Authors: A.F. Novokreshchenov, Yu.V. Drozdenko, A.I. Kopytov

Title of the article: Intelligent blasting control system: digital modeling, parameter optimization and ai-based prediction

Year: 2026, Issue: 1, Pages: 116-124

Branch of knowledge: 2.8.6. Geomechanics, destruction of rocks by explosion, mine aerogasdynamics and mining thermophysics

Index UDK: 622.235.2:004.8

DOI: 10.26730/1999-4125-2026-1-116-124

Abstract: The article addresses the pressing issue of developing an intelligent control system for blasting operations based on a hybrid approach that integrates artificial intelligence (AI) methods with classical physical models. The necessity of this approach for digital modeling, parameter optimization, and predicting the outcomes of blasting operations is substantiated. A review of modern Russian research in this field is provided, identifying key problems of traditional methods: insufficient modeling accuracy, inefficiency of manual parameter optimization, and the lack of predictive systems for risk assessment. A methodological framework for creating a hybrid system is presented, where deterministic physical models (Kutuzov's for charge calculation, Kuznetsov-Rammler for fragment size distribution) are combined with adaptive AI algorithms. To test the approach, a demonstration prototype was developed in Python using the TensorFlow and DEAP libraries, implementing modules for simulation, prediction based on neural networks, and parameter optimization using a genetic algorithm. The discussion section analyzes the key advantages of the hybrid approach, including improved forecasting accuracy, savings in explosives (up to 15-20%), and reduced environmental risks through minimized seismic impact. The strategic importance of this development for import substitution in the mining industry is emphasized. It is concluded that the proposed solution forms the basis for transitioning to predictive management of blasting operations and requires further testing at real-world sites to accumulate data and address organizational and legal challenges.

Key words: blasting artificial intelligence hybrid systems digital modeling optimization forecasting digital twin import substitution

Receiving date: 31.10.2025

Approval date: 15.01.2026

Publication date: 19.03.2026

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