2411-3336 2541-9404 Записки Горного института Journal of Mining Institute Санкт-Петербургский горный университет императрицы Екатерины ΙΙ Empress Catherine II Saint Petersburg Mining University 10.31897/PMI.2022.89 pmi-15842 https://pmi.spmi.ru/pmi/article/view/15842 Металлургия и обогащение Metallurgy and concentration Rapid detection of coal ash based on machine learning and X-ray fluorescence Оперативный контроль элементного состава угольной золы на основе машинного обучения и рентгеновской флуоресценции Huang Jinzhan Хуан Цзиньчжань Huang Jinzhan dongl@cumt.edu.cn 0000-0003-2531-4834 Университет горного дела и технологий (Китай) China University of Mining & Technology (China) Li Zhiqiang Ли Чжицян Li Zhiqiang dongl@cumt.edu.cn 0000-0002-3661-6443 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) Chen Biao Чэнь Бяо Chen Biao dongliang@cumt.edu.cn 0000-0002-9304-6037 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) Cui Sen Цуй Сен Cui Sen dongliang@cumt.edu.cn 0000-0003-3250-5292 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) Lu Zhaolin Лу Чжаолинь Lu Zhaolin dongliang@cumt.edu.cn 0000-0002-9251-0758 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) Dai Wei Дай Вэй Dai Wei dongliang@cumt.edu.cn 0000-0003-3057-7225 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) Zhao Yuemin Чжао Юэминь Zhao Yuemin dongliang@cumt.edu.cn 0000-0003-2848-5962 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) Duan Chenlong Дуань Чэньлун Duan Chenlong dongliang@cumt.edu.cn 0000-0002-8093-6719 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) Dong Liang Дон Лян Dong Liang dongliang@cumt.edu.cn 0000-0003-0264-8807 Университет горного дела и технологий (Россия) China University of Mining & Technology (Russia) 03 11 2022 2022 256 663 676 13 05 2022 24 09 2022 03 11 2022 © 2022 Цзиньчжань Хуан, Чжицян Ли, Бяо Чэнь, Сен Цуй, Чжаолинь Лу, Вэй Дай, Юэминь Чжао, Чэньлун Дуань, Лян Дон © 2022 Jinzhan Huang, Zhiqiang Li, Biao Chen, Sen Cui, Zhaolin Lu, Wei Dai, Yuemin Zhao, Chenlong Duan, Liang Dong 2022 Цзиньчжань Хуан, Чжицян Ли, Бяо Чэнь, Сен Цуй, Чжаолинь Лу, Вэй Дай, Юэминь Чжао, Чэньлун Дуань, Лян Дон Jinzhan Huang, Zhiqiang Li, Biao Chen, Sen Cui, Zhaolin Lu, Wei Dai, Yuemin Zhao, Chenlong Duan, Liang Dong Эта статья доступна по лицензии Creative Commons Attribution 4.0 International (CC BY 4.0) This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0) https://pmi.spmi.ru/pmi/article/view/15842

Тестирование угольной золы в режиме реального времени играет жизненно важную роль в химической промышленности, энергетике, металлургии и разделении угля. Оперативное онлайн-тестирование угольной золы с использованием измерения радиации в качестве основной технологии сопряжено с такими проблемами, как жесткие требования к пробе угля, неудовлетворительная радиационная безопасность, низкая точность и сложность замены оборудования. В данном исследовании предлагается метод обнаружения, основанный на нейронных сетях прямой связи и улучшенной оптимизации скопления частиц (IPSO-FNN) для быстрого, точного, безопасного и удобного прогнозирования содержания золы. Набор данных был получен путем тестирования элементного содержания 198 образцов угля с помощью рентгенофлуоресценции (XRF). Типы исходных элементов для машинного обучения (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) были определены путем объединения данных рентгеновской фотоэлектронной спектроскопии (XPS) с изменением физической фазы каждого элемента в образцах угля во время сгорания. В качестве показателей эффективности модели были выбраны среднеквадратичная ошибка и коэффициент детерминации. Результаты показывают, что алгоритм IPSO полезен для настройки оптимального количества узлов в скрытом слое. Модель IPSO-FNN обладает и высокой точностью прогнозирования угольной золы. Изучено влияние содержания исходных элементов в модели IPSO-FNN на зольность и обнаружено, что содержание калия является наиболее значительным фактором, влияющим на зольность. Исследование имеет важное значение для онлайн-прогнозирования в режиме реального времени, точного и быстрого определения количества угольной золы.

Real-time testing of coal ash plays a vital role in the chemical, power generation, metallurgical, and coal separation sectors. The rapid online testing of coal ash using radiation measurement as the mainstream technology has problems such as strict coal sample requirements, poor radiation safety, low accuracy, and complicated equipment replacement. In this study, an intelligent detection technique based on feed-forward neural networks and improved particle swarm optimization (IPSO-FNN) is proposed to predict coal quality ash content in a fast, accurate, safe，and convenient manner. The data set was obtained by testing the elemental content of 198 coal samples with X-ray fluorescence (XRF). The types of input elements for machine learning (Si, Al, Fe, K, Ca, Mg, Ti, Zn, Na, P) were determined by combining the X-ray photoelectron spectroscopy (XPS) data with the change in the physical phase of each element in the coal samples during combustion. The mean squared error and coefficient of determination were chosen as the performance measures for the model. The results show that the IPSO algorithm is useful in adjusting the optimal number of nodes in the hidden layer. The IPSO-FNN model has strong prediction ability and good accuracy in coal ash prediction. The effect of the input element content of the IPSO-FNN model on the ash content was investigated, and it was found that the potassium content was the most significant factor affecting the ash content. This study is essential for real-time online, accurate, and fast prediction of coal ash.

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