The Central Institute of Mining and Fuel Research (CIMFR), Dhanbad, has developed an Artificial Intelligence (AI) based model to predict the hazardous effects of blasting in mines. The model is designed to estimate Air Over Pressure (AOP) generated during blasting, which often causes structural damage, vibrations, and health hazards in mining areas.
Model Created Using Data from Five States
According to scientists, the model was created using data from 699 blasts across 33 mines including Coal, Iron ore, Manganese, Uranium, Limestone, and Stone quarries in Jharkhand, West Bengal, Odisha, Rajasthan, and Meghalaya. Ten key parameters such as hole diameter, depth, number of holes, charge per hole, and stemming length were analyzed. The AI system then generated predictive designs for blast planning without repeated field trials, reducing risks to people and the environment.
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The Model Provides Safer Mining to Reduced Disputes
Experts explained that in conventional blasting, only 20 to 30% of the explosive energy is used to break rocks, while the rest creates unwanted side-effects like vibrations, flying debris, and AOP. At levels above 120 dB, AOP can cause discomfort to humans and damage to buildings, sometimes leading to disputes between mine operators and local communities. The AI model provides precise blast designs, limiting these harmful effects and supporting safer, smarter mining practices.
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The Study Published in International Journal
The project, led by Dr. Aditya Rana, an involved scientists including Dr. C. Saumliana, Hemant Agrawal, and R.K. Singh, along with Nigerian researcher Charles Komadza, who has since moved to the United States as a research scholar. The study has been published in the Noise Control Engineering Journal (USA), bringing international recognition to Indian mining research. Scientists believe the technology can be widely applied in different mining environments, marking a significant step towards sustainable mineral extraction.
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