Adequate estimation of equipment costs is a key factor in a feasibility study and in evaluation of design alternatives for mining and mineral processing projects. Inaccurate early cost estimation could lead to the elimination of investment benefits. However, this estimation is complex and time consuming because of data accessibility problems and consequently access to a rapid, up-to-date and accurate tool is desirable for carrying out cost estimations. This paper describes the development of models for estimating capital and operating costs for a jaw crusher, which is used as the primary crusher for a wide variety of mineral beneficiation plants, applying single and multivariate regression analyses, together with the design of experiment approach, based on principal component analysis. Explanatory variables include gape size, length of feed opening and power as numeric (quantitative) variables and toggle type (single or double) and the existence of extra heavy duty construction as categorical (qualitative) variables. The performance of each model has been investigated and comparing the estimated R-squared (R2) of each model, the best was selected and its validity examined. The cost models include the capital and operating costs along with the operating cost components. The results indicate that the suggested best cost model is sufficiently accurate for use in feasibility studies.
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