Оптимизация взрывного дробления горных пород с использованием гибридных методов искусственного интеллекта на алмазном руднике Орапа (Ботсвана)
- 1 — магистр научный сотрудник Ботсванский международный университет науки и технологий ▪ Orcid
- 2 — Ph.D. преподаватель Ботсванский международный университет науки и технологий ▪ Orcid
- 3 — Ph.D. заведующий кафедрой Ботсванский международный университет науки и технологий ▪ Orcid
- 4 — Ph.D. заведующий кафедрой Ботсванский международный университет науки и технологий ▪ Orcid
Аннотация
В данном исследовании демонстрируются различные методы искусственного интеллекта для прогнозирования и оптимизации взрывного дробления породы на алмазном руднике Орапа в Ботсване. Эти методы включают искусственные нейронные сети (ИНС/ANN), адаптивную нейро-нечеткую систему вывода (ANFIS), генетический алгоритм с ИНС (GA-ANN) и метод роя частиц с ИНС (PSO-ANN). Для выполнения задачи была использована база данных, включающая информацию о 120 взрывных работах на руднике с девятью входными параметрами. Результаты показывают, что модель PSO-ANN превосходит другие в прогнозировании взрывного дробления породы. Оптимальная модель включает девять входных параметров, два скрытых слоя с 65 и 30 нейронами и один выходной параметр (7-65-30-1). Это десятимерное пространство решений исследовалось с использованием градиентного спуска для определения оптимизированных параметров проектирования взрыва, и достигнуто оптимальное значение дробления приблизительно 86 %. Результаты анализа чувствительности показывают, что входными параметрами, имеющими наибольшее влияние на дробление, являются коэффициент крепости породы (15,3 %), индекс взрываемости (14,7 %) и коэффициент сближения скважин (14,7 %). Наименьшее влияние на дробление оказывает коэффициент жесткости (6,3 %).
Финансирование
Исследовательский проект финансировался алмазной компанией Debswana, грант № P00064.
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