Прогнозирование выбросов пыли (PM2.5) на угольных разрезах с помощью нейронной сети с функциональными связями, оптимизированной различными алгоритмами

Прогнозирование выбросов пыли (PM2.5) на угольных разрезах с помощью нейронной сети с функциональными связями, оптимизированной различными алгоритмами

Автор: Буи Суан-Нам, Нгуен Хоанг, Ле Ки-Тао, Ле Туан-Нгок

Журнал: Горные науки и технологии @gornye-nauki-tekhnologii

Рубрика: Технологическая безопасность в минерально-сырьевом комплексе и охрана окружающей среды

Статья в выпуске: 2 т.7, 2022 года.

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Загрязнение воздуха PM2.5 (твердые частицы размером 2,5 мк и менее) представляет собой не только значительную опасность для здоровья человека в повседневной жизни, но и опасный риск для рабочих при открытых горных работах, особенно на угольных разрезах. PM2.5 на угольных разрезах могут вызывать заболевания легких (например, пневмокониоз, рак легких) и сердечно-сосудистые заболевания из-за длительного воздействия вдыхаемой пыли. Поэтому точное прогнозирование PM2.5 имеет большое значение для минимизации загрязнения PM2.5 и улучшения качества воздуха на рабочих местах. В данном исследовании изучались метеорологические условия и выбросы PM2.5 на угольном разрезе во Вьетнаме с целью разработки новой интеллектуальной модели для прогнозирования выбросов и загрязнения PM2.5, применялась нейронная сеть с функциональными связями (FLNN) для прогнозирования загрязнения PM2.5 в зависимости от метеорологических условий (в частности, температуры, влажности, атмосферного давления, направления и скорости ветра). Вместо традиционных алгоритмов для обучения модели FLNN был использован алгоритм поиска методом голодных игр (HGS). Важнейшая роль HGS в данном исследовании заключается в оптимизации весов в модели FLNN, которая была названа моделью HGS-FLNN. Также были рассмотрены три другие гибридные модели, основанные на FLNN и метаэвристических алгоритмах, т.е. ABC (искусственная пчелиная колония)-FLNN, GA (генетический алгоритм)-FLNN и PSO (оптимизация роя частиц)-FLNN, для оценки возможности прогнозирования PM2.5 на угольных разрезах и сравнения их результатов с результатами модели HGS-FLNN. Исследования показали, что HGS-FLNN является лучшей моделью с самой высокой точностью прогнозирования загрязнения воздуха PM2.5 (в среднем до 94-95 %, при этом точность других моделей варьировалась от 87 до 90 %), а также наиболее стабильной моделью с наименьшей относительной ошибкой (в диапазоне от -0,3 до 0,5 %).

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Угольный разрез, загрязнение воздуха, пыль, pm2.5, здоровье человека, поиск методом голодных игр, нейронная сеть с функциональными связями, оптимизация, разрез кок сау, провинция куангнинь, вьетнам

Короткий адрес: https://sciup.org/140295675

IDR: 140295675   |   DOI: 10.17073/2500-0632-2022-2-111-125

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