Digital and AI‑based evaluation of ANFO and SME explosives in surface coal mining: fragmentation, powder factor, ground vibrations, sustainability implications and safety outcomes

In open-cast coal mining, blasting should be optimized. The efficiency of blast fragmentation must be improved in order to enable this. The sustainability of Ammonium Nitrate Fuel Oil (ANFO) and Site Mixed Emulsion (SME) explosives is examined in this paper using a comprehensive framework that goes beyond conventional productivity metrics. The study evaluates 115 controlled blastings carried out at an operational open cast coal mine in India using digital technologies like drone photogrammetry, AI-based predictive modelling, and automated image analysis. Important performance indicators include the powder factor, fragmentation efficiency, and blast-induced ground vibrations (peak particle velocity, PPV). The results showed that in dry geological conditions, ANFO works better than SME. For instance, the average D80 of ANFO is 230 mm instead of 280 mm, and its powder factor is 3.27 m3/kg, while the SME’s is 2.00 m3/kg. Furthermore, the PPV values of ANFO blasts were lower. It has been discovered that the choice of explosives varies by location, and in certain situations, ANFO may be more effective and ecologically friendly. The study presents a technology-driven, integrated blasting framework that can support the global sustainable development goals agenda, increase productivity, and lessen environmental impact.

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Publication Details

Journal
Journal of Engineering and Applied Science
Published
2026-01-26
DOI
https://doi.org/10.1186/s44147-026-00898-8
Primary Topic
Coal Properties and Utilization
Type
article
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article

Digital and AI‑based evaluation of ANFO and SME explosives in surface coal mining: fragmentation, powder factor, ground vibrations, sustainability implications and safety outcomes

Appani Ravikumar, Merugu Uma Shankar, Ravi Pandey, Harsha Vardhan
Journal of Engineering and Applied Science
Coal Properties and Utilization
article

Digital and AI‑based evaluation of ANFO and SME explosives in surface coal mining: fragmentation, powder factor, ground vibrations, sustainability implications and safety outcomes

Appani Ravikumar, Merugu Uma Shankar, Ravi Pandey, Harsha Vardhan
article en

Abstract

In open-cast coal mining, blasting should be optimized. The efficiency of blast fragmentation must be improved in order to enable this. The sustainability of Ammonium Nitrate Fuel Oil (ANFO) and Site Mixed Emulsion (SME) explosives is examined in this paper using a comprehensive framework that goes beyond conventional productivity metrics. The study evaluates 115 controlled blastings carried out at an operational open cast coal mine in India using digital technologies like drone photogrammetry, AI-based predictive modelling, and automated image analysis. Important performance indicators include the powder factor, fragmentation efficiency, and blast-induced ground vibrations (peak particle velocity, PPV). The results showed that in dry geological conditions, ANFO works better than SME. For instance, the average D80 of ANFO is 230 mm instead of 280 mm, and its powder factor is 3.27 m3/kg, while the SME’s is 2.00 m3/kg. Furthermore, the PPV values of ANFO blasts were lower. It has been discovered that the choice of explosives varies by location, and in certain situations, ANFO may be more effective and ecologically friendly. The study presents a technology-driven, integrated blasting framework that can support the global sustainable development goals agenda, increase productivity, and lessen environmental impact.

Journal of Engineering and Applied ScienceVol. 73(1)
National Institute of Technology Karnataka (IN), Bharat Heavy Electricals (India) (IN), Fauji Fertilizer (Pakistan) (PK)
Industry, innovation and infrastructure
Openalex Percentile: Top 78%
Coal Properties and Utilization
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Digital and AI‑based evaluation of ANFO and SME explosives in surface coal mining: fragmentation, powder factor, ground vibrations, sustainability implications and safety outcomes — Appani Ravikumar, Merugu Uma Shankar, et al. · Journal of Engineering and Applied Science (2026) | TGRS Research Map | TGRS