Open AccessOPTIMIZING THE CONTENT OF THE SEDIMENTS IN THE PROCESS OF HUDRON′S HYDROCRACKING WITH THE USE OF MACHINE LEARNING METHODS
Author(s) -
A. S. Nuzhny,
Ivan Odnolko,
A. O. Glukhov,
Maxim Butyrin,
Ė. N. Levchenko,
Alexander Starikov,
Igor Karasev,
Svetlana Lapinova
Publication year - 2021
Publication title -
prikladnaâ matematika i voprosy upravleniâ
Language(s) - English
Resource type - Journals
eISSN - 2782-4500
pISSN - 2499-9873
DOI - 10.15593/2499-9873/2021.1.01
Subject(s) - refinery , computer science , process (computing) , mode (computer interface) , process engineering , mathematical optimization , engineering , mathematics , waste management , operating system
The paper proposes a mathematical model to optimize the operation of the tar hydrocracking unit. The purpose of modeling is to improve the economic effect of product output by selecting optimal parameters, such as hydrogen flow rate and reactor temperature. Hot Filtered Precipitation (HFT) is used as a target. The model involves the search for the minimum value of the functional with restrictions presented in the form of a fine imposed when the parameters go beyond the permissible values, as well as when the target parameter deviates from the specified value. The execution of the algorithm includes two stages. The first stage is the simulation of the HFT value for a given state of the installation at the selected parameters of temperature and hydrogen flow rate using a virtual analyzer, the second stage is to solve the optimization problem by selecting the control parameters of the installation. For the first stage, a model for assessing the HFT indicator by technological indicators was built, including the main factors determining it; machine learning methods were used to find the parameters of the models. The free standard library of optimum search tools scipy.optimize was used to solve the optimization problem. Powell's algorithm was chosen as the optimization method. The paper presents the results of testing the model on real data provided by an oil refinery in the city of Burgas in Bulgaria. The study period includes several operating modes of the installation, in particular, the intensive load mode during 2018-2019 and low load during the 2020 period. The results of testing the model on real data presented in the work have been verified by experts in the field of oil refining for compliance with real conditions.