The benefits of automated metallurgical accounting for plant sampling

The Australasian Institute of Mining and Metallurgy
J P. Vagenas D W. Wall
Organization:
The Australasian Institute of Mining and Metallurgy
Pages:
8
File Size:
1425 KB
Publication Date:
May 9, 2017

Abstract

Questions of sampling precision and accuracy affect every process plant in differing ways and to differing extents. To address these perceived issues, chemists, metallurgists and engineers are often asked to check their results to confirm their reliability. However, it is not possible to achieve the level of detail required to check every sample, especially if the event has already occurred. No operation has the resources to frequently sample every point in the process for the purpose of verification.One way to address this scenario is by generating a sufficiently detailed comparative data set from a dynamic mass balance simulation of the processing plant. This mass balance uses both sample and plant instrument data to model every flow, while also taking inventory changes and process chemistry into account.The mass balance data can be used to identify any bias present in plant sampling and analysis. It can also be used to verify any unexpected assay results by taking a holistic view of the process. Improving the precision and accuracy of sampling data helps support operational decision-?making and understanding. It also improves the accuracy of the dynamic mass balance used in this process, facilitating further improvements.CITATION:Vagenas, J P and Wall, D W, 2017. The benefits of automated metallurgical accounting for plant sampling, in Proceedings Eighth World Conference on Sampling and Blending , pp 203–210 (The Australasian Institute of Mining and Metallurgy: Melbourne).
Citation

APA: J P. Vagenas D W. Wall  (2017)  The benefits of automated metallurgical accounting for plant sampling

MLA: J P. Vagenas D W. Wall The benefits of automated metallurgical accounting for plant sampling. The Australasian Institute of Mining and Metallurgy, 2017.

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