kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2025-22-2-76-93kaz29-1988Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEОПТИМИЗАЦИЯ ПАРАМЕТРОВ ПИД-РЕГУЛЯТОРА С ИСПОЛЬЗОВАНИЕМ АЛГОРИТМОВ МАШИННОГО ОБУЧЕНИЯ НА ОСНОВЕ ДАННЫХ ПРОЦЕССА СЕПАРАЦИИ НЕФТИOPTIMIZATION OF PID CONTROLLER PARAMETERS USING MACHINE LEARNING ALGORITHMS BASED ON OIL SEPARATION PROCESS DATAhttps://orcid.org/0000-0002-5862-6415СамигулинаЗ.SamigulinaZ. I.

 PhD, доцент 

 г. Алматы 

 PhD, Associate Professor 

 Almaty 

z.samigulina@kbtu.kzhttps://orcid.org/0009-0003-7991-9836АманғалиеваА.AmangaliyevaA. G.

 бакалавр 

 г. Алматы 

 Bachelor 

 Almaty 

a_amangaliyeva@kbtu.kzҚазақстан-Британ техникалық университетіКазахстанKazakh-British Technical UniversityKazakhstan2025060720252227693Copyright © Самигулина З.I., Аманғалиева А.G., 20252025Самигулина З., Аманғалиева А.Samigulina Z.I., Amangaliyeva A.G.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/1988

В работе исследуется процесс оптимизации параметров ПИД-регулятора с помощью использования алгоритмов машинного обучения для системы управления процессом сепарации нефти. Оптимизация параметров контроллера (Kp, Ki, Kd) важна для повышения качества управления и уменьшения количества ошибок в динамических процессах. Для решения этой проблемы было рассмотрено несколько инновационных методов, таких как алгоритм поиска кукушки (CSA), алгоритм светлячков (FA), оптимизация роя частиц (PSO) и метод опорных векторов (SVM). Все данные, включая текущие значения процессов (PV), уставки (SP) и выходные сигналы (OP), были получены от «Тенгизшевройла». Кроме того, для оценки эффективности оптимизированных регуляторов использовались такие показатели, как среднеквадратичная ошибка (MSE), время настройки, превышение и установившаяся ошибка. В целом результаты исследования свидетельствуют о значительном улучшении динамических характеристик системы за счет использования алгоритмов машинного обучения по сравнению с традиционными подходами. Полученные параметры оптимизации достигли целевого значения, оставаясь при этом более быстрыми и стабильными, что позволило повысить производительность управления технологическим процессом.

This paper presents the investigation of the process of optimizing the parameters of a PID controller using machine learning algorithms for the oil separation process control system. The optimization of the controller parameters (Kp, Ki, Kd) is important, in order to improve control quality and reduce the number of errors in dynamic processes. To solve this issue, several innovative methods were considered, such as the cuckoo search algorithm (CSA), the firefly algorithm (FA), particle swarm optimization (PSO), and the support vector machine (SVM). All the data, including the current process values (PV), setpoints (SP) and output signals (OP) were obtained from Tengizchevroil. In addition, the metrics, such as root-mean-square error (MSE), adjustment time, overshoot, and steady-state error were used to assess the effectiveness of optimized regulators. Overall, the results of the research indicate that there was a significant improvement of the dynamic characteristics of the system due to the usage of machine learning algorithms compared to the traditional approaches. The obtained parameters of optimization achieved the target value while being faster and more stable, thus increasing the productivity of control in the technological process.

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The authors declare that there are no conflicts of interest present.