of neural network controller for control of debutanization process in rectification column of gas-fractionating plant

In process systems, stabilizing parameters and bringing the process to technological mode is a rather difficult task. Control and control of parameters in manual mode does not produce effective results, and for this reason the introduction of an improved control system is required to eliminate human factor errors. Currently, the best efficiency is shown by neural networks, which are used for an advanced control and prediction system. The purpose of the study: in this project, a neural network controller is being developed to control the debutanization process of the gas fractionation plant in the Matlab Simulink environment. Cascade neural networks consisting of a certain number of layers and neurons are used to develop the project. During the work, a cascade neural network was selected, in which the parameters of the technological system were trained.