AI Predicts Air Pressure in Mine Blasting, Developed in Jharkhand

Dhanbad: A team of Indian scientists has developed an Artificial Intelligence (AI) system to predict hazardous air overpressure (AOP) generated during mine blasting, promising safer mining operations and reduced environmental and human risks. The breakthrough comes from the Central Institute of Mining and Fuel Research (CIMFR), Dhanbad, a leading institute in mining research.

AI Model Created Using Data from Five States

The AI model was developed using data from 699 blasts across 33 mines in Jharkhand, West Bengal, Odisha, Rajasthan, and Meghalaya. The mines include coal, iron ore, manganese, uranium, limestone, and stone quarries. Scientists analyzed ten critical parameters, including hole diameter, depth, number of holes, charge per hole, and stemming length.

The AI system generates predictive blast designs, reducing the need for repeated field trials. This allows mine operators to plan safer blasts while minimizing environmental and structural hazards.

Safer Mining and Reduced Disputes

Experts explained that in conventional blasting, only 20–30% of explosive energy is utilized to break rocks. The remaining energy produces vibrations, flying debris, and harmful air overpressure. When AOP levels exceed 120 dB, it can cause damage to buildings, discomfort to humans, and conflicts between mining companies and local communities.

By providing precise blast designs, the AI system limits these side effects, enhancing safety for workers and nearby residents and fostering sustainable mining practices.

Published in International Journal

The project was led by Dr. Aditya Rana, along with Dr. C. Saumliana, Hemant Agrawal, and R.K. Singh. Nigerian researcher Charles Komadza, now a research scholar in the United States, also contributed.

Their study has been published in the Noise Control Engineering Journal (USA), giving international recognition to Indian mining research. Scientists suggest the technology can be applied across different mining environments, marking a significant step towards sustainable and responsible mineral extraction.

Key Highlights

• Institute: Central Institute of Mining and Fuel Research (CIMFR), Dhanbad
• AI System Purpose: Predict air overpressure (AOP) during blasting
• Data Used: 699 blasts across 33 mines in 5 states
• Impact: Safer mining, reduced structural damage, fewer disputes
• Publication: Noise Control Engineering Journal, USA

At a Glance: AI-Powered Mine Blasting Safety

• What: AI system to predict Air Over Pressure (AOP) during mine blasts
• Who: Developed by CIMFR, Dhanbad team led by Dr. Aditya Rana
• Data Source: 699 blasts across 33 mines in Jharkhand, West Bengal, Odisha, Rajasthan, Meghalaya
• Parameters Analyzed: Hole diameter, depth, number of holes, charge per hole, stemming length, and more
• Benefits:
  • Safer mining operations
  • Reduced structural damage
  • Lower health hazards for workers and nearby communities
  • Minimizes disputes between mines and local residents
• Recognition: Published in Noise Control Engineering Journal (USA)
• Application: Can be adapted for various mining environments