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.

Wireless networks are crucial to modern communication infrastructure, supporting a wide range of applications from personal use to industrial operations. The rapid growth of mobile devices, IoT, and advanced technologies like 5G has heightened the demand for scalable, efficient, and reliable wireless networks. Routing in wireless networks is particularly complex due to the dynamic nature of the medium, device mobility, and diverse topologies. Traditional routing algorithms, though effective in some cases, frequently fail to handle the complexities of contemporary wireless networks. Integrating machine learning technologies with routing algorithms offers a promising solution. In particular, reinforcement learning (RL) has become prevalent in routing due to its ability to operate without the need for extensive datasets. This article evaluates two RL-based routing algorithms, by comparing their average delivery times under various loads across various topologies. This study offers a comprehensive analysis that addresses the importance of different network structures, providing insights into how each algorithm performs across diverse topologies, an aspect not covered in previous surveys.

Данная статья посвящена к разработке математической модели, пригодная для автоматизированного управления процессом каталитического крекинга в условиях неопределенности, вызванной случайного и нечеткого характера исходной, доступной информации. На основе разработанной и других моделей объекта реакторно-регенераторного блока установки каталитического крекинга далее предполагается решению задачи принятия решений по управлению режимами их работы основаны с привлечением лица, принимающего решения и экспертов. Разработанная математическая модель способствует повышению ресурс эффективности установки каталитического крекинга путем определения оптимального режима работы реактора и регенератора установки. Для определения коэффициентов множественной регрессии написан програмный код на языке Python, результаты моделирования визуализированы с помощью програмного обеспечения Power BI.

Мақалада Python бағдарламалық ортасында регрессия әдістерін қолдану арқылы каталитикалық крекинг қондырғысынан өнімнің шығуын анықтау тапсырмасы қарастырылады. Каталитикалық крекинг мұнай өндірудің негізгі процесі болғандықтан, өнімнің шығуын нақты болжау технологиялық параметрлерді оңтайландыру және өнімнің тиімділігін арттыру үшін өте маңызды. Мақалада температура, қысым, шикізат тығыздығы және катализатор шығыны сияқты әр түрлі технологиялық параметрлердің әсер етуін талдау нәтижесінде модельдерді жасап шығару әдістемесі қарастырылған. Pandas, Scikit-learn және Matplotlib сияқты Python мәліметтерді талдау инструменттері мен кітапханаларын қолдану толық талдау жасап, нәтижелерін визуализациялауға мүмкіндік береді. Модельдің сапасын бағалау детерминациялау коэфициенті (R2) және қалдық қателерді талдау арқылы жүргізіледі. Алынған нәтижелер моделдің жоғарғы дәлдігі мен ұсынылған тәсілдің өндірістегі мұнай өндіру процестерін болжау және оңтайландыру мүмкіндігін дәлелдейді.

This paper discusses the application of nonlinear regression to forecast and optimize the operation of catalytic cracking units under conditions of fuzzy information. Catalytic cracking is a crucial process in oil refining that produces high-quality gasoline and other light hydrocarbon products. However, the complexity of the process and the uncertainty of initial data complicate the modeling and optimization of plant operations. To address this issue, a nonlinear regression method is proposed that accommodates the fuzziness of input and output parameters described by linguistic variables. The methodology includes the collection and formalization of expert knowledge, the construction of fuzzy models, and their integration into the process control system. Forecasting is performed by creating regression models that describe the relationships between operational parameters and product quality characteristics. The paper presents a procedure for developing and applying nonlinear regression models, describes algorithms for synthesizing linguistic models, and provides examples of their use to optimize the operation of catalytic cracking units. The modeling results demonstrate the high adequacy and accuracy of the proposed method, as well as its advantages over traditional approaches in conditions of uncertainty and data scarcity. The scientific novelty of the research lies in the development and testing of advanced nonlinear regression models adapted for analyzing and optimizing catalytic cracking processes based on fuzzy data. These methods take into account the specificity and uncertainty of process data, improving the accuracy and reliability of forecasts, which facilitates more effective management of production processes in the petrochemical industry. The main reason for conducting this study is the need to improve the control of oil refining processes, particularly catalytic cracking, which plays an important role in producing high-quality gasoline. The complexity of this process and the presence of fuzzy information caused by fuzzy initial data require the development of new modeling and optimization methods. Existing traditional models based on deterministic methods are often insufficient under uncertainty. This leads to a decrease in the accuracy of process control, which can negatively affect the quality of the final product and production efficiency. The use of nonlinear regression in combination with fuzzy logic is a more flexible and adaptive approach that allows you to take into account the fuzziness and uncertainty of data and use expert knowledge to build models that match the actual operating conditions of the units. Thus, this study aims to solve the key problems associated with data uncertainty and the complexity of the catalytic cracking process, which will improve the accuracy of forecasting and optimization of the units. The main contribution is creating a model that uses nonlinear regression methods in combination with fuzzy logic. This allows uncertainty in input data (such as reactor temperature or pressure) to be effectively considered and processed to improve gasoline and other product yield forecasts. It is shown that using nonlinear regression combined with fuzzy logic significantly improves the management of technological processes, increases the output and quality of products, and reduces production costs. The conclusion of the paper discusses the prospects for further development of the methodology and its application to solve similar tasks in other areas of chemical technology.