###### 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. c pacs: 88.85.-b, 88. The process of formalizing and solving problems of developing mathematical models, optimization problems in the presence of conflicting criteria, i.e., decision-making and management tasks for many technological objects of oil refining production, are complicated by the multi-criteria and fuzzy initial information [4; 5; 6]. Under these conditions, a more effective approach for solving these problems is the use of search methods of multiciteria (vector) optimization [7; 8] and the mathematical apparatus of the theory of fuzzy [6; 8; 9; 10], as well as the creation of a knowledge base and intellectualized decision support systems for the optimal management of technological objects and processes [8; 11; 12; 13; 14; 15]. Thus, the development of mathematical support for intellectualized decision support systems for the optimal management of oil refining facilities in a fuzzy environment based on the knowledge and experience of experts are currently very important and urgent tasks of science and oil refining. Currently, all refineries use catalytic cracking and catalytic reforming process units for the production of high-quality gasoline, which uses various catalysts and includes various blocks and interconnected process units [3; 16; 17]. In this work, the objects of modeling are the main hydrotreating units of the catalytic reforming unit of the LG-35-11/300-95 type, operating at the Atyrau refinery (AOR) since 1971 [18]. The design capacity of this unit was 300 thousand tons per year. As a result of modernization carried out at the Atyrau refinery, the capacity has now been increased to 450 thousand tons per year. The LG unit carries out the process of catalytic reforming of straight-run gasoline from the primary oil refining unit, which is the most important technological process of modern oil refining and petrochemistry. The catalytic reforming unit LG-35-11/300-95 uses the catalysts UOP--S-12T (in the hydrotreating unit) and UOP--R-56 (in the reforming unit). In the investigated hydrotreating block of the catalytic reforming unit LG-35-11/300-95 of the Atyrau refinery for which the model is being built, an aluminum-cobalt-molybdenum catalyst of the S-12T type, developed by the American company UOP, is used.