Stochastic Non-Linear Optimization of Equipment Productivity in Multi-Seam Formations | OMICS International | Abstract
ISSN: 2168-9806

Journal of Powder Metallurgy & Mining
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Special Issue Article

Stochastic Non-Linear Optimization of Equipment Productivity in Multi-Seam Formations

Elijah Adadzi* and Samuel Frimpong

Missouri University of Science and Technology, USA

*Corresponding Author:
Elijah Adadzi, Professor
Missouri University of Science and Technology
E-mail: [email protected]

Received Date: January 15, 2013; Accepted Date: January 17, 2013; Published Date: January 26, 2013

Citation: Adadzi E, Frimpong S (2013) Stochastic Non-Linear Optimization of Equipment Productivity in Multi-Seam Formations. J Powder Metall Min S1:001. doi: 10.4172/2168-9806.S1-001

Copyright: © 2013 Adadzi, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


The deployment of large mining equipment has resulted in low-cost, high efficiency and bulk production
operations in surface mines. In strip coal mining operations, these economies of scale favor increasingly the use of draglines, shovels, dozers and other support equipment for overburden and coal extraction. However, the selection of particular equipment units, with fixed design geometry, might be economically inefficient in varying geological and operating domains. This study formulates stochastic-optimization (SOP) models of the dynamics of resource allocation in multi-seam deposits. The SOP model maximizes the productivities of draglines as well as other ancillary excavation operations, and determines optimal blends of coal products. Future uncertainties are also characterized for a comprehensive risk modeling and analysis. Application of the SOP model is presented with a bituminous coal mining case. The results show a 3.51% improvement in mining cost and 0.19% increment in net present value. This approach optimizes equipment scheduling in strip mines and ensures high economic efficiencies.