Abstract:
Methods for the development of fuzzy and linguistic models of technological objects,
which are characterized by the fuzzy output parameters and linguistic values of the input and output
parameters of the object are proposed. The hydrotreating unit of the catalytic reforming unit was
investigated and described. On the basis of experimental and statistical data and fuzzy information
from experts and using the proposed methods, mathematical models of a hydrotreating reactor and a
hydrotreating furnace were developed. To determine the volume of production from the outlet of
the reactor and furnace, nonlinear regression models were built, and fuzzy models were developed
in the form of fuzzy regression equations to determine the quality indicators of the hydrotreating
unit—the hydrogenated product. To identify the structure of the models, the ideas of sequential
inclusion regressors are used, and for parametric identification, a modified method of least squares
is used, adapted to work in a fuzzy environment. To determine the optimal temperature of the
hydrotreating process on the basis of expert information and logical rules of conditional conclusions,
rule bases are built. The constructed rule bases for determining the optimal temperature of the
hydrotreating process depending on the thermal stability of the feedstock and the pressure in the
hydrotreating furnace are implemented using the Fuzzy Logic Toolbox application of the MatLab
package. Comparison results of data obtained with the known models, developed models and
real, experimental data from the hydrotreating unit of the reforming unit are presented and the
effectiveness of the proposed approach to modeling is shown.
