Imputation of Gold Recovery Data from Low Grade Gold Ore Using Artificial Neural Network

Abstract

In a multivariate database, the missing data can be obtained through several imputation techniques, which are particularly useful for data that are difficult to obtain, for any reason, or have high uncertainties or scarce variables. A Self-Organizing Maps (SOM) neural network is an effective tool for the analysis of multidimensional data applied for the imputation of data. In this paper, data from drilling were used for training, testing, and validation using the variables: total Au recovery (%), which means gold recovery from a gravity concentration plus hydrometallurgical process, Au (g/t), As (ppm), S (%), Al2O3 (%), CaO (%), K2O (%), and MgO (%). After training, the partial omission of Au content and recovery was carried out, from 10% to 50%, to evaluate the data imputation performance for those variables. The results imputed by the SOM were compared with the original data values and evaluated according to descriptive statistics; the results indicated a determination coefficient of 85% when 50% of the data were omitted and 93% when 10% of the data were omitted. Once demonstrated, the correlation between the original data and SOM imputation analysis can help geologists and metallurgists to obtain results with a high degree of reliability of metallurgical recovery through related chemical variables, making it possible to implement SOM analysis as a powerful tool to input analytical data. One of the practical applications of the proposed model is to produce a pattern of imputed data that can be a good alternative in the construction or generation of a synthetic geometallurgical database with missing data.