Abstract:
This research develops a method for synthesizing linguistic models of fuzzy systems
with fuzzy input and output parameters that are described by linguistic variables. Based on the
proposed method, linguistic models of the Title 1000 catalytic cracking unit for heavy residues at
the Shymkent oil refinery are developed, describing the dependence of the volume and quality of
gasoline on the input and operating parameters of the facility, which are fuzzy. It is substantiated
that the use of a fuzzy approach, which allows the use of the experience, knowledge, and intuition
(intelligence) of the decision maker and subject matter experts, is the most suitable effective method
for synthesizing models of complex, fuzzily described objects and processes for comparison with
other methods. The main idea of the proposed work is to solve the problems of shortage and fuzziness
of initial information when developing models and optimizing the operating modes of a catalytic
cracking unit through the use of knowledge, experience, and intuition of experts in this field. To
solve the problems of the shortage of initial quantitative information and the fuzziness of available
information when developing mathematical models, it is proposed to systematically use statistical
methods, expert assessment methods, and a heuristic method based on fuzzy logic. The scientific
novelty of the research lies in the development of a method for synthesizing linguistic models in a
fuzzy environment and an algorithm for its implementation, which makes it possible to describe the
dependence of the fuzzy values of the object’s output parameters on its fuzzy input and operating
parameters. The proposed approach allows the formalization and synthesis of models of fuzzily
described objects when other methods of model development are not applicable or do not give the
expected results. The results of the work were simulated in the MATLAB Fuzzy Logic Toolbox.
