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 %).
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
Forecasting PM2.5 emissions in open-pit mines using a functional link neural network optimized by various optimization algorithms
PM2.5 air pollution is not only a significant hazard to human health in everyday life but also a dangerous risk to workers operating in open-pit mines OPMs), especially open-pit coal mines (OPCMs). PM2.5 in OPCMs can cause lung-related (e.g., pneumoconiosis, lung cancer) and cardiovascular diseases due to exposure to airborne respirable dust over a long time. Therefore, the precise prediction of PM2.5 is of great importance in the mitigation of PM2.5 pollution and improving air quality at the workplace. This study investigated the meteorological conditions and PM2.5 emissions at an OPCM in Vietnam, in order to develop a novel intelligent model to predict PM2.5 emissions and pollution. We applied functional link neural network (FLNN) to predict PM2.5 pollution based on meteorological conditions (e.g., temperature, humidity, atmospheric pressure, wind direction and speed). Instead of using traditional algorithms, the Hunger Games Search (HGS) algorithm was used to train the FLNN model. The vital role of HGS in this study is to optimize the weights in the FLNN model, which was finally referred to as the HGS-FLNN model. We also considered three other hybrid models based on FLNN and metaheuristic algorithms, i.e., ABC (Artificial Bee Colony)-FLNN, GA (Genetic Algorithm)- FLNN, and PSO (Particle Swarm Optimization)-FLNN to assess the feasibility of PM2.5 prediction in OPCMs and compare their results with those of the HGS-FLNN model. The study findings showed that HGS-FLNN was the best model with the highest accuracy (up to 94-95 % in average) to predict PM2.5 air pollution. Meanwhile, the accuracy of the other models ranged 87 % to 90 % only. The obtained results also indicated that HGS-FLNN was the most stable model with the lowest relative error (in the range of -0.3 to 0.5 %).
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