Modeling of a delayed coking plant in the MatLab program

In order to prevent the occurrence of an emergency situation in the oil and gas industry, it is necessary to use control, automation and alarm systems. In this regard, artificial intelligence technologies have recently become in-creasingly popular. Neural networks are of particular interest. To implement the task of regulating, automating and predicting technological processes in the oil industry, it is possible to use neural network modeling of chemical and technological processes. Examples of using neural network modeling in practice are given. The results of neural network modeling of a delayed coking plant of one of the operating enterprises are presented. A delayed coking installation was modeled in the UniSim Design software package, which allowed us to obtain an initial data array for a neural network. The neural network was built in the MatLab program, and the program code was created. Graphs of error and regression are presented. The analysis of the results presented on the regression and error graphs is given. As a result of testing the model, minimal discrepancies between experimental and predicted data were obtained, which indicates the adequacy of the neural network model. An additional test of the program was also performed. The results of training and testing of the model are presented. The results obtained can later be used to create programs at different levels of control, since the model allows you to estimate the amount of losses during the operation of the installation at a certain flow rate of feed water supplied to the installation as a turbocharger.