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has been cited by the following article:

• TITLE: Theoretical Basis in Regression Model Based Selection of the Most Cost Effective Parameters of Hard Rock Surface Mining

  AUTHORS: Antipas T. S. Massawe, Karim R. Baruti, Paul S. M. Gongo

  KEYWORDS: Parameters of Rockmass, Parameters of Mining Design, Regression Model, Algorithm of Selection

  JOURNAL NAME: Engineering, Vol.3 No.2, March 18, 2011

  ABSTRACT: What determines selection of the most cost effective parameters of hard rock surface mining is consideration of all alternative variants of mine design and the conflicting effect of their parameters on cost. Consideration could be realized based on the mathematical model of the cumulative influence of rockmass and mine design variables on the overall cost per ton of the hard rock drilled, blasted, hauled and primary crushed. Available works on the topic mostly dwelt on four processes of hard rock surface mining separately. This paper dwells on the theoretical part of a research proposed to enhance effectiveness in the selection of the parameters of hard rock surface mining design based on the regression model of overall cost per tonne of the rock mined fit on the determinant variations of rockmass and mine design. The regression model could be developed based on the statistical data generated by many of the hard rock surface mines operating in variable conditions of rockmass and mine design worldwide. Also, a regression model based general algorithm has been formulated for the development of software and computer aided selection of the most cost effective parameters of hard rock surface mining.