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Raw Ash to Yield Relationships

Open Cut » Geology

Published: February 22Project Number: C28033

Get ReportAuthor: Chris McMahon | McMahon Coal Quality Resources

Correct outcomes in yield predictions for product ash from coal borecore exploration can be critical to resource predictive potential, market evaluation and economic mining outcomes.

An observation has been made over many projects that product yield can be estimated from borecores from a variety of testing and interpretive mechanisms. A commonly utilised method is estimating product yield at a certain set ash outcome via raw ash. This method can be valid and useful but on occasion badly misrepresent actual outcomes and the data produced misleading. This can occur as significant variations and biases can be incorporated for a variety of reasons including geological event changes (oxidised and heat affected coals) and coal quality changes (including changing washability character) thus using this method can incorrectly misrepresented resource potential for product ash and yield.

This project's objective is to seek to collate and evaluate data attained in raw ash and product yield / ash outcomes across several deposits and methodologies to define when the methods can be usefully used or not and define the error limits for the process.

The outcomes of this project were the definition of accuracy (via correlation coefficients and standard error) information for varying coal types and efficacy of use of methods. That is, when to use and not use the method and what the variation (standard error) from more traditional methods such as sample pre-treated wash data and sized and float and sunk / froth flotation tested data. This benefits the industry by providing a guide for use and defined accuracy of outcomes for this method via a variety of testing methods, and potentially saving very expensive mine and resource misrepresentation. The outcomes then will provide a financial risk reduction and / or evaluation criteria.

The outcome of this work is a guide produced for use in industry to improve resource accuracy, save time for technicians and reduce risk in evaluation.

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