ОПТИМИЗАЦИЯ ПАРАМЕТРОВ ПИД-РЕГУЛЯТОРА С ИСПОЛЬЗОВАНИЕМ АЛГОРИТМОВ МАШИННОГО ОБУЧЕНИЯ НА ОСНОВЕ ДАННЫХ ПРОЦЕССА СЕПАРАЦИИ НЕФТИ

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.

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