Identification of Most Influencing Blast Design Parameters On Mean Fragmentation Size And Muckpile By Principal Component Analysis
N. Sri Chandrahasa1, B.S.Choudhary2, M.S.Venkataramayya3
1N. Sri Chandrahasa, Research Scholar, Department of Mining Engineering, Indian Institute of Technology, Indian School of Mines, Malla Reddy Engineering College, Hyderabad, Telangana, India.
2B.S.Choudhary, Associat Professor, Department of Mining Engineering, Indian Institute of Technology, Indian School of Mines, Dhanbad, India.
3M.S.Venkataramayya, Professor, Department of Mining Engineering, Malla Reddy Engineering College, Telangana, India.
Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 489-495 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0106028419/2019©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 identify the most influencing blast design parameters on mean fragmentation size and muck pile. The intent of the research was achieved through collection of field data related to blast design parameters which are drill hole depth, drill hole 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 different limestone mines 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 and Warehousing