Advance Data Handling to Support Modeling and Simulations in Mineral Processing Plant Optimization

14 August, 2015 | White paper

JORGE TORREALBA, P.Eng. Ph. D.

Director, Business Line – Mining optimization

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Abstract

Data handling is an important step in the development of a simulator of a mineral processing circuit. The model requires representative data of the scenario to be modeled. The model development involves protocols that must be followed very closely to complete a successful project.

Data handling of large and diverse datasets (metallurgical test results, geological data, mining models, historical data, etc.) is a key element of the model. The information needs to be processed, reconciled, interpreted, modeled and presented to support the design or the optimization of a mineral processing plant.

Best practice recommends that teams from different disciplines (such as mining, metallurgy, geology and environmental) interact to define the most important variables that could influence the simulator. For example by adopting a geometallurgical approach in the earlier stages of the project, it is possible to consolidate the number of main ore rock types in a testwork program (saving money and time) by involving geologists and metallurgists in the sample selection and data interpretation.

Planning the development of a model involves an understanding of the software capabilities but also the limitations and the magnitude of the data required to calibrate the model in case of changes in the project.

A successful model requires a deep understanding of the circuit to be modeled and thus plant personnel should be considered as members of the team.

This paper describes the use of the “state of the art” tools for data analysis, simulation and optimization and shows three practical examples of the use those tools have in the design of mineral processing plants.

Note that this white paper was written in collaboration with Ricardo Esteban, David Runnels and Geneviève Couture.

To receive a copy, email us at marketing@bba.ca.

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