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mixingtank

Operate cost models for major flotation equipment

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Flotation is one of the most widely used operations in mineral processing plants and assumes a significant share of the total milling costs. The purpose of this paper is to introduce a new set of capital and operating cost models for major Flotation Equipment based on the application of single (SRA) and multiple regression analysis (MRA). Thirty-seven major flotation machines were analysed for this purpose. Depending on the machinery type, different technical variables such as diameter, required air flow rate, required floor space, cell volume, required air pressure, and power were considered as predictor variables, individually (in SRA) or simultaneously (in MRA). Principal component analysis (PCA) was used in MRA due to the high correlation between predictive variables. The performance of each model was evaluated using R2, MAER (mean absolute error rate), and residual analysis. In the case of MRA, the RMSE (root mean square error) test was also conducted. Maximum obtained MAER of 13.5% and minimum R2 of 0.86 indicated that these models could be applied as credible tools in estimation of capital and operating costs of flotation machines for design and feasibility studies.

Mineral processing is a vital part of mining projects and mainly involves comminution, sizing, concentration, extractive metallurgical processes, and dewatering. Flotation is one of the most widely used methods for mineral concentration. Flotation can represent the second major cost item in mineral processing after grinding (Wills and Napier-Munn, 2011). Accordingly, it is a main concern of mining project managers to select and optimize flotation circuits in order to decrease costs and increase productivity. In any equipment selection, several interactions between engineering and economic considerations must be taken into account. Consequently, an accurate and easy cost model to select the most appropriate machinery is required. Moreover, cost models could be used in flow sheet simulations applied in design and optimization. Models of unit operations built into the simulators could be improved by linking the equipment cost models.

Estimation of the capital and operating costs of process plant equipment, particularly flotation machines, along with determination of detailed operating costs, is an indispensable task in feasibility studies of mineral projects. Almost all of the current models are obsolete and need to be updated. Moreover, the majority of the available models have a univariate structure, and the role of other operative variables has simply been disregarded. A new up-to-date statistical cost model for flotation machines (column as well as coal and sulphide, self-aerating, and standard) has been developed.

Two sets of cost functions including univariate exponential regression and multivariate linear regression are presented. Individual cost functions are presented for each operational cost item category such as overhaul (parts and labour), maintenance (parts and labour), power and lubrication items. However, costs can vary from mine to mine and from time to time, and should be adjusted for conditions specific to the operation based on local unit costs (such as electrical power, lubricants, and repair labour), and annual cost index of mineral processing equipment. The proposed cost models are reliable in device specifications ranged, and over- extrapolation could result in misguiding estimates.

The heart of a mechanical Flotation Machine is known as the impeller. It behaves a lot like the agitator in a washing machine, but is more powerful and moves in only one direction. Ore and chemicals are introduced at or near the bottom of the flotation tank, known as a "cell," and air is sucked in either from the top or the side of the machine. The air intakes are designed to promote the production of small bubbles. The lower portion of the machine is known as the turbulent zone.

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