Control of the Pyrolysis Fraction Cleaning Process Using a Neural Network

Abstract

The process of purification of the pyrolysis fraction from acetylene compounds is one of the stages in the production of butadiene. The efficiency of purification of the pyrolysis fraction from acetylene compounds and the selectivity of the reaction are affected by the volumetric feed rate of the fraction, the mass concentration of butadiene before hydrogenation, the mass content of alpha-acetylene compounds, the content of pure acetylene, and hydrogen consumption. Artificial neural networks are selected for the development of a process control system due to the fact that they are fault tolerant. In a neural network, information is distributed throughout the network, which means if a neuron fails, the behavior of the network will be changed slightly, the behavior of neurons will change, but the network itself continues to function successfully. It is necessary to develop a neural network to control the process of purification of the pyrolysis fraction from acetylene compounds. To minimize the loss of butadiene, it is proposed to use a more efficient control system that will take into account the optimal ratio of butadiene to acetylene and the flow rate of the fraction, which significantly affect the yield of butadiene. As a result of the training, a neural network was obtained which, without reconfiguring the connection weights, generates output signals when any set of input signals from the training set is fed to the network input.