Development and Synthesis of Linguistic Models for Catalytic Cracking Unit in a Fuzzy Environment

Author:

Orazbayev Batyr1ORCID,Boranbayeva Narkez1,Makhatova Valentina2,Rzayeva Leila3,Ospanov Yerbol4,Kurmashev Ildar5ORCID,Kurmangaziyeva Lyailya2

Affiliation:

1. Faculty of Information Technologies, L. N. Gumilyov Eurasian National University, Astana 010000, Kazakhstan

2. Faculty of Physics, Mathematics and Information Technology, Kh. Dosmukhamedov Atyrau University, Atyrau 060011, Kazakhstan

3. Department Intelligent Systems and Cybersecurity, Astana IT University, Astana 010000, Kazakhstan

4. Faculty of Engineering and Technology, Shakarim University, Semey 070014, Kazakhstan

5. Department of Information and Communication Technologies, M. Kozybayev North Kazakhstan University, Petropavlovsk 150000, Kazakhstan

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.

Publisher

MDPI AG

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