This work is devoted to the study and solution of the problems of modeling complex objects on the example of the atmospheric block of the primary oil refining unit, associated with the deficit and fuzziness of the necessary initial information. Since many real technological objects of oil refining and other industries are often characterized by a deficit and fuzziness of the necessary information for their study, modeling, and optimization, this work allows solving an urgent scientific and practical problem. An effective method has been proposed that allows, based on a system approach, expert assessment methods, theories of fuzzy sets, and available information of various natures to develop hybrid models of complex objects in conditions of deficiency and fuzzy initial information. Based on the proposed hybrid method and available statistical and fuzzy information, effective hybrid models of atmospheric block columns of the primary oil refining unit were developed. In this case, statistical models were developed based on experimental and statistical data. With crisp input, mode parameters, and fuzzy output parameters, atmospheric block fuzzy models based on the proposed method, determining the quality of the manufactured products, were developed. Moreover, with the fuzzy input, mode, and output parameters of the atmospheric block columns, linguistic models based on the methods of expert assessments, logical rules of conditional inference, and the proposed method, assessing the quality of the produced gasoline, were developed. The linguistic models developed in Fuzzy Logic Toolbox allow for the assessment of the quality of gasoline from the atmospheric block depending on the content of chloride salts and the mass fraction of sulfur in the raw material. The results obtained using the proposed modeling method show their advantages in comparison with known modeling methods.

Optimizing the operating modes of hydrotreating units is difficult because process parameters are numerous and interdependent, and product-quality indicators are often available only as fuzzy expert assessments. This study integrates fuzzy mathematical modeling, a heuristic decision-making method, and machine learning into a unified framework for controlling a hydrotreating unit. A system of models was developed for the reactor, three columns, and the furnace, combining nonlinear regression equations for measurable parameters, fuzzy regression equations for quality indicators, and a heat balance equation. The reactor control problem with fuzzy quality constraints was solved using the heuristic AorRA+PO method (absolute or relative allowance with Pareto optimality), which takes the decision-maker’s priorities into account. In parallel, eight regression algorithms were compared on a synthetic dataset, and the best gradient-boosting models (R² = 0.989–0.992) were used as surrogates in a two-stage optimization. The fuzzy approach increased the hydrotreated product volume to 78.0 m³/h, which is 2.0% higher than the deterministic method and 0.6% higher than the production mode; the machine learning approach yielded 79.6 m³/h with the quality constraints satisfied to a degree of 0.96–1.00. The lower reactor temperature in the recommended mode also reduces the furnace heat duty by about 2.5%. Because the machine learning models were trained on synthetic data, the recommended mode requires experimental verification at the unit.

In practice, when developing models of many production facilities, problems often arise related to the fuzziness of some of the initial information, affecting important indicators of the quality of the facility’s operation. The purpose of the study is to develop a systematic method for synthesizing statistical, fuzzy and linguistic models of complex objects in conditions of shortage and fuzziness of initial information. Then, using the proposed method, various models of the atmospheric unit of a primary oil refining plant are developed. At the same time, statistical models are developed on the basis of traditional methods. With crisp input and operating parameters and fuzzy output parameters, based on the proposed method, fuzzy models of the atmospheric unit are developed that determine the quality of the manufactured products. And when the input, operating and output parameters of the object are fuzzy, there have been developed linguistic models that evaluate the qualities of the target product of the atmospheric unit based on expert assessment methods, logical rules of conditional inference and the proposed method. The developed linguistic models in Fuzzy Logic Toolbox make it possible to evaluate the quality of gasoline from an atmospheric unit depending on the content of chloride salts and the mass fraction of sulfur in the raw material. The advantages of the proposed modeling method compared to known ones are shown.

Исследуются проблемы разработки моделей и управления процессом первичной переработки нефти в условиях неопределенности из-за дефицита и нечеткости исходной информации и предлагается подходы к их решению. В результате систематизации выделены и описаны три подходы к разработке моделей сложных объектов в условиях дефицита и нечеткости исходной информации. Путем комбинации и модификации различных принципов оптимальности к нечеткости формулируется математическая постановка задачи принятия решений для управления режимами работы установки первичной переработки нефти и предложен подход к ее решению. Преимуществом предложенного подхода к управлению режимами работы объекта исследования на основе его моделей является возможность максимального использования исходной нечеткой информации, представляющего собой знания, опыт, интуицию лица, принимающего решения, экспертов. Это позволяет получить более адекватные решения реальных производственных задач, характеризуемые нечеткостью исходной информации.