<p>Loosening of hard rock mass by highly reactive chemical explosives often not only influences surface excavation processes, but also results in undesirable effects in the fragmentation dimensions. Mineral prices have a direct relation with the fragment size, which is a major challenge considering the wide variability in the Earth strata. It involves risks in optimizing the particle size distribution in any blasting processes. Hence, risk assessment in estimating the size of the fragments to the highest accuracy possible is desirable. In this research work, the risk of fragmentation in an iron ore mining was assessed and the average fragment size (<i>X</i><sub>50</sub>) of blasted rocks was also estimated. The rock engineering system (RES) was adopted to consider different factors that affect the blasting of the rock mass in 26 events. On-site data were obtained for 20 effective key parameters to generate a reliable RES-based predictive model. Analysis showed medium to high likelihood of risks associated with obtaining optimal fragmentation. The developed RES model from those 26 events was compared with similar available models as multiple regression analysis, and existing models, namely, Kuz–Ram and modified Kuz–Ram. The results of the new model were validated with the measured fragmentation of eight new blast events. The investigation establishes that the RES model exhibits the best results <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="603_2025_4678_Article_IEq1.gif" Format="GIF" Height="20" Rendition="HTML" Resolution="72" Type="Linedraw" Width="506" /> </InlineMediaObject> <EquationSource Format="TEX">\(({R}^{2}=0.81, \text{RMSE}=2.24,\text{ MAE}=2.03,\text{ MAPE}=14.47,\text{ VAF}=63.4)\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">(</mo> <msup> <mrow> <mi>R</mi> </mrow> <mn>2</mn> </msup> <mo>=</mo> <mn>0.81</mn> <mo>,</mo> <mtext>RMSE</mtext> <mo>=</mo> <mn>2.24</mn> <mo>,</mo> <mspace width="0.333333em" /> <mtext>MAE</mtext> <mo>=</mo> <mn>2.03</mn> <mo>,</mo> <mspace width="0.333333em" /> <mtext>MAPE</mtext> <mo>=</mo> <mn>14.47</mn> <mo>,</mo> <mspace width="0.333333em" /> <mtext>VAF</mtext> <mo>=</mo> <mn>63.4</mn> <mo stretchy="false">)</mo> </mrow> </math></EquationSource> </InlineEquation> comparing favorably with the measured fragment sizes. It establishes the better performance efficiency of the RES-based model for predicting rock fragmentation in iron ore mines accurately.</p>

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A Robust Framework for Blast Fragmentation and Risk Mitigation: A Comparative Analysis

  • Chandrakanta Behera,
  • Subhamoy Ghosh,
  • Tushar Gupta,
  • Manoj Kumar Mishra

摘要

Loosening of hard rock mass by highly reactive chemical explosives often not only influences surface excavation processes, but also results in undesirable effects in the fragmentation dimensions. Mineral prices have a direct relation with the fragment size, which is a major challenge considering the wide variability in the Earth strata. It involves risks in optimizing the particle size distribution in any blasting processes. Hence, risk assessment in estimating the size of the fragments to the highest accuracy possible is desirable. In this research work, the risk of fragmentation in an iron ore mining was assessed and the average fragment size (X50) of blasted rocks was also estimated. The rock engineering system (RES) was adopted to consider different factors that affect the blasting of the rock mass in 26 events. On-site data were obtained for 20 effective key parameters to generate a reliable RES-based predictive model. Analysis showed medium to high likelihood of risks associated with obtaining optimal fragmentation. The developed RES model from those 26 events was compared with similar available models as multiple regression analysis, and existing models, namely, Kuz–Ram and modified Kuz–Ram. The results of the new model were validated with the measured fragmentation of eight new blast events. The investigation establishes that the RES model exhibits the best results \(({R}^{2}=0.81, \text{RMSE}=2.24,\text{ MAE}=2.03,\text{ MAPE}=14.47,\text{ VAF}=63.4)\) ( R 2 = 0.81 , RMSE = 2.24 , MAE = 2.03 , MAPE = 14.47 , VAF = 63.4 ) comparing favorably with the measured fragment sizes. It establishes the better performance efficiency of the RES-based model for predicting rock fragmentation in iron ore mines accurately.