Identification of Most Influencing Blast Design Parameters on Mean Fragmentation Size and Muckpile by Principal Component Analysis
N. Sri Chandrahas1, B.S. Choudhary2, M.S.Venkataramayya3
1N. Sri Chandrahas, Assistant Professor, Department of Mining Engineering, Malla Reddy Engineering College, Hyderabad, India.
2B.S. Choudhary, Assistant Professor, Department of Mining Engineering, Malla Reddy Engineering College, Hyderabad, India.
3M.S.Venkataramayya, Professor, Department of Mining Engineering, Malla Reddy Engineering College, Hyderabad, India.
Manuscript received on 01 December 2018 | Revised Manuscript received on 06 December 2018 | Manuscript Published on 26 December 2018 | PP: 23-30 | Volume-8 Issue- 2S2 December 2018 | Retrieval Number: BS2003128218/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mean fragmentation size, muck pile are the most emphasis factors in terms of economic and safe production in mining. It is needful to maintain certain limits to reach optimum level of blast results. The motive of study is to categorize the most influencing blast design parametric values on average sized fragmentation and muck pile. The commitment of the research was dealt on time through field data collection that related to blast design parametric values such as drill hole depth, its diameter, no of holes, no of rows, burden, spacing, average charge per hole, explosive, firing pattern, length width ratio, powder factor, mean fragmentation size, throw from three limestone mines positioned at different vicinity in Rajasthan. The collected data has analyzed statistically using principal component analysis (PCA) in IBM SPSS and XLSTAT software’s. Most influencing significant and non-significant parameters on mean fragmentation size and muck pile were drawn from regression analysis by considering P, F and R square values in IBM SPSS, For more robust results further analysis has done with XLSTST by considering influenced parameters from correlation circle according to their respective coordinates.
Keywords: Blast Design Parameters, IBM SPSS, XLSTAT, PCA.
Scope of the Article: Data Mining Methods, Techniques, and Tools