International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Large amounts of liquidity flow into several precious metals every day. Investment decisions are mainly based on predicting the future movements of the instrument(s) in question. However, high frequency financial data are somewhat hard to model or predict as stochastic processes and many other random factors are involved. It would be valuable information for the investor if he or she knew which precious metals were quantitatively more predictable, that would also be a good basis for more robust trading decisions. The objective in this study is to build predictive models on high frequency precious metal data and compare predictabilities of different metals using only past price and volume values which should be a basis for robust trading decisions. The data used consist of various frequencies from 1-minute to 4-hour covering a period of almost 20 years for each instrument and frequency. Artificial Neural Network (ANN) and Gradient Boosted Decision Tree (XGB) methods are applied. Comparable results are achieved.

Large amounts of liquidity flow into several precious metals every day. Investment decisions are mainly based on predicting the future movements of the instrument(s) in question. However, high frequency financial data are somewhat hard to model or predict as stochastic processes and many other random factors are involved. It would be valuable information for the investor if he or she knew which precious metals were quantitatively more predictable, that would also be a good basis for more robust trading decisions. The objective in this study is to build predictive models on high frequency precious metal data and compare predictabilities of different metals using only past price and volume values which should be a basis for robust trading decisions. The data used consist of various frequencies from 1-minute to 4-hour covering a period of almost 20 years for each instrument and frequency. Artificial Neural Network (ANN) and Gradient Boosted Decision Tree (XGB) methods are applied. Comparable results are achieved

A sulphide flotation concentrate containing 15.2 g/t gold and 893 g/t silver finely disseminated in pyrite (4.1 % sulphidic sulphur in the concentrate) was treated by a two-stage process to recover these precious metals. Initially the concentrate was subjected to microbial oxidation by means of different acidophilic chemolithotrophic microorganisms (bacteria at 37 ^oC and archaea at 59 and 86 ^oC) to expose the precious metals encapsulated in the pyrite. The precious metals liberated in this way were then subjected to leaching by means of solutions containing different reagents (protein hydrolysate, thiosulphate, cyanide and some chemical oxidizers). The leaching was carried out in agitated reactors and up to 93.6 % of the gold and 80.8 % of the silver were solubilised in this way for 48 hours from a pulp density of 20 % at 57 ^oC.