GOLD REMOVAL FROM GOLD-BEARING ORE USING ALPHA-CYCLODEXTRIN: RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL ANALYSIS NETWORK OPTIMIZATIONS

Abstract

This paper explores the use of alpha cyclodextrin (α-CD) for gold extraction from gold-bearing ore in the Democratic Republic of Congo (D.R.C). The research aims to identify the optimal gold removal conditions using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). Initially, ore samples were collected and processed to enhance leachability by reducing particle size. The leaching process employed a modified aqua regia with hydrobromic acid due to the strong molecular recognition between gold bromide ion and α-CD. Various leaching parameters, such as time, HBr concentration, pH, and stirring speed, were adjusted during experimentation. RSM yielded optimal values of 7.27 hours, 50 g/L, 1, and 200 rpm, resulting in 98.54% gold removal, while ANN produced slightly lower values of 7.5 hours, 50 g/L, 1, and 200 rpm, with 97.16% gold removal. For α-CD gold recovery, RSM and ANN optimization resulted in 40 minutes, 11.61 g/L, and pH 5, achieving 98.87% and 39.8 minutes, 10.71 g/L, and pH 5, with 99.98% gold removal, respectively. Validation tests supported these findings, indicating that RSM and ANN are successful methods for optimizing gold recovery from gold-bearing ore in the D.R.C.