A application of big data analytics for effective rock fragmentation through blasting

Abstract

Rock fragmentation is the major route of obtaining minerals even today. The civil and military applications of the rock fragmentation are also increasing in scale and frequency due to enhanced developmental and defence activities world over. Till date blasting has been accepted as the cheapest and functional mode of rock fragmentation at large. Attempts have been made to substitute it through mechanized excavation but they are few and far between and it would take some time to catch up with blasting. Therefore, effectiveness of blasting can influence the economics and the ecology of the rock fragmentation. Coming to blasting, its success is the outcome of the interplay of the rockmass, blast design and explosive parameters. The variations in the rockmass properties and their site specificity make the consequence of the blasting at any location complex. Drilling for production i.e. blasting related drilling provides most important real-time and realistic data that can be utilized for planning blasting - particularly explosive density determination and loading if well assimilated within the procedure. But this data is seldom kept and used-particularly in Indian mines and civil and military sites. With rock mass changing multipally within the same hole and even on surface, each hole generates huge amount of data. This coupled with past best performance of the explosive for the rock type can generate huge data converting it to big data. Hence, I see an opportunity in this scenario. The big data analytics if that is deployed in this case then our entire blasting operations can be optimized and an effective blasting can provide an economic and ecologically acceptable blasting for rock fragmentation. The energy partitioning and distribution within the blasting phenomenon could probably be better regulated. Present paper thus makes a case for the application of big data analytics in blasting related activities for informed and analysed decisions.