Abstract
The nuclear decay of uranium is one of the cleanest ways to meet the growing energy demand. The uranium needed for power plants is mainly extracted by two methods in roughly equal amounts: quarries (underground and open pit) and in-situ leaching (ISL). The effective use of ISL requires, among other things, the correct determination of the filtration characteristics of the host rocks. In Kazakhstan, this calculation is still based on methods that were developed more than 50 years ago, and in some cases, give inaccurate results. At the same time, knowledge of filtration characteristics is necessary for the calculation of recoverable reserves, prediction of production dynamics, calculation of the optimum number of wells, etc. This paper describes a method for calculating the filtration coefficient of ore-bearing rocks using machine learning. The proposed method is based on nonlinear regression models. It also allows the estimation of the filtration properties of rocks within the process acidification zone, where the existing method is not applicable. The proposed method applies to approximately half of the uranium mined in the world and makes it possible to significantly (by 22 %–70%) increase the accuracy of the filtration coefficient determination and, accordingly, improve the accuracy of recoverable reserves calculation and economic indicators of mining processes.
Highlights
Nuclear power, despite the environmental risks involved, remains one of the cleanest ways to meet the growing demand for energy without increasing greenhouse gas emissions
Companies use two main mining methods: open pit, which accounted for 45.9% of the production, and in-situ leaching (ISL), which accounts for 48.3% of the world's uranium production
The work consists of the following sections: - The first section briefly describes the existing techniques for determining the filtration coefficient and its shortcomings. - In the second section, we provide an overview of the work devoted to the application of machine learning methods to mining problems. - In the third section, we present the methodological scheme of the study, describe the machine learning models we applied, and the metrics for evaluating the quality of their performance
Summary
Despite the environmental risks involved, remains one of the cleanest ways to meet the growing demand for energy without increasing greenhouse gas emissions. Nuclear power plants require the mining of uranium ore to power them. Uranium is mined in 28 countries, of which ten countries account for more than 90% of the established reserves [1] (Fig. 1). According to the World Nuclear Association, in 2018, the largest uranium mining companies produced 86% of the world's total uranium production [2], of which NAC Kazatomprom JSC accounted for 21%. Companies use two main mining methods: open pit (underground and open-pit), which accounted for 45.9% of the production, and in-situ leaching (ISL), which accounts for 48.3% of the world's uranium production. 5.8% of uranium is mined as a byproduct, such as in gold mining [3]. Appendix A provides a detailed overview of uranium mining worldwide Appendix A provides a detailed overview of uranium mining worldwide (https://www.dropbox.com/s/ijl3my3z9dhfg4z/Appendix_A. pdf?dl=0)
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