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.